System

Abstract

An object of the system according to the embodiment is to create a comic that effectively conveys service content without expert knowledge.SOLUTION: A system according to an embodiment includes a service content acquisition section, a scenario generation section, and a comic creation section. The service content acquisition unit acquires service content from a user. The scenario generation unit generates a scenario based on the service content acquired by the service content acquisition unit. The comic creation section creates a comic on the basis of the scenario generated by the scenario generation section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of creating manga to effectively communicate service content was complicated and difficult to execute without specialized knowledge.

[0005] The system according to the embodiment aims to create manga that effectively communicates service content, even without specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a service content acquisition unit, a scenario generation unit, and a manga creation unit. The service content acquisition unit acquires service content from a user. The scenario generation unit generates a scenario based on the service content acquired by the service content acquisition unit. The manga creation unit creates a manga based on the scenario generated by the scenario generation unit. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible to create manga that effectively communicates service content, even without specialized knowledge. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The image creation tool according to an embodiment of the present invention is a system that allows users to clearly communicate their company's services in the form of manga. This system allows users to create manga with the design they desire from a series of multiple frames. This allows the image creation tool to visually communicate their company's services in an easy-to-understand manner.

[0029] An image creation tool according to an embodiment includes a service content acquisition unit, a scenario generation unit, and a manga creation unit. The service content acquisition unit acquires service content from a user. For example, the user inputs the service content through an online form. The service content acquisition unit can also allow the user to provide the service content using voice input. The service content acquisition unit can also allow the user to provide the service content using handwritten input. For example, the user inputs the service content by hand and converts it into digital data. The scenario generation unit generates a scenario based on the service content acquired by the service content acquisition unit. For example, the generation AI generates a scenario that highlights the features and benefits of the service provided by the user. The generation AI can also customize the scenario based on user instructions. For example, the generation AI generates a scenario based on a theme specified by the user. The manga creation unit creates a manga based on the scenario generated by the scenario generation unit. For example, the generation AI creates a manga depicting a character learning online based on the scenario. The generation AI can also finish the manga according to a design envisioned by the user. For example, the generation AI creates a manga based on a design style specified by the user. This allows the image creation tool according to an embodiment to easily communicate the company's services in manga format. For example, if a user provides an online learning support service, the generation AI will draw a "scenario that emphasizes the benefits of online learning" and create a manga based on that scenario. If a user provides a health food sales service, the generation AI will draw a "scenario that emphasizes the effects of health food" and create a manga based on that scenario. If a user advertises a sports club, the generation AI will draw a "scenario that emphasizes the appeal of the sports club" and create a manga based on that scenario.

[0030] The service content acquisition unit allows the generation AI to automatically generate related questions for the service content entered by the user, thereby eliciting detailed information. For example, when a user enters "online learning support service," the service content acquisition unit automatically generates related questions such as "What age group is the target?" and "What subjects do you offer?" to elicit detailed information. The generation AI uses natural language processing technology to analyze the user's input and generate related questions. For example, the generation AI selects appropriate questions based on the user's input and presents them to the user. This makes it easier for the user to provide detailed information.

[0031] When the service content acquisition unit inputs the service content, the generation AI can present past success stories or failure stories for the user to refer to. For example, if the user inputs "online learning support service," the service content acquisition unit can present "examples of successful learning programs specialized for specific age groups" as past success stories for the user to refer to. The generation AI refers to a past database and selects success stories and failure stories related to the user's input. For example, the generation AI extracts the case most relevant to the user's input from the past case database and presents it to the user. This makes it easier for the user to refer to past cases.

[0032] The service content acquisition unit can also accept voice or handwritten input of service content, improving user convenience. For example, when a user vocally inputs "online learning support service," the service content acquisition unit causes the generation AI to analyze the content using voice recognition technology and convert it into text data. The generation AI then analyzes the user's voice using a voice recognition algorithm and converts it into text data. For example, the generation AI collects the user's voice data and performs voice recognition. Based on the results of the voice recognition, the generation AI converts the user's voice into text data. The service content acquisition unit can also allow users to input service content by hand and convert it into digital data. For example, the generation AI can analyze the user's handwritten input using handwriting recognition technology and convert it into text data. This allows users to use a variety of input methods.

[0033] The service content acquisition unit can automatically refer to service content from different industries or fields to provide the user with a new perspective. For example, when a user inputs "online learning support service," the generation AI will refer to successful examples from outside the education industry (e.g., online services in the entertainment industry) to provide a new perspective. The generation AI will refer to databases from different industries and fields to select examples related to the user's input. For example, the generation AI will extract the examples most relevant to the user's input from databases from different industries and fields and present them to the user. This makes it easier for the user to gain a new perspective.

