System

Abstract

An object of the system according to the embodiment is to improve the efficiency of the process of line drawing and coloring in cartoon production and to quickly generate a high-quality work.SOLUTION: A system includes a reception part, a line drawing generation part, a coloring part, a learning data collection part, and a user interface part. The receiving unit receives a line drawing and a coloring instruction from a user. The line drawing generation portion generates a line drawing on the basis of the command received by the reception portion. The coloring unit colors the line drawing generated by the line drawing generation unit. The learning data collection unit collects public learning data. The user interface unit controls the reception unit, the line drawing generation unit, the coloring unit, and the learning data collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, the line drawing and coloring processes in manga production were time-consuming and labor-intensive, making it difficult to carry out efficiently.

[0005] The system according to the embodiment aims to streamline the line drawing and coloring processes in manga production and to quickly produce high-quality works. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a line drawing generation unit, a coloring unit, a training data collection unit, and a user interface unit. The reception unit receives instructions for line drawing and coloring from a user. The line drawing generation unit generates a line drawing based on the instructions received by the reception unit. The coloring unit colors the line drawing generated by the line drawing generation unit. The training data collection unit collects publicly available training data. The user interface unit controls the reception unit, line drawing generation unit, coloring unit, and training data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment streamlines the line drawing and coloring processes in manga production, enabling high-quality works to be produced quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A manga production support system according to an embodiment of the present invention accepts line drawing and coloring instructions from a user, and an AI uses open-source training data to generate line drawings and automatically color the line drawings. In this manga production support system, a user inputs line drawing and coloring instructions with simple operations, and an AI generates line drawings using open-source training data and automatically colors the line drawings. This process solves the issues of a shortage of artists and long production times, allowing artists to produce more works. It also accommodates a variety of styles and genres, promoting connections with readers. For example, a user inputs line drawing and coloring instructions with simple operations. The user can select their desired style and genre. For example, if a user wants to create a fantasy-style manga, they select that style. This information is input into the AI. The AI ​​then analyzes the input information and generates line drawings using open-source training data. The AI ​​has learned from past manga data and can generate line drawings that match the user's desired style. For example, when generating a fantasy-style line drawing, the AI ​​generates the line drawing using fantasy-style training data. The AI ​​then automatically colors the generated line drawing. The AI ​​can color according to the user's desired color palette and atmosphere. For example, if a user requests a bright, fantasy-style manga, the AI ​​can color according to that request. This allows the manga production support system to solve the problems of a shortage of artists and long production times, allowing artists to produce more works. It can also accommodate a variety of styles and genres, promoting connections with readers. This allows artists to enjoy creative freedom and produce more works. For example, even if an artist is creating a manga of a different genre, the AI ​​can create line drawings and coloring that are appropriate for that genre. The user interface is also designed for easy operation, allowing artists to operate it intuitively. This allows artists to focus on their creations without having to waste time on detailed tasks.

[0029] A manga production support system according to an embodiment includes a reception unit, a line drawing generation unit, a coloring unit, a learning data collection unit, and a user interface unit. The reception unit receives instructions for line drawing and coloring from a user. The user can select, for example, a desired style or genre. For example, if a user wants to create a fantasy-style manga, the reception unit provides a function for selecting the style. The line drawing generation unit generates a line drawing based on the instructions received by the reception unit using AI. The line drawing generation unit generates the line drawing using, for example, open-source learning data. The AI ​​learns from past manga data and can generate line drawings that match the user's desired style. For example, when generating a fantasy-style line drawing, the AI ​​generates the line drawing using fantasy-style learning data. The coloring unit uses AI to color the line drawing generated by the line drawing generation unit. The coloring unit colors the line drawing according to, for example, the user's desired color scheme and atmosphere. The AI ​​can color the line drawing according to the user's desired color scheme and atmosphere. For example, if a user desires a bright, fantasy-style manga, the AI ​​colors the line drawing according to the user's desire. The training data collection unit collects open-source training data. The training data collection unit collects training data corresponding to, for example, various styles and genres. The user interface unit oversees the reception unit, line drawing generation unit, coloring unit, and training data collection unit, and provides an interface that is easy for users to operate. For example, the user simply selects the desired style and color, and the AI ​​automatically performs line drawing and coloring, allowing the artist to concentrate on creation without having to waste time on detailed work. As a result, the manga production support system according to the embodiment automates line drawing and coloring based on user instructions, achieving efficient and high-quality manga production.

[0030] The line drawing generation unit can generate line drawings using open source training data. Examples of open source training data include, but are not limited to, specific dataset names and license conditions. The line drawing generation unit can generate line drawings corresponding to various styles and genres using, for example, open source training data. For example, when generating fantasy-style line drawings, the line drawing generation unit can use training data specialized for fantasy. Furthermore, when generating science fiction-style line drawings, the line drawing generation unit can also use training data specialized for science fiction. Furthermore, when generating comedy-style line drawings, the line drawing generation unit can also use training data specialized for comedy. In this way, by using open source training data, line drawings corresponding to various styles and genres can be generated.

[0031] The coloring unit can perform coloring according to the color tone and atmosphere specified by the user. Examples of color tone and atmosphere include, but are not limited to, color palettes and atmosphere categories. The coloring unit performs coloring according to the color tone and atmosphere desired by the user. For example, if a user desires a fantasy-style manga with bright colors, the coloring unit can perform coloring according to that desire. Alternatively, if a user desires a horror-style manga with dark colors, the coloring unit can perform coloring according to that desire. Furthermore, if a user desires a science fiction-style manga with realistic colors, the coloring unit can perform coloring according to that desire. This allows the user to color with the color tone and atmosphere desired, thereby providing a more satisfying work.

[0032] The learning data collection unit can collect learning data corresponding to a variety of styles and genres. Examples of the various styles and genres include, but are not limited to, anime-style, realistic, and fantasy. For example, the learning data collection unit collects anime-style learning data. The learning data collection unit can also collect realistic-style learning data. Furthermore, the learning data collection unit can also collect fantasy-style learning data. This allows for the collection of learning data corresponding to a variety of styles and genres, thereby meeting a wide range of needs.

[0033] The user interface unit can provide an interface that the user can operate intuitively. Examples of an intuitively operable interface include, but are not limited to, usability test results and design guidelines. For example, the user interface unit can provide an interface in which the AI ​​automatically performs line drawing and coloring by simply selecting the user's desired style and color. The user interface unit can also provide an interface in which instructions for line drawing and coloring can be input with simple operations. Furthermore, the user interface unit can provide an interface with a devised screen layout and operation method that allows the user to operate intuitively. This improves operability by providing an interface that the user can operate intuitively.

