system

Abstract

The system according to the embodiment aims to provide learning content in a visually appealing manner and to improve the motivation of learners. [Solution] A system according to an embodiment includes a quantification unit, a generation unit, and a provision unit. The quantification unit uses facial expression recognition technology to quantify emotions. The generation unit generates a story flow based on the emotions quantified by the quantification unit. The provision unit provides the manga generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have limited means for presenting learning content in a visually appealing way, making it difficult to maintain learners' motivation.

[0005] The system according to the embodiment aims to provide learning content in a visually appealing manner and to improve the motivation of learners. [Means for solving the problem]

[0006] The system according to the embodiment includes a quantification unit, a generation unit, and a provision unit. The quantification unit uses facial expression recognition technology to quantify emotions. The generation unit generates a story flow based on the emotions quantified by the quantification unit. The provision unit provides the manga generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide learning content in a visually appealing manner, thereby improving the motivation of learners. [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 learning system according to an embodiment of the present invention quantifies emotions, creates a narrative flow, and provides manga. The learning system uses free manga materials and a generation AI to quantify emotions in each frame and create a narrative flow. This allows test-takers to absorb important concepts and information in a visually appealing format. For example, the learning system can select frames in which a character has a surprised expression and then place explanatory frames after them to emphasize important concepts. Furthermore, the generated manga can help test-takers gain a deeper understanding of important events and people in history classes. For example, the learning system can depict important historical events and people as manga characters and convey their background and influence as stories. Furthermore, the learning system plans to recruit manga artists and introduce a system that offers incentives each time their work is used. This will allow creators to realize that their work is useful for education, increasing their motivation. Furthermore, the provision of high-quality content will increase learners' motivation. In this way, the learning system offers a new learning method for studying for exams easily and efficiently, creating a system that is beneficial to both learners and creators. This allows learning systems to help students absorb important concepts and information in a visually appealing way. For example, it helps students gain a deeper understanding of important events and people in history classes. Creators also realize that their work is useful for education, which increases their motivation. This creates a system that is beneficial to both learners and creators.

[0029] A learning system according to an embodiment includes a quantification unit, a creation unit, and a provision unit. The quantification unit uses facial expression recognition technology to quantify emotions. For example, the quantification unit analyzes the facial expressions of characters using a facial expression recognition algorithm and quantifies the results. The quantification unit can also quantify emotions based on the type of emotion and the scale of quantification. For example, the quantification unit quantifies the characters' smiles and surprised expressions to capture changes in emotion. The creation unit generates a story flow based on the emotions quantified by the quantification unit. The creation unit generates a story flow using, for example, a storytelling algorithm. The creation unit can also generate a story flow based on scenario generation rules. For example, the creation unit selects and arranges appropriate frames according to changes in emotion. The provision unit provides the manga generated by the creation unit. For example, the provision unit provides the manga in digital format. The provision unit can also provide the manga in print format. For example, the provision unit provides the manga through a web platform. As a result, the learning system according to the embodiment can provide learners with a visually appealing learning experience by quantifying emotions, creating a storyline, and providing comics.

[0030] The learning system further includes an incentive providing unit that recruits manga artists and provides them with an incentive each time their work is used. The incentive providing unit recruits manga artists and provides them with an incentive each time their work is used. The incentive providing unit provides, for example, monetary rewards. The incentive providing unit can also implement a point system and award points according to the number of times the work is used. For example, the incentive providing unit awards points each time the work is used and exchanges the points for rewards. In this way, the incentive providing unit can provide incentives to manga artists, thereby providing high-quality content and increasing learners' motivation to learn.

[0031] The incentive providing unit can introduce a reward or ranking system according to the number of uses. The incentive providing unit, for example, provides a reward according to the number of uses. For example, the incentive providing unit provides a higher reward the more times the service is used. The incentive providing unit can also introduce a ranking system and create a ranking based on the number of uses. For example, the incentive providing unit updates the ranking based on the number of uses and provides special benefits to manga artists with higher rankings. In this way, the incentive providing unit can improve the motivation of manga artists by introducing a reward or ranking system according to the number of uses.

[0032] The quantification unit can quantify not only the facial expressions of characters but also the emotional elements of backgrounds and props. For example, the quantification unit analyzes the color and brightness of the background and quantifies its emotional impact. The quantification unit can also analyze the placement and shape of props and quantify their emotional meaning. The quantification unit can also analyze background sounds and sound effects and quantify their emotional impact. In this way, the quantification unit can quantify the emotional elements of backgrounds and props, enabling richer emotional expression. The specific types and quantification methods of the emotional elements of backgrounds and props are based on color, placement, shape, etc. For example, the quantification unit inputs the color and brightness of the background into the generation AI, which then quantifies the emotional impact.

[0033] The quantification unit updates the quantified emotional data in real time, allowing it to change dynamically in accordance with the progress of the story. For example, the quantification unit updates the emotional data of characters in real time in accordance with the progress of the story. The quantification unit can also update the emotional data of backgrounds and props in real time in accordance with the progress of the story. The quantification unit can also update the overall emotional data in real time and visually express it in accordance with the progress of the story. In this way, the quantification unit can update the emotional data in real time in accordance with the progress of the story, enabling a more dynamic story expression. The specific method and criteria for updating in real time are determined based on the timing of the update and the method of acquiring the data. For example, the quantification unit inputs story progress data to the generation AI, and the generation AI updates the emotional data in real time.

[0034] The quantification unit can visually express the inner emotions of the characters using the quantified emotional data. For example, the quantification unit visually expresses the inner emotions of the characters using colors or effects based on the quantified emotional data. The quantification unit can also visually express the inner emotions of the characters using animation based on the quantified emotional data. The quantification unit can also visually express the inner emotions of the characters using text based on the quantified emotional data. In this way, the quantification unit can visually express the inner emotions of the characters, thereby deepening understanding of the story. The specific method and criteria for visually expressing the inner emotions are based on visual effects, animation, etc. For example, the quantification unit inputs the emotional data into a generation AI, which then visually expresses the inner emotions.

[0035] The quantification unit can also analyze the tone and speed of the characters' voices and quantify their emotions. For example, the quantification unit can analyze the tone of the characters' voices and quantify their emotions. The quantification unit can also analyze the speed of the characters' voices and quantify their emotions. The quantification unit can also analyze the strength and weakness of the characters' voices and quantify their emotions. In this way, the quantification unit can analyze the tone and speed of the voices to more accurately quantify emotions. The specific analysis method and criteria for the tone and speed of the voices are based on a voice analysis algorithm and tone classification criteria. For example, the quantification unit inputs the data of the characters' voices into a generation AI, which then analyzes the tone and speed of the voices and quantifies their emotions.

