system

Abstract

The system according to the embodiment aims to automatically convert digital comics into voice comics. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a playback unit. The reception unit inputs digital comic data. The analysis unit analyzes the digital comic data input by the reception unit. The generation unit generates text-to-speech or sound effects based on the data analyzed by the analysis unit. The playback unit plays back the text-to-speech or sound effects 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] With conventional technology, the cost of creating voice comics was high, making it difficult to turn many works into voice comics.

[0005] The system according to the embodiment aims to automatically convert digital comics into voice comics. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a playback unit. The reception unit inputs digital comic data. The analysis unit analyzes the digital comic data input by the reception unit. The generation unit generates text-to-speech or sound effects based on the data analyzed by the analysis unit. The playback unit plays back the text-to-speech or sound effects generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically convert digital comics into voice comics. [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 voice comic automatic generation system according to an embodiment of the present invention automatically generates a voice comic by inputting, analyzing, generating, and playing back digital comic data. When a digital comic is input, the voice comic automatic generation system analyzes each page of the comic and automatically generates and plays back a reading that takes into account the emotions of the characters and the drawings in the comic, as well as sound effects based on onomatopoeia and other elements. For example, in a voice comic automatic generation system, a user inputs a digital comic into the system. For example, the digital comic data is input as image data or text data. The voice comic automatic generation system then analyzes the input digital comic. The generation AI analyzes each page of the comic and identifies the emotions of the characters and the drawings in the comic. For example, the AI ​​determines whether the character is happy, angry, or sad based on the character's facial expressions and the content of the dialogue. The AI ​​also analyzes drawing elements such as onomatopoeia and effect lines and uses them to generate sound effects. The voice comic automatic generation system then generates a reading and sound effects based on the character's emotions and the drawing elements. The generation AI adjusts the voice tone and tempo according to the character's emotions, resulting in a natural reading. For example, if the character is angry, the voice tone is lowered and the tempo is increased. It also generates appropriate sound effects based on onomatopoeia. For example, if the onomatopoeia is "boom," a corresponding explosion sound is generated. Finally, the voice comic automatic generation system plays back the generated reading and sound effects. This makes it easy to turn any manga into a voice comic. This significantly reduces the cost of creating voice comics, making it easy for even individual manga artists and small publishers to create voice comics. Furthermore, turning existing manga works into voice comics can create new revenue streams.

[0029] A voice comic automatic generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a playback unit. The reception unit inputs digital comic data. Digital comic data includes, but is not limited to, image data, text data, and audio data. The reception unit inputs, for example, a PDF digital comic or comic pages saved as image files. The analysis unit analyzes the digital comic data input by the reception unit. The analysis may be performed using, for example, image analysis, text analysis, or audio analysis, but is not limited to, the methods. The analysis unit, for example, uses a generation AI to analyze each page of the comic and identify the character's emotions and the drawings in the comic. The generation AI determines, for example, from the character's facial expressions and the content of the dialogue whether the character is happy, angry, or sad. The analysis unit also analyzes drawing elements such as onomatopoeia and effect lines and uses the results to generate sound effects. The generation unit generates readings and sound effects based on the data analyzed by the analysis unit. The generation is performed using, for example, voice synthesis technology, but is not limited to, such an example. The generation unit uses a generation AI to adjust the tone and tempo of the voice according to the character's emotions, achieving natural reading. For example, if the character is angry, the tone of the voice is lowered and the tempo is increased. The generation unit also generates appropriate sound effects based on onomatopoeia. For example, if the onomatopoeia is "bang," a corresponding explosion sound is generated. The playback unit plays the reading and sound effects generated by the generation unit. The playback is performed using, for example, a playback device, but is not limited to such an example. The playback unit plays the generated reading and sound effects using, for example, speakers or headphones. As a result, the voice comic automatic generation system according to the embodiment can automatically generate a voice comic by inputting digital comic data and performing analysis, generation, and playback.

[0030] The analysis unit includes an emotion determination unit that determines the emotion of the character. The emotion determination unit determines the emotion of the character. The emotion of the character includes, but is not limited to, joy, anger, sadness, and the like. The emotion determination unit, for example, analyzes the character's facial expression to determine the emotion. For example, the emotion determination unit analyzes the character's smile to determine the emotion of joy. The emotion determination unit can also analyze the content of the character's lines to determine the emotion. For example, the emotion determination unit determines the emotion of anger from the character's lines. The emotion determination unit can also determine the emotion by combining the character's facial expression and the content of the lines. For example, the emotion determination unit combines the character's smile with a line of joy to determine the emotion of joy. In this way, by determining the character's emotion, more natural reading and sound effects can be generated.

[0031] The analysis unit includes an onomatopoeia analysis unit that analyzes onomatopoeia. The onomatopoeia analysis unit analyzes onomatopoeia. Onomatopoeia includes, for example, onomatopoeic words and mimetic words, but is not limited to these examples. The onomatopoeia analysis unit analyzes onomatopoeia using, for example, text analysis technology. For example, the onomatopoeia analysis unit analyzes the onomatopoeia "boom" (bang) and generates an explosion sound. The onomatopoeia analysis unit can also analyze the frequency of use of onomatopoeia and use the results to generate sound effects. For example, the onomatopoeia analysis unit prioritizes analysis of frequently used onomatopoeia and generates sound effects. The onomatopoeia analysis unit can also analyze the context of onomatopoeia and use the results to generate sound effects. For example, the onomatopoeia analysis unit analyzes the sentences before and after the onomatopoeia and generates an appropriate sound effect. This makes it possible to generate appropriate sound effects by analyzing onomatopoeia.

[0032] The generation unit includes a sound effect adjustment unit that adjusts the generated sound effect. The sound effect adjustment unit adjusts the generated sound effect. Examples of sound effect adjustment include, but are not limited to, volume adjustment, echo effect, and pitch adjustment. The sound effect adjustment unit adjusts the volume of the generated sound effect. For example, the sound effect adjustment unit adjusts the volume of an explosion sound and plays it at an appropriate volume. The sound effect adjustment unit can also add an echo effect to the generated sound effect. For example, the sound effect adjustment unit adds an echo effect to the explosion sound to enhance realism. The sound effect adjustment unit can also adjust the pitch of the generated sound effect. For example, the sound effect adjustment unit adjusts the pitch of the explosion sound to provide a more natural sound effect. In this way, by adjusting the generated sound effect, a more natural sound effect can be provided.

[0033] The generation unit can adjust the tone and tempo of the voice according to the character's emotions. The generation unit adjusts the tone and tempo of the voice according to the character's emotions. Adjustments to the tone and tempo of the voice include, but are not limited to, changes in tone and tempo according to the emotion. For example, if the character is angry, the generation unit generates a voice with a low tone and a fast tempo. For example, the generation unit generates a voice with a low tone and a fast tempo to express the emotion of anger. The generation unit can also generate a voice with a high tone and a bright tempo if the character is happy. For example, the generation unit generates a voice with a high tone and a bright tempo to express the emotion of joy. The generation unit can also generate a voice with a low tone and a slow tempo if the character is sad. For example, the generation unit generates a voice with a low tone and a slow tempo to express the emotion of sadness. In this way, by adjusting the tone and tempo of the voice according to the character's emotions, more natural reading can be achieved.

[0034] The playback unit can play the generated readings and sound effects. The playback unit plays the generated readings and sound effects. Playback includes, but is not limited to, a playback device, a playback order, and a playback timing. The playback unit plays the generated readings and sound effects using, for example, a speaker or headphones. For example, the playback unit plays the generated readings and sound effects using a speaker to provide a user with a sense of realism. The playback unit can also play the generated readings and sound effects using headphones. For example, the playback unit uses headphones to provide a personalized experience for the user. The playback unit can also adjust the playback order and playback timing. For example, the playback unit adjusts the playback order depending on the scene and plays the readings and sound effects at the appropriate timing. In this way, by playing the generated readings and sound effects, the user can enjoy a sense of realism while reading the digital comic.

[0035] The reception unit analyzes the user's past usage history and selects an appropriate reception method when receiving digital comic data. The reception unit analyzes the user's past usage history and selects an appropriate reception method when receiving digital comic data. The past usage history includes, but is not limited to, browsing history, operation history, and feedback. For example, the reception unit preferentially receives digital comics in a format that the user has frequently used in the past. For example, the reception unit analyzes the user's past browsing history and preferentially receives digital comics in a format that the user has frequently used in the past. The reception unit can also select the optimal reception method for a specific time period based on the user's past usage history. For example, the reception unit analyzes the user's past operation history and selects the optimal reception method for a specific time period. The reception unit can also automatically select an interface that the user has preferred in the past and use it to receive data. For example, the reception unit analyzes the user's past feedback and selects the preferred interface. In this way, the optimal reception method can be selected by analyzing the user's past usage history.

[0036] The reception unit filters the e-comic data based on the user's current interests and concerns when receiving the e-comic data. The reception unit filters the e-comic data based on the user's current interests and concerns when receiving the e-comic data. Current interests and concerns include, but are not limited to, search history, social media activity, etc. For example, the reception unit preferentially receives e-comics in a genre in which the user is currently interested. For example, the reception unit analyzes the user's recent search history and preferentially receives e-comics in a genre in which the user is interested. The reception unit can also filter and receive related e-comics based on the user's recent search history. For example, the reception unit analyzes the user's search history and filters related e-comics. The reception unit can also filter and receive e-comics that the user may be interested in based on content mentioned by the user on social media. For example, the reception unit analyzes the user's social media activity and filters related e-comics. In this way, highly relevant data can be received by filtering based on the user's current interests and concerns.

[0037] When receiving digital comic data, the reception unit selects an appropriate reception means depending on the user's input method. When receiving digital comic data, the reception unit selects an appropriate reception means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the reception unit uses voice recognition technology to receive digital comic data. For example, the reception unit uses voice recognition technology to analyze the user's voice input and receive digital comic data. Furthermore, when the user uses text input, the reception unit can also use text analysis technology to receive digital comic data. For example, the reception unit uses text analysis technology to analyze the user's text input and receive digital comic data. Furthermore, when the user uses image input, the reception unit can also use image recognition technology to receive digital comic data. For example, the reception unit uses image recognition technology to analyze the user's image input and receive digital comic data. This improves the efficiency of data reception by selecting an appropriate reception means depending on the user's input method.

[0038] When receiving e-comic data, the reception unit prioritizes receiving highly relevant data by taking into account the user's geographical location information. When receiving e-comic data, the reception unit prioritizes receiving highly relevant data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the reception unit prioritizes receiving e-comics related to that area. For example, the reception unit analyzes the user's GPS data and prioritizes receiving e-comics related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving e-comics related to the user's travel destination. For example, the reception unit analyzes the user's IP address and prioritizes receiving e-comics related to the user's travel destination. Furthermore, when the user is participating in a specific event, the reception unit can also prioritize receiving e-comics related to the event. For example, the reception unit analyzes the user's geographical location information and prioritizes receiving e-comics related to the event. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account.

[0039] The reception unit analyzes the user's social media activity when receiving data on the e-comic and receives related data. The reception unit analyzes the user's social media activity when receiving data on the e-comic and receives related data. Social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the reception unit preferentially receives e-comics related to manga or characters mentioned by the user on social media. For example, the reception unit analyzes the content of the user's social media posts and preferentially receives related e-comics. The reception unit can also analyze the content of the user's social media posts and receive related e-comics. For example, the reception unit analyzes the content of the user's social media posts and receives related e-comics. The reception unit can also receive related e-comics based on the activity of the user's friends on social media. For example, the reception unit analyzes the social media activity of the user's friends and receives related e-comics. In this way, related data can be received by analyzing the user's social media activity.

[0040] The reception unit customizes the reception method by reflecting the user's past feedback when receiving digital comic data. The reception unit customizes the reception method by reflecting the user's past feedback when receiving digital comic data. Past feedback includes, but is not limited to, user ratings, comments, and survey results. For example, the reception unit preferentially receives digital comic formats that the user has previously rated highly. For example, the reception unit analyzes the user's past ratings and preferentially receives digital comic formats that have been highly rated. The reception unit can also select an optimal reception method based on the user's past feedback. For example, the reception unit analyzes the user's past comments and selects an optimal reception method. The reception unit can also avoid reception methods that the user has previously been dissatisfied with and provide a customized reception method. For example, the reception unit analyzes the user's past survey results and avoids reception methods that the user has previously been dissatisfied with. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0041] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the manga. During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the manga. The importance of the manga includes, for example, popularity, sales, review ratings, etc., but is not limited to these examples. The analysis unit performs a detailed analysis, for example, of scenes in which major characters appear. For example, the analysis unit identifies scenes in which major characters appear and performs a detailed analysis. The analysis unit can also perform a concise analysis of scenes that focus on backgrounds or scenery. For example, the analysis unit identifies scenes that focus on backgrounds or scenery and performs a concise analysis. The analysis unit can also perform a particularly detailed analysis of climax scenes. For example, the analysis unit identifies climax scenes and performs a particularly detailed analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the manga, more important scenes can be analyzed in detail.