[0034] When the generation AI draws a scenario, the scenario generation unit can refer to the user's past input data and generate an individually customized scenario. For example, when a user inputs "online learning support service," the scenario generation unit causes the generation AI to refer to the past input data (e.g., previously entered learning content and target age) and generate a customized scenario. The generation AI references the past input data from a database and customizes the scenario based on the user's input. For example, the generation AI analyzes the user's past input data and generates an optimal scenario. This makes it possible to generate a scenario tailored to the user.

[0035] When generating a scenario, the scenario generation unit can have the generation AI present multiple scenario proposals and allow the user to select from them. For example, when a user inputs "online learning support service," the scenario generation unit can have the generation AI present multiple scenario proposals, such as "a scenario that emphasizes the fun of learning" and "a scenario that emphasizes the results of learning," and allow the user to select from them. The generation AI generates multiple scenario proposals using a scenario generation algorithm and presents them to the user. For example, the generation AI generates multiple scenario proposals based on the user's input and presents them to the user. This allows the user to select from multiple scenario proposals.

[0036] The scenario generation unit can incorporate perspectives from different cultures or regions when generating a scenario, generating a scenario that is applicable globally. For example, when the generation AI generates a scenario for an "online learning support service," the scenario generation unit incorporates perspectives from different cultures and regions to generate a scenario that is applicable globally. The generation AI references data from different cultures and regions and reflects this in the scenario. For example, the generation AI takes into account the differences between educational systems in Asia and Europe to generate a scenario that is applicable globally. This makes it possible to generate a scenario that is applicable globally.

[0037] When generating a scenario, the scenario generation unit allows the generation AI to automatically suggest related visual materials, thereby improving the quality of the scenario. For example, when the generation AI generates a scenario for an "online learning support service," the scenario generation unit automatically suggests related visual materials (e.g., images and videos of learning scenes), thereby improving the quality of the scenario. The generation AI references a database of visual materials and selects materials related to the scenario. For example, the generation AI selects appropriate images and videos based on the content of the scenario and suggests them to the user. This improves the quality of the scenario.

[0038] When the generation AI creates a manga, the manga creation unit can learn the user's past design preferences and provide an individually customized design. For example, when a user creates a manga for the "online learning support service," the generation AI learns the user's past design preferences (e.g., character style and color usage) and provides a customized design. The generation AI references the user's past design preference data and generates an optimal design. For example, the generation AI analyzes the user's past design preferences and provides a design tailored to the user. This makes it possible to provide a design tailored to the user.

[0039] When creating a manga, the manga creation unit can have the generation AI present multiple design proposals and allow the user to select from them. For example, when a user creates a manga for an "online learning support service," the manga creation unit can have the generation AI present multiple design proposals, such as "simple design" and "colorful design," and allow the user to select from them. The generation AI generates multiple design proposals using a design generation algorithm and presents them to the user. For example, the generation AI generates multiple design proposals based on user input and presents them to the user. This allows the user to select from multiple design proposals.

[0040] The manga creation unit can incorporate different art styles or themes when creating a manga, providing the user with a variety of options. For example, when a user creates a manga for an "online learning support service," the generation AI can suggest different art styles, such as "anime style" or "realistic style," providing the user with a variety of options. The generation AI refers to a database of art styles and themes to suggest appropriate options to the user. For example, the generation AI can suggest different art styles or themes based on the user's input. This allows the user to choose from a variety of options.

[0041] The manga creation unit allows the generation AI to automatically add audio and sound effects to each frame of the manga, creating more interactive content. For example, when the generation AI creates a manga for an "online learning support service," the manga creation unit adds audio and sound effects to each frame to create interactive content. The generation AI references a database of audio and sound effects and selects appropriate audio and sound effects. For example, the generation AI selects appropriate audio and sound effects based on the content of the manga and adds them to each frame. This allows interactive content to be created.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The service content acquisition unit allows the generation AI to automatically generate related questions for the service content entered by the user, thereby eliciting detailed information. For example, if a user enters "online learning support service," the generation AI automatically generates related questions such as "What age group is the target?" and "What subjects do you offer?" to elicit detailed information. The generation AI uses natural language processing technology to analyze the user's input and generate related questions. For example, the generation AI selects appropriate questions based on the user's input and presents them to the user. This makes it easier for the user to provide detailed information.

[0044] The service content acquisition unit allows the generation AI to automatically generate related questions for the service content entered by the user, thereby eliciting detailed information. For example, if a user enters "online learning support service," the generation AI automatically generates related questions such as "What age group is the target?" and "What subjects do you offer?" to elicit detailed information. The generation AI uses natural language processing technology to analyze the user's input and generate related questions. For example, the generation AI selects appropriate questions based on the user's input and presents them to the user. This makes it easier for the user to provide detailed information.