[0036] The quantification unit can select an appropriate facial expression recognition algorithm by referring to the user's past learning history. The quantification unit selects the optimal facial expression recognition algorithm based on the user's past learning history, for example. The quantification unit can also select an facial expression recognition algorithm that emphasizes a specific emotion based on the user's past learning history. The quantification unit can also select an facial expression recognition algorithm that captures changes in emotion based on the user's past learning history. In this way, the quantification unit can select the optimal facial expression recognition algorithm by referring to the past learning history. The specific content of the past learning history and the method of referring to it are determined based on the learning history database and the storage period of the history. For example, the quantification unit inputs the user's learning history data into the generation AI, and the generation AI selects the optimal facial expression recognition algorithm.

[0037] The quantification unit can quantify region-specific emotional expressions taking into account the user's geographical location information. For example, the quantification unit quantifies region-specific facial expressions based on the user's geographical location information. The quantification unit can also quantify region-specific emotional expressions based on the user's geographical location information. The quantification unit can also quantify emotions taking into account the region's cultural background based on the user's geographical location information. In this way, the quantification unit can quantify region-specific emotional expressions, enabling emotional expressions that are more in line with the cultural background. The specific method of acquiring and using the geographical location information is based on the accuracy of GPS data and location information. For example, the quantification unit inputs the user's geographical location data into the generation AI, and the generation AI quantifies region-specific emotional expressions.

[0038] The creation department can select frames taking into consideration not only changes in emotions but also the relationships between characters and the background story. For example, the creation department can select frames that correspond to changes in emotions by considering the relationships between characters. The creation department can also select frames that correspond to changes in emotions by considering the background story. The creation department can also select the optimal frames by comprehensively considering the relationships between characters and the background story. In this way, the creation department can generate a more consistent story by taking into consideration the relationships between characters and the background story. The specific content and consideration method of the relationships between characters and the background story are determined based on the definition of the relationships and the elements of the background story. For example, the creation department inputs character relationship data into the generation AI, which then selects the optimal frames.

[0039] The creation unit can adjust the difficulty of the story according to the user's learning progress. For example, if the user's learning progress is rapid, the creation unit can increase the difficulty of the story. Also, if the user's learning progress is slow, the creation unit can lower the difficulty of the story. Also, the creation unit can dynamically adjust the difficulty of the story according to the user's learning progress. In this way, the creation unit can maximize the learning effect by adjusting the difficulty of the story according to the learning progress. The specific method and criteria for adjusting the difficulty of the story are based on difficulty evaluation criteria and adjustment algorithms. For example, the creation unit inputs the user's learning progress data into the generation AI, and the generation AI adjusts the difficulty of the story.

[0040] The creation unit can dynamically change the flow of the story by reflecting user feedback. For example, the creation unit changes the flow of the story based on user feedback. The creation unit can also add specific scenes based on user feedback. The creation unit can also change the ending of the story based on user feedback. In this way, the creation unit can provide a story that is more suitable for the user by reflecting user feedback. The specific method and criteria for dynamically changing the flow of the story are based on a change algorithm and a reference value. For example, the creation unit inputs user feedback data into a generation AI, and the generation AI dynamically changes the flow of the story.

[0041] The creation unit can customize the story genre based on the user's interests. For example, if the user is interested in history, the creation unit can customize a story in the history genre. If the user is interested in science, the creation unit can also customize a story in the science genre. If the user is interested in fantasy, the creation unit can also customize a story in the fantasy genre. In this way, the creation unit can provide more appealing stories by customizing the story genre based on the user's interests. The specific method and criteria for customizing the story genre are based on genre classification criteria and customization algorithms. For example, the creation unit inputs the user's interest data into the generation AI, and the generation AI customizes the story genre.

[0042] The creation unit can generate an appropriate story flow by referring to the user's past learning history. The creation unit generates an optimal story flow based on the user's past learning history, for example. The creation unit can also generate a story flow that emphasizes a specific theme based on the user's past learning history. The creation unit can also generate a story flow that enhances learning effectiveness based on the user's past learning history. In this way, the creation unit can generate an optimal story flow by referring to the past learning history. The specific method and criteria for generating an optimal story flow are based on a generation algorithm and evaluation criteria. For example, the creation unit inputs the user's learning history data into a generation AI, and the generation AI generates an optimal story flow.

[0043] The creation unit can incorporate regional story elements taking into account the user's geographic location information. For example, the creation unit can incorporate regional characters into the story based on the user's geographic location information. The creation unit can also incorporate regional backgrounds into the story based on the user's geographic location information. The creation unit can also incorporate regional culture into the story based on the user's geographic location information. In this way, the creation unit can provide a more familiar story by incorporating regional story elements. The specific content and method of incorporating regional story elements are determined based on the culture, customs, history, etc. of the region. For example, the creation unit inputs the user's geographic location data into the generation AI, which then incorporates regional story elements.

[0044] The providing unit can customize the content of the manga to be provided according to the user's learning progress. For example, if the user's learning progress is rapid, the providing unit can provide more difficult content. Also, if the user's learning progress is slow, the providing unit can provide more basic content. The providing unit can also dynamically customize the content according to the user's learning progress. In this way, the providing unit can maximize the learning effect by customizing the content according to the user's learning progress. The specific method and criteria for customizing the manga content are based on content selection criteria and a customization algorithm. For example, the providing unit inputs the user's learning progress data into the generating AI, and the generating AI customizes the manga content.

[0045] The providing unit can dynamically change the content of the manga to be provided by reflecting user feedback. For example, the providing unit changes the content of the manga to be provided based on user feedback. The providing unit can also add specific scenes based on user feedback. The providing unit can also change the ending of the manga based on user feedback. In this way, the providing unit can provide manga that is more suitable for users by reflecting user feedback. The specific method and criteria for dynamically changing the content of the manga are based on a change algorithm and a reference value. For example, the providing unit inputs user feedback data into a generation AI, and the generation AI dynamically changes the content of the manga.

[0046] The providing unit can select an appropriate display method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, the providing unit can select a display method based on the device information, thereby enabling a more highly visible display. The specific method of obtaining and using the device information is based on the type, specifications, and performance of the device. For example, the providing unit inputs the user's device information into the generation AI, which then selects the optimal display method.