[0034] The reception unit can provide a selection function for selecting a style or genre specified by the user. Examples of styles and genres include, but are not limited to, fantasy, science fiction, and comedy. For example, if a user wants to create a fantasy-style manga, the reception unit can provide a function for selecting that style. Furthermore, if a user wants to create a science fiction-style manga, the reception unit can also provide a function for selecting that style. Furthermore, if a user wants to create a comedy-style manga, the reception unit can also provide a function for selecting that style. This allows the user to select their desired style or genre, making it possible to provide a more personalized work.

[0035] The reception unit can analyze the user's past instruction history and select an appropriate reception method. For example, the reception unit preferentially suggests instruction methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an instruction method to be used in a specific time period based on the user's past instruction history. Furthermore, the reception unit can select the optimal reception method based on the style or genre that the user has preferred in the past. In this way, the optimal reception method can be provided to the user by analyzing the past instruction history.

[0036] The reception unit can perform filtering based on the user's current project or field of interest at the time of reception. For example, the reception unit preferentially receives only instructions related to a project currently in progress by the user. The reception unit can also filter and receive related instructions based on the user's field of interest. Furthermore, the reception unit can filter optimal instructions based on projects or fields in which the user has shown interest in the past. In this way, by filtering instructions based on the user's current project or field of interest, more relevant instructions can be received.

[0037] The reception unit can select an appropriate reception means depending on the user's input method when receiving the input. For example, if the user prefers voice input, the reception unit can preferentially receive voice input. Furthermore, if the user prefers text input, the reception unit can also preferentially receive text input. Furthermore, if the user prefers image input, the reception unit can also preferentially receive image input. This allows the optimal reception means to be selected depending on the user's input method, making it possible to provide a system that is easier to use.

[0038] The reception unit can prioritize receiving highly relevant instructions by taking into account the user's geographical location information when receiving instructions. For example, when the user is in a specific area, the reception unit prioritizes receiving instructions related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving instructions related to the user's destination. Furthermore, when the user is staying in a specific location, the reception unit can also prioritize receiving instructions related to that location. In this way, by taking into account the user's geographical location information, it is possible to prioritize receiving more relevant instructions.

[0039] The reception unit can analyze the user's social media activity and receive related instructions at the time of reception. The reception unit can receive related instructions based on, for example, content shared by the user on social media. The reception unit can also analyze the user's social media activity history and receive related instructions. Furthermore, the reception unit can also receive related instructions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to receive more relevant instructions.

[0040] The reception unit can customize the reception method based on the user's past feedback when receiving a call. For example, the reception unit suggests the optimal reception method based on the user's past feedback. The reception unit can also customize a specific reception method based on the user's past feedback. Furthermore, the reception unit can reflect the user's feedback and continuously improve the reception method. In this way, a more optimal reception method can be provided by reflecting the user's past feedback.

[0041] When generating a line drawing, the line drawing generation unit can adjust the level of detail of the line drawing based on the style specified by the user. For example, if the user desires a simple style, the line drawing generation unit generates a line drawing with a low level of detail. Alternatively, if the user desires a realistic style, the line drawing generation unit can generate a line drawing with a high level of detail. Furthermore, if the user desires an anime-style style, the line drawing generation unit can generate a line drawing with a medium level of detail. In this way, by adjusting the level of detail of the line drawing based on the style desired by the user, a line drawing that more closely matches the user's desire can be generated.

[0042] The line drawing generation unit can apply different generation algorithms according to different styles or genres when generating line drawings. For example, when generating a fantasy-style line drawing, the line drawing generation unit applies a generation algorithm specialized for fantasy. Furthermore, when generating a science fiction-style line drawing, the line drawing generation unit can also apply a generation algorithm specialized for science fiction. Furthermore, when generating a comedy-style line drawing, the line drawing generation unit can also apply a generation algorithm specialized for comedy. In this way, by applying generation algorithms according to different styles and genres, a wider variety of line drawings can be generated.

[0043] When generating a line drawing, the line drawing generation unit can improve the accuracy of the generation by referring to the user's past line drawing results. For example, the line drawing generation unit generates a new line drawing in a similar style based on a line drawing previously generated by the user. The line drawing generation unit can also analyze the user's past line drawing results and optimize the generation algorithm. Furthermore, the line drawing generation unit can also generate a new line drawing by incorporating features of line drawings that the user previously preferred. In this way, the accuracy of the generation can be improved by referring to the user's past line drawing results.

[0044] When generating line drawings, the line drawing generation unit can determine the priority of the line drawings based on the time of submission by the user. For example, if the user submits the line drawing early, the line drawing generation unit generates the line drawing with priority. Also, if the user submits the line drawing close to the deadline, the line drawing generation unit can also generate the line drawing with priority. Furthermore, if the user submits the line drawing at a specific date and time, the line drawing generation unit can also determine the priority of the line drawing based on the date and time. In this way, by determining the priority of the line drawings based on the time of submission by the user, efficient line drawing generation is possible.

[0045] The line drawing generation unit can adjust the order of the line drawings based on the user's level of interest when generating the line drawings. For example, the line drawing generation unit preferentially generates line drawings that the user associates with a specific project. The line drawing generation unit can also preferentially generate line drawings that the user associates with a specific genre. Furthermore, the line drawing generation unit can also preferentially generate line drawings that the user associates with a specific style. In this way, by adjusting the order of the line drawings based on the user's level of interest, more relevant line drawings can be preferentially generated.

[0046] When generating a line drawing, the line drawing generation unit can adjust the use of technical terms in the line drawing according to the user's knowledge level. For example, if the user is a beginner, the line drawing generation unit generates a line drawing with fewer technical terms. Furthermore, if the user is an intermediate user, the line drawing generation unit can also generate a line drawing with an appropriate amount of technical terms. Furthermore, if the user is an advanced user, the line drawing generation unit can also generate a line drawing with a lot of technical terms. In this way, by adjusting the use of technical terms according to the user's knowledge level, it is possible to provide a line drawing that is easier to understand.

[0047] During coloring, the coloring unit can adjust the level of detail of the coloring based on the user's desired color tone and atmosphere. For example, if the user desires simple coloring, the coloring unit performs coloring with a low level of detail. Alternatively, if the user desires realistic coloring, the coloring unit can perform coloring with a high level of detail. Furthermore, if the user desires anime-style coloring, the coloring unit can perform coloring with a medium level of detail. This allows coloring that more closely matches the user's desire by adjusting the level of detail of the coloring based on the user's desired color tone and atmosphere.