[0045] When the service content acquisition unit inputs service content, the generation AI can present past success stories or failure stories for the user to refer to. For example, if the user inputs "online learning support service," the generation AI can present "examples of successful learning programs specialized for specific age groups" as past success stories for the user to refer to. The generation AI refers to a past database and selects success stories and failure stories related to the user's input. For example, the generation AI extracts the case most relevant to the user's input from the past case database and presents it to the user. This makes it easier for the user to refer to past cases.

[0046] The service content acquisition unit can also accept voice or handwritten input of service content, improving user convenience. For example, when a user voice-inputs "online learning support service," the generation AI analyzes the content using voice recognition technology and converts it into text data. The generation AI analyzes the user's voice using a voice recognition algorithm and converts it into text data. For example, the generation AI collects the user's voice data and performs voice recognition. Based on the results of the voice recognition, the generation AI converts the user's voice into text data. The service content acquisition unit can also allow users to input service content by hand and convert it into digital data. For example, the generation AI analyzes the user's handwritten input using handwriting recognition technology and converts it into text data. This allows users to use a variety of input methods.

[0047] The service content acquisition unit can automatically reference service content from different industries or fields to provide the user with a new perspective. For example, if a user inputs "online learning support service," the generation AI will reference successful examples from outside the education industry (e.g., online services in the entertainment industry) to provide a new perspective. The generation AI will reference databases from different industries and fields to select examples related to the user's input. For example, the generation AI will extract the examples most relevant to the user's input from databases from different industries and fields and present them to the user. This makes it easier for the user to gain a new perspective.

[0048] When the generation AI creates a scenario, the scenario generation unit can reference the user's past input data and generate an individually customized scenario. For example, if a user inputs "online learning support service," the generation AI references the past input data (e.g., previously entered learning content and target age) and generates a customized scenario. The generation AI references the past input data from a database and customizes the scenario based on the user's input. For example, the generation AI analyzes the user's past input data and generates an optimal scenario. This allows it to generate a scenario tailored to the user.

[0049] When generating a scenario, the scenario generation unit can have the generation AI present multiple scenario proposals and allow the user to choose from them. For example, if a user inputs "online learning support service," the generation AI will present multiple scenario proposals, such as "a scenario that emphasizes the fun of learning" and "a scenario that emphasizes the results of learning," and allow the user to choose from them. The generation AI uses a scenario generation algorithm to generate multiple scenario proposals and present them to the user. For example, the generation AI generates multiple scenario proposals based on the user's input and presents them to the user. This allows the user to choose from multiple scenario proposals.

[0050] When generating a scenario, the scenario generation unit can incorporate perspectives from different cultures or regions to generate a globally applicable scenario. For example, when the generation AI generates a scenario for an "online learning support service," it incorporates perspectives from different cultures and regions to generate a globally applicable scenario. The generation AI references data from different cultures and regions and reflects this in the scenario. For example, the generation AI takes into account the differences between educational systems in Asia and Europe to generate a globally applicable scenario. This makes it possible to generate a globally applicable scenario.

[0051] When generating a scenario, the scenario generation unit allows the generation AI to automatically suggest related visual materials, improving the quality of the scenario. For example, when the generation AI generates a scenario for an "online learning support service," it automatically suggests related visual materials (e.g., images and videos of learning scenes), improving the quality of the scenario. The generation AI references a database of visual materials and selects materials relevant to the scenario. For example, the generation AI selects appropriate images and videos based on the content of the scenario and suggests them to the user. This improves the quality of the scenario.

[0052] When the generation AI creates a manga, the manga creation unit can learn the user's past design preferences and provide an individually customized design. For example, when a user creates a manga for the "online learning support service," the generation AI learns the user's past design preferences (e.g., character style and color usage) and provides a customized design. The generation AI references the user's past design preference data and generates an optimal design. For example, the generation AI analyzes the user's past design preferences and provides a design tailored to the user. This makes it possible to provide a design tailored to the user.

[0053] When creating a manga, the manga creation unit can have the generation AI present multiple design proposals, allowing the user to choose from them. For example, when a user creates a manga for an "online learning support service," the generation AI presents multiple design proposals, such as "simple design" and "colorful design," allowing the user to choose from them. The generation AI generates multiple design proposals using a design generation algorithm and presents them to the user. For example, the generation AI generates multiple design proposals based on user input and presents them to the user. This allows the user to choose from multiple design proposals.

[0054] The manga creation unit can incorporate different art styles or themes when creating manga, providing the user with a variety of options. For example, when a user creates a manga for an "online learning support service," the generation AI can suggest different art styles, such as "anime style" or "realistic style," to provide the user with a variety of options. The generation AI refers to a database of art styles and themes to suggest appropriate options to the user. For example, the generation AI can suggest different art styles or themes based on the user's input. This allows the user to choose from a variety of options.