[0047] The providing unit can customize the genre of manga to be provided based on the user's interests. For example, if the user is interested in history, the providing unit can provide manga in the history genre. Furthermore, if the user is interested in science, the providing unit can provide manga in the science genre. Furthermore, if the user is interested in fantasy, the providing unit can provide manga in the fantasy genre. In this way, the providing unit can provide more attractive manga by customizing the manga genre based on the user's interests. The specific method and criteria for customizing the manga genre are based on genre classification criteria and customization algorithms. For example, the providing unit inputs the user's interest data into the generating AI, and the generating AI customizes the manga genre.

[0048] The providing unit can provide appropriate manga by referring to the user's past learning history. The providing unit, for example, provides the optimal manga based on the user's past learning history. The providing unit can also provide manga that emphasizes a specific theme based on the user's past learning history. The providing unit can also provide manga that enhances learning effectiveness based on the user's past learning history. In this way, the providing unit can provide the optimal manga by referring to the past learning history. The specific method and criteria for providing the optimal manga are based on the providing algorithm and evaluation criteria. For example, the providing unit inputs the user's learning history data into a generation AI, and the generation AI provides the optimal manga.

[0049] The providing unit can provide region-specific manga taking into account the user's geographical location information. For example, the providing unit can provide manga featuring region-specific characters based on the user's geographical location information. The providing unit can also provide manga with region-specific backgrounds based on the user's geographical location information. The providing unit can also provide manga that reflects region-specific culture based on the user's geographical location information. In this way, the providing unit can provide a more familiar learning experience by providing region-specific manga. The specific content and provision method of the region-specific manga are based on the culture, customs, history, etc. of the region. For example, the providing unit inputs the user's geographical location data into a generating AI, which then provides region-specific manga.

[0050] The incentive providing unit can determine the remuneration by referring to the evaluations of the manga artist's past works. For example, the incentive providing unit determines the remuneration based on the evaluations of the manga artist's past works. The incentive providing unit can also determine the remuneration by setting specific evaluation criteria based on the evaluations of the manga artist's past works. The incentive providing unit can also determine an increase or decrease in the remuneration based on the evaluations of the manga artist's past works. In this way, the incentive providing unit can provide a fairer remuneration by referring to the evaluations of past works. The specific criteria and reference method for the evaluation of past works are based on the evaluation scale and the evaluator's criteria. For example, the incentive providing unit inputs the manga artist's work evaluation data into the generation AI, and the generation AI determines the remuneration.

[0051] The incentive providing unit can analyze the activity history of the manga artist and provide appropriate incentives. For example, the incentive providing unit can provide optimal incentives based on the activity history of the manga artist. The incentive providing unit can also provide incentives for specific activities based on the activity history of the manga artist. The incentive providing unit can also customize the content of incentives based on the activity history of the manga artist. This allows the incentive providing unit to analyze the activity history and provide more appropriate incentives. The specific content of the activity history and the analysis method are determined based on the type of activity and the storage period of the history. For example, the incentive providing unit inputs the activity history data of the manga artist into a generation AI, which then provides the optimal incentive.

[0052] The incentive providing unit can improve the content of the incentives by reflecting the cartoonist's feedback. For example, the incentive providing unit improves the content of the incentives based on the cartoonist's feedback. The incentive providing unit can also add specific incentives based on the cartoonist's feedback. The incentive providing unit can also dynamically change the content of the incentives based on the cartoonist's feedback. In this way, the incentive providing unit can provide more effective incentives by reflecting the feedback. The specific methods and standards for improving the content of the incentives are determined based on improvement algorithms and reference values. For example, the incentive providing unit inputs the cartoonist's feedback data into the generation AI, and the generation AI improves the content of the incentives.

[0053] The incentive providing unit can provide region-specific incentives by taking into account the geographical location information of the cartoonist. For example, the incentive providing unit can provide region-specific incentives based on the geographical location information of the cartoonist. The incentive providing unit can also provide incentives that reflect region-specific culture based on the geographical location information of the cartoonist. The incentive providing unit can also provide incentives related to region-specific events based on the geographical location information of the cartoonist. In this way, the incentive providing unit can provide region-specific incentives that are more familiar to the user. The specific content and provision method of the region-specific incentives are determined based on the culture, customs, history, etc. of the region. For example, the incentive providing unit inputs the geographical location data of the cartoonist into the generation AI, which then provides region-specific incentives.

[0054] The incentive providing unit can analyze the social media activity of the manga artist and provide incentives. For example, the incentive providing unit provides incentives based on the social media activity of the manga artist. The incentive providing unit can also provide incentives for specific activities based on the social media activity of the manga artist. The incentive providing unit can also customize the content of incentives based on the social media activity of the manga artist. This allows the incentive providing unit to provide more appropriate incentives by analyzing social media activity. The specific content of social media activity and the analysis method are determined based on the type of activity and the analysis criteria. For example, the incentive providing unit inputs the social media activity data of the manga artist into a generation AI, and the generation AI provides incentives.

[0055] The incentive providing unit can customize the content of the incentive by reflecting the manga artist's past feedback. For example, the incentive providing unit customizes the content of the incentive based on the manga artist's past feedback. The incentive providing unit can also add specific incentives based on the manga artist's past feedback. The incentive providing unit can also dynamically change the content of the incentive based on the manga artist's past feedback. This allows the incentive providing unit to provide more effective incentives by reflecting past feedback. The specific method and criteria for customizing the content of the incentive are based on a customization algorithm and a reference value. For example, the incentive providing unit inputs the manga artist's feedback data into a generation AI, and the generation AI customizes the content of the incentive.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The quantification unit can also analyze the user's learning style and select the optimal facial expression recognition algorithm. For example, if the user is a visual learner, an algorithm that emphasizes visual changes in facial expressions can be selected. If the user is an auditory learner, an algorithm that emphasizes changes in vocal tone can be selected. Furthermore, if the user is an experiential learner, an algorithm that dynamically captures changes in emotions can be selected. In this way, the quantification unit can provide a more effective learning experience by selecting the optimal facial expression recognition algorithm according to the user's learning style.

[0058] The creation unit can adjust the speed of the story by referring to the user's learning history. For example, if the user has an extensive learning history, the speed of the story can be increased. Alternatively, if the user has a limited learning history, the speed of the story can be decreased. Furthermore, specific scenes can be emphasized based on the user's learning history. In this way, the creation unit can provide a more effective learning experience by adjusting the speed of the story according to the user's learning history.