[0042] The analysis unit applies different analysis algorithms depending on the category of the manga during analysis. The analysis unit applies different analysis algorithms depending on the category of the manga during analysis. Manga categories include, but are not limited to, genre, target age, and theme, for example. For example, for action manga, the analysis unit applies an algorithm specialized for analyzing movements and fight scenes. For example, the analysis unit identifies movements and fight scenes in action manga and applies a specialized algorithm. The analysis unit can also apply an algorithm specialized for analyzing character emotions and relationships in romance manga. For example, the analysis unit identifies character emotions and relationships in romance manga and applies a specialized algorithm. The analysis unit can also apply an algorithm specialized for analyzing humor and comedy in comedy manga. For example, the analysis unit identifies humor and comedy in comedy manga and applies a specialized algorithm. In this way, by applying different analysis algorithms depending on the category of the manga, more appropriate analysis results can be provided.

[0043] The analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, improvements in analysis accuracy and pattern recognition. The analysis unit improves the accuracy of the analysis by referring to, for example, analysis results that the user has previously rated highly. For example, the analysis unit analyzes the user's past highly rated analysis results and improves the accuracy. The analysis unit can also adjust the analysis algorithm based on the user's past feedback. For example, the analysis unit analyzes the user's past feedback and adjusts the algorithm. The analysis unit can also avoid analysis results that the user has previously dissatisfied with and provide a highly accurate analysis. For example, the analysis unit analyzes the user's past dissatisfied analysis results and provides a highly accurate analysis. In this way, the analysis accuracy can be improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit determines the analysis priority based on the publication date of the manga. During analysis, the analysis unit determines the analysis priority based on the publication date of the manga. Publication date includes, but is not limited to, for example, the publication date or the release date. For example, the analysis unit prioritizes analysis of the latest manga and provides the latest information. For example, the analysis unit identifies the latest manga and analyzes it preferentially. The analysis unit can also analyze classic manga and provide results that take historical background into consideration. For example, the analysis unit identifies classic manga and analyzes it taking historical background into consideration. The analysis unit can also prioritize analysis of manga from a period in which the user is particularly interested. For example, the analysis unit identifies manga from a period in which the user is interested and analyzes it preferentially. In this way, by determining the analysis priority based on the publication date of the manga, the latest information can be provided preferentially.

[0045] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the manga. During analysis, the analysis unit adjusts the order of analysis based on the relevance of the manga. The relevance of the manga includes, but is not limited to, for example, story continuity and character relationships. For example, the analysis unit prioritizes analysis of scenes related to the main storyline. For example, the analysis unit identifies the main storyline and prioritizes analysis of related scenes. The analysis unit can also postpone analysis of side stories and supplementary scenes. For example, the analysis unit identifies side stories and supplementary scenes and analyzes them later. The analysis unit can also prioritize analysis of scenes related to characters in which the user is particularly interested. For example, the analysis unit identifies characters in which the user is interested and prioritizes analysis of related scenes. In this way, by adjusting the order of analysis based on the relevance of the manga, it is possible to prioritize analysis of the main storyline.

[0046] During analysis, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. During analysis, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past usage history. For example, if the user is a beginner, the analysis unit provides the analysis result in simple language. For example, the analysis unit determines that the user is a beginner and provides the analysis result in simple language. Furthermore, if the user is an intermediate user, the analysis unit can provide the analysis result using appropriate technical terms. For example, the analysis unit determines that the user is an intermediate user and provides the analysis result using appropriate technical terms. Furthermore, if the user is an advanced user, the analysis unit can provide the analysis result using detailed technical terms. For example, the analysis unit determines that the user is an advanced user and provides the analysis result using detailed technical terms. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0047] The generation unit can adjust the tone and tempo of the voice based on the character's emotion during generation. The generation unit adjusts the tone and tempo of the voice based on the character's emotion during generation. Adjustments to the tone and tempo of the voice include, but are not limited to, changes in tone and tempo depending on the emotion. For example, if the character is angry, the generation unit generates a voice with a low tone and a fast tempo. For example, the generation unit generates a voice with a low tone and a fast tempo to express the emotion of anger. The generation unit can also generate a voice with a high tone and a bright tempo when the character is happy. For example, the generation unit generates a voice with a high tone and a bright tempo to express the emotion of joy. The generation unit can also generate a voice with a low tone and a slow tempo when the character is sad. For example, the generation unit generates a voice with a low tone and a slow tempo to express the emotion of sadness. In this way, by adjusting the tone and tempo of the voice based on the character's emotion, more natural reading can be achieved.

[0048] The generation unit can apply different sound effect generation algorithms depending on the type of onomatopoeia during generation. The generation unit applies different sound effect generation algorithms depending on the type of onomatopoeia during generation. Sound effect generation algorithms include, but are not limited to, physics-based acoustic models and sample-based sound generation. For example, the generation unit applies an algorithm that generates an explosion sound to the onomatopoeia ``boom.'' For example, the generation unit applies a physics-based acoustic model to generate an explosion sound. The generation unit can also apply an algorithm that generates a flash sound to the onomatopoeia ``pika.'' For example, the generation unit applies sample-based sound generation to generate a flash sound. The generation unit can also apply an algorithm that generates a crowd noise to the onomatopoeia ``buzz.'' For example, the generation unit applies a physics-based acoustic model to generate the crowd noise. In this way, by applying different sound effect generation algorithms depending on the type of onomatopoeia, more appropriate sound effects can be generated.

[0049] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit improves the accuracy of generation by referring to the user's past generation results during generation. Past generation results include, but are not limited to, improvements in generation accuracy and pattern recognition. The generation unit improves the accuracy of generation by referring to, for example, generation results that the user has previously rated highly. For example, the generation unit analyzes the user's past highly rated generation results and improves the accuracy. The generation unit can also adjust the generation algorithm based on the user's past feedback. For example, the generation unit analyzes the user's past feedback and adjusts the algorithm. The generation unit can also avoid generation results that the user has previously dissatisfied with and provide a highly accurate generation. For example, the generation unit analyzes the user's past generation results that the user has previously dissatisfied with and provides a highly accurate generation. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0050] The generation unit can determine the generation priority based on the publication date of the manga during generation. The generation unit can determine the generation priority based on the publication date of the manga during generation. The publication date includes, but is not limited to, for example, the publication date or the release date. For example, the generation unit can prioritize generating the latest manga and provide the latest information. For example, the generation unit can identify the latest manga and generate it preferentially. The generation unit can also generate classic manga and provide results taking historical background into consideration. For example, the generation unit can identify classic manga and generate it taking historical background into consideration. The generation unit can also prioritize generating manga from a period in which the user is particularly interested. For example, the generation unit can identify manga from a period in which the user is interested and generate it preferentially. In this way, by determining the generation priority based on the publication date of the manga, the latest information can be preferentially provided.

[0051] The generation unit can adjust the order of generation based on the relevance of the manga during generation. The generation unit can adjust the order of generation based on the relevance of the manga during generation. The relevance of the manga includes, but is not limited to, for example, the continuity of the story and the relationships between the characters. For example, the generation unit prioritizes generating scenes related to the main storyline. For example, the generation unit identifies the main storyline and prioritizes generating related scenes. The generation unit can also postpone generating side stories and supplementary scenes. For example, the generation unit identifies side stories and supplementary scenes and generates them later. The generation unit can also prioritize generating scenes related to characters in which the user is particularly interested. For example, the generation unit identifies characters in which the user is interested and prioritizes generating related scenes. In this way, by adjusting the order of generation based on the relevance of the manga, the main storyline can be prioritized.

[0052] The generation unit may adjust the use of technical terminology in the generation according to the user's level of expertise during generation. The generation unit may adjust the use of technical terminology in the generation according to the user's level of expertise during generation. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past usage history. For example, if the user is a beginner, the generation unit may provide the generated result in simple language. For example, the generation unit may determine that the user is a beginner and provide the generated result in simple language. Furthermore, if the user is an intermediate user, the generation unit may provide the generated result using appropriate technical terminology. For example, the generation unit may determine that the user is an intermediate user and provide the generated result using appropriate technical terminology. Furthermore, if the user is an advanced user, the generation unit may provide the generated result using detailed technical terminology. For example, the generation unit may determine that the user is an advanced user and provide the generated result using detailed technical terminology. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, it is possible to provide a generated result that is easier to understand.

[0053] The playback unit can adjust the level of detail of playback based on the importance of the manga during playback. The playback unit adjusts the level of detail of playback based on the importance of the manga during playback. The importance of the manga includes, for example, popularity, sales, review ratings, etc., but is not limited to these examples. The playback unit, for example, performs detailed playback of scenes featuring major characters. For example, the playback unit identifies scenes featuring major characters and performs detailed playback. The playback unit can also perform concise playback of scenes focusing on backgrounds and scenery. For example, the playback unit identifies scenes focusing on backgrounds and scenery and performs concise playback. The playback unit can also perform particularly detailed playback of climax scenes. For example, the playback unit identifies climax scenes and performs particularly detailed playback. In this way, by adjusting the level of detail of playback based on the importance of the manga, more important scenes can be played in detail.

[0054] The playback unit can apply different playback algorithms during playback depending on the category of the manga. The playback unit applies different playback algorithms during playback depending on the category of the manga. Examples of playback algorithms include, but are not limited to, a physics-based acoustic model and sample-based audio generation. For example, the playback unit applies an algorithm specialized for playing back action and battle scenes to an action manga. For example, the playback unit identifies the actions and battle scenes of an action manga and applies the specialized algorithm. The playback unit can also apply an algorithm specialized for playing back character emotions and relationships to a romance manga. For example, the playback unit identifies the emotions and relationships of characters in a romance manga and applies the specialized algorithm. The playback unit can also apply an algorithm specialized for playing back humor and gags to a comedy manga. For example, the playback unit identifies the humor and gags in a comedy manga and applies the specialized algorithm. In this way, by applying different playback algorithms depending on the category of the manga, more appropriate playback results can be provided.

[0055] The playback unit can improve playback accuracy by referring to the user's past playback results during playback. The playback unit can improve playback accuracy by referring to the user's past playback results during playback. Past playback results include, but are not limited to, improvements in playback accuracy and pattern recognition. The playback unit can improve playback accuracy by referring to playback results that the user has previously rated highly. For example, the playback unit can analyze the user's past highly rated playback results and improve accuracy. The playback unit can also adjust a playback algorithm based on the user's past feedback. For example, the playback unit can analyze the user's past feedback and adjust the algorithm. The playback unit can also avoid playback results that the user has previously dissatisfied with and provide highly accurate playback. For example, the playback unit can analyze the user's past dissatisfied playback results and provide highly accurate playback. In this way, the playback accuracy can be improved by referring to the user's past playback results.

[0056] The playback unit can determine playback priorities based on the publication dates of the manga during playback. The playback unit can determine playback priorities based on the publication dates of the manga during playback. Publication dates include, but are not limited to, for example, publication dates and release dates. For example, the playback unit can prioritize playback of the latest manga to provide the latest information. For example, the playback unit can identify the latest manga and prioritize playback. The playback unit can also play classic manga and provide results that take historical background into consideration. For example, the playback unit can identify classic manga and play them while taking historical background into consideration. The playback unit can also prioritize playback of manga from a period in which the user is particularly interested. For example, the playback unit can identify manga from a period in which the user is interested and prioritize playback. In this way, by determining playback priorities based on the publication dates of the manga, the latest information can be prioritized.

[0057] The playback unit can adjust the playback order based on the relevance of the manga during playback. The playback unit adjusts the playback order based on the relevance of the manga during playback. The relevance of the manga includes, but is not limited to, for example, the continuity of the story and the relationships between the characters. For example, the playback unit prioritizes playback of scenes related to the main storyline. For example, the playback unit identifies the main storyline and prioritizes playback of related scenes. The playback unit can also postpone playback of side stories or supplementary scenes. For example, the playback unit identifies side stories or supplementary scenes and prioritizes playback of them. The playback unit can also prioritize playback of scenes related to characters in which the user is particularly interested. For example, the playback unit identifies characters in which the user is interested and prioritizes playback of related scenes. In this way, by adjusting the playback order based on the relevance of the manga, it is possible to prioritize playback of the main storyline.

[0058] The playback unit may adjust the use of technical terms during playback according to the user's level of expertise. The playback unit may adjust the use of technical terms during playback according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past usage history. For example, if the user is a beginner, the playback unit may provide playback results in simple language. For example, the playback unit may determine that the user is a beginner and provide playback results in simple language. Furthermore, if the user is an intermediate user, the playback unit may provide playback results using appropriate technical terms. For example, the playback unit may determine that the user is an intermediate user and provide playback results using appropriate technical terms. Furthermore, if the user is an advanced user, the playback unit may provide playback results using detailed technical terms. For example, the playback unit may determine that the user is an advanced user and provide playback results using detailed technical terms. In this way, by adjusting the use of technical terms during playback according to the user's level of expertise, it is possible to provide playback results that are easier to understand.

[0059] The emotion determination unit can improve the accuracy of emotion determination by analyzing in detail the character's facial expressions and the content of the lines when determining the emotion. The emotion determination unit can improve the accuracy of emotion determination by analyzing in detail the character's facial expressions and the content of the lines when determining the emotion. The character's facial expressions and the content of the lines can be determined using, for example, facial expression recognition technology, natural language processing technology, etc., but are not limited to these examples. The emotion determination unit can improve the accuracy of emotion determination by analyzing, for example, the character's facial expressions in detail. For example, the emotion determination unit can analyze in detail the character's smile to determine the emotion of joy. The emotion determination unit can also improve the accuracy of emotion determination by analyzing in detail the content of the character's lines. For example, the emotion determination unit can determine the emotion of anger from the character's lines. The emotion determination unit can also improve the accuracy of emotion determination by combining the character's facial expressions and the content of the lines. For example, the emotion determination unit can determine the emotion of joy by combining the character's smile and the content of the lines. In this way, the accuracy of emotion determination can be improved by analyzing the character's facial expressions and the content of the lines in detail.