[0055] The manga creation unit allows the generation AI to automatically add audio and sound effects to each frame of the manga, creating more interactive content. For example, when the generation AI creates a manga for an "online learning support service," it adds audio and sound effects to each frame to create interactive content. The generation AI references a database of audio and sound effects and selects appropriate audio and sound effects. For example, the generation AI selects appropriate audio and sound effects based on the content of the manga and adds them to each frame. This allows interactive content to be created.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The service content acquisition unit acquires service content from the user. For example, the user inputs the service content through an online form. The service content acquisition unit can also allow the user to provide the service content using voice input. Furthermore, the service content acquisition unit can also allow the user to provide the service content using handwritten input. For example, the user inputs the service content by handwriting and converts it into digital data. Step 2: The scenario generation unit generates a scenario based on the service content acquired by the service content acquisition unit. For example, the generation AI generates a scenario that emphasizes the features and benefits of the service provided by the user. The generation AI can also customize the scenario based on user instructions. For example, the generation AI generates a scenario based on a theme specified by the user. Step 3: The manga creation unit creates a manga based on the scenario generated by the scenario generation unit. For example, the generation AI creates a manga depicting a character learning online based on the scenario. The generation AI can also finish the manga according to a design envisioned by the user. For example, the generation AI creates a manga based on a design style specified by the user.

[0058] (Example 2) The image creation tool according to an embodiment of the present invention is a system that allows users to clearly communicate their company's services in the form of manga. This system allows users to create manga with the design they desire from a series of multiple frames. This allows the image creation tool to visually communicate their company's services in an easy-to-understand manner.

[0059] An image creation tool according to an embodiment includes a service content acquisition unit, a scenario generation unit, and a manga creation unit. The service content acquisition unit acquires service content from a user. For example, the user inputs the service content through an online form. The service content acquisition unit can also allow the user to provide the service content using voice input. The service content acquisition unit can also allow the user to provide the service content using handwritten input. For example, the user inputs the service content by hand and converts it into digital data. The scenario generation unit generates a scenario based on the service content acquired by the service content acquisition unit. For example, the generation AI generates a scenario that highlights the features and benefits of the service provided by the user. The generation AI can also customize the scenario based on user instructions. For example, the generation AI generates a scenario based on a theme specified by the user. The manga creation unit creates a manga based on the scenario generated by the scenario generation unit. For example, the generation AI creates a manga depicting a character learning online based on the scenario. The generation AI can also finish the manga according to a design envisioned by the user. For example, the generation AI creates a manga based on a design style specified by the user. This allows the image creation tool according to an embodiment to easily communicate the company's services in manga format. For example, if a user provides an online learning support service, the generation AI will draw a "scenario that emphasizes the benefits of online learning" and create a manga based on that scenario. If a user provides a health food sales service, the generation AI will draw a "scenario that emphasizes the effects of health food" and create a manga based on that scenario. If a user advertises a sports club, the generation AI will draw a "scenario that emphasizes the appeal of the sports club" and create a manga based on that scenario.

[0060] The service content acquisition unit allows the generation AI to automatically generate related questions for the service content entered by the user, thereby eliciting detailed information. For example, when a user enters "online learning support service," the service content acquisition unit automatically generates related questions such as "What age group is the target?" and "What subjects do you offer?" to elicit detailed information. The generation AI uses natural language processing technology to analyze the user's input and generate related questions. For example, the generation AI selects appropriate questions based on the user's input and presents them to the user. This makes it easier for the user to provide detailed information.

[0061] When the service content acquisition unit inputs the service content, the generation AI can present past success stories or failure stories for the user to refer to. For example, if the user inputs "online learning support service," the service content acquisition unit can present "examples of successful learning programs specialized for specific age groups" as past success stories for the user to refer to. The generation AI refers to a past database and selects success stories and failure stories related to the user's input. For example, the generation AI extracts the case most relevant to the user's input from the past case database and presents it to the user. This makes it easier for the user to refer to past cases.

[0062] The service content acquisition unit uses the emotion estimation function to analyze the emotions a user feels when entering service content and can provide advice to elicit positive emotions. For example, when a user enters "online learning support service," the service content acquisition unit causes the generation AI to analyze the user's facial expressions and voice and display an encouraging message to elicit positive emotions. The generation AI uses an emotion analysis algorithm to analyze the user's emotions and provide appropriate advice. For example, the generation AI collects the user's facial expression data and voice data and performs emotion analysis. Based on the results of the emotion analysis, the generation AI provides the user with advice to elicit positive emotions. This allows the user to enter service content with positive emotions.

[0063] The service content acquisition unit can also accept voice or handwritten input of service content, improving user convenience. For example, when a user vocally inputs "online learning support service," the service content acquisition unit causes the generation AI to analyze the content using voice recognition technology and convert it into text data. The generation AI then analyzes the user's voice using a voice recognition algorithm and converts it into text data. For example, the generation AI collects the user's voice data and performs voice recognition. Based on the results of the voice recognition, the generation AI converts the user's voice into text data. The service content acquisition unit can also allow users to input service content by hand and convert it into digital data. For example, the generation AI can analyze the user's handwritten input using handwriting recognition technology and convert it into text data. This allows users to use a variety of input methods.