[0048] The coloring unit can apply different coloring algorithms according to different styles and genres when coloring. For example, when performing fantasy-style coloring, the coloring unit applies a coloring algorithm specialized for fantasy. Furthermore, when performing science fiction-style coloring, the coloring unit can also apply a coloring algorithm specialized for science fiction. Furthermore, when performing comedy-style coloring, the coloring unit can also apply a coloring algorithm specialized for comedy. In this way, by applying coloring algorithms according to different styles and genres, more diverse coloring can be performed.

[0049] When coloring, the coloring unit can improve the accuracy of the coloring by referring to the user's past coloring results. For example, the coloring unit performs new coloring in a similar style based on coloring performed by the user in the past. The coloring unit can also analyze the user's past coloring results and optimize the coloring algorithm. Furthermore, the coloring unit can perform new coloring by incorporating colors that the user has previously preferred. In this way, the accuracy of the coloring can be improved by referring to the user's past coloring results.

[0050] When coloring, the coloring unit can determine the priority of coloring based on the time of submission by the user. For example, if the user submits early, the coloring unit can prioritize coloring of that work. In addition, if the user submits close to the deadline, the coloring unit can also prioritize coloring of that work. Furthermore, if the user submits at a specific date and time, the coloring unit can also determine the priority of coloring based on that date and time. In this way, by determining the priority of coloring based on the time of submission by the user, efficient coloring is possible.

[0051] The coloring unit can adjust the order of coloring based on the user's level of interest during coloring. For example, the coloring unit can prioritize coloring related to a specific project by the user. The coloring unit can also prioritize coloring related to a specific genre by the user. Furthermore, the coloring unit can also prioritize coloring related to a specific style by the user. In this way, by adjusting the order of coloring based on the user's level of interest, more relevant coloring can be prioritized.

[0052] The coloring unit can adjust the use of technical terms in the coloring in accordance with the user's knowledge level when coloring. For example, if the user is a beginner, the coloring unit can perform coloring using fewer technical terms. Furthermore, if the user is an intermediate user, the coloring unit can perform coloring using an appropriate amount of technical terms. Furthermore, if the user is an advanced user, the coloring unit can perform coloring using a lot of technical terms. In this way, by adjusting the use of technical terms in accordance with the user's knowledge level, it is possible to provide coloring that is easier to understand.

[0053] The learning data collection unit can optimize the collection algorithm by referring to past learning data when collecting learning data. For example, the learning data collection unit preferentially collects similar data based on previously collected learning data. The learning data collection unit can also analyze past learning data and optimize the collection algorithm. Furthermore, the learning data collection unit can also adjust the collection method for new data by referring to trends in past learning data. In this way, by referring to past learning data, the collection algorithm can be optimized and more effective learning data can be collected.

[0054] The training data collection unit can update the collected data by reflecting user feedback when collecting training data. The training data collection unit updates the collected data based on, for example, feedback provided by the user. The training data collection unit can also optimize the collection algorithm by reflecting user feedback. Furthermore, the training data collection unit can also adjust the method of collecting new data by referring to user feedback. In this way, by reflecting user feedback, the collected data can be continuously improved and more appropriate training data can be collected.

[0055] When collecting learning data, the learning data collection unit can weight the collected data based on the time of data submission. For example, the learning data collection unit preferentially collects data submitted early. The learning data collection unit can also preferentially collect data submitted close to a deadline. Furthermore, the learning data collection unit can weight and collect data submitted at a specific date and time. In this way, by weighting the collected data based on the time of data submission, more important data can be preferentially collected.

[0056] When collecting training data, the training data collection unit can integrate information from multiple data sources to expand the collected data. For example, the training data collection unit integrates open source data to expand the collected data. The training data collection unit can also integrate data provided by a user to expand the collected data. Furthermore, the training data collection unit can also integrate information from an external database to expand the collected data. This allows for the collection of richer training data by integrating information from different data sources.

[0057] When displaying an interface, the user interface unit can select an appropriate display method by referring to the user's past operation history. For example, the user interface unit may preferentially display an interface design that the user has used in the past. The user interface unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the user interface unit can customize the interface based on functions that the user has used in the past. In this way, by referring to the user's past operation history, it is possible to provide an interface that is easier to use.

[0058] The user interface unit can customize the display content according to the user's current task when displaying the interface. For example, if the user is creating a line drawing, the user interface unit can prioritize displaying functions related to the line drawing. Also, if the user is coloring, the user interface unit can prioritize displaying functions related to coloring. Furthermore, if the user is collecting learning data, the user interface unit can prioritize displaying functions related to collection. This allows for more efficient operation by customizing the display content according to the user's current task.

[0059] The user interface unit can select an appropriate display method based on the user's device information when displaying the interface. For example, if the user is using a smartphone, the user interface unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the user interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the user interface unit can provide a display method optimized for a wide screen. This makes it possible to provide a more appropriate display method by taking the user's device information into consideration.

[0060] The user interface unit can make the display content multilingual in accordance with the user's language setting when displaying the interface. For example, the user interface unit automatically sets the interface language based on the language setting of the user's device. The user interface unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the user interface unit can display the interface in that language. This makes it possible to provide a more user-friendly interface by providing multilingual display content in accordance with the user's language setting.

[0061] The user interface unit can adjust the display content based on the user's customization settings when displaying the interface. For example, the user interface unit displays the interface based on the customization content previously set by the user. The user interface unit can also adjust the display content by reflecting the user's customization settings. Furthermore, when the user makes new customization settings, the user interface unit can immediately reflect those settings and display the interface. This makes it possible to provide a more personalized interface by reflecting the user's customization settings.

[0062] The user interface unit can analyze the user's social media activity and provide related information when displaying the interface. The user interface unit can display related information on the interface based on, for example, content shared by the user on social media. The user interface unit can also analyze the user's social media activity history and display related information on the interface. Furthermore, the user interface unit can also display related information on the interface by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant information can be provided.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The reception unit can analyze the user's past instruction history and select an appropriate reception method. For example, it can preferentially suggest instruction methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an instruction method to be used in a specific time period based on the user's past instruction history. Furthermore, the reception unit can select the optimal reception method based on the style or genre that the user has preferred in the past. In this way, by analyzing the past instruction history, it is possible to provide the user with the optimal reception method.

[0065] When generating a line drawing, the line drawing generation unit can adjust the level of detail of the line drawing based on the style specified by the user. For example, if the user desires a simple style, the line drawing generation unit generates a line drawing with a low level of detail. Alternatively, if the user desires a realistic style, the line drawing generation unit can generate a line drawing with a high level of detail. Furthermore, if the user desires an anime-style style, the line drawing generation unit can generate a line drawing with a medium level of detail. In this way, by adjusting the level of detail of the line drawing based on the style desired by the user, a line drawing that more closely matches the user's desire can be generated.