[0060] The emotion determination unit can make an emotion determination by referring to the character's past emotion history. The emotion determination unit makes an emotion determination by referring to the character's past emotion history. The past emotion history includes, for example, the character's emotion change patterns, past scenes, etc., but is not limited to these examples. The emotion determination unit, for example, refers to the character's past emotion history to determine the character's current emotion. For example, the emotion determination unit analyzes the character's past emotion history to determine the character's current emotion. The emotion determination unit can also determine a change in emotion based on the character's past emotion history. For example, the emotion determination unit analyzes the character's past emotion history to determine a change in emotion. The emotion determination unit can also improve the accuracy of emotion determination by referring to the character's past emotion history. For example, the emotion determination unit analyzes the character's past emotion history to improve the accuracy of emotion determination. In this way, by referring to the character's past emotion history, the accuracy of emotion determination can be improved.

[0061] The emotion determination unit can determine emotions by taking into consideration the relationships between characters when determining emotions. The emotion determination unit determines emotions by taking into consideration the relationships between characters when determining emotions. Character relationships include, for example, dialogue between characters, progress of a story, etc., but are not limited to these examples. The emotion determination unit, for example, improves the accuracy of emotion determination by taking into consideration the relationships between characters. For example, the emotion determination unit analyzes the relationships between characters to improve the accuracy of emotion determination. The emotion determination unit can also determine the strength of an emotion based on the relationships between characters. For example, the emotion determination unit analyzes the relationships between characters and determines the strength of an emotion. The emotion determination unit can also determine changes in emotions by referring to the relationships between characters. For example, the emotion determination unit analyzes the relationships between characters and determines changes in emotion. In this way, the accuracy of emotion determination can be improved by taking the relationships between characters into consideration.

[0062] The emotion determination unit can determine an emotion by taking into consideration the geographical background of the character when determining the emotion. The emotion determination unit determines an emotion by taking into consideration the geographical background of the character when determining the emotion. The geographical background includes, for example, the character's hometown and the location of the scene, but is not limited to these examples. The emotion determination unit, for example, improves the accuracy of emotion determination by taking into consideration the geographical background of the character. For example, the emotion determination unit analyzes the character's hometown and improves the accuracy of emotion determination. The emotion determination unit can also determine the strength of an emotion based on the character's geographical background. For example, the emotion determination unit analyzes the location of the character's scene and determines the strength of the emotion. The emotion determination unit can also determine a change in emotion by referring to the character's geographical background. For example, the emotion determination unit analyzes the character's geographical background and determines a change in emotion. In this way, the accuracy of emotion determination can be improved by taking the character's geographical background into consideration.

[0063] The emotion determination unit can improve the accuracy of emotion determination by referring to literature related to the character when determining the emotion. The emotion determination unit improves the accuracy of emotion determination by referring to literature related to the character when determining the emotion. Related literature includes, but is not limited to, past works and related research papers, for example. The emotion determination unit improves the accuracy of emotion determination by referring to literature related to the character. For example, the emotion determination unit analyzes past works of the character to improve the accuracy of emotion determination. The emotion determination unit can also determine the strength of emotion based on literature related to the character. For example, the emotion determination unit analyzes related research papers to determine the strength of emotion. The emotion determination unit can also determine changes in emotion by referring to literature related to the character. For example, the emotion determination unit analyzes past works of the character to determine changes in emotion. In this way, the accuracy of emotion determination can be improved by referring to literature related to the character.

[0064] The emotion determination unit can determine the emotion by taking into consideration the market value of the character when determining the emotion. The emotion determination unit determines the emotion by taking into consideration the market value of the character when determining the emotion. Market value includes, for example, the popularity of the character, sales data, etc., but is not limited to these examples. The emotion determination unit, for example, improves the accuracy of the emotion determination by taking into consideration the market value of the character. For example, the emotion determination unit analyzes the popularity of the character and improves the accuracy of the emotion determination. The emotion determination unit can also determine the strength of the emotion based on the market value of the character. For example, the emotion determination unit analyzes the sales data of the character and determines the strength of the emotion. The emotion determination unit can also determine a change in emotion by referring to the market value of the character. For example, the emotion determination unit analyzes the popularity of the character and determines a change in emotion. In this way, the accuracy of the emotion determination can be improved by taking into consideration the market value of the character.

[0065] The onomatopoeia analysis unit can improve the accuracy of the analysis by analyzing the types and frequency of use of onomatopoeia in detail during onomatopoeia analysis. The onomatopoeia analysis unit can improve the accuracy of the analysis by analyzing the types and frequency of use of onomatopoeia in detail during onomatopoeia analysis. Examples of the types and frequency of use of onomatopoeia include, but are not limited to, text analysis and frequency analysis. The onomatopoeia analysis unit can improve the accuracy of the analysis by analyzing the types of onomatopoeia in detail. For example, the onomatopoeia analysis unit can analyze the onomatopoeia "boom" in detail to generate an explosion sound. The onomatopoeia analysis unit can also improve the accuracy of the analysis by analyzing the frequency of use of onomatopoeia in detail. For example, the onomatopoeia analysis unit can prioritize analysis of frequently used onomatopoeia to generate sound effects. The onomatopoeia analysis unit can also improve the accuracy of the analysis by combining the types and frequency of use of onomatopoeia. For example, the onomatopoeia analysis unit generates an explosion sound by analyzing the type of onomatopoeia "bang" (dokan) and its frequency of use in combination. This allows for a detailed analysis of the type and frequency of use of onomatopoeia, improving the accuracy of the analysis.

[0066] When analyzing onomatopoeia, the onomatopoeia analysis unit can perform the analysis by referring to the past usage history of the onomatopoeia. When analyzing onomatopoeia, the onomatopoeia analysis unit performs the analysis by referring to the past usage history of the onomatopoeia. The past usage history includes, for example, the frequency of use of the onomatopoeia and the situations in which it is used, but is not limited to these examples. For example, the onomatopoeia analysis unit performs the current analysis by referring to the past usage history of the onomatopoeia. For example, the onomatopoeia analysis unit performs the current analysis by referring to the history of past uses of the onomatopoeia "dokan." The onomatopoeia analysis unit can also analyze usage patterns based on the past usage history of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the past usage history and analyzes the usage pattern of the onomatopoeia "dokan." The onomatopoeia analysis unit can also improve the accuracy of the analysis by referring to the past usage history of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the past usage history to improve the accuracy of the analysis. By referring to the past usage history of onomatopoeia, the accuracy of the analysis can be improved.

[0067] The onomatopoeia analysis unit can perform the analysis while taking into consideration the context of the onomatopoeia. The onomatopoeia analysis unit performs the analysis while taking into consideration the context of the onomatopoeia. The context of the onomatopoeia includes, for example, preceding and following sentences, related scenes, etc., but is not limited to these examples. The onomatopoeia analysis unit, for example, improves the accuracy of the analysis by taking into consideration the context of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the sentences before and after the onomatopoeia "boom" to generate the sound of an explosion. The onomatopoeia analysis unit can also analyze usage patterns based on the context of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the scenes before and after the onomatopoeia "boom" to analyze the usage pattern. The onomatopoeia analysis unit can also improve the accuracy of the analysis by referring to the context of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the context of the onomatopoeia "dokan" to improve the accuracy of the analysis. By taking the context of the onomatopoeia into consideration, the accuracy of the analysis can be improved.

[0068] The onomatopoeia analysis unit can perform the analysis by taking into consideration the geographical background of the onomatopoeia. The onomatopoeia analysis unit performs the analysis by taking into consideration the geographical background of the onomatopoeia. The geographical background includes, for example, the region where the onomatopoeia originates, the location of the scene, etc., but is not limited to these examples. The onomatopoeia analysis unit improves the accuracy of the analysis by taking into consideration the geographical background of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the region where the onomatopoeia "boom" originates and generates an explosion sound. The onomatopoeia analysis unit can also analyze usage patterns based on the geographical background of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the location of the scene where the onomatopoeia "boom" occurs and analyzes the usage pattern. The onomatopoeia analysis unit can also improve the accuracy of the analysis by referring to the geographical background of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the geographical background of the onomatopoeia "dokan" (big boom) and improves the accuracy of the analysis. By taking the geographical background of the onomatopoeia into consideration, the accuracy of the analysis can be improved.

[0069] The onomatopoeia analysis unit can improve the accuracy of the analysis by referring to literature related to the onomatopoeia when analyzing the onomatopoeia. The onomatopoeia analysis unit can improve the accuracy of the analysis by referring to literature related to the onomatopoeia when analyzing the onomatopoeia. Related literature includes, but is not limited to, past works and related research papers, for example. The onomatopoeia analysis unit can improve the accuracy of the analysis by referring to literature related to the onomatopoeia. For example, the onomatopoeia analysis unit can analyze past works using the onomatopoeia "boom" (crash) to generate an explosion sound. The onomatopoeia analysis unit can also analyze usage patterns based on literature related to the onomatopoeia. For example, the onomatopoeia analysis unit can analyze research papers related to the onomatopoeia "boom" (crash) to analyze usage patterns. The onomatopoeia analysis unit can also improve the accuracy of the analysis by referring to literature related to the onomatopoeia. For example, the onomatopoeia analysis unit analyzes past works that use the onomatopoeia "dokan" (thud) to improve the accuracy of the analysis. This allows the accuracy of the analysis to be improved by referring to literature related to the onomatopoeia.

[0070] The onomatopoeia analysis unit can perform the analysis by taking into account the market value of the onomatopoeia. The onomatopoeia analysis unit performs the analysis by taking into account the market value of the onomatopoeia. Market value includes, for example, the popularity and frequency of use of the onomatopoeia, but is not limited to these examples. The onomatopoeia analysis unit improves the accuracy of the analysis by taking into account the market value of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the popularity of the onomatopoeia "boom" (crash) and generates an explosion sound. The onomatopoeia analysis unit can also analyze usage patterns based on the market value of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the frequency of use of the onomatopoeia "boom" (crash) and analyzes the usage pattern. The onomatopoeia analysis unit can also improve the accuracy of the analysis by referring to the market value of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the popularity of the onomatopoeia "dokan" (boom) to improve the accuracy of the analysis. This allows the accuracy of the analysis to be improved by taking into account the market value of the onomatopoeia.

[0071] The sound effect adjustment unit can improve the accuracy of the adjustment by analyzing in detail the type and frequency of use of the sound effect when adjusting the sound effect. The sound effect adjustment unit can improve the accuracy of the adjustment by analyzing in detail the type and frequency of use of the sound effect when adjusting the sound effect. Examples of the type and frequency of use of the sound effect include, but are not limited to, text analysis and frequency analysis. The sound effect adjustment unit can improve the accuracy of the adjustment by analyzing in detail the type of the sound effect. For example, the sound effect adjustment unit can analyze in detail the sound effect "boom" to generate an explosion sound. The sound effect adjustment unit can also improve the accuracy of the adjustment by analyzing in detail the frequency of use of the sound effect. For example, the sound effect adjustment unit can prioritize and adjust frequently used sound effects to generate sound effects. The sound effect adjustment unit can also improve the accuracy of the adjustment by combining the type of the sound effect and the frequency of use. For example, the sound effect adjustment unit can combine the type of the sound effect "boom" and the frequency of use to adjust it to generate an explosion sound. In this way, the accuracy of the adjustment can be improved by analyzing in detail the type and frequency of use of the sound effect.

[0072] The sound effect adjustment unit can make adjustments by referring to the past usage history of the sound effect when adjusting the sound effect. The sound effect adjustment unit makes adjustments by referring to the past usage history of the sound effect when adjusting the sound effect. The past usage history includes, but is not limited to, for example, the frequency of use of the sound effect and the scenes in which it was used. The sound effect adjustment unit, for example, makes current adjustments by referring to the past usage history of the sound effect. For example, the sound effect adjustment unit makes current adjustments by referring to the history of past uses of the sound effect "boom." The sound effect adjustment unit can also adjust usage patterns based on the past usage history of the sound effect. For example, the sound effect adjustment unit analyzes the past usage history and adjusts the usage pattern of the sound effect "boom." The sound effect adjustment unit can also improve the accuracy of the adjustment by referring to the past usage history of the sound effect. For example, the sound effect adjustment unit analyzes the past usage history and improves the accuracy of the adjustment. In this way, the accuracy of the adjustment can be improved by referring to the past usage history of the sound effect.

[0073] The sound effect adjustment unit can adjust the sound effect while taking into account the context of the sound effect. The sound effect adjustment unit can adjust the sound effect while taking into account the context of the sound effect. The context of the sound effect includes, for example, previous and following scenes, related actions, etc., but is not limited to these examples. The sound effect adjustment unit can, for example, improve the accuracy of the adjustment by taking into account the context of the sound effect. For example, the sound effect adjustment unit analyzes the scenes before and after the sound effect "boom" to generate an explosion sound. The sound effect adjustment unit can also adjust the usage pattern based on the context of the sound effect. For example, the sound effect adjustment unit analyzes the actions before and after the sound effect "boom" to adjust the usage pattern. The sound effect adjustment unit can also improve the accuracy of the adjustment by referring to the context of the sound effect. For example, the sound effect adjustment unit analyzes the context of the sound effect "boom" to improve the accuracy of the adjustment. In this way, the accuracy of the adjustment can be improved by taking the context of the sound effect into account.