[0064] The service content acquisition unit can automatically refer to service content from different industries or fields to provide the user with a new perspective. For example, when a user inputs "online learning support service," the generation AI will refer to successful examples from outside the education industry (e.g., online services in the entertainment industry) to provide a new perspective. The generation AI will refer to databases from different industries and fields to select examples related to the user's input. For example, the generation AI will extract the examples most relevant to the user's input from databases from different industries and fields and present them to the user. This makes it easier for the user to gain a new perspective.

[0065] The service content acquisition unit can use the emotion estimation function to display other users' emotional reactions to the service content entered by the user in real time, thereby promoting feedback. For example, when a user enters "online learning support service," the service content acquisition unit causes the generation AI to display other users' emotional reactions (e.g., joy or excitement) in real time, thereby promoting feedback. The generation AI analyzes other users' emotional reactions using emotion analysis technology and displays them in real time. For example, the generation AI collects emotional data of other users and performs emotion analysis. Based on the results of the emotion analysis, the generation AI displays other users' emotional reactions in real time. This allows the user to check other users' emotional reactions in real time.

[0066] When the generation AI draws a scenario, the scenario generation unit can refer to the user's past input data and generate an individually customized scenario. For example, when a user inputs "online learning support service," the scenario generation unit causes the generation AI to refer to the past input data (e.g., previously entered learning content and target age) and generate a customized scenario. The generation AI references the past input data from a database and customizes the scenario based on the user's input. For example, the generation AI analyzes the user's past input data and generates an optimal scenario. This makes it possible to generate a scenario tailored to the user.

[0067] When generating a scenario, the scenario generation unit can have the generation AI present multiple scenario proposals and allow the user to select from them. For example, when a user inputs "online learning support service," the scenario generation unit can have the generation AI present multiple scenario proposals, such as "a scenario that emphasizes the fun of learning" and "a scenario that emphasizes the results of learning," and allow the user to select from them. The generation AI generates multiple scenario proposals using a scenario generation algorithm and presents them to the user. For example, the generation AI generates multiple scenario proposals based on the user's input and presents them to the user. This allows the user to select from multiple scenario proposals.

[0068] The scenario generation unit can use the emotion estimation function to analyze the user's emotional response to the generated scenario and prioritize presenting the scenario that elicits the most positive response. For example, the scenario generation unit generates a scenario for an "online learning support service" using a generation AI, analyzes the user's emotional response, and prioritizes presenting the scenario that elicits the most positive response. The generation AI analyzes the user's emotional response using emotion analysis technology and selects the optimal scenario. For example, the generation AI collects user emotion data and performs emotion analysis. Based on the results of the emotion analysis, the generation AI selects the scenario that elicits the most positive response and presents it to the user. This allows the user to select the most positive scenario.

[0069] The scenario generation unit can incorporate perspectives from different cultures or regions when generating a scenario, generating a scenario that is applicable globally. For example, when the generation AI generates a scenario for an "online learning support service," the scenario generation unit incorporates perspectives from different cultures and regions to generate a scenario that is applicable globally. The generation AI references data from different cultures and regions and reflects this in the scenario. For example, the generation AI takes into account the differences between educational systems in Asia and Europe to generate a scenario that is applicable globally. This makes it possible to generate a scenario that is applicable globally.

[0070] When generating a scenario, the scenario generation unit allows the generation AI to automatically suggest related visual materials, thereby improving the quality of the scenario. For example, when the generation AI generates a scenario for an "online learning support service," the scenario generation unit automatically suggests related visual materials (e.g., images and videos of learning scenes), thereby improving the quality of the scenario. The generation AI references a database of visual materials and selects materials related to the scenario. For example, the generation AI selects appropriate images and videos based on the content of the scenario and suggests them to the user. This improves the quality of the scenario.

[0071] The scenario generation unit can use the emotion estimation function to monitor the user's emotional reactions to the scenario in real time and suggest modifications to the scenario. For example, in the scenario generation unit, the generation AI generates a scenario for an "online learning support service," monitors the user's emotional reactions in real time, and suggests modifications to the scenario as necessary. The generation AI analyzes the user's emotional reactions using emotion analysis technology and suggests modifications to the scenario. For example, the generation AI collects user emotion data and performs emotion analysis. Based on the results of the emotion analysis, the generation AI suggests modifications to the scenario. This allows the scenario to be modified based on the user's emotional reactions.

[0072] When the generation AI creates a manga, the manga creation unit can learn the user's past design preferences and provide an individually customized design. For example, when a user creates a manga for the "online learning support service," the generation AI learns the user's past design preferences (e.g., character style and color usage) and provides a customized design. The generation AI references the user's past design preference data and generates an optimal design. For example, the generation AI analyzes the user's past design preferences and provides a design tailored to the user. This makes it possible to provide a design tailored to the user.