[0066] During coloring, the coloring unit can adjust the level of detail of the coloring based on the user's desired color tone and atmosphere. For example, if the user desires simple coloring, the coloring unit performs coloring with a low level of detail. Alternatively, if the user desires realistic coloring, the coloring unit can perform coloring with a high level of detail. Furthermore, if the user desires anime-style coloring, the coloring unit can perform coloring with a medium level of detail. This allows coloring that more closely matches the user's desire by adjusting the level of detail of the coloring based on the user's desired color tone and atmosphere.

[0067] When collecting learning data, the learning data collection unit can optimize the collection algorithm by referring to past learning data. For example, similar data is preferentially collected based on previously collected learning data. The learning data collection unit can also analyze past learning data and optimize the collection algorithm. Furthermore, the learning data collection unit can also adjust the method for collecting new data by referring to trends in past learning data. In this way, by referring to past learning data, the collection algorithm can be optimized and more effective learning data can be collected.

[0068] When displaying an interface, the user interface unit can select an appropriate display method by referring to the user's past operation history. For example, it can preferentially display an interface design that the user has used favorably in the past. The user interface unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the user interface unit can customize the interface based on the functions that the user has used in the past. In this way, by referring to the user's past operation history, it is possible to provide an interface that is easier to use.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit receives instructions from the user for line drawing and coloring. The user can select the desired style and genre. For example, if the user wants to create a fantasy-style manga, the system provides a function to select that style. Step 2: The line drawing generation unit uses AI to generate line drawings based on the instructions received by the reception unit. For example, it generates line drawings using open-source learning data. The AI ​​has learned from past manga data and can generate line drawings that match the user's desired style. For example, when generating fantasy-style line drawings, the AI ​​generates line drawings using fantasy-style learning data. Step 3: The coloring unit uses AI to color the line drawing generated by the line drawing generation unit. For example, the coloring is performed according to the user's desired color and atmosphere. The AI ​​can perform coloring according to the user's desired color and atmosphere. For example, if a user desires a bright, fantasy-style manga, the AI ​​will perform coloring according to that desire. Step 4: The training data collection unit collects open-source training data, for example, training data corresponding to various styles and genres. Step 5: The user interface unit oversees the reception unit, line drawing generation unit, coloring unit, and learning data collection unit, providing an interface that is easy for users to operate. For example, the user simply selects the desired style and color, and the AI ​​automatically performs the line drawing and coloring, allowing the artist to focus on their creation without having to waste time on detailed work.

[0071] (Example 2) A manga production support system according to an embodiment of the present invention accepts line drawing and coloring instructions from a user, and an AI uses open-source training data to generate line drawings and automatically color the line drawings. In this manga production support system, a user inputs line drawing and coloring instructions with simple operations, and an AI generates line drawings using open-source training data and automatically colors the line drawings. This process solves the issues of a shortage of artists and long production times, allowing artists to produce more works. It also accommodates a variety of styles and genres, promoting connections with readers. For example, a user inputs line drawing and coloring instructions with simple operations. The user can select their desired style and genre. For example, if a user wants to create a fantasy-style manga, they select that style. This information is input into the AI. The AI ​​then analyzes the input information and generates line drawings using open-source training data. The AI ​​has learned from past manga data and can generate line drawings that match the user's desired style. For example, when generating a fantasy-style line drawing, the AI ​​generates the line drawing using fantasy-style training data. The AI ​​then automatically colors the generated line drawing. The AI ​​can color according to the user's desired color palette and atmosphere. For example, if a user requests a bright, fantasy-style manga, the AI ​​can color according to that request. This allows the manga production support system to solve the problems of a shortage of artists and long production times, allowing artists to produce more works. It can also accommodate a variety of styles and genres, promoting connections with readers. This allows artists to enjoy creative freedom and produce more works. For example, even if an artist is creating a manga of a different genre, the AI ​​can create line drawings and coloring that are appropriate for that genre. The user interface is also designed for easy operation, allowing artists to operate it intuitively. This allows artists to focus on their creations without having to waste time on detailed tasks.

[0072] A manga production support system according to an embodiment includes a reception unit, a line drawing generation unit, a coloring unit, a learning data collection unit, and a user interface unit. The reception unit receives instructions for line drawing and coloring from a user. The user can select, for example, a desired style or genre. For example, if a user wants to create a fantasy-style manga, the reception unit provides a function for selecting the style. The line drawing generation unit generates a line drawing based on the instructions received by the reception unit using AI. The line drawing generation unit generates the line drawing using, for example, open-source learning data. The AI ​​learns from past manga data and can generate line drawings that match the user's desired style. For example, when generating a fantasy-style line drawing, the AI ​​generates the line drawing using fantasy-style learning data. The coloring unit uses AI to color the line drawing generated by the line drawing generation unit. The coloring unit colors the line drawing according to, for example, the user's desired color scheme and atmosphere. The AI ​​can color the line drawing according to the user's desired color scheme and atmosphere. For example, if a user desires a bright, fantasy-style manga, the AI ​​colors the line drawing according to the user's desire. The training data collection unit collects open-source training data. The training data collection unit collects training data corresponding to, for example, various styles and genres. The user interface unit oversees the reception unit, line drawing generation unit, coloring unit, and training data collection unit, and provides an interface that is easy for users to operate. For example, the user simply selects the desired style and color, and the AI ​​automatically performs line drawing and coloring, allowing the artist to concentrate on creation without having to waste time on detailed work. As a result, the manga production support system according to the embodiment automates line drawing and coloring based on user instructions, achieving efficient and high-quality manga production.

[0073] The line drawing generation unit can generate line drawings using open source training data. Examples of open source training data include, but are not limited to, specific dataset names and license conditions. The line drawing generation unit can generate line drawings corresponding to various styles and genres using, for example, open source training data. For example, when generating fantasy-style line drawings, the line drawing generation unit can use training data specialized for fantasy. Furthermore, when generating science fiction-style line drawings, the line drawing generation unit can also use training data specialized for science fiction. Furthermore, when generating comedy-style line drawings, the line drawing generation unit can also use training data specialized for comedy. In this way, by using open source training data, line drawings corresponding to various styles and genres can be generated.