[0074] The sound effect adjustment unit can take into account the geographical background of the sound effect when adjusting the sound effect. The sound effect adjustment unit can take into account the geographical background of the sound effect when adjusting the sound effect. The geographical background includes, but is not limited to, the region where the sound effect occurs and the location of the scene. The sound effect adjustment unit can improve the accuracy of the adjustment by taking into account the geographical background of the sound effect. For example, the sound effect adjustment unit analyzes the region where the sound effect "boom" occurs and generates an explosion sound. The sound effect adjustment unit can also adjust the usage pattern based on the geographical background of the sound effect. For example, the sound effect adjustment unit analyzes the location of the scene where the sound effect "boom" occurs and adjusts the usage pattern. The sound effect adjustment unit can also improve the accuracy of the adjustment by referring to the geographical background of the sound effect. For example, the sound effect adjustment unit analyzes the geographical background of the sound effect "boom" and improves the accuracy of the adjustment. In this way, the accuracy of the adjustment can be improved by taking the geographical background of the sound effect into account.

[0075] The sound effect adjustment unit can improve the accuracy of the adjustment by referring to literature related to the sound effect when adjusting the sound effect. The sound effect adjustment unit can improve the accuracy of the adjustment by referring to literature related to the sound effect when adjusting the sound effect. Related literature includes, but is not limited to, past works and related research papers. The sound effect adjustment unit can improve the accuracy of the adjustment by referring to literature related to the sound effect. For example, the sound effect adjustment unit analyzes past works that use the sound effect "boom" to generate an explosion sound. The sound effect adjustment unit can also adjust the usage pattern based on literature related to the sound effect. For example, the sound effect adjustment unit analyzes research papers that use the sound effect "boom" to adjust the usage pattern. The sound effect adjustment unit can also improve the accuracy of the adjustment by referring to literature related to the sound effect. For example, the sound effect adjustment unit analyzes past works that use the sound effect "boom" to improve the accuracy of the adjustment. In this way, the accuracy of the adjustment can be improved by referring to literature related to the sound effect.

[0076] The sound effect adjustment unit can adjust the sound effect taking into account the market value of the sound effect when adjusting the sound effect. The sound effect adjustment unit can adjust the sound effect taking into account the market value of the sound effect when adjusting the sound effect. Market value includes, but is not limited to, the popularity and frequency of use of the sound effect. For example, the sound effect adjustment unit can improve the accuracy of the adjustment by taking into account the market value of the sound effect. For example, the sound effect adjustment unit can analyze the popularity of the sound effect "boom" and generate an explosion sound. The sound effect adjustment unit can also adjust the usage pattern based on the market value of the sound effect. For example, the sound effect adjustment unit can analyze the frequency of use of the sound effect "boom" and adjust the usage pattern. The sound effect adjustment unit can also improve the accuracy of the adjustment by referring to the market value of the sound effect. For example, the sound effect adjustment unit can analyze the popularity of the sound effect "boom" and improve the accuracy of the adjustment. In this way, the accuracy of the adjustment can be improved by taking the market value of the sound effect into account.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] When receiving digital comic data, the reception unit can analyze the user's past usage history and select an appropriate reception method. For example, it can prioritize reception of digital comics in formats that the user has frequently used in the past. It can also select the optimal reception method for a specific time period based on the user's past usage history. It can also automatically select an interface that the user has preferred in the past to receive data. This makes it possible to select the optimal reception method by analyzing the user's past usage history.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the manga. For example, a detailed analysis can be performed for scenes featuring major characters. A concise analysis can also be performed for scenes focusing on backgrounds and landscapes. Furthermore, a particularly detailed analysis can be performed for climax scenes. In this way, by adjusting the level of detail of the analysis based on the importance of the manga, more important scenes can be analyzed in detail.

[0080] During generation, the generation unit can apply different sound effect generation algorithms depending on the type of onomatopoeia. For example, an algorithm that generates the sound of an explosion can be applied to the onomatopoeia "boom!". Also, an algorithm that generates the sound of a flash can be applied to the onomatopoeia "pika!". Furthermore, an algorithm that generates the sound of a crowd murmuring can be applied to the onomatopoeia "wazawa!". In this way, by applying different sound effect generation algorithms depending on the type of onomatopoeia, more appropriate sound effects can be generated.

[0081] During playback, the playback unit can apply different playback algorithms depending on the category of the manga. For example, for action manga, an algorithm specialized for playing back movement and battle scenes can be applied. For romance manga, an algorithm specialized for playing back character emotions and relationships can be applied. Furthermore, for comedy manga, an algorithm specialized for playing back humor and gags can be applied. In this way, by applying different playback algorithms depending on the category of the manga, more appropriate playback results can be provided.

[0082] When adjusting sound effects, the sound effect adjustment unit can improve the accuracy of the adjustment by analyzing in detail the type and frequency of use of the sound effects. For example, it can analyze in detail the sound effect "bang" to generate an explosion sound. It can also generate sound effects by prioritizing adjustment of frequently used sound effects. Furthermore, it can improve the accuracy of the adjustment by combining the type and frequency of use of the sound effects. In this way, the accuracy of the adjustment can be improved by analyzing in detail the type and frequency of use of the sound effects.

[0083] The processing flow of the first embodiment will be briefly explained below.

[0084] Step 1: The reception unit inputs the data of the digital comic. The digital comic data includes image data, text data, audio data, etc. For example, the reception unit inputs the digital comic in PDF format or the pages of a comic saved as an image file. Step 2: The analysis unit analyzes the digital comic data entered by the reception unit. The analysis is performed using methods such as image analysis, text analysis, and audio analysis. For example, a generation AI is used to analyze each page of the comic and identify the emotions of the characters and the drawings on the comic. Drawing elements such as onomatopoeia and effect lines are also analyzed and used to generate sound effects. Step 3: The generator generates the reading and sound effects based on the data analyzed by the analyzer. This is done using voice synthesis technology. The generator AI adjusts the tone and tempo of the voice according to the character's emotions, achieving natural reading. Appropriate sound effects are generated based on the onomatopoeia. Step 4: The playback unit plays back the speech and sound effects generated by the generation unit. The playback is performed using a playback device. For example, the generated speech and sound effects are played back using speakers or headphones.

[0085] (Example 2) A voice comic automatic generation system according to an embodiment of the present invention automatically generates a voice comic by inputting, analyzing, generating, and playing back digital comic data. When a digital comic is input, the voice comic automatic generation system analyzes each page of the comic and automatically generates and plays back a reading that takes into account the emotions of the characters and the drawings in the comic, as well as sound effects based on onomatopoeia and other elements. For example, in a voice comic automatic generation system, a user inputs a digital comic into the system. For example, the digital comic data is input as image data or text data. The voice comic automatic generation system then analyzes the input digital comic. The generation AI analyzes each page of the comic and identifies the emotions of the characters and the drawings in the comic. For example, the AI ​​determines whether the character is happy, angry, or sad based on the character's facial expressions and the content of the dialogue. The AI ​​also analyzes drawing elements such as onomatopoeia and effect lines and uses them to generate sound effects. The voice comic automatic generation system then generates a reading and sound effects based on the character's emotions and the drawing elements. The generation AI adjusts the voice tone and tempo according to the character's emotions, resulting in a natural reading. For example, if the character is angry, the voice tone is lowered and the tempo is increased. It also generates appropriate sound effects based on onomatopoeia. For example, if the onomatopoeia is "boom," a corresponding explosion sound is generated. Finally, the voice comic automatic generation system plays back the generated reading and sound effects. This makes it easy to turn any manga into a voice comic. This significantly reduces the cost of creating voice comics, making it easy for even individual manga artists and small publishers to create voice comics. Furthermore, turning existing manga works into voice comics can create new revenue streams.

[0086] A voice comic automatic generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a playback unit. The reception unit inputs digital comic data. Digital comic data includes, but is not limited to, image data, text data, and audio data. The reception unit inputs, for example, a PDF digital comic or comic pages saved as image files. The analysis unit analyzes the digital comic data input by the reception unit. The analysis may be performed using, for example, image analysis, text analysis, or audio analysis, but is not limited to, the methods. The analysis unit, for example, uses a generation AI to analyze each page of the comic and identify the character's emotions and the drawings in the comic. The generation AI determines, for example, from the character's facial expressions and the content of the dialogue whether the character is happy, angry, or sad. The analysis unit also analyzes drawing elements such as onomatopoeia and effect lines and uses the results to generate sound effects. The generation unit generates readings and sound effects based on the data analyzed by the analysis unit. The generation is performed using, for example, voice synthesis technology, but is not limited to, such an example. The generation unit uses a generation AI to adjust the tone and tempo of the voice according to the character's emotions, achieving natural reading. For example, if the character is angry, the tone of the voice is lowered and the tempo is increased. The generation unit also generates appropriate sound effects based on onomatopoeia. For example, if the onomatopoeia is "bang," a corresponding explosion sound is generated. The playback unit plays the reading and sound effects generated by the generation unit. The playback is performed using, for example, a playback device, but is not limited to such an example. The playback unit plays the generated reading and sound effects using, for example, speakers or headphones. As a result, the voice comic automatic generation system according to the embodiment can automatically generate a voice comic by inputting digital comic data and performing analysis, generation, and playback.

[0087] The analysis unit includes an emotion determination unit that determines the emotion of the character. The emotion determination unit determines the emotion of the character. The emotion of the character includes, but is not limited to, joy, anger, sadness, and the like. The emotion determination unit, for example, analyzes the character's facial expression to determine the emotion. For example, the emotion determination unit analyzes the character's smile to determine the emotion of joy. The emotion determination unit can also analyze the content of the character's lines to determine the emotion. For example, the emotion determination unit determines the emotion of anger from the character's lines. The emotion determination unit can also determine the emotion by combining the character's facial expression and the content of the lines. For example, the emotion determination unit combines the character's smile with a line of joy to determine the emotion of joy. In this way, by determining the character's emotion, more natural reading and sound effects can be generated.

[0088] The analysis unit includes an onomatopoeia analysis unit that analyzes onomatopoeia. The onomatopoeia analysis unit analyzes onomatopoeia. Onomatopoeia includes, for example, onomatopoeic words and mimetic words, but is not limited to these examples. The onomatopoeia analysis unit analyzes onomatopoeia using, for example, text analysis technology. For example, the onomatopoeia analysis unit analyzes the onomatopoeia "boom" (bang) and generates an explosion sound. The onomatopoeia analysis unit can also analyze the frequency of use of onomatopoeia and use the results to generate sound effects. For example, the onomatopoeia analysis unit prioritizes analysis of frequently used onomatopoeia and generates sound effects. The onomatopoeia analysis unit can also analyze the context of onomatopoeia and use the results to generate sound effects. For example, the onomatopoeia analysis unit analyzes the sentences before and after the onomatopoeia and generates an appropriate sound effect. This makes it possible to generate appropriate sound effects by analyzing onomatopoeia.

[0089] The generation unit includes a sound effect adjustment unit that adjusts the generated sound effect. The sound effect adjustment unit adjusts the generated sound effect. Examples of sound effect adjustment include, but are not limited to, volume adjustment, echo effect, and pitch adjustment. The sound effect adjustment unit adjusts the volume of the generated sound effect. For example, the sound effect adjustment unit adjusts the volume of an explosion sound and plays it at an appropriate volume. The sound effect adjustment unit can also add an echo effect to the generated sound effect. For example, the sound effect adjustment unit adds an echo effect to the explosion sound to enhance realism. The sound effect adjustment unit can also adjust the pitch of the generated sound effect. For example, the sound effect adjustment unit adjusts the pitch of the explosion sound to provide a more natural sound effect. In this way, by adjusting the generated sound effect, a more natural sound effect can be provided.

[0090] The generation unit can adjust the tone and tempo of the voice according to the character's emotions. The generation unit adjusts the tone and tempo of the voice according to the character's emotions. Adjustments to the tone and tempo of the voice include, but are not limited to, changes in tone and tempo according to the emotion. For example, if the character is angry, the generation unit generates a voice with a low tone and a fast tempo. For example, the generation unit generates a voice with a low tone and a fast tempo to express the emotion of anger. The generation unit can also generate a voice with a high tone and a bright tempo if the character is happy. For example, the generation unit generates a voice with a high tone and a bright tempo to express the emotion of joy. The generation unit can also generate a voice with a low tone and a slow tempo if the character is sad. For example, the generation unit generates a voice with a low tone and a slow tempo to express the emotion of sadness. In this way, by adjusting the tone and tempo of the voice according to the character's emotions, more natural reading can be achieved.

[0091] The playback unit can play the generated readings and sound effects. The playback unit plays the generated readings and sound effects. Playback includes, but is not limited to, a playback device, a playback order, and a playback timing. The playback unit plays the generated readings and sound effects using, for example, a speaker or headphones. For example, the playback unit plays the generated readings and sound effects using a speaker to provide a user with a sense of realism. The playback unit can also play the generated readings and sound effects using headphones. For example, the playback unit uses headphones to provide a personalized experience for the user. The playback unit can also adjust the playback order and playback timing. For example, the playback unit adjusts the playback order depending on the scene and plays the readings and sound effects at the appropriate timing. In this way, by playing the generated readings and sound effects, the user can enjoy a sense of realism while reading the digital comic.