[0073] When creating a manga, the manga creation unit can have the generation AI present multiple design proposals and allow the user to select from them. For example, when a user creates a manga for an "online learning support service," the manga creation unit can have the generation AI present multiple design proposals, such as "simple design" and "colorful design," and allow the user to select from them. The generation AI generates multiple design proposals using a design generation algorithm and presents them to the user. For example, the generation AI generates multiple design proposals based on user input and presents them to the user. This allows the user to select from multiple design proposals.

[0074] The manga creation unit can use the emotion estimation function to analyze the user's emotional response to the generated manga and prioritize presenting the design that elicits the most positive response. For example, the generation AI creates a manga for an "online learning support service," analyzes the user's emotional response, and prioritizes presenting the design that elicits the most positive response. The generation AI analyzes the user's emotional response using emotion analysis technology and selects the optimal design. For example, the generation AI collects user emotion data and performs emotion analysis. Based on the results of the emotion analysis, the generation AI selects the design that elicits the most positive response and presents it to the user. This allows the user to select the most positive design.

[0075] The manga creation unit can incorporate different art styles or themes when creating a manga, providing the user with a variety of options. For example, when a user creates a manga for an "online learning support service," the generation AI can suggest different art styles, such as "anime style" or "realistic style," providing the user with a variety of options. The generation AI refers to a database of art styles and themes to suggest appropriate options to the user. For example, the generation AI can suggest different art styles or themes based on the user's input. This allows the user to choose from a variety of options.

[0076] The manga creation unit allows the generation AI to automatically add audio and sound effects to each frame of the manga, creating more interactive content. For example, when the generation AI creates a manga for an "online learning support service," the manga creation unit adds audio and sound effects to each frame to create interactive content. The generation AI references a database of audio and sound effects and selects appropriate audio and sound effects. For example, the generation AI selects appropriate audio and sound effects based on the content of the manga and adds them to each frame. This allows interactive content to be created.

[0077] The manga creation unit can use the emotion estimation function to monitor users' emotional reactions to the manga in real time and suggest design modifications. For example, the manga creation unit has a generation AI create a manga for an "online learning support service," monitor users' emotional reactions in real time, and suggest design modifications as needed. The generation AI analyzes users' emotional reactions using emotion analysis technology and suggests design modifications. For example, the generation AI collects users' emotional data and performs emotion analysis. Based on the results of the emotion analysis, the generation AI suggests design modifications. This allows the design to be modified based on the users' emotional reactions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The service content acquisition unit allows the generation AI to automatically generate related questions for the service content entered by the user, thereby eliciting detailed information. For example, if a user enters "online learning support service," the generation AI automatically generates related questions such as "What age group is the target?" and "What subjects do you offer?" to elicit detailed information. The generation AI uses natural language processing technology to analyze the user's input and generate related questions. For example, the generation AI selects appropriate questions based on the user's input and presents them to the user. This makes it easier for the user to provide detailed information.

[0080] The service content acquisition unit allows the generation AI to automatically generate related questions for the service content entered by the user, thereby eliciting detailed information. For example, if a user enters "online learning support service," the generation AI automatically generates related questions such as "What age group is the target?" and "What subjects do you offer?" to elicit detailed information. The generation AI uses natural language processing technology to analyze the user's input and generate related questions. For example, the generation AI selects appropriate questions based on the user's input and presents them to the user. This makes it easier for the user to provide detailed information.

[0081] When the service content acquisition unit inputs service content, the generation AI can present past success stories or failure stories for the user to refer to. For example, if the user inputs "online learning support service," the generation AI can present "examples of successful learning programs specialized for specific age groups" as past success stories for the user to refer to. The generation AI refers to a past database and selects success stories and failure stories related to the user's input. For example, the generation AI extracts the case most relevant to the user's input from the past case database and presents it to the user. This makes it easier for the user to refer to past cases.

[0082] The service content acquisition unit can use the emotion estimation function to analyze the emotions a user feels when entering service content and provide advice to elicit positive emotions. For example, when a user enters "online learning support service," the generation AI analyzes the user's facial expressions and voice and displays an encouraging message to elicit positive emotions. The generation AI uses an emotion analysis algorithm to analyze the user's emotions and provide appropriate advice. For example, the generation AI collects the user's facial expression data and voice data and performs emotion analysis. Based on the results of the emotion analysis, the generation AI provides the user with advice to elicit positive emotions. This allows the user to enter service content with positive emotions.