[0059] The providing unit can analyze the user's learning style and select the optimal manga display method. For example, if the user is a visual learner, a visually appealing display method can be provided. If the user is an auditory learner, a display method with audio guidance can be provided. Furthermore, if the user is an experiential learner, an interactive display method can be provided. In this way, the providing unit can provide a more effective learning experience by selecting the optimal display method according to the user's learning style.

[0060] The incentive providing unit can analyze the user's learning progress and provide appropriate incentives. For example, if the user's learning progress is fast, a high reward can be provided. Also, if the user's learning progress is slow, an encouraging message can be provided. Furthermore, specific incentives can be added depending on the user's learning progress. In this way, the incentive providing unit can increase the user's motivation to learn by providing appropriate incentives depending on the user's learning progress.

[0061] The creation department can incorporate region-specific story elements by taking into account the user's geographic location information. For example, if the user is in Japan, story elements related to Japanese history and culture can be incorporated. If the user is in America, story elements related to American history and culture can be incorporated. Furthermore, if the user is in Europe, story elements related to European history and culture can be incorporated. In this way, the creation department can provide a more familiar story by incorporating region-specific story elements.

[0062] The providing unit can dynamically change the content of the manga it provides by reflecting user feedback. For example, it can add specific scenes based on user feedback. It can also change the ending of the story based on user feedback. It can also change character settings based on user feedback. In this way, the providing unit can provide manga that is more suitable for the user by reflecting user feedback.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The quantification unit uses facial expression recognition technology to quantify emotions. Specifically, it uses a facial expression recognition algorithm to analyze the characters' facial expressions and quantify the results. It can also quantify emotions based on the type of emotion and the scale of quantification. For example, it can quantify the characters' smiling or surprised expressions to capture changes in their emotions. Step 2: The generator generates a story flow based on the emotions quantified by the quantification unit. Specifically, the generator generates a story flow using a storytelling algorithm, and can also generate a story flow based on scenario generation rules. For example, it selects and arranges appropriate frames according to changes in emotions. Step 3: The provider provides the comics generated by the generator. Specifically, the provider provides the comics in digital format, and can also provide the comics in print format. For example, the provider provides the comics through a web platform.

[0065] (Example 2) A learning system according to an embodiment of the present invention quantifies emotions, creates a narrative flow, and provides manga. The learning system uses free manga materials and a generation AI to quantify emotions in each frame and create a narrative flow. This allows test-takers to absorb important concepts and information in a visually appealing format. For example, the learning system can select frames in which a character has a surprised expression and then place explanatory frames after them to emphasize important concepts. Furthermore, the generated manga can help test-takers gain a deeper understanding of important events and people in history classes. For example, the learning system can depict important historical events and people as manga characters and convey their background and influence as stories. Furthermore, the learning system plans to recruit manga artists and introduce a system that offers incentives each time their work is used. This will allow creators to realize that their work is useful for education, increasing their motivation. Furthermore, the provision of high-quality content will increase learners' motivation. In this way, the learning system offers a new learning method for studying for exams easily and efficiently, creating a system that is beneficial to both learners and creators. This allows learning systems to help students absorb important concepts and information in a visually appealing way. For example, it helps students gain a deeper understanding of important events and people in history classes. Creators also realize that their work is useful for education, which increases their motivation. This creates a system that is beneficial to both learners and creators.

[0066] A learning system according to an embodiment includes a quantification unit, a creation unit, and a provision unit. The quantification unit uses facial expression recognition technology to quantify emotions. For example, the quantification unit analyzes the facial expressions of characters using a facial expression recognition algorithm and quantifies the results. The quantification unit can also quantify emotions based on the type of emotion and the scale of quantification. For example, the quantification unit quantifies the characters' smiles and surprised expressions to capture changes in emotion. The creation unit generates a story flow based on the emotions quantified by the quantification unit. The creation unit generates a story flow using, for example, a storytelling algorithm. The creation unit can also generate a story flow based on scenario generation rules. For example, the creation unit selects and arranges appropriate frames according to changes in emotion. The provision unit provides the manga generated by the creation unit. For example, the provision unit provides the manga in digital format. The provision unit can also provide the manga in print format. For example, the provision unit provides the manga through a web platform. As a result, the learning system according to the embodiment can provide learners with a visually appealing learning experience by quantifying emotions, creating a storyline, and providing comics.

[0067] The learning system further includes an incentive providing unit that recruits manga artists and provides them with an incentive each time their work is used. The incentive providing unit recruits manga artists and provides them with an incentive each time their work is used. The incentive providing unit provides, for example, monetary rewards. The incentive providing unit can also implement a point system and award points according to the number of times the work is used. For example, the incentive providing unit awards points each time the work is used and exchanges the points for rewards. In this way, the incentive providing unit can provide incentives to manga artists, thereby providing high-quality content and increasing learners' motivation to learn.

[0068] The incentive providing unit can introduce a reward or ranking system according to the number of uses. The incentive providing unit, for example, provides a reward according to the number of uses. For example, the incentive providing unit provides a higher reward the more times the service is used. The incentive providing unit can also introduce a ranking system and create a ranking based on the number of uses. For example, the incentive providing unit updates the ranking based on the number of uses and provides special benefits to manga artists with higher rankings. In this way, the incentive providing unit can improve the motivation of manga artists by introducing a reward or ranking system according to the number of uses.

[0069] The quantifying unit can estimate the user's emotions and dynamically adjust the accuracy of facial expression recognition based on the estimated user emotions. For example, if the user is nervous, the quantifying unit increases the accuracy of facial expression recognition to capture even subtle changes in facial expressions. Furthermore, if the user is relaxed, the quantifying unit can return the accuracy of facial expression recognition to normal and capture natural expressions. Furthermore, if the user is excited, the quantifying unit can adjust the accuracy of facial expression recognition to emphasize and quantify specific emotions. This allows the quantifying unit to adjust the accuracy of facial expression recognition according to the user's emotions, enabling more accurate emotion quantification. Specific methods and criteria for dynamically adjusting the accuracy of facial expression recognition are based on adjustment algorithms and accuracy evaluation criteria. For example, the quantifying unit inputs the user's emotional data into the generation AI, which analyzes the emotional data and dynamically adjusts the accuracy of facial expression recognition.