[0092] The reception unit estimates the user's emotions and adjusts the timing of receiving digital comic data based on the user's emotions. The reception unit estimates the user's emotions and adjusts the timing of receiving digital comic data based on the user's emotions. User emotions include, but are not limited to, relaxation, stress, and excitement. For example, if the user is relaxed, the reception unit immediately receives digital comic data, providing a smooth experience. For example, the reception unit determines that the user is relaxed using an emotion estimation algorithm and immediately receives the data. Furthermore, if the user is stressed, the reception unit can slightly delay data reception to give the user time to calm down. For example, the reception unit determines that the user is stressed using an emotion estimation algorithm and delays data reception. Furthermore, if the user is excited, the reception unit can quickly receive data and immediately begin analysis. For example, the reception unit determines that the user is excited using an emotion estimation algorithm and quickly receives the data. This allows the timing of data reception to be adjusted according to the user's emotions, providing a smoother experience.

[0093] The reception unit analyzes the user's past usage history and selects an appropriate reception method when receiving digital comic data. The reception unit analyzes the user's past usage history and selects an appropriate reception method when receiving digital comic data. The past usage history includes, but is not limited to, browsing history, operation history, and feedback. For example, the reception unit preferentially receives digital comics in a format that the user has frequently used in the past. For example, the reception unit analyzes the user's past browsing history and preferentially receives digital comics in a format that the user has frequently used in the past. The reception unit can also select the optimal reception method for a specific time period based on the user's past usage history. For example, the reception unit analyzes the user's past operation history and selects the optimal reception method for a specific time period. The reception unit can also automatically select an interface that the user has preferred in the past and use it to receive data. For example, the reception unit analyzes the user's past feedback and selects the preferred interface. In this way, the optimal reception method can be selected by analyzing the user's past usage history.

[0094] The reception unit filters the e-comic data based on the user's current interests and concerns when receiving the e-comic data. The reception unit filters the e-comic data based on the user's current interests and concerns when receiving the e-comic data. Current interests and concerns include, but are not limited to, search history, social media activity, etc. For example, the reception unit preferentially receives e-comics in a genre in which the user is currently interested. For example, the reception unit analyzes the user's recent search history and preferentially receives e-comics in a genre in which the user is interested. The reception unit can also filter and receive related e-comics based on the user's recent search history. For example, the reception unit analyzes the user's search history and filters related e-comics. The reception unit can also filter and receive e-comics that the user may be interested in based on content mentioned by the user on social media. For example, the reception unit analyzes the user's social media activity and filters related e-comics. In this way, highly relevant data can be received by filtering based on the user's current interests and concerns.

[0095] When receiving digital comic data, the reception unit selects an appropriate reception means depending on the user's input method. When receiving digital comic data, the reception unit selects an appropriate reception means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the reception unit uses voice recognition technology to receive digital comic data. For example, the reception unit uses voice recognition technology to analyze the user's voice input and receive digital comic data. Furthermore, when the user uses text input, the reception unit can also use text analysis technology to receive digital comic data. For example, the reception unit uses text analysis technology to analyze the user's text input and receive digital comic data. Furthermore, when the user uses image input, the reception unit can also use image recognition technology to receive digital comic data. For example, the reception unit uses image recognition technology to analyze the user's image input and receive digital comic data. This improves the efficiency of data reception by selecting an appropriate reception means depending on the user's input method.

[0096] The reception unit estimates the user's emotions and determines the priority of e-comics to be received based on the estimated user emotions. The reception unit estimates the user's emotions and determines the priority of e-comics to be received based on the estimated user emotions. The priority of e-comics includes, but is not limited to, the user's emotions, past usage history, and current interests. For example, when the user is relaxed, the reception unit preferentially receives e-comics in the user's favorite genre. For example, the reception unit determines that the user is relaxed using an emotion estimation algorithm and preferentially receives e-comics in the user's favorite genre. Furthermore, when the user is stressed, the reception unit can preferentially receive e-comics that have a relaxing effect. For example, the reception unit determines that the user is stressed using an emotion estimation algorithm and preferentially receives e-comics that have a relaxing effect. Furthermore, when the user is excited, the reception unit can preferentially receive e-comics in the action or adventure genre. For example, the reception unit determines that the user is excited using an emotion estimation algorithm and preferentially receives e-comics in the action or adventure genre. This allows the priority of electronic comics to be determined based on the user's feelings, making it possible to preferentially accept more appropriate data.

[0097] When receiving e-comic data, the reception unit prioritizes receiving highly relevant data by taking into account the user's geographical location information. When receiving e-comic data, the reception unit prioritizes receiving highly relevant data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the reception unit prioritizes receiving e-comics related to that area. For example, the reception unit analyzes the user's GPS data and prioritizes receiving e-comics related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving e-comics related to the user's travel destination. For example, the reception unit analyzes the user's IP address and prioritizes receiving e-comics related to the user's travel destination. Furthermore, when the user is participating in a specific event, the reception unit can also prioritize receiving e-comics related to the event. For example, the reception unit analyzes the user's geographical location information and prioritizes receiving e-comics related to the event. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account.

[0098] The reception unit analyzes the user's social media activity when receiving data on the e-comic and receives related data. The reception unit analyzes the user's social media activity when receiving data on the e-comic and receives related data. Social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the reception unit preferentially receives e-comics related to manga or characters mentioned by the user on social media. For example, the reception unit analyzes the content of the user's social media posts and preferentially receives related e-comics. The reception unit can also analyze the content of the user's social media posts and receive related e-comics. For example, the reception unit analyzes the content of the user's social media posts and receives related e-comics. The reception unit can also receive related e-comics based on the activity of the user's friends on social media. For example, the reception unit analyzes the social media activity of the user's friends and receives related e-comics. In this way, related data can be received by analyzing the user's social media activity.

[0099] The reception unit customizes the reception method by reflecting the user's past feedback when receiving digital comic data. The reception unit customizes the reception method by reflecting the user's past feedback when receiving digital comic data. Past feedback includes, but is not limited to, user ratings, comments, and survey results. For example, the reception unit preferentially receives digital comic formats that the user has previously rated highly. For example, the reception unit analyzes the user's past ratings and preferentially receives digital comic formats that have been highly rated. The reception unit can also select an optimal reception method based on the user's past feedback. For example, the reception unit analyzes the user's past comments and selects an optimal reception method. The reception unit can also avoid reception methods that the user has previously been dissatisfied with and provide a customized reception method. For example, the reception unit analyzes the user's past survey results and avoids reception methods that the user has previously been dissatisfied with. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0100] The analysis unit estimates the user's emotion and adjusts the presentation method of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the presentation method of the analysis based on the estimated user's emotion. Examples of presentation methods of the analysis include, but are not limited to, graph display, text display, and audio explanation. For example, when the user is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit determines that the user is relaxed using an emotion estimation algorithm and provides detailed analysis results. Furthermore, when the user is stressed, the analysis unit can provide concise and to-the-point analysis results. For example, the analysis unit determines that the user is stressed using an emotion estimation algorithm and provides concise analysis results. Furthermore, when the user is excited, the analysis unit can provide visually stimulating analysis results. For example, the analysis unit determines that the user is excited using an emotion estimation algorithm and provides visually stimulating analysis results. In this way, by adjusting the presentation method of the analysis based on the user's emotion, more appropriate analysis results can be provided.

[0101] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the manga. During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the manga. The importance of the manga includes, for example, popularity, sales, review ratings, etc., but is not limited to these examples. The analysis unit performs a detailed analysis, for example, of scenes in which major characters appear. For example, the analysis unit identifies scenes in which major characters appear and performs a detailed analysis. The analysis unit can also perform a concise analysis of scenes that focus on backgrounds or scenery. For example, the analysis unit identifies scenes that focus on backgrounds or scenery and performs a concise analysis. The analysis unit can also perform a particularly detailed analysis of climax scenes. For example, the analysis unit identifies climax scenes and performs a particularly detailed analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the manga, more important scenes can be analyzed in detail.

[0102] The analysis unit applies different analysis algorithms depending on the category of the manga during analysis. The analysis unit applies different analysis algorithms depending on the category of the manga during analysis. Manga categories include, but are not limited to, genre, target age, and theme, for example. For example, for action manga, the analysis unit applies an algorithm specialized for analyzing movements and fight scenes. For example, the analysis unit identifies movements and fight scenes in action manga and applies a specialized algorithm. The analysis unit can also apply an algorithm specialized for analyzing character emotions and relationships in romance manga. For example, the analysis unit identifies character emotions and relationships in romance manga and applies a specialized algorithm. The analysis unit can also apply an algorithm specialized for analyzing humor and comedy in comedy manga. For example, the analysis unit identifies humor and comedy in comedy manga and applies a specialized algorithm. In this way, by applying different analysis algorithms depending on the category of the manga, more appropriate analysis results can be provided.

[0103] The analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, improvements in analysis accuracy and pattern recognition. The analysis unit improves the accuracy of the analysis by referring to, for example, analysis results that the user has previously rated highly. For example, the analysis unit analyzes the user's past highly rated analysis results and improves the accuracy. The analysis unit can also adjust the analysis algorithm based on the user's past feedback. For example, the analysis unit analyzes the user's past feedback and adjusts the algorithm. The analysis unit can also avoid analysis results that the user has previously dissatisfied with and provide a highly accurate analysis. For example, the analysis unit analyzes the user's past dissatisfied analysis results and provides a highly accurate analysis. In this way, the analysis accuracy can be improved by referring to the user's past analysis results.

[0104] The analysis unit estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. The length of the analysis includes, but is not limited to, the user's emotion, the importance of the scene, and the like. For example, if the user is relaxed, the analysis unit performs a detailed analysis and provides a longer result. For example, the analysis unit determines that the user is relaxed using an emotion estimation algorithm and performs a detailed analysis. Furthermore, if the user is feeling stressed, the analysis unit can provide a concise and short analysis result. For example, the analysis unit determines that the user is feeling stressed using an emotion estimation algorithm and provides a concise analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit determines that the user is excited using an emotion estimation algorithm and provides a visually stimulating analysis result. In this way, by adjusting the length of the analysis based on the user's emotion, more appropriate analysis results can be provided.

[0105] During analysis, the analysis unit determines the analysis priority based on the publication date of the manga. During analysis, the analysis unit determines the analysis priority based on the publication date of the manga. Publication date includes, but is not limited to, for example, the publication date or the release date. For example, the analysis unit prioritizes analysis of the latest manga and provides the latest information. For example, the analysis unit identifies the latest manga and analyzes it preferentially. The analysis unit can also analyze classic manga and provide results that take historical background into consideration. For example, the analysis unit identifies classic manga and analyzes it taking historical background into consideration. The analysis unit can also prioritize analysis of manga from a period in which the user is particularly interested. For example, the analysis unit identifies manga from a period in which the user is interested and analyzes it preferentially. In this way, by determining the analysis priority based on the publication date of the manga, the latest information can be provided preferentially.

[0106] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the manga. During analysis, the analysis unit adjusts the order of analysis based on the relevance of the manga. The relevance of the manga includes, but is not limited to, for example, story continuity and character relationships. For example, the analysis unit prioritizes analysis of scenes related to the main storyline. For example, the analysis unit identifies the main storyline and prioritizes analysis of related scenes. The analysis unit can also postpone analysis of side stories and supplementary scenes. For example, the analysis unit identifies side stories and supplementary scenes and analyzes them later. The analysis unit can also prioritize analysis of scenes related to characters in which the user is particularly interested. For example, the analysis unit identifies characters in which the user is interested and prioritizes analysis of related scenes. In this way, by adjusting the order of analysis based on the relevance of the manga, it is possible to prioritize analysis of the main storyline.

[0107] During analysis, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. During analysis, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past usage history. For example, if the user is a beginner, the analysis unit provides the analysis result in simple language. For example, the analysis unit determines that the user is a beginner and provides the analysis result in simple language. Furthermore, if the user is an intermediate user, the analysis unit can provide the analysis result using appropriate technical terms. For example, the analysis unit determines that the user is an intermediate user and provides the analysis result using appropriate technical terms. Furthermore, if the user is an advanced user, the analysis unit can provide the analysis result using detailed technical terms. For example, the analysis unit determines that the user is an advanced user and provides the analysis result using detailed technical terms. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0108] The generation unit estimates the user's emotion and adjusts the expression method of the generated speech and sound effects based on the estimated user emotion. The generation unit estimates the user's emotion and adjusts the expression method of the generated speech and sound effects based on the estimated user emotion. The expression method of the speech and sound effects includes, but is not limited to, the tone of the voice and the type of sound effects. For example, if the user is relaxed, the generation unit reads in a calm voice and reduces the sound effects. For example, the generation unit determines that the user is relaxed using an emotion estimation algorithm and reads in a calm voice. Furthermore, if the user is excited, the generation unit can read in a powerful voice and use more flashy sound effects. For example, the generation unit determines that the user is excited using an emotion estimation algorithm and reads in a powerful voice. Furthermore, if the user is sad, the generation unit can read in a calm voice and reduce the sound effects. For example, the generation unit determines that the user is sad using an emotion estimation algorithm and reads in a calm voice. This allows for more appropriate expression by adjusting the way reading and sound effects are expressed based on the user's emotions.