[0083] The service content acquisition unit can also accept voice or handwritten input of service content, improving user convenience. For example, when a user voice-inputs "online learning support service," the generation AI analyzes the content using voice recognition technology and converts it into text data. The generation AI analyzes the user's voice using a voice recognition algorithm and converts it into text data. For example, the generation AI collects the user's voice data and performs voice recognition. Based on the results of the voice recognition, the generation AI converts the user's voice into text data. The service content acquisition unit can also allow users to input service content by hand and convert it into digital data. For example, the generation AI analyzes the user's handwritten input using handwriting recognition technology and converts it into text data. This allows users to use a variety of input methods.

[0084] The service content acquisition unit can automatically reference service content from different industries or fields to provide the user with a new perspective. For example, if a user inputs "online learning support service," the generation AI will reference successful examples from outside the education industry (e.g., online services in the entertainment industry) to provide a new perspective. The generation AI will reference databases from different industries and fields to select examples related to the user's input. For example, the generation AI will extract the examples most relevant to the user's input from databases from different industries and fields and present them to the user. This makes it easier for the user to gain a new perspective.

[0085] The service content acquisition unit can use the emotion estimation function to display other users' emotional reactions to the service content entered by the user in real time, thereby promoting feedback. For example, when a user enters "online learning support service," the generation AI displays other users' emotional reactions (e.g., joy or excitement) in real time, promoting feedback. The generation AI analyzes other users' emotional reactions using emotion analysis technology and displays them in real time. For example, the generation AI collects emotional data of other users and performs emotion analysis. Based on the results of the emotion analysis, the generation AI displays other users' emotional reactions in real time. This allows the user to check other users' emotional reactions in real time.

[0086] When the generation AI creates a scenario, the scenario generation unit can reference the user's past input data and generate an individually customized scenario. For example, if a user inputs "online learning support service," the generation AI references the past input data (e.g., previously entered learning content and target age) and generates a customized scenario. The generation AI references the past input data from a database and customizes the scenario based on the user's input. For example, the generation AI analyzes the user's past input data and generates an optimal scenario. This allows it to generate a scenario tailored to the user.

[0087] When generating a scenario, the scenario generation unit can have the generation AI present multiple scenario proposals and allow the user to choose from them. For example, if a user inputs "online learning support service," the generation AI will present multiple scenario proposals, such as "a scenario that emphasizes the fun of learning" and "a scenario that emphasizes the results of learning," and allow the user to choose from them. The generation AI uses a scenario generation algorithm to generate multiple scenario proposals and present them to the user. For example, the generation AI generates multiple scenario proposals based on the user's input and presents them to the user. This allows the user to choose from multiple scenario proposals.

[0088] The scenario generation unit can use the emotion estimation function to analyze the user's emotional response to the generated scenario and prioritize presenting the scenario that elicits the most positive response. For example, the generation AI generates a scenario for an "online learning support service," analyzes the user's emotional response, and prioritizes presenting the scenario that elicits the most positive response. The generation AI analyzes the user's emotional response using emotion analysis technology and selects the optimal scenario. For example, the generation AI collects user emotion data and performs emotion analysis. Based on the results of the emotion analysis, the generation AI selects the scenario that elicits the most positive response and presents it to the user. This allows the user to select the most positive scenario.

[0089] When generating a scenario, the scenario generation unit can incorporate perspectives from different cultures or regions to generate a globally applicable scenario. For example, when the generation AI generates a scenario for an "online learning support service," it incorporates perspectives from different cultures and regions to generate a globally applicable scenario. The generation AI references data from different cultures and regions and reflects this in the scenario. For example, the generation AI takes into account the differences between educational systems in Asia and Europe to generate a globally applicable scenario. This makes it possible to generate a globally applicable scenario.

[0090] When generating a scenario, the scenario generation unit allows the generation AI to automatically suggest related visual materials, improving the quality of the scenario. For example, when the generation AI generates a scenario for an "online learning support service," it automatically suggests related visual materials (e.g., images and videos of learning scenes), improving the quality of the scenario. The generation AI references a database of visual materials and selects materials relevant to the scenario. For example, the generation AI selects appropriate images and videos based on the content of the scenario and suggests them to the user. This improves the quality of the scenario.

[0091] The scenario generation unit can use the emotion estimation function to monitor the user's emotional reactions to the scenario in real time and suggest modifications to the scenario. For example, the generation AI generates a scenario for an "online learning support service," monitors the user's emotional reactions in real time, and suggests modifications to the scenario as necessary. The generation AI analyzes the user's emotional reactions using emotion analysis technology and suggests modifications to the scenario. For example, the generation AI collects user emotion data and performs emotion analysis. Based on the results of the emotion analysis, the generation AI suggests modifications to the scenario. This allows the scenario to be modified based on the user's emotional reactions.

[0092] When the generation AI creates a manga, the manga creation unit can learn the user's past design preferences and provide an individually customized design. For example, when a user creates a manga for the "online learning support service," the generation AI learns the user's past design preferences (e.g., character style and color usage) and provides a customized design. The generation AI references the user's past design preference data and generates an optimal design. For example, the generation AI analyzes the user's past design preferences and provides a design tailored to the user. This makes it possible to provide a design tailored to the user.