[0074] The coloring unit can perform coloring according to the color tone and atmosphere specified by the user. Examples of color tone and atmosphere include, but are not limited to, color palettes and atmosphere categories. The coloring unit performs coloring according to the color tone and atmosphere desired by the user. For example, if a user desires a fantasy-style manga with bright colors, the coloring unit can perform coloring according to that desire. Alternatively, if a user desires a horror-style manga with dark colors, the coloring unit can perform coloring according to that desire. Furthermore, if a user desires a science fiction-style manga with realistic colors, the coloring unit can perform coloring according to that desire. This allows the user to color with the color tone and atmosphere desired, thereby providing a more satisfying work.

[0075] The learning data collection unit can collect learning data corresponding to a variety of styles and genres. Examples of the various styles and genres include, but are not limited to, anime-style, realistic, and fantasy. For example, the learning data collection unit collects anime-style learning data. The learning data collection unit can also collect realistic-style learning data. Furthermore, the learning data collection unit can also collect fantasy-style learning data. This allows for the collection of learning data corresponding to a variety of styles and genres, thereby meeting a wide range of needs.

[0076] The user interface unit can provide an interface that the user can operate intuitively. Examples of an intuitively operable interface include, but are not limited to, usability test results and design guidelines. For example, the user interface unit can provide an interface in which the AI ​​automatically performs line drawing and coloring by simply selecting the user's desired style and color. The user interface unit can also provide an interface in which instructions for line drawing and coloring can be input with simple operations. Furthermore, the user interface unit can provide an interface with a devised screen layout and operation method that allows the user to operate intuitively. This improves operability by providing an interface that the user can operate intuitively.

[0077] The reception unit can provide a selection function for selecting a style or genre specified by the user. Examples of styles and genres include, but are not limited to, fantasy, science fiction, and comedy. For example, if a user wants to create a fantasy-style manga, the reception unit can provide a function for selecting that style. Furthermore, if a user wants to create a science fiction-style manga, the reception unit can also provide a function for selecting that style. Furthermore, if a user wants to create a comedy-style manga, the reception unit can also provide a function for selecting that style. This allows the user to select their desired style or genre, making it possible to provide a more personalized work.

[0078] The reception unit can estimate the user's emotions and adjust the timing for accepting instructions for line drawing and coloring based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit uses AI to estimate the user's emotions and accept instructions for line drawing and coloring at a timing when the user is able to relax. Furthermore, if the user is concentrating, the reception unit can also use AI to estimate the user's emotions and accept instructions at a timing when the user's concentration is at its highest. Furthermore, if the user is tired, the reception unit can also use AI to estimate the user's emotions and adjust the timing to accept instructions after a break. This allows instructions to be accepted at a more appropriate timing by adjusting the timing for accepting instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The reception unit can analyze the user's past instruction history and select an appropriate reception method. For example, the reception unit preferentially suggests instruction methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an instruction method to be used in a specific time period based on the user's past instruction history. Furthermore, the reception unit can select the optimal reception method based on the style or genre that the user has preferred in the past. In this way, the optimal reception method can be provided to the user by analyzing the past instruction history.

[0080] The reception unit can perform filtering based on the user's current project or field of interest at the time of reception. For example, the reception unit preferentially receives only instructions related to a project currently in progress by the user. The reception unit can also filter and receive related instructions based on the user's field of interest. Furthermore, the reception unit can filter optimal instructions based on projects or fields in which the user has shown interest in the past. In this way, by filtering instructions based on the user's current project or field of interest, more relevant instructions can be received.

[0081] The reception unit can select an appropriate reception means depending on the user's input method when receiving the input. For example, if the user prefers voice input, the reception unit can preferentially receive voice input. Furthermore, if the user prefers text input, the reception unit can also preferentially receive text input. Furthermore, if the user prefers image input, the reception unit can also preferentially receive image input. This allows the optimal reception means to be selected depending on the user's input method, making it possible to provide a system that is easier to use.

[0082] The reception unit can estimate the user's emotions and determine the priority of instructions to be received based on the estimated user's emotions. For example, if the user is nervous, the reception unit can prioritize receiving instructions that will help the user relax. Furthermore, if the user is relaxed, the reception unit can also prioritize receiving complex instructions. Furthermore, if the user is in a hurry, the reception unit can also prioritize receiving instructions that can be processed quickly. In this way, by determining the priority of instructions according to the user's emotions, more appropriate instructions can be preferentially received. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The reception unit can prioritize receiving highly relevant instructions by taking into account the user's geographical location information when receiving instructions. For example, when the user is in a specific area, the reception unit prioritizes receiving instructions related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving instructions related to the user's destination. Furthermore, when the user is staying in a specific location, the reception unit can also prioritize receiving instructions related to that location. In this way, by taking into account the user's geographical location information, it is possible to prioritize receiving more relevant instructions.

[0084] The reception unit can analyze the user's social media activity and receive related instructions at the time of reception. The reception unit can receive related instructions based on, for example, content shared by the user on social media. The reception unit can also analyze the user's social media activity history and receive related instructions. Furthermore, the reception unit can also receive related instructions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to receive more relevant instructions.

[0085] The reception unit can customize the reception method based on the user's past feedback when receiving a call. For example, the reception unit suggests the optimal reception method based on the user's past feedback. The reception unit can also customize a specific reception method based on the user's past feedback. Furthermore, the reception unit can reflect the user's feedback and continuously improve the reception method. In this way, a more optimal reception method can be provided by reflecting the user's past feedback.

[0086] The line drawing generation unit can estimate the user's emotion and adjust the way the line drawing is expressed based on the estimated user's emotion. For example, if the user is relaxed, the line drawing generation unit can generate a line drawing drawn with soft lines. If the user is excited, the line drawing generation unit can also generate a line drawing drawn with strong lines. If the user is sad, the line drawing generation unit can also generate a line drawing drawn with soft lines. In this way, by adjusting the way the line drawing is expressed according to the user's emotion, a line drawing that more closely matches the emotion can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] When generating a line drawing, the line drawing generation unit can adjust the level of detail of the line drawing based on the style specified by the user. For example, if the user desires a simple style, the line drawing generation unit generates a line drawing with a low level of detail. Alternatively, if the user desires a realistic style, the line drawing generation unit can generate a line drawing with a high level of detail. Furthermore, if the user desires an anime-style style, the line drawing generation unit can generate a line drawing with a medium level of detail. In this way, by adjusting the level of detail of the line drawing based on the style desired by the user, a line drawing that more closely matches the user's desire can be generated.

[0088] The line drawing generation unit can apply different generation algorithms according to different styles or genres when generating line drawings. For example, when generating a fantasy-style line drawing, the line drawing generation unit applies a generation algorithm specialized for fantasy. Furthermore, when generating a science fiction-style line drawing, the line drawing generation unit can also apply a generation algorithm specialized for science fiction. Furthermore, when generating a comedy-style line drawing, the line drawing generation unit can also apply a generation algorithm specialized for comedy. In this way, by applying generation algorithms according to different styles and genres, a wider variety of line drawings can be generated.