[0070] The quantification unit can quantify not only the facial expressions of characters but also the emotional elements of backgrounds and props. For example, the quantification unit analyzes the color and brightness of the background and quantifies its emotional impact. The quantification unit can also analyze the placement and shape of props and quantify their emotional meaning. The quantification unit can also analyze background sounds and sound effects and quantify their emotional impact. In this way, the quantification unit can quantify the emotional elements of backgrounds and props, enabling richer emotional expression. The specific types and quantification methods of the emotional elements of backgrounds and props are based on color, placement, shape, etc. For example, the quantification unit inputs the color and brightness of the background into the generation AI, which then quantifies the emotional impact.

[0071] The quantification unit updates the quantified emotional data in real time, allowing it to change dynamically in accordance with the progress of the story. For example, the quantification unit updates the emotional data of characters in real time in accordance with the progress of the story. The quantification unit can also update the emotional data of backgrounds and props in real time in accordance with the progress of the story. The quantification unit can also update the overall emotional data in real time and visually express it in accordance with the progress of the story. In this way, the quantification unit can update the emotional data in real time in accordance with the progress of the story, enabling a more dynamic story expression. The specific method and criteria for updating in real time are determined based on the timing of the update and the method of acquiring the data. For example, the quantification unit inputs story progress data to the generation AI, and the generation AI updates the emotional data in real time.

[0072] The quantification unit can visually express the inner emotions of the characters using the quantified emotional data. For example, the quantification unit visually expresses the inner emotions of the characters using colors or effects based on the quantified emotional data. The quantification unit can also visually express the inner emotions of the characters using animation based on the quantified emotional data. The quantification unit can also visually express the inner emotions of the characters using text based on the quantified emotional data. In this way, the quantification unit can visually express the inner emotions of the characters, thereby deepening understanding of the story. The specific method and criteria for visually expressing the inner emotions are based on visual effects, animation, etc. For example, the quantification unit inputs the emotional data into a generation AI, which then visually expresses the inner emotions.

[0073] The quantification unit can estimate the user's emotions and filter the quantified emotional data based on the estimated user's emotions. For example, if the user is nervous, the quantification unit can prioritize and display positive emotional data. Furthermore, if the user is relaxed, the quantification unit can display all emotional data in a balanced manner. Furthermore, if the user is excited, the quantification unit can suppress and display negative emotional data. In this way, the quantification unit can filter the emotional data according to the user's emotions, enabling more appropriate emotional expression. The specific method and criteria for filtering the emotional data are based on a filtering algorithm and a reference value. For example, the quantification unit inputs the user's emotional data into a generation AI, and the generation AI filters the emotional data.

[0074] The quantification unit can also analyze the tone and speed of the characters' voices and quantify their emotions. For example, the quantification unit can analyze the tone of the characters' voices and quantify their emotions. The quantification unit can also analyze the speed of the characters' voices and quantify their emotions. The quantification unit can also analyze the strength and weakness of the characters' voices and quantify their emotions. In this way, the quantification unit can analyze the tone and speed of the voices to more accurately quantify emotions. The specific analysis method and criteria for the tone and speed of the voices are based on a voice analysis algorithm and tone classification criteria. For example, the quantification unit inputs the data of the characters' voices into a generation AI, which then analyzes the tone and speed of the voices and quantifies their emotions.

[0075] The quantification unit can select an appropriate facial expression recognition algorithm by referring to the user's past learning history. The quantification unit selects the optimal facial expression recognition algorithm based on the user's past learning history, for example. The quantification unit can also select an facial expression recognition algorithm that emphasizes a specific emotion based on the user's past learning history. The quantification unit can also select an facial expression recognition algorithm that captures changes in emotion based on the user's past learning history. In this way, the quantification unit can select the optimal facial expression recognition algorithm by referring to the past learning history. The specific content of the past learning history and the method of referring to it are determined based on the learning history database and the storage period of the history. For example, the quantification unit inputs the user's learning history data into the generation AI, and the generation AI selects the optimal facial expression recognition algorithm.

[0076] The quantification unit can quantify region-specific emotional expressions taking into account the user's geographical location information. For example, the quantification unit quantifies region-specific facial expressions based on the user's geographical location information. The quantification unit can also quantify region-specific emotional expressions based on the user's geographical location information. The quantification unit can also quantify emotions taking into account the region's cultural background based on the user's geographical location information. In this way, the quantification unit can quantify region-specific emotional expressions, enabling emotional expressions that are more in line with the cultural background. The specific method of acquiring and using the geographical location information is based on the accuracy of GPS data and location information. For example, the quantification unit inputs the user's geographical location data into the generation AI, and the generation AI quantifies region-specific emotional expressions.

[0077] The creation unit can estimate the user's emotions and adjust the speed of the story based on the estimated user emotions. For example, the creation unit can slow down the speed of the story if the user is relaxed. The creation unit can also speed up the speed of the story if the user is in a hurry. The creation unit can also adjust the speed of the story to emphasize specific scenes if the user is excited. In this way, the creation unit can provide a more appropriate learning experience by adjusting the speed of the story according to the user's emotions. The specific method and criteria for adjusting the speed of the story are based on a speed adjustment algorithm and reference values. For example, the creation unit inputs the user's emotional data into the generation AI, and the generation AI adjusts the speed of the story.

[0078] The creation department can select frames taking into consideration not only changes in emotions but also the relationships between characters and the background story. For example, the creation department can select frames that correspond to changes in emotions by considering the relationships between characters. The creation department can also select frames that correspond to changes in emotions by considering the background story. The creation department can also select the optimal frames by comprehensively considering the relationships between characters and the background story. In this way, the creation department can generate a more consistent story by taking into consideration the relationships between characters and the background story. The specific content and consideration method of the relationships between characters and the background story are determined based on the definition of the relationships and the elements of the background story. For example, the creation department inputs character relationship data into the generation AI, which then selects the optimal frames.

[0079] The creation unit can adjust the difficulty of the story according to the user's learning progress. For example, if the user's learning progress is rapid, the creation unit can increase the difficulty of the story. Also, if the user's learning progress is slow, the creation unit can lower the difficulty of the story. Also, the creation unit can dynamically adjust the difficulty of the story according to the user's learning progress. In this way, the creation unit can maximize the learning effect by adjusting the difficulty of the story according to the learning progress. The specific method and criteria for adjusting the difficulty of the story are based on difficulty evaluation criteria and adjustment algorithms. For example, the creation unit inputs the user's learning progress data into the generation AI, and the generation AI adjusts the difficulty of the story.

[0080] The creation unit can dynamically change the flow of the story by reflecting user feedback. For example, the creation unit changes the flow of the story based on user feedback. The creation unit can also add specific scenes based on user feedback. The creation unit can also change the ending of the story based on user feedback. In this way, the creation unit can provide a story that is more suitable for the user by reflecting user feedback. The specific method and criteria for dynamically changing the flow of the story are based on a change algorithm and a reference value. For example, the creation unit inputs user feedback data into a generation AI, and the generation AI dynamically changes the flow of the story.