[0093] When creating a manga, the manga creation unit can have the generation AI present multiple design proposals, allowing the user to choose from them. For example, when a user creates a manga for an "online learning support service," the generation AI presents multiple design proposals, such as "simple design" and "colorful design," allowing the user to choose from them. The generation AI generates multiple design proposals using a design generation algorithm and presents them to the user. For example, the generation AI generates multiple design proposals based on user input and presents them to the user. This allows the user to choose from multiple design proposals.

[0094] The manga creation unit can use the emotion estimation function to analyze users' emotional reactions to the generated manga and prioritize presenting designs that elicit the most positive reactions. For example, the generation AI creates manga for an "online learning support service," analyzes users' emotional reactions, and prioritizes presenting designs that elicit the most positive reactions. The generation AI uses emotion analysis technology to analyze users' emotional reactions and select the optimal design. For example, the generation AI collects users' emotional data and performs emotion analysis. Based on the results of the emotion analysis, the generation AI selects designs that elicit the most positive reactions and presents them to the user. This allows users to select the most positive design.

[0095] The manga creation unit can incorporate different art styles or themes when creating manga, providing the user with a variety of options. For example, when a user creates a manga for an "online learning support service," the generation AI can suggest different art styles, such as "anime style" or "realistic style," to provide the user with a variety of options. The generation AI refers to a database of art styles and themes to suggest appropriate options to the user. For example, the generation AI can suggest different art styles or themes based on the user's input. This allows the user to choose from a variety of options.

[0096] The manga creation unit allows the generation AI to automatically add audio and sound effects to each frame of the manga, creating more interactive content. For example, when the generation AI creates a manga for an "online learning support service," it adds audio and sound effects to each frame to create interactive content. The generation AI references a database of audio and sound effects and selects appropriate audio and sound effects. For example, the generation AI selects appropriate audio and sound effects based on the content of the manga and adds them to each frame. This allows interactive content to be created.

[0097] The manga creation unit can use the emotion estimation function to monitor users' emotional reactions to the manga in real time and suggest design modifications. For example, the generation AI creates manga for an "online learning support service," monitors users' emotional reactions in real time, and suggests design modifications as necessary. The generation AI analyzes users' emotional reactions using emotion analysis technology and suggests design modifications. For example, the generation AI collects users' emotional data and performs emotion analysis. Based on the results of the emotion analysis, the generation AI suggests design modifications. This allows the design to be modified based on the users' emotional reactions.

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

[0099] Step 1: The service content acquisition unit acquires service content from the user. For example, the user inputs the service content through an online form. The service content acquisition unit can also allow the user to provide the service content using voice input. Furthermore, the service content acquisition unit can also allow the user to provide the service content using handwritten input. For example, the user inputs the service content by handwriting and converts it into digital data. Step 2: The scenario generation unit generates a scenario based on the service content acquired by the service content acquisition unit. For example, the generation AI generates a scenario that emphasizes the features and benefits of the service provided by the user. The generation AI can also customize the scenario based on user instructions. For example, the generation AI generates a scenario based on a theme specified by the user. Step 3: The manga creation unit creates a manga based on the scenario generated by the scenario generation unit. For example, the generation AI creates a manga depicting a character learning online based on the scenario. The generation AI can also finish the manga according to a design envisioned by the user. For example, the generation AI creates a manga based on a design style specified by the user.

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

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

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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, in order to avoid confusion and to 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.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 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 service content acquisition unit that acquires service content from a user; a scenario generation unit that generates a scenario based on the service content acquired by the service content acquisition unit; a manga creation unit that creates a manga based on the scenario generated by the scenario generation unit; A system characterized by:

2. The service content acquisition unit The AI ​​automatically generates related questions based on the service details entered by the user, and extracts detailed information.

2. The system of claim 1.

3. The service content acquisition unit Automatically reference service content from different industries or fields to provide users with new perspectives 2. The system of claim 1.

4. The scenario generation unit When the generation AI creates a scenario, it references the user's past input data and generates an individually customized scenario.

2. The system of claim 1.

5. The manga creation unit When creating manga, the generative AI learns the user's past design preferences and provides individually customized designs.

2. The system of claim 1.

6. The service content acquisition unit Using emotion estimation, the system analyzes the emotions users feel when entering service details and provides advice to elicit positive emotions.

2. The system of claim 1.

7. The scenario generation unit Using emotion estimation, the system analyzes the user's emotional response to the generated scenarios and prioritizes the scenarios that elicit the most positive responses.

2. The system of claim 1.

8. The manga creation unit Using emotion estimation, the system analyzes users' emotional reactions to the generated manga and prioritizes the designs that elicit the most positive reactions.

2. The system of claim 1.

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