[0089] When generating a line drawing, the line drawing generation unit can improve the accuracy of the generation by referring to the user's past line drawing results. For example, the line drawing generation unit generates a new line drawing in a similar style based on a line drawing previously generated by the user. The line drawing generation unit can also analyze the user's past line drawing results and optimize the generation algorithm. Furthermore, the line drawing generation unit can also generate a new line drawing by incorporating features of line drawings that the user previously preferred. In this way, the accuracy of the generation can be improved by referring to the user's past line drawing results.

[0090] The line drawing generation unit can estimate the user's emotion and adjust the length of the line drawing based on the estimated user's emotion. For example, the line drawing generation unit generates a longer line drawing when the user is relaxed. The line drawing generation unit can also generate a shorter line drawing when the user is in a hurry. Furthermore, the line drawing generation unit can also generate a line drawing of an appropriate length when the user is concentrating. This allows for the generation of a more appropriate line drawing by adjusting the length of the line drawing according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] When generating line drawings, the line drawing generation unit can determine the priority of the line drawings based on the time of submission by the user. For example, if the user submits the line drawing early, the line drawing generation unit generates the line drawing with priority. Also, if the user submits the line drawing close to the deadline, the line drawing generation unit can also generate the line drawing with priority. Furthermore, if the user submits the line drawing at a specific date and time, the line drawing generation unit can also determine the priority of the line drawing based on the date and time. In this way, by determining the priority of the line drawings based on the time of submission by the user, efficient line drawing generation is possible.

[0092] The line drawing generation unit can adjust the order of the line drawings based on the user's level of interest when generating the line drawings. For example, the line drawing generation unit preferentially generates line drawings that the user associates with a specific project. The line drawing generation unit can also preferentially generate line drawings that the user associates with a specific genre. Furthermore, the line drawing generation unit can also preferentially generate line drawings that the user associates with a specific style. In this way, by adjusting the order of the line drawings based on the user's level of interest, more relevant line drawings can be preferentially generated.

[0093] When generating a line drawing, the line drawing generation unit can adjust the use of technical terms in the line drawing according to the user's knowledge level. For example, if the user is a beginner, the line drawing generation unit generates a line drawing with fewer technical terms. Furthermore, if the user is an intermediate user, the line drawing generation unit can also generate a line drawing with an appropriate amount of technical terms. Furthermore, if the user is an advanced user, the line drawing generation unit can also generate a line drawing with a lot of technical terms. In this way, by adjusting the use of technical terms according to the user's knowledge level, it is possible to provide a line drawing that is easier to understand.

[0094] The coloring unit can estimate the user's emotion and adjust the coloring expression method based on the estimated user's emotion. For example, if the user is relaxed, the coloring unit can use soft colors. If the user is excited, the coloring unit can also use bright colors. If the user is sad, the coloring unit can also use pale colors. This allows the coloring expression method to be adjusted according to the user's emotion, making it possible to perform coloring that more closely matches the emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] During coloring, the coloring unit can adjust the level of detail of the coloring based on the user's desired color tone and atmosphere. For example, if the user desires simple coloring, the coloring unit performs coloring with a low level of detail. Alternatively, if the user desires realistic coloring, the coloring unit can perform coloring with a high level of detail. Furthermore, if the user desires anime-style coloring, the coloring unit can perform coloring with a medium level of detail. This allows coloring that more closely matches the user's desire by adjusting the level of detail of the coloring based on the user's desired color tone and atmosphere.

[0096] The coloring unit can apply different coloring algorithms according to different styles and genres when coloring. For example, when performing fantasy-style coloring, the coloring unit applies a coloring algorithm specialized for fantasy. Furthermore, when performing science fiction-style coloring, the coloring unit can also apply a coloring algorithm specialized for science fiction. Furthermore, when performing comedy-style coloring, the coloring unit can also apply a coloring algorithm specialized for comedy. In this way, by applying coloring algorithms according to different styles and genres, more diverse coloring can be performed.

[0097] When coloring, the coloring unit can improve the accuracy of the coloring by referring to the user's past coloring results. For example, the coloring unit performs new coloring in a similar style based on coloring performed by the user in the past. The coloring unit can also analyze the user's past coloring results and optimize the coloring algorithm. Furthermore, the coloring unit can perform new coloring by incorporating colors that the user has previously preferred. In this way, the accuracy of the coloring can be improved by referring to the user's past coloring results.

[0098] The coloring unit can estimate the user's emotion and adjust the length of the coloring based on the estimated user's emotion. For example, the coloring unit can perform longer coloring when the user is relaxed. Furthermore, the coloring unit can also perform shorter coloring when the user is in a hurry. Furthermore, the coloring unit can perform coloring of an appropriate length when the user is concentrating. This allows for more appropriate coloring by adjusting the length of the coloring according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] When coloring, the coloring unit can determine the priority of coloring based on the time of submission by the user. For example, if the user submits early, the coloring unit can prioritize coloring of that work. In addition, if the user submits close to the deadline, the coloring unit can also prioritize coloring of that work. Furthermore, if the user submits at a specific date and time, the coloring unit can also determine the priority of coloring based on that date and time. In this way, by determining the priority of coloring based on the time of submission by the user, efficient coloring is possible.

[0100] The coloring unit can adjust the order of coloring based on the user's level of interest during coloring. For example, the coloring unit can prioritize coloring related to a specific project by the user. The coloring unit can also prioritize coloring related to a specific genre by the user. Furthermore, the coloring unit can also prioritize coloring related to a specific style by the user. In this way, by adjusting the order of coloring based on the user's level of interest, more relevant coloring can be prioritized.

[0101] The coloring unit can adjust the use of technical terms in the coloring in accordance with the user's knowledge level when coloring. For example, if the user is a beginner, the coloring unit can perform coloring using fewer technical terms. Furthermore, if the user is an intermediate user, the coloring unit can perform coloring using an appropriate amount of technical terms. Furthermore, if the user is an advanced user, the coloring unit can perform coloring using a lot of technical terms. In this way, by adjusting the use of technical terms in accordance with the user's knowledge level, it is possible to provide coloring that is easier to understand.