[0081] The creation unit can estimate the user's emotions and select a story theme based on the estimated user emotions. For example, if the user is relaxed, the creation unit can select a story with a relaxing theme. If the user is excited, the creation unit can select a story with a stimulating theme. If the user is sad, the creation unit can select a story with a comforting theme. In this way, the creation unit can provide a more interesting story by selecting a story theme according to the user's emotions. The specific method and criteria for selecting a story theme are based on theme classification criteria and selection algorithms. For example, the creation unit inputs the user's emotional data into a generation AI, and the generation AI selects a story theme.

[0082] The creation unit can customize the story genre based on the user's interests. For example, if the user is interested in history, the creation unit can customize a story in the history genre. If the user is interested in science, the creation unit can also customize a story in the science genre. If the user is interested in fantasy, the creation unit can also customize a story in the fantasy genre. In this way, the creation unit can provide more appealing stories by customizing the story genre based on the user's interests. The specific method and criteria for customizing the story genre are based on genre classification criteria and customization algorithms. For example, the creation unit inputs the user's interest data into the generation AI, and the generation AI customizes the story genre.

[0083] The creation unit can generate an appropriate story flow by referring to the user's past learning history. The creation unit generates an optimal story flow based on the user's past learning history, for example. The creation unit can also generate a story flow that emphasizes a specific theme based on the user's past learning history. The creation unit can also generate a story flow that enhances learning effectiveness based on the user's past learning history. In this way, the creation unit can generate an optimal story flow by referring to the past learning history. The specific method and criteria for generating an optimal story flow are based on a generation algorithm and evaluation criteria. For example, the creation unit inputs the user's learning history data into a generation AI, and the generation AI generates an optimal story flow.

[0084] The creation unit can incorporate regional story elements taking into account the user's geographic location information. For example, the creation unit can incorporate regional characters into the story based on the user's geographic location information. The creation unit can also incorporate regional backgrounds into the story based on the user's geographic location information. The creation unit can also incorporate regional culture into the story based on the user's geographic location information. In this way, the creation unit can provide a more familiar story by incorporating regional story elements. The specific content and method of incorporating regional story elements are determined based on the culture, customs, history, etc. of the region. For example, the creation unit inputs the user's geographic location data into the generation AI, which then incorporates regional story elements.

[0085] The providing unit can estimate the user's emotions and adjust the display method of the provided manga based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method that includes detailed information. If the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, the providing unit can provide a more appropriate learning experience by adjusting the display method according to the user's emotions. The specific method and criteria for adjusting the display method of the manga are based on the display format and timing of display. For example, the providing unit inputs the user's emotional data into the generation AI, and the generation AI adjusts the display method of the manga.

[0086] The providing unit can customize the content of the manga to be provided according to the user's learning progress. For example, if the user's learning progress is rapid, the providing unit can provide more difficult content. Also, if the user's learning progress is slow, the providing unit can provide more basic content. The providing unit can also dynamically customize the content according to the user's learning progress. In this way, the providing unit can maximize the learning effect by customizing the content according to the user's learning progress. The specific method and criteria for customizing the manga content are based on content selection criteria and a customization algorithm. For example, the providing unit inputs the user's learning progress data into the generating AI, and the generating AI customizes the manga content.

[0087] The providing unit can dynamically change the content of the manga to be provided by reflecting user feedback. For example, the providing unit changes the content of the manga to be provided based on user feedback. The providing unit can also add specific scenes based on user feedback. The providing unit can also change the ending of the manga based on user feedback. In this way, the providing unit can provide manga that is more suitable for users by reflecting user feedback. The specific method and criteria for dynamically changing the content of the manga are based on a change algorithm and a reference value. For example, the providing unit inputs user feedback data into a generation AI, and the generation AI dynamically changes the content of the manga.

[0088] The providing unit can select an appropriate display method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, the providing unit can select a display method based on the device information, thereby enabling a more highly visible display. The specific method of obtaining and using the device information is based on the type, specifications, and performance of the device. For example, the providing unit inputs the user's device information into the generation AI, which then selects the optimal display method.

[0089] The providing unit can estimate the user's emotions and adjust the order of the comics to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can first provide comics with relaxing content. Furthermore, if the user is relaxed, the providing unit can also first provide comics with content that is highly effective for learning. Furthermore, if the user is in a hurry, the providing unit can also first provide comics with content that covers the main points. In this way, the providing unit can adjust the order of the comics according to the user's emotions, enabling more effective learning. The specific method and criteria for adjusting the order of the comics are based on an order determination algorithm and reference values. For example, the providing unit inputs the user's emotional data into the generation AI, and the generation AI adjusts the order of the comics.

[0090] The providing unit can customize the genre of manga to be provided based on the user's interests. For example, if the user is interested in history, the providing unit can provide manga in the history genre. Furthermore, if the user is interested in science, the providing unit can provide manga in the science genre. Furthermore, if the user is interested in fantasy, the providing unit can provide manga in the fantasy genre. In this way, the providing unit can provide more attractive manga by customizing the manga genre based on the user's interests. The specific method and criteria for customizing the manga genre are based on genre classification criteria and customization algorithms. For example, the providing unit inputs the user's interest data into the generating AI, and the generating AI customizes the manga genre.

[0091] The providing unit can provide appropriate manga by referring to the user's past learning history. The providing unit, for example, provides the most suitable manga based on the user's past learning history. The providing unit can also provide manga that emphasizes a specific theme based on the user's past learning history. The providing unit can also provide manga that enhances learning effectiveness based on the user's past learning history. In this way, the providing unit can provide the most suitable manga by referring to the user's past learning history. The specific method and criteria for providing the most suitable manga are based on a providing algorithm and evaluation criteria. For example, the providing unit inputs the user's learning history data into a generation AI, and the generation AI provides the most suitable manga.

[0092] The providing unit can provide region-specific manga taking into account the user's geographical location information. For example, the providing unit can provide manga featuring region-specific characters based on the user's geographical location information. The providing unit can also provide manga with region-specific backgrounds based on the user's geographical location information. The providing unit can also provide manga that reflects region-specific culture based on the user's geographical location information. In this way, the providing unit can provide a more familiar learning experience by providing region-specific manga. The specific content and provision method of the region-specific manga are based on the culture, customs, history, etc. of the region. For example, the providing unit inputs the user's geographical location data into a generating AI, which then provides region-specific manga.