[0109] The generation unit can adjust the tone and tempo of the voice based on the character's emotion during generation. The generation unit adjusts the tone and tempo of the voice based on the character's emotion during generation. Adjustments to the tone and tempo of the voice include, but are not limited to, changes in tone and tempo depending on the emotion. For example, if the character is angry, the generation unit generates a voice with a low tone and a fast tempo. For example, the generation unit generates a voice with a low tone and a fast tempo to express the emotion of anger. The generation unit can also generate a voice with a high tone and a bright tempo when the character is happy. For example, the generation unit generates a voice with a high tone and a bright tempo to express the emotion of joy. The generation unit can also generate a voice with a low tone and a slow tempo when the character is sad. For example, the generation unit generates a voice with a low tone and a slow tempo to express the emotion of sadness. In this way, by adjusting the tone and tempo of the voice based on the character's emotion, more natural reading can be achieved.

[0110] The generation unit can apply different sound effect generation algorithms depending on the type of onomatopoeia during generation. The generation unit applies different sound effect generation algorithms depending on the type of onomatopoeia during generation. Sound effect generation algorithms include, but are not limited to, physics-based acoustic models and sample-based sound generation. For example, the generation unit applies an algorithm that generates an explosion sound to the onomatopoeia ``boom.'' For example, the generation unit applies a physics-based acoustic model to generate an explosion sound. The generation unit can also apply an algorithm that generates a flash sound to the onomatopoeia ``pika.'' For example, the generation unit applies sample-based sound generation to generate a flash sound. The generation unit can also apply an algorithm that generates a crowd noise to the onomatopoeia ``buzz.'' For example, the generation unit applies a physics-based acoustic model to generate the crowd noise. In this way, by applying different sound effect generation algorithms depending on the type of onomatopoeia, more appropriate sound effects can be generated.

[0111] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit improves the accuracy of generation by referring to the user's past generation results during generation. Past generation results include, but are not limited to, improvements in generation accuracy and pattern recognition. The generation unit improves the accuracy of generation by referring to, for example, generation results that the user has previously rated highly. For example, the generation unit analyzes the user's past highly rated generation results and improves the accuracy. The generation unit can also adjust the generation algorithm based on the user's past feedback. For example, the generation unit analyzes the user's past feedback and adjusts the algorithm. The generation unit can also avoid generation results that the user has previously dissatisfied with and provide a highly accurate generation. For example, the generation unit analyzes the user's past generation results that the user has previously dissatisfied with and provides a highly accurate generation. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0112] The generation unit can estimate the user's emotion and adjust the length of the generated speech and sound effects based on the estimated user's emotion. The generation unit can estimate the user's emotion and adjust the length of the generated speech and sound effects based on the estimated user's emotion. The length of the speech and sound effects can include, but is not limited to, the user's emotion and the importance of the scene. For example, if the user is relaxed, the generation unit generates longer speech and sound effects. For example, the generation unit determines that the user is relaxed using an emotion estimation algorithm and generates longer speech and sound effects. Furthermore, if the user is in a hurry, the generation unit can generate shorter speech and sound effects that are more to the point. For example, the generation unit determines that the user is in a hurry using an emotion estimation algorithm and generates shorter speech and sound effects that are more to the point. Furthermore, if the user is excited, the generation unit can generate speech and sound effects with visually stimulating effects. For example, the generation unit determines that the user is excited using an emotion estimation algorithm and generates speech and sound effects with visually stimulating effects. This allows for more appropriate expression to be provided by adjusting the length of the reading and sound effects based on the user's emotions.

[0113] The generation unit can determine the generation priority based on the publication date of the manga during generation. The generation unit can determine the generation priority based on the publication date of the manga during generation. The publication date includes, but is not limited to, for example, the publication date or the release date. For example, the generation unit can prioritize generating the latest manga and provide the latest information. For example, the generation unit can identify the latest manga and generate it preferentially. The generation unit can also generate classic manga and provide results taking historical background into consideration. For example, the generation unit can identify classic manga and generate it taking historical background into consideration. The generation unit can also prioritize generating manga from a period in which the user is particularly interested. For example, the generation unit can identify manga from a period in which the user is interested and generate it preferentially. In this way, by determining the generation priority based on the publication date of the manga, the latest information can be preferentially provided.

[0114] The generation unit can adjust the order of generation based on the relevance of the manga during generation. The generation unit can adjust the order of generation based on the relevance of the manga during generation. The relevance of the manga includes, but is not limited to, for example, the continuity of the story and the relationships between the characters. For example, the generation unit prioritizes generating scenes related to the main storyline. For example, the generation unit identifies the main storyline and prioritizes generating related scenes. The generation unit can also postpone generating side stories and supplementary scenes. For example, the generation unit identifies side stories and supplementary scenes and generates them later. The generation unit can also prioritize generating scenes related to characters in which the user is particularly interested. For example, the generation unit identifies characters in which the user is interested and prioritizes generating related scenes. In this way, by adjusting the order of generation based on the relevance of the manga, the main storyline can be prioritized.

[0115] The generation unit may adjust the use of technical terminology in the generation according to the user's level of expertise during generation. The generation unit may adjust the use of technical terminology in the generation according to the user's level of expertise during generation. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past usage history. For example, if the user is a beginner, the generation unit may provide the generated result in simple language. For example, the generation unit may determine that the user is a beginner and provide the generated result in simple language. Furthermore, if the user is an intermediate user, the generation unit may provide the generated result using appropriate technical terminology. For example, the generation unit may determine that the user is an intermediate user and provide the generated result using appropriate technical terminology. Furthermore, if the user is an advanced user, the generation unit may provide the generated result using detailed technical terminology. For example, the generation unit may determine that the user is an advanced user and provide the generated result using detailed technical terminology. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, it is possible to provide a generated result that is easier to understand.

[0116] The playback unit estimates the user's emotion and adjusts the playback expression method based on the estimated user's emotion. The playback unit estimates the user's emotion and adjusts the playback expression method based on the estimated user's emotion. Examples of playback expression methods include, but are not limited to, voice tone and type of sound effects. For example, when the user is relaxed, the playback unit plays back in a calm voice and reduces sound effects. For example, the playback unit determines that the user is relaxed using an emotion estimation algorithm and plays back in a calm voice. Furthermore, when the user is excited, the playback unit can play back in a powerful voice and increase sound effects. For example, the playback unit determines that the user is excited using an emotion estimation algorithm and plays back in a powerful voice. Furthermore, when the user is sad, the playback unit can play back in a calm voice and reduce sound effects. For example, the playback unit determines that the user is sad using an emotion estimation algorithm and plays back in a calm voice. In this way, by adjusting the playback expression method based on the user's emotion, more appropriate expression can be provided.

[0117] The playback unit can adjust the level of detail of playback based on the importance of the manga during playback. The playback unit adjusts the level of detail of playback based on the importance of the manga during playback. The importance of the manga includes, for example, popularity, sales, review ratings, etc., but is not limited to these examples. The playback unit, for example, performs detailed playback of scenes featuring major characters. For example, the playback unit identifies scenes featuring major characters and performs detailed playback. The playback unit can also perform concise playback of scenes focusing on backgrounds and scenery. For example, the playback unit identifies scenes focusing on backgrounds and scenery and performs concise playback. The playback unit can also perform particularly detailed playback of climax scenes. For example, the playback unit identifies climax scenes and performs particularly detailed playback. In this way, by adjusting the level of detail of playback based on the importance of the manga, more important scenes can be played in detail.

[0118] The playback unit can apply different playback algorithms during playback depending on the category of the manga. The playback unit applies different playback algorithms during playback depending on the category of the manga. Examples of playback algorithms include, but are not limited to, a physics-based acoustic model and sample-based audio generation. For example, the playback unit applies an algorithm specialized for playing back action and battle scenes to an action manga. For example, the playback unit identifies the actions and battle scenes of an action manga and applies the specialized algorithm. The playback unit can also apply an algorithm specialized for playing back character emotions and relationships to a romance manga. For example, the playback unit identifies the emotions and relationships of characters in a romance manga and applies the specialized algorithm. The playback unit can also apply an algorithm specialized for playing back humor and gags to a comedy manga. For example, the playback unit identifies the humor and gags in a comedy manga and applies the specialized algorithm. In this way, by applying different playback algorithms depending on the category of the manga, more appropriate playback results can be provided.

[0119] The playback unit can improve playback accuracy by referring to the user's past playback results during playback. The playback unit can improve playback accuracy by referring to the user's past playback results during playback. Past playback results include, but are not limited to, improvements in playback accuracy and pattern recognition. The playback unit can improve playback accuracy by referring to playback results that the user has previously rated highly. For example, the playback unit can analyze the user's past highly rated playback results and improve accuracy. The playback unit can also adjust a playback algorithm based on the user's past feedback. For example, the playback unit can analyze the user's past feedback and adjust the algorithm. The playback unit can also avoid playback results that the user has previously dissatisfied with and provide highly accurate playback. For example, the playback unit can analyze the user's past dissatisfied playback results and provide highly accurate playback. In this way, the playback accuracy can be improved by referring to the user's past playback results.

[0120] The playback unit estimates the user's emotion and adjusts the playback length based on the estimated user's emotion. The playback unit estimates the user's emotion and adjusts the playback length based on the estimated user's emotion. The playback length includes, but is not limited to, the user's emotion, the importance of the scene, and the like. For example, if the user is relaxed, the playback unit performs a longer playback. For example, the playback unit determines that the user is relaxed using an emotion estimation algorithm and performs a longer playback. Furthermore, if the user is in a hurry, the playback unit can perform a shorter, more to-the-point playback. For example, the playback unit determines that the user is in a hurry using an emotion estimation algorithm and performs a shorter, more to-the-point playback. Furthermore, if the user is excited, the playback unit can perform a playback with a visually stimulating effect. For example, the playback unit determines that the user is excited using an emotion estimation algorithm and performs a playback with a visually stimulating effect. In this way, by adjusting the playback length based on the user's emotion, a more appropriate expression can be provided.

[0121] The playback unit can determine playback priorities based on the publication dates of the manga during playback. The playback unit can determine playback priorities based on the publication dates of the manga during playback. Publication dates include, but are not limited to, for example, publication dates and release dates. For example, the playback unit can prioritize playback of the latest manga to provide the latest information. For example, the playback unit can identify the latest manga and prioritize playback. The playback unit can also play classic manga and provide results that take historical background into consideration. For example, the playback unit can identify classic manga and play them while taking historical background into consideration. The playback unit can also prioritize playback of manga from a period in which the user is particularly interested. For example, the playback unit can identify manga from a period in which the user is interested and prioritize playback. In this way, by determining playback priorities based on the publication dates of the manga, the latest information can be prioritized.

[0122] The playback unit can adjust the playback order based on the relevance of the manga during playback. The playback unit adjusts the playback order based on the relevance of the manga during playback. The relevance of the manga includes, but is not limited to, for example, the continuity of the story and the relationships between the characters. For example, the playback unit prioritizes playback of scenes related to the main storyline. For example, the playback unit identifies the main storyline and prioritizes playback of related scenes. The playback unit can also postpone playback of side stories or supplementary scenes. For example, the playback unit identifies side stories or supplementary scenes and prioritizes playback of them. The playback unit can also prioritize playback of scenes related to characters in which the user is particularly interested. For example, the playback unit identifies characters in which the user is interested and prioritizes playback of related scenes. In this way, by adjusting the playback order based on the relevance of the manga, it is possible to prioritize playback of the main storyline.

[0123] The playback unit may adjust the use of technical terms during playback according to the user's level of expertise. The playback unit may adjust the use of technical terms during playback according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past usage history. For example, if the user is a beginner, the playback unit may provide playback results in simple language. For example, the playback unit may determine that the user is a beginner and provide playback results in simple language. Furthermore, if the user is an intermediate user, the playback unit may provide playback results using appropriate technical terms. For example, the playback unit may determine that the user is an intermediate user and provide playback results using appropriate technical terms. Furthermore, if the user is an advanced user, the playback unit may provide playback results using detailed technical terms. For example, the playback unit may determine that the user is an advanced user and provide playback results using detailed technical terms. In this way, by adjusting the use of technical terms during playback according to the user's level of expertise, it is possible to provide playback results that are easier to understand.

[0124] The emotion determination unit estimates the user's emotion and adjusts the criteria for determining the character's emotion based on the estimated user's emotion. The emotion determination unit estimates the user's emotion and adjusts the criteria for determining the character's emotion based on the estimated user's emotion. Criteria for determining the character's emotion include, but are not limited to, facial expression analysis and dialogue content analysis. For example, the emotion determination unit may moderate the character's emotion determination if the user is relaxed. For example, the emotion determination unit may determine that the user is relaxed using an emotion estimation algorithm and moderate the character's emotion determination. The emotion determination unit may also emphasize the character's emotion determination if the user is excited. For example, the emotion determination unit may determine that the user is excited using an emotion estimation algorithm and emphasize the character's emotion determination. The emotion determination unit may also moderate the character's emotion determination if the user is sad. For example, the emotion determination unit may determine that the user is sad using an emotion estimation algorithm and moderate the character's emotion determination. This allows for more appropriate emotion determination by adjusting the criteria for determining the character's emotion based on the user's emotion.

[0125] The emotion determination unit can improve the accuracy of emotion determination by analyzing in detail the character's facial expressions and the content of the lines when determining the emotion. The emotion determination unit can improve the accuracy of emotion determination by analyzing in detail the character's facial expressions and the content of the lines when determining the emotion. The character's facial expressions and the content of the lines can be determined using, for example, facial expression recognition technology, natural language processing technology, etc., but are not limited to these examples. The emotion determination unit can improve the accuracy of emotion determination by analyzing, for example, the character's facial expressions in detail. For example, the emotion determination unit can analyze in detail the character's smile to determine the emotion of joy. The emotion determination unit can also improve the accuracy of emotion determination by analyzing in detail the content of the character's lines. For example, the emotion determination unit can determine the emotion of anger from the character's lines. The emotion determination unit can also improve the accuracy of emotion determination by combining the character's facial expressions and the content of the lines. For example, the emotion determination unit can determine the emotion of joy by combining the character's smile and the content of the lines. In this way, the accuracy of emotion determination can be improved by analyzing the character's facial expressions and the content of the lines in detail.

[0126] The emotion determination unit can make an emotion determination by referring to the character's past emotion history. The emotion determination unit makes an emotion determination by referring to the character's past emotion history. The past emotion history includes, for example, the character's emotion change patterns, past scenes, etc., but is not limited to these examples. The emotion determination unit, for example, refers to the character's past emotion history to determine the character's current emotion. For example, the emotion determination unit analyzes the character's past emotion history to determine the character's current emotion. The emotion determination unit can also determine a change in emotion based on the character's past emotion history. For example, the emotion determination unit analyzes the character's past emotion history to determine a change in emotion. The emotion determination unit can also improve the accuracy of emotion determination by referring to the character's past emotion history. For example, the emotion determination unit analyzes the character's past emotion history to improve the accuracy of emotion determination. In this way, by referring to the character's past emotion history, the accuracy of emotion determination can be improved.

[0127] The emotion determination unit can determine emotions by taking into consideration the relationships between characters when determining emotions. The emotion determination unit determines emotions by taking into consideration the relationships between characters when determining emotions. Character relationships include, for example, dialogue between characters, progress of a story, etc., but are not limited to these examples. The emotion determination unit, for example, improves the accuracy of emotion determination by taking into consideration the relationships between characters. For example, the emotion determination unit analyzes the relationships between characters to improve the accuracy of emotion determination. The emotion determination unit can also determine the strength of an emotion based on the relationships between characters. For example, the emotion determination unit analyzes the relationships between characters and determines the strength of an emotion. The emotion determination unit can also determine changes in emotions by referring to the relationships between characters. For example, the emotion determination unit analyzes the relationships between characters and determines changes in emotion. In this way, the accuracy of emotion determination can be improved by taking the relationships between characters into consideration.

[0128] The emotion determination unit estimates the user's emotion and adjusts the order in which the emotion determination results are displayed based on the estimated user's emotion. The emotion determination unit estimates the user's emotion and adjusts the order in which the emotion determination results are displayed based on the estimated user's emotion. The order in which the emotion determination results are displayed includes, but is not limited to, the user's emotion, the importance of the scene, and the like. For example, if the user is relaxed, the emotion determination unit may preferentially display a calm emotion determination result. For example, the emotion determination unit may determine that the user is relaxed using an emotion estimation algorithm and preferentially display a calm emotion determination result. Furthermore, if the user is excited, the emotion determination unit may preferentially display an emphasized emotion determination result. For example, the emotion determination unit may determine that the user is excited using an emotion estimation algorithm and preferentially display an emphasized emotion determination result. Furthermore, if the user is sad, the emotion determination unit may preferentially display a subdued emotion determination result. For example, the emotion determination unit may determine that the user is sad using an emotion estimation algorithm and preferentially display a subdued emotion determination result. In this way, by adjusting the order in which the emotion determination results are displayed based on the user's emotion, more appropriate results can be provided.

[0129] The emotion determination unit can determine an emotion by taking into consideration the geographical background of the character when determining the emotion. The emotion determination unit determines an emotion by taking into consideration the geographical background of the character when determining the emotion. The geographical background includes, for example, the character's hometown and the location of the scene, but is not limited to these examples. The emotion determination unit, for example, improves the accuracy of emotion determination by taking into consideration the geographical background of the character. For example, the emotion determination unit analyzes the character's hometown and improves the accuracy of emotion determination. The emotion determination unit can also determine the strength of an emotion based on the character's geographical background. For example, the emotion determination unit analyzes the location of the character's scene and determines the strength of the emotion. The emotion determination unit can also determine a change in emotion by referring to the character's geographical background. For example, the emotion determination unit analyzes the character's geographical background and determines a change in emotion. In this way, the accuracy of emotion determination can be improved by taking the character's geographical background into consideration.

[0130] The emotion determination unit can improve the accuracy of emotion determination by referring to literature related to the character when determining the emotion. The emotion determination unit improves the accuracy of emotion determination by referring to literature related to the character when determining the emotion. Related literature includes, but is not limited to, past works and related research papers, for example. The emotion determination unit improves the accuracy of emotion determination by referring to literature related to the character. For example, the emotion determination unit analyzes past works of the character to improve the accuracy of emotion determination. The emotion determination unit can also determine the strength of emotion based on literature related to the character. For example, the emotion determination unit analyzes related research papers to determine the strength of emotion. The emotion determination unit can also determine changes in emotion by referring to literature related to the character. For example, the emotion determination unit analyzes past works of the character to determine changes in emotion. In this way, the accuracy of emotion determination can be improved by referring to literature related to the character.

[0131] The emotion determination unit can determine the emotion by taking into consideration the market value of the character when determining the emotion. The emotion determination unit determines the emotion by taking into consideration the market value of the character when determining the emotion. Market value includes, for example, the popularity of the character, sales data, etc., but is not limited to these examples. The emotion determination unit, for example, improves the accuracy of the emotion determination by taking into consideration the market value of the character. For example, the emotion determination unit analyzes the popularity of the character and improves the accuracy of the emotion determination. The emotion determination unit can also determine the strength of the emotion based on the market value of the character. For example, the emotion determination unit analyzes the sales data of the character and determines the strength of the emotion. The emotion determination unit can also determine a change in emotion by referring to the market value of the character. For example, the emotion determination unit analyzes the popularity of the character and determines a change in emotion. In this way, the accuracy of the emotion determination can be improved by taking into consideration the market value of the character.

[0132] The onomatopoeia analysis unit estimates the user's emotion and adjusts the criteria for onomatopoeia analysis based on the estimated user's emotion. The onomatopoeia analysis unit estimates the user's emotion and adjusts the criteria for onomatopoeia analysis based on the estimated user's emotion. The criteria for onomatopoeia analysis include, but are not limited to, the type of onomatopoeia and the frequency of use. For example, if the user is relaxed, the onomatopoeia analysis unit performs a gentle onomatopoeia analysis. For example, the onomatopoeia analysis unit determines that the user is relaxed using an emotion estimation algorithm and performs a gentle onomatopoeia analysis. Furthermore, the onomatopoeia analysis unit can also perform an emphasized onomatopoeia analysis if the user is excited. For example, the onomatopoeia analysis unit determines that the user is excited using an emotion estimation algorithm and performs an emphasized onomatopoeia analysis. Furthermore, the onomatopoeia analysis unit can also perform a subdued onomatopoeia analysis if the user is sad. For example, the onomatopoeia analysis unit may use an emotion estimation algorithm to determine that the user is sad and perform a more conservative onomatopoeia analysis. This allows the onomatopoeia analysis criteria to be adjusted based on the user's emotions, resulting in a more appropriate analysis.

[0133] The onomatopoeia analysis unit can improve the accuracy of the analysis by analyzing the types and frequency of use of onomatopoeia in detail during onomatopoeia analysis. The onomatopoeia analysis unit can improve the accuracy of the analysis by analyzing the types and frequency of use of onomatopoeia in detail during onomatopoeia analysis. Examples of the types and frequency of use of onomatopoeia include, but are not limited to, text analysis and frequency analysis. The onomatopoeia analysis unit can improve the accuracy of the analysis by analyzing the types of onomatopoeia in detail. For example, the onomatopoeia analysis unit can analyze the onomatopoeia "boom" in detail to generate an explosion sound. The onomatopoeia analysis unit can also improve the accuracy of the analysis by analyzing the frequency of use of onomatopoeia in detail. For example, the onomatopoeia analysis unit can prioritize analysis of frequently used onomatopoeia to generate sound effects. The onomatopoeia analysis unit can also improve the accuracy of the analysis by combining the types and frequency of use of onomatopoeia. For example, the onomatopoeia analysis unit generates an explosion sound by analyzing the type of onomatopoeia "bang" (dokan) and its frequency of use in combination. This allows for a detailed analysis of the type and frequency of use of onomatopoeia, improving the accuracy of the analysis.

[0134] When analyzing onomatopoeia, the onomatopoeia analysis unit can perform the analysis by referring to the past usage history of the onomatopoeia. When analyzing onomatopoeia, the onomatopoeia analysis unit performs the analysis by referring to the past usage history of the onomatopoeia. The past usage history includes, for example, the frequency of use of the onomatopoeia and the situations in which it is used, but is not limited to these examples. For example, the onomatopoeia analysis unit performs the current analysis by referring to the past usage history of the onomatopoeia. For example, the onomatopoeia analysis unit performs the current analysis by referring to the history of past uses of the onomatopoeia "dokan." The onomatopoeia analysis unit can also analyze usage patterns based on the past usage history of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the past usage history and analyzes the usage pattern of the onomatopoeia "dokan." The onomatopoeia analysis unit can also improve the accuracy of the analysis by referring to the past usage history of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the past usage history to improve the accuracy of the analysis. By referring to the past usage history of onomatopoeia, the accuracy of the analysis can be improved.

[0135] The onomatopoeia analysis unit can perform the analysis while taking into consideration the context of the onomatopoeia. The onomatopoeia analysis unit performs the analysis while taking into consideration the context of the onomatopoeia. The context of the onomatopoeia includes, for example, preceding and following sentences, related scenes, etc., but is not limited to these examples. The onomatopoeia analysis unit, for example, improves the accuracy of the analysis by taking into consideration the context of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the sentences before and after the onomatopoeia "boom" to generate the sound of an explosion. The onomatopoeia analysis unit can also analyze usage patterns based on the context of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the scenes before and after the onomatopoeia "boom" to analyze the usage pattern. The onomatopoeia analysis unit can also improve the accuracy of the analysis by referring to the context of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the context of the onomatopoeia "dokan" to improve the accuracy of the analysis. By taking the context of the onomatopoeia into consideration, the accuracy of the analysis can be improved.

[0136] The onomatopoeia analysis unit estimates the user's emotion and adjusts the order in which the onomatopoeia analysis results are displayed based on the estimated user's emotion. The onomatopoeia analysis unit estimates the user's emotion and adjusts the order in which the onomatopoeia analysis results are displayed based on the estimated user's emotion. The order in which the onomatopoeia analysis results are displayed includes, but is not limited to, the user's emotion, the importance of the scene, and the like. For example, if the user is relaxed, the onomatopoeia analysis unit may preferentially display calm onomatopoeia analysis results. For example, the onomatopoeia analysis unit may determine that the user is relaxed using an emotion estimation algorithm and preferentially display calm onomatopoeia analysis results. Furthermore, if the user is excited, the onomatopoeia analysis unit may preferentially display emphasized onomatopoeia analysis results. For example, the onomatopoeia analysis unit may determine that the user is excited using an emotion estimation algorithm and preferentially display emphasized onomatopoeia analysis results. Furthermore, if the user is sad, the onomatopoeia analysis unit can prioritize displaying onomatopoeia analysis results that are more subdued. For example, the onomatopoeia analysis unit determines that the user is sad using an emotion estimation algorithm and prioritizes displaying onomatopoeia analysis results that are more subdued. This allows the order in which onomatopoeia analysis results are displayed to be adjusted based on the user's emotions, thereby providing more appropriate results.

[0137] The onomatopoeia analysis unit can perform the analysis by taking into consideration the geographical background of the onomatopoeia. The onomatopoeia analysis unit performs the analysis by taking into consideration the geographical background of the onomatopoeia. The geographical background includes, for example, the region where the onomatopoeia originates, the location of the scene, etc., but is not limited to these examples. The onomatopoeia analysis unit improves the accuracy of the analysis by taking into consideration the geographical background of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the region where the onomatopoeia "boom" originates and generates an explosion sound. The onomatopoeia analysis unit can also analyze usage patterns based on the geographical background of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the location of the scene where the onomatopoeia "boom" occurs and analyzes the usage pattern. The onomatopoeia analysis unit can also improve the accuracy of the analysis by referring to the geographical background of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the geographical background of the onomatopoeia "dokan" (big boom) and improves the accuracy of the analysis. By taking the geographical background of the onomatopoeia into consideration, the accuracy of the analysis can be improved.

[0138] The onomatopoeia analysis unit can improve the accuracy of the analysis by referring to literature related to the onomatopoeia when analyzing the onomatopoeia. The onomatopoeia analysis unit can improve the accuracy of the analysis by referring to literature related to the onomatopoeia when analyzing the onomatopoeia. Related literature includes, but is not limited to, past works and related research papers, for example. The onomatopoeia analysis unit can improve the accuracy of the analysis by referring to literature related to the onomatopoeia. For example, the onomatopoeia analysis unit can analyze past works using the onomatopoeia "boom" (crash) to generate an explosion sound. The onomatopoeia analysis unit can also analyze usage patterns based on literature related to the onomatopoeia. For example, the onomatopoeia analysis unit can analyze research papers related to the onomatopoeia "boom" (crash) to analyze usage patterns. The onomatopoeia analysis unit can also improve the accuracy of the analysis by referring to literature related to the onomatopoeia. For example, the onomatopoeia analysis unit analyzes past works that use the onomatopoeia "dokan" (thud) to improve the accuracy of the analysis. This allows the accuracy of the analysis to be improved by referring to literature related to the onomatopoeia.