[0102] The training data collection unit can estimate the user's emotions and adjust the training data collection method based on the estimated user's emotions. For example, if the user is relaxed, the training data collection unit collects training data with soft expressions. Furthermore, if the user is excited, the training data collection unit can also collect training data with stimulating expressions. Furthermore, if the user is sad, the training data collection unit can also collect training data with mild expressions. This allows more appropriate training data to be collected by adjusting the training data collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The learning data collection unit can optimize the collection algorithm by referring to past learning data when collecting learning data. For example, the learning data collection unit preferentially collects similar data based on previously collected learning data. The learning data collection unit can also analyze past learning data and optimize the collection algorithm. Furthermore, the learning data collection unit can also adjust the collection method for new data by referring to trends in past learning data. In this way, by referring to past learning data, the collection algorithm can be optimized and more effective learning data can be collected.

[0104] The training data collection unit can update the collected data by reflecting user feedback when collecting training data. The training data collection unit updates the collected data based on, for example, feedback provided by the user. The training data collection unit can also optimize the collection algorithm by reflecting user feedback. Furthermore, the training data collection unit can also adjust the method of collecting new data by referring to user feedback. In this way, by reflecting user feedback, the collected data can be continuously improved and more appropriate training data can be collected.

[0105] The training data collection unit can estimate the user's emotions and adjust the frequency of training data collection based on the estimated user's emotions. For example, the training data collection unit collects training data frequently when the user is relaxed. Furthermore, the training data collection unit can also collect training data at an appropriate frequency when the user is excited. Furthermore, the training data collection unit can also reduce the collection frequency when the user is sad. In this way, by adjusting the training data collection frequency according to the user's emotions, training data can be collected at a more appropriate frequency. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0106] When collecting learning data, the learning data collection unit can weight the collected data based on the time of data submission. For example, the learning data collection unit preferentially collects data submitted early. The learning data collection unit can also preferentially collect data submitted close to a deadline. Furthermore, the learning data collection unit can weight and collect data submitted at a specific date and time. In this way, by weighting the collected data based on the time of data submission, more important data can be preferentially collected.

[0107] When collecting training data, the training data collection unit can integrate information from multiple data sources to expand the collected data. For example, the training data collection unit integrates open source data to expand the collected data. The training data collection unit can also integrate data provided by a user to expand the collected data. Furthermore, the training data collection unit can also integrate information from an external database to expand the collected data. This allows for the collection of richer training data by integrating information from different data sources.

[0108] The user interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user's emotions. For example, if the user is nervous, the user interface unit can provide an interface with subdued colors. If the user is having fun, the user interface unit can also provide an interface with bright colors. Furthermore, if the user is tired, the user interface unit can also provide a simple, highly visible interface. This allows for adjusting the interface display method according to the user's emotions, thereby providing a more comfortable operating environment. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0109] When displaying an interface, the user interface unit can select an appropriate display method by referring to the user's past operation history. For example, the user interface unit may preferentially display an interface design that the user has used in the past. The user interface unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the user interface unit can customize the interface based on functions that the user has used in the past. In this way, by referring to the user's past operation history, it is possible to provide an interface that is easier to use.

[0110] The user interface unit can customize the display content according to the user's current task when displaying the interface. For example, if the user is creating a line drawing, the user interface unit can prioritize displaying functions related to the line drawing. Also, if the user is coloring, the user interface unit can prioritize displaying functions related to coloring. Furthermore, if the user is collecting learning data, the user interface unit can prioritize displaying functions related to collection. This allows for more efficient operation by customizing the display content according to the user's current task.

[0111] The user interface unit can select an appropriate display method based on the user's device information when displaying the interface. For example, if the user is using a smartphone, the user interface unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the user interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the user interface unit can provide a display method optimized for a wide screen. This makes it possible to provide a more appropriate display method by taking the user's device information into consideration.

[0112] The user interface unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user's emotions. For example, if the user is nervous, the user interface unit can provide simple and intuitive operation procedures. Furthermore, if the user is enjoying themselves, the user interface unit can also provide detailed operation procedures. Furthermore, if the user is tired, the user interface unit can minimize the operation procedures. This allows for adjusting the operation procedures according to the user's emotions, thereby providing a more comfortable operating environment. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0113] The user interface unit can make the display content multilingual in accordance with the user's language setting when displaying the interface. For example, the user interface unit automatically sets the interface language based on the language setting of the user's device. The user interface unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the user interface unit can display the interface in that language. This makes it possible to provide a more user-friendly interface by providing multilingual display content in accordance with the user's language setting.

[0114] The user interface unit can adjust the display content based on the user's customization settings when displaying the interface. For example, the user interface unit displays the interface based on the customization content previously set by the user. The user interface unit can also adjust the display content by reflecting the user's customization settings. Furthermore, when the user makes new customization settings, the user interface unit can immediately reflect those settings and display the interface. This makes it possible to provide a more personalized interface by reflecting the user's customization settings.

[0115] The user interface unit can analyze the user's social media activity and provide related information when displaying the interface. The user interface unit can display related information on the interface based on, for example, content shared by the user on social media. The user interface unit can also analyze the user's social media activity history and display related information on the interface. Furthermore, the user interface unit can also display related information on the interface by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant information can be provided. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, line drawing generation unit, coloring unit, training data collection unit, and user interface unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives instructions for line drawing and coloring from a user. For example, the line drawing generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates line drawings using open-source training data. For example, the coloring unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically colors the generated line drawings. For example, the training data collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects training data corresponding to various styles and genres. For example, the user interface unit is implemented by the control unit 46A of the smart device 14 and provides an interface that is easy for the user to operate. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, line drawing generation unit, coloring unit, training data collection unit, and user interface unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives instructions for line drawing and coloring from a user. For example, the line drawing generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates line drawings using open-source training data. For example, the coloring unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically colors the generated line drawings. For example, the training data collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects training data corresponding to various styles and genres. For example, the user interface unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface that can be easily operated by a user. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, line drawing generation unit, coloring unit, training data collection unit, and user interface unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the headset-type terminal 314 and receives instructions for line drawing and coloring from a user. For example, the line drawing generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates line drawings using open-source training data. For example, the coloring unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically colors the generated line drawings. For example, the training data collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects training data corresponding to various styles and genres. For example, the user interface unit is implemented by the control unit 46A of the headset-type terminal 314 and provides an interface that can be easily operated by a user. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, line drawing generation unit, coloring unit, learning data collection unit, and user interface unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives instructions for line drawing and coloring from a user. For example, the line drawing generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates line drawings using open-source learning data. For example, the coloring unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically colors the generated line drawings. For example, the learning data collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects learning data corresponding to various styles and genres. For example, the user interface unit is implemented by the control unit 46A of the robot 414 and provides an interface that can be easily operated by a user.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The reception unit can estimate the user's emotions and adjust the timing for accepting instructions for line drawing and coloring based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can estimate the user's emotions and accept instructions for line drawing and coloring at a timing when the user is able to relax. In addition, if the user is concentrating, the reception unit can estimate the user's emotions and accept instructions at a timing when the user's concentration is at its highest. Furthermore, if the user is tired, the reception unit can estimate the user's emotions and adjust the timing so that instructions are accepted after a break. In this way, by adjusting the timing for accepting instructions according to the user's emotions, instructions can be accepted at more appropriate times.