[0093] The incentive providing unit can estimate the user's emotions and adjust the content of the incentive based on the estimated user's emotions. For example, if the user is nervous, the incentive providing unit can provide an incentive that helps the user relax. Furthermore, if the user is relaxed, the incentive providing unit can also provide an incentive that increases the user's motivation to learn. Furthermore, if the user is excited, the incentive providing unit can also provide an incentive that gives the user a sense of accomplishment. In this way, the incentive providing unit can provide more effective incentives by adjusting the content of the incentive according to the user's emotions. The specific method and criteria for adjusting the content of the incentive are based on an adjustment algorithm and a reference value. For example, the incentive providing unit inputs the user's emotional data into a generation AI, and the generation AI adjusts the content of the incentive.

[0094] The incentive providing unit can determine the remuneration by referring to the evaluations of the manga artist's past works. For example, the incentive providing unit determines the remuneration based on the evaluations of the manga artist's past works. The incentive providing unit can also determine the remuneration by setting specific evaluation criteria based on the evaluations of the manga artist's past works. The incentive providing unit can also determine an increase or decrease in the remuneration based on the evaluations of the manga artist's past works. In this way, the incentive providing unit can provide a fairer remuneration by referring to the evaluations of past works. The specific criteria and reference method for the evaluation of past works are based on the evaluation scale and the evaluator's criteria. For example, the incentive providing unit inputs the manga artist's work evaluation data into the generation AI, and the generation AI determines the remuneration.

[0095] The incentive providing unit can analyze the activity history of the manga artist and provide appropriate incentives. For example, the incentive providing unit can provide optimal incentives based on the activity history of the manga artist. The incentive providing unit can also provide incentives for specific activities based on the activity history of the manga artist. The incentive providing unit can also customize the content of incentives based on the activity history of the manga artist. This allows the incentive providing unit to analyze the activity history and provide more appropriate incentives. The specific content of the activity history and the analysis method are determined based on the type of activity and the storage period of the history. For example, the incentive providing unit inputs the activity history data of the manga artist into a generation AI, which then provides the optimal incentive.

[0096] The incentive providing unit can improve the content of the incentives by reflecting the cartoonist's feedback. For example, the incentive providing unit improves the content of the incentives based on the cartoonist's feedback. The incentive providing unit can also add specific incentives based on the cartoonist's feedback. The incentive providing unit can also dynamically change the content of the incentives based on the cartoonist's feedback. In this way, the incentive providing unit can provide more effective incentives by reflecting the feedback. The specific methods and standards for improving the content of the incentives are determined based on improvement algorithms and reference values. For example, the incentive providing unit inputs the cartoonist's feedback data into the generation AI, and the generation AI improves the content of the incentives.

[0097] The incentive providing unit can estimate the user's emotions and adjust the timing of providing the incentive based on the estimated user's emotions. For example, if the user is tense, the incentive providing unit can provide the incentive at a timing that will help the user relax. Furthermore, if the user is relaxed, the incentive providing unit can also provide the incentive at a timing that will increase the user's motivation to learn. Furthermore, if the user is excited, the incentive providing unit can also provide the incentive at a timing that will help the user feel a sense of accomplishment. In this way, the incentive providing unit can provide more effective incentives by adjusting the timing of providing the incentive according to the user's emotions. The specific method and criteria for adjusting the timing of providing the incentive are based on an adjustment algorithm and a reference value. For example, the incentive providing unit inputs the user's emotional data into the generation AI, and the generation AI adjusts the timing of providing the incentive.

[0098] The incentive providing unit can provide region-specific incentives by taking into account the geographical location information of the cartoonist. For example, the incentive providing unit can provide region-specific incentives based on the geographical location information of the cartoonist. The incentive providing unit can also provide incentives that reflect region-specific culture based on the geographical location information of the cartoonist. The incentive providing unit can also provide incentives related to region-specific events based on the geographical location information of the cartoonist. In this way, the incentive providing unit can provide region-specific incentives that are more familiar to the user. The specific content and provision method of the region-specific incentives are determined based on the culture, customs, history, etc. of the region. For example, the incentive providing unit inputs the geographical location data of the cartoonist into the generation AI, which then provides region-specific incentives.

[0099] The incentive providing unit can analyze the social media activity of the manga artist and provide incentives. For example, the incentive providing unit provides incentives based on the social media activity of the manga artist. The incentive providing unit can also provide incentives for specific activities based on the social media activity of the manga artist. The incentive providing unit can also customize the content of incentives based on the social media activity of the manga artist. This allows the incentive providing unit to provide more appropriate incentives by analyzing social media activity. The specific content of social media activity and the analysis method are determined based on the type of activity and the analysis criteria. For example, the incentive providing unit inputs the social media activity data of the manga artist into a generation AI, and the generation AI provides incentives.

[0100] The incentive providing unit can customize the content of the incentive by reflecting the manga artist's past feedback. For example, the incentive providing unit customizes the content of the incentive based on the manga artist's past feedback. The incentive providing unit can also add specific incentives based on the manga artist's past feedback. The incentive providing unit can also dynamically change the content of the incentive based on the manga artist's past feedback. This allows the incentive providing unit to provide more effective incentives by reflecting past feedback. The specific method and criteria for customizing the content of the incentive are based on a customization algorithm and a reference value. For example, the incentive providing unit inputs the manga artist's feedback data into a generation AI, and the generation AI customizes the content of the incentive. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned quantification unit, creation unit, provision unit, and incentive provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the quantification unit analyzes the facial expressions of the characters using the camera 42 of the smart device 14 and quantifies the emotions using the control unit 46A. The creation unit generates the story flow using, for example, the specific processing unit 290 of the data processing device 12. The provision unit provides the generated manga through, for example, the output device 40 of the smart device 14. The incentive provision unit provides incentives to the manga artist using, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned quantification unit, creation unit, provision unit, and incentive provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the quantification unit analyzes the facial expressions of the characters using the camera 42 of the smart glasses 214 and quantifies the emotions using the control unit 46A. The creation unit generates a story flow using, for example, the specific processing unit 290 of the data processing device 12. The provision unit provides the generated manga through, for example, the speaker 240 of the smart glasses 214. The incentive provision unit provides incentives to the manga artist using, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned quantification unit, creation unit, provision unit, and incentive provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the quantification unit analyzes the facial expressions of the characters using the camera 42 of the headset type terminal 314 and quantifies the emotions using the control unit 46A. The creation unit generates the flow of the story using, for example, the specific processing unit 290 of the data processing device 12. The provision unit provides the generated manga through, for example, the display 343 of the headset type terminal 314. The incentive provision unit provides incentives to the manga artist using, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned quantification unit, creation unit, provision unit, and incentive provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the quantification unit analyzes the facial expressions of the characters using the camera 42 of the robot 414 and quantifies the emotions using the control unit 46A. The creation unit generates the flow of the story using, for example, the specific processing unit 290 of the data processing device 12. The provision unit provides the generated manga through, for example, the speaker 240 of the robot 414. The incentive provision unit provides incentives to the manga artist using, for example, the specific processing unit 290 of the data processing device 12.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The quantification unit can also analyze the user's learning style and select the optimal facial expression recognition algorithm. For example, if the user is a visual learner, an algorithm that emphasizes visual changes in facial expressions can be selected. If the user is an auditory learner, an algorithm that emphasizes changes in vocal tone can be selected. Furthermore, if the user is an experiential learner, an algorithm that dynamically captures changes in emotions can be selected. In this way, the quantification unit can provide a more effective learning experience by selecting the optimal facial expression recognition algorithm according to the user's learning style.