[0139] The onomatopoeia analysis unit can perform the analysis by taking into account the market value of the onomatopoeia. The onomatopoeia analysis unit performs the analysis by taking into account the market value of the onomatopoeia. Market value includes, for example, the popularity and frequency of use of the onomatopoeia, but is not limited to these examples. The onomatopoeia analysis unit improves the accuracy of the analysis by taking into account the market value of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the popularity of the onomatopoeia "boom" (crash) and generates an explosion sound. The onomatopoeia analysis unit can also analyze usage patterns based on the market value of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the frequency of use of the onomatopoeia "boom" (crash) and analyzes the usage pattern. The onomatopoeia analysis unit can also improve the accuracy of the analysis by referring to the market value of the onomatopoeia. For example, the onomatopoeia analysis unit analyzes the popularity of the onomatopoeia "dokan" (boom) to improve the accuracy of the analysis. This allows the accuracy of the analysis to be improved by taking into account the market value of the onomatopoeia.

[0140] The sound effect adjustment unit estimates the user's emotion and adjusts the sound effect adjustment method based on the estimated user's emotion. The sound effect adjustment unit estimates the user's emotion and adjusts the sound effect adjustment method based on the estimated user's emotion. Sound effect adjustment methods include, but are not limited to, volume adjustment, echo effect, and pitch adjustment. For example, when the user is relaxed, the sound effect adjustment unit provides a gentle sound effect. For example, the sound effect adjustment unit determines that the user is relaxed using an emotion estimation algorithm and provides a gentle sound effect. Furthermore, the sound effect adjustment unit can provide an emphasized sound effect when the user is excited. For example, the sound effect adjustment unit determines that the user is excited using an emotion estimation algorithm and provides an emphasized sound effect. Furthermore, the sound effect adjustment unit can provide a subdued sound effect when the user is sad. For example, the sound effect adjustment unit determines that the user is sad using an emotion estimation algorithm and provides a subdued sound effect. In this way, by adjusting the sound effect adjustment method based on the user's emotion, more appropriate sound effects can be provided.

[0141] The sound effect adjustment unit can improve the accuracy of the adjustment by analyzing in detail the type and frequency of use of the sound effect when adjusting the sound effect. The sound effect adjustment unit can improve the accuracy of the adjustment by analyzing in detail the type and frequency of use of the sound effect when adjusting the sound effect. Examples of the type and frequency of use of the sound effect include, but are not limited to, text analysis and frequency analysis. The sound effect adjustment unit can improve the accuracy of the adjustment by analyzing in detail the type of the sound effect. For example, the sound effect adjustment unit can analyze in detail the sound effect "boom" to generate an explosion sound. The sound effect adjustment unit can also improve the accuracy of the adjustment by analyzing in detail the frequency of use of the sound effect. For example, the sound effect adjustment unit can prioritize and adjust frequently used sound effects to generate sound effects. The sound effect adjustment unit can also improve the accuracy of the adjustment by combining the type of the sound effect and the frequency of use. For example, the sound effect adjustment unit can combine the type of the sound effect "boom" and the frequency of use to adjust it to generate an explosion sound. In this way, the accuracy of the adjustment can be improved by analyzing in detail the type and frequency of use of the sound effect.

[0142] The sound effect adjustment unit can make adjustments by referring to the past usage history of the sound effect when adjusting the sound effect. The sound effect adjustment unit makes adjustments by referring to the past usage history of the sound effect when adjusting the sound effect. The past usage history includes, but is not limited to, for example, the frequency of use of the sound effect and the scenes in which it was used. The sound effect adjustment unit, for example, makes current adjustments by referring to the past usage history of the sound effect. For example, the sound effect adjustment unit makes current adjustments by referring to the history of past uses of the sound effect "boom." The sound effect adjustment unit can also adjust usage patterns based on the past usage history of the sound effect. For example, the sound effect adjustment unit analyzes the past usage history and adjusts the usage pattern of the sound effect "boom." The sound effect adjustment unit can also improve the accuracy of the adjustment by referring to the past usage history of the sound effect. For example, the sound effect adjustment unit analyzes the past usage history and improves the accuracy of the adjustment. In this way, the accuracy of the adjustment can be improved by referring to the past usage history of the sound effect.

[0143] The sound effect adjustment unit can adjust the sound effect while taking into account the context of the sound effect. The sound effect adjustment unit can adjust the sound effect while taking into account the context of the sound effect. The context of the sound effect includes, for example, previous and following scenes, related actions, etc., but is not limited to these examples. The sound effect adjustment unit can, for example, improve the accuracy of the adjustment by taking into account the context of the sound effect. For example, the sound effect adjustment unit analyzes the scenes before and after the sound effect "boom" to generate an explosion sound. The sound effect adjustment unit can also adjust the usage pattern based on the context of the sound effect. For example, the sound effect adjustment unit analyzes the actions before and after the sound effect "boom" to adjust the usage pattern. The sound effect adjustment unit can also improve the accuracy of the adjustment by referring to the context of the sound effect. For example, the sound effect adjustment unit analyzes the context of the sound effect "boom" to improve the accuracy of the adjustment. In this way, the accuracy of the adjustment can be improved by taking the context of the sound effect into account.

[0144] The sound effect adjustment unit estimates the user's emotion and adjusts the order in which the sound effect adjustment results are displayed based on the estimated user's emotion. The sound effect adjustment unit estimates the user's emotion and adjusts the order in which the sound effect adjustment results are displayed based on the estimated user's emotion. The order in which the sound effect adjustment results are displayed includes, but is not limited to, the user's emotion, the importance of a scene, and the like. For example, if the user is relaxed, the sound effect adjustment unit may preferentially display a gentle sound effect adjustment result. For example, the sound effect adjustment unit may determine that the user is relaxed using an emotion estimation algorithm and preferentially display a gentle sound effect adjustment result. Furthermore, the sound effect adjustment unit may preferentially display an emphasized sound effect adjustment result if the user is excited. For example, the sound effect adjustment unit may determine that the user is excited using an emotion estimation algorithm and preferentially display an emphasized sound effect adjustment result. Furthermore, the sound effect adjustment unit may preferentially display a muted sound effect adjustment result if the user is sad. For example, the sound effect adjustment unit may determine that the user is sad using an emotion estimation algorithm and preferentially display a muted sound effect adjustment result. This allows the order in which the results of sound effect adjustment are displayed to be adjusted based on the user's emotions, thereby providing more appropriate results.

[0145] The sound effect adjustment unit can take into account the geographical background of the sound effect when adjusting the sound effect. The sound effect adjustment unit can take into account the geographical background of the sound effect when adjusting the sound effect. The geographical background includes, but is not limited to, the region where the sound effect occurs and the location of the scene. The sound effect adjustment unit can improve the accuracy of the adjustment by taking into account the geographical background of the sound effect. For example, the sound effect adjustment unit analyzes the region where the sound effect "boom" occurs and generates an explosion sound. The sound effect adjustment unit can also adjust the usage pattern based on the geographical background of the sound effect. For example, the sound effect adjustment unit analyzes the location of the scene where the sound effect "boom" occurs and adjusts the usage pattern. The sound effect adjustment unit can also improve the accuracy of the adjustment by referring to the geographical background of the sound effect. For example, the sound effect adjustment unit analyzes the geographical background of the sound effect "boom" and improves the accuracy of the adjustment. In this way, the accuracy of the adjustment can be improved by taking the geographical background of the sound effect into account.

[0146] The sound effect adjustment unit can improve the accuracy of the adjustment by referring to literature related to the sound effect when adjusting the sound effect. The sound effect adjustment unit can improve the accuracy of the adjustment by referring to literature related to the sound effect when adjusting the sound effect. Related literature includes, but is not limited to, past works and related research papers. The sound effect adjustment unit can improve the accuracy of the adjustment by referring to literature related to the sound effect. For example, the sound effect adjustment unit analyzes past works that use the sound effect "boom" to generate an explosion sound. The sound effect adjustment unit can also adjust the usage pattern based on literature related to the sound effect. For example, the sound effect adjustment unit analyzes research papers that use the sound effect "boom" to adjust the usage pattern. The sound effect adjustment unit can also improve the accuracy of the adjustment by referring to literature related to the sound effect. For example, the sound effect adjustment unit analyzes past works that use the sound effect "boom" to improve the accuracy of the adjustment. In this way, the accuracy of the adjustment can be improved by referring to literature related to the sound effect.

[0147] The sound effect adjustment unit can adjust the sound effect taking into account the market value of the sound effect when adjusting the sound effect. The sound effect adjustment unit can adjust the sound effect taking into account the market value of the sound effect when adjusting the sound effect. Market value includes, but is not limited to, the popularity and frequency of use of the sound effect. For example, the sound effect adjustment unit can improve the accuracy of the adjustment by taking into account the market value of the sound effect. For example, the sound effect adjustment unit can analyze the popularity of the sound effect "boom" and generate an explosion sound. The sound effect adjustment unit can also adjust the usage pattern based on the market value of the sound effect. For example, the sound effect adjustment unit can analyze the frequency of use of the sound effect "boom" and adjust the usage pattern. The sound effect adjustment unit can also improve the accuracy of the adjustment by referring to the market value of the sound effect. For example, the sound effect adjustment unit can analyze the popularity of the sound effect "boom" and improve the accuracy of the adjustment. In this way, the accuracy of the adjustment can be improved by taking the market value of the sound effect into account. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and playback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs digital comic data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the digital comic data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates text-to-speech and sound effects. The playback unit is realized by the control unit 46A of the smart device 14 and plays back the generated text-to-speech and sound effects. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and playback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs digital comic data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the digital comic data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates text-to-speech and sound effects. The playback unit is realized by the control unit 46A of the smart glasses 214 and plays back the generated text-to-speech and sound effects. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and playback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs digital comic data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the digital comic data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates text-to-speech and sound effects. The playback unit is realized by the control unit 46A of the headset type terminal 314 and plays back the generated text-to-speech and sound effects. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and playback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs digital comic data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the digital comic data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates text-to-speech and sound effects. The playback unit is realized by the control unit 46A of the robot 414 and plays back the generated text-to-speech and sound effects.

[0148] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0149] The reception unit can estimate the user's emotions and adjust the timing of receiving digital comic data based on the estimated user emotions. For example, if the user is relaxed, the digital comic data can be received immediately, providing a smooth experience. If the user is feeling stressed, the reception of the data can be delayed slightly to give the user time to calm down. Furthermore, if the user is excited, the data can be received quickly and analysis can begin immediately. In this way, a smoother experience can be provided by adjusting the timing of receiving data according to the user's emotions.

[0150] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is stressed, concise analysis results can be provided. If the user is excited, visually stimulating analysis results can be provided. In this way, by adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided.

[0151] The generation unit can estimate the user's emotions and adjust the expression method of the generated reading and sound effects based on the estimated user's emotions. For example, if the user is relaxed, the reading can be performed in a calm voice and the sound effects can be subdued. If the user is excited, the reading can be performed in a powerful voice and the sound effects can be louder. Furthermore, if the user is sad, the reading can be performed in a calm voice and the sound effects can be quieter. In this way, by adjusting the expression method of the reading and sound effects based on the user's emotions, more appropriate expressions can be provided.

[0152] The playback unit can estimate the user's emotions and adjust the playback expression method based on the estimated user's emotions. For example, if the user is relaxed, playback can be performed in a calm voice and with less sound effects. If the user is excited, playback can be performed in a powerful voice and with more flashy sound effects. Furthermore, if the user is sad, playback can be performed in a calm voice and with quieter sound effects. In this way, by adjusting the playback expression method based on the user's emotions, more appropriate expressions can be provided.

[0153] The emotion determination unit can estimate the user's emotion and adjust the criteria for determining the character's emotion based on the estimated user's emotion. For example, if the user is relaxed, the character's emotion determination can be made gentler. Also, if the user is excited, the character's emotion determination can be made more emphatically. Furthermore, if the user is sad, the character's emotion determination can be made more modestly. In this way, by adjusting the criteria for determining the character's emotion based on the user's emotion, more appropriate emotion determination can be performed.

[0154] When receiving digital comic data, the reception unit can analyze the user's past usage history and select an appropriate reception method. For example, it can prioritize reception of digital comics in formats that the user has frequently used in the past. It can also select the optimal reception method for a specific time period based on the user's past usage history. It can also automatically select an interface that the user has preferred in the past to receive data. This makes it possible to select the optimal reception method by analyzing the user's past usage history.

[0155] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the manga. For example, a detailed analysis can be performed for scenes featuring major characters. A concise analysis can also be performed for scenes focusing on backgrounds and landscapes. Furthermore, a particularly detailed analysis can be performed for climax scenes. In this way, by adjusting the level of detail of the analysis based on the importance of the manga, more important scenes can be analyzed in detail.

[0156] During generation, the generation unit can apply different sound effect generation algorithms depending on the type of onomatopoeia. For example, an algorithm that generates the sound of an explosion can be applied to the onomatopoeia "boom!". Also, an algorithm that generates the sound of a flash can be applied to the onomatopoeia "pika!". Furthermore, an algorithm that generates the sound of a crowd murmuring can be