[0118] The line drawing generation unit can estimate the user's emotion and adjust the way the line drawing is expressed based on the estimated user's emotion. For example, if the user is relaxed, the line drawing generation unit can generate a line drawing drawn with soft lines. If the user is excited, the line drawing generation unit can also generate a line drawing drawn with strong lines. Furthermore, if the user is sad, the line drawing generation unit can also generate a line drawing drawn with soft lines. In this way, by adjusting the way the line drawing is expressed based on the user's emotion, it is possible to generate a line drawing that better matches the emotion.

[0119] The coloring unit can estimate the user's emotion and adjust the coloring expression method based on the estimated user's emotion. For example, if the user is relaxed, the coloring unit can use soft colors. If the user is excited, the coloring unit can also use bright colors. If the user is sad, the coloring unit can also use pale colors. In this way, by adjusting the coloring expression method according to the user's emotion, it is possible to perform coloring that more closely matches the emotion.

[0120] The training data collection unit can estimate the user's emotions and adjust the training data collection method based on the estimated user's emotions. For example, if the user is relaxed, training data with soft expressions is collected. Also, if the user is excited, the training data collection unit can collect training data with stimulating expressions. Furthermore, if the user is sad, the training data collection unit can collect training data with neutral expressions. In this way, by adjusting the training data collection method according to the user's emotions, more appropriate training data can be collected.

[0121] The user interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user's emotions. For example, if the user is nervous, the user interface unit can provide an interface with subdued colors. If the user is having fun, the user interface unit can also provide an interface with bright colors. Furthermore, if the user is tired, the user interface unit can also provide a simple, highly visible interface. In this way, by adjusting the interface display method according to the user's emotions, a more comfortable operating environment can be provided.

[0122] The reception unit can analyze the user's past instruction history and select an appropriate reception method. For example, it can preferentially suggest instruction methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an instruction method to be used in a specific time period based on the user's past instruction history. Furthermore, the reception unit can select the optimal reception method based on the style or genre that the user has preferred in the past. In this way, by analyzing the past instruction history, it is possible to provide the user with the optimal reception method.

[0123] When generating a line drawing, the line drawing generation unit can adjust the level of detail of the line drawing based on the style specified by the user. For example, if the user desires a simple style, the line drawing generation unit generates a line drawing with a low level of detail. Alternatively, if the user desires a realistic style, the line drawing generation unit can generate a line drawing with a high level of detail. Furthermore, if the user desires an anime-style style, the line drawing generation unit can generate a line drawing with a medium level of detail. In this way, by adjusting the level of detail of the line drawing based on the style desired by the user, a line drawing that more closely matches the user's desire can be generated.

[0124] During coloring, the coloring unit can adjust the level of detail of the coloring based on the user's desired color tone and atmosphere. For example, if the user desires simple coloring, the coloring unit performs coloring with a low level of detail. Alternatively, if the user desires realistic coloring, the coloring unit can perform coloring with a high level of detail. Furthermore, if the user desires anime-style coloring, the coloring unit can perform coloring with a medium level of detail. This allows coloring that more closely matches the user's desire by adjusting the level of detail of the coloring based on the user's desired color tone and atmosphere.

[0125] When collecting learning data, the learning data collection unit can optimize the collection algorithm by referring to past learning data. For example, similar data is preferentially collected based on previously collected learning data. The learning data collection unit can also analyze past learning data and optimize the collection algorithm. Furthermore, the learning data collection unit can also adjust the method for collecting new data by referring to trends in past learning data. In this way, by referring to past learning data, the collection algorithm can be optimized and more effective learning data can be collected.

[0126] When displaying an interface, the user interface unit can select an appropriate display method by referring to the user's past operation history. For example, it can preferentially display an interface design that the user has used favorably in the past. The user interface unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the user interface unit can customize the interface based on the functions that the user has used in the past. In this way, by referring to the user's past operation history, it is possible to provide an interface that is easier to use.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The reception unit receives instructions from the user for line drawing and coloring. The user can select the desired style and genre. For example, if the user wants to create a fantasy-style manga, the system provides a function to select that style. Step 2: The line drawing generation unit uses AI to generate line drawings based on the instructions received by the reception unit. For example, it generates line drawings using open-source learning data. The AI ​​has learned from past manga data and can generate line drawings that match the user's desired style. For example, when generating fantasy-style line drawings, the AI ​​generates line drawings using fantasy-style learning data. Step 3: The coloring unit uses AI to color the line drawing generated by the line drawing generation unit. For example, the coloring is performed according to the user's desired color and atmosphere. The AI ​​can perform coloring according to the user's desired color and atmosphere. For example, if a user desires a bright, fantasy-style manga, the AI ​​will perform coloring according to that desire. Step 4: The training data collection unit collects open-source training data, for example, training data corresponding to various styles and genres. Step 5: The user interface unit oversees the reception unit, line drawing generation unit, coloring unit, and learning data collection unit, providing an interface that is easy for users to operate. For example, the user simply selects the desired style and color, and the AI ​​automatically performs the line drawing and coloring, allowing the artist to focus on their creation without having to waste time on detailed work.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] 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.

[0200] [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives instructions for line drawing and coloring from a user; a line drawing generating unit that generates a line drawing based on the instruction received by the receiving unit; a coloring unit that colors the line drawing generated by the line drawing generation unit; a learning data collection unit that collects publicly available learning data; a user interface unit that controls the reception unit, the line drawing generation unit, the coloring unit, and the learning data collection unit; A system characterized by:

2. The line drawing generation unit Generate line drawings using open-source training data 2. The system of claim 1.

3. The colored portion is Coloring is performed according to the color and atmosphere specified by the user.

2. The system of claim 1.

4. The learning data collection unit Collect learning data covering a variety of styles and genres 2. The system of claim 1.

5. The user interface unit Provide an interface that users can operate intuitively 2. The system of claim 1.

6. The reception unit Providing a selection feature for users to choose their preferred style or genre 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing for accepting instructions for line drawing and coloring based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past instruction history and select the appropriate reception method 2. The system of claim 1.

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