[0103] The incentive providing unit can estimate the user's emotions and select the type of incentive based on the estimated user's emotions. For example, if the user is tired, it can provide a relaxing incentive. If the user is highly motivated, it can provide a challenging incentive. Furthermore, if the user is depressed, it can provide an incentive that includes an encouraging message. In this way, the incentive providing unit can more effectively improve motivation by selecting the type of incentive according to the user's emotions.

[0104] The creation unit can estimate the user's emotions and adjust the tone of the story based on the estimated user's emotions. For example, if the user is sad, the tone of the story can be made brighter. If the user is excited, the tone of the story can be made calmer. Furthermore, if the user is relaxed, the tone of the story can be maintained as it is. In this way, the creation unit can provide a more appropriate learning experience by adjusting the tone of the story according to the user's emotions.

[0105] The providing unit can estimate the user's emotions and adjust the format of the manga to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible format can be provided. If the user is relaxed, a format including detailed information can be provided. Furthermore, if the user is in a hurry, a format that focuses on the main points can be provided. In this way, the providing unit can provide a more effective learning experience by adjusting the format according to the user's emotions.

[0106] The quantification unit can estimate the user's emotions and customize the display method of the emotional data based on the estimated user emotions. For example, if the user is nervous, positive emotional data can be emphasized and displayed. If the user is relaxed, all emotional data can be displayed in a balanced manner. Furthermore, if the user is excited, negative emotional data can be suppressed and displayed. In this way, the quantification unit can customize the display method of the emotional data according to the user's emotions, making it possible to express emotions more appropriately.

[0107] The creation unit can adjust the speed of the story by referring to the user's learning history. For example, if the user has an extensive learning history, the speed of the story can be increased. Alternatively, if the user has a limited learning history, the speed of the story can be decreased. Furthermore, specific scenes can be emphasized based on the user's learning history. In this way, the creation unit can provide a more effective learning experience by adjusting the speed of the story according to the user's learning history.

[0108] The providing unit can analyze the user's learning style and select the optimal manga display method. For example, if the user is a visual learner, a visually appealing display method can be provided. If the user is an auditory learner, a display method with audio guidance can be provided. Furthermore, if the user is an experiential learner, an interactive display method can be provided. In this way, the providing unit can provide a more effective learning experience by selecting the optimal display method according to the user's learning style.

[0109] The incentive providing unit can analyze the user's learning progress and provide appropriate incentives. For example, if the user's learning progress is fast, a high reward can be provided. Also, if the user's learning progress is slow, an encouraging message can be provided. Furthermore, specific incentives can be added depending on the user's learning progress. In this way, the incentive providing unit can increase the user's motivation to learn by providing appropriate incentives depending on the user's learning progress.

[0110] The creation department can incorporate region-specific story elements by taking into account the user's geographic location information. For example, if the user is in Japan, story elements related to Japanese history and culture can be incorporated. If the user is in America, story elements related to American history and culture can be incorporated. Furthermore, if the user is in Europe, story elements related to European history and culture can be incorporated. In this way, the creation department can provide a more familiar story by incorporating region-specific story elements.

[0111] The providing unit can dynamically change the content of the manga it provides by reflecting user feedback. For example, it can add specific scenes based on user feedback. It can also change the ending of the story based on user feedback. It can also change character settings based on user feedback. In this way, the providing unit can provide manga that is more suitable for the user by reflecting user feedback.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The quantification unit uses facial expression recognition technology to quantify emotions. Specifically, it uses a facial expression recognition algorithm to analyze the characters' facial expressions and quantify the results. It can also quantify emotions based on the type of emotion and the scale of quantification. For example, it can quantify the characters' smiling or surprised expressions to capture changes in their emotions. Step 2: The generator generates a story flow based on the emotions quantified by the quantification unit. Specifically, the generator generates a story flow using a storytelling algorithm, and can also generate a story flow based on scenario generation rules. For example, it selects and arranges appropriate frames according to changes in emotions. Step 3: The provider provides the comics generated by the generator. Specifically, the provider provides the comics in digital format, and can also provide the comics in print format. For example, the provider provides the comics through a web platform.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 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.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0128] 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.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0144] 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.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 quantification unit that uses facial expression recognition technology to quantify emotions; a generation unit that generates a story flow based on the emotions quantified by the quantification unit; a providing unit that provides the comic book generated by the generating unit; Equipped with A system characterized by:

2. A department will recruit manga artists and provide incentives each time their work is used.

2. The system of claim 1.

3. The incentive providing unit: Implement a reward or ranking system based on usage 3. The system of claim 2.

4. The digitizing unit Estimate the user's emotions and dynamically adjust the accuracy of facial expression recognition based on the estimated user emotions.

2. The system of claim 1.

5. The digitizing unit Quantify the emotional impact of not only the characters' facial expressions but also the scenery and props 2. The system of claim 1.

6. The digitizing unit Equipped with a function that updates quantified emotional data in real time and dynamically changes it according to the progress of the story.

2. The system of claim 1.

7. The digitizing unit It has the function of visually expressing the inner feelings of characters using quantified emotional data.

2. The system of claim 1.

8. The digitizing unit It has the function of estimating the user's emotions and filtering the quantified emotional data based on the estimated user emotions.

2. The system of claim 1.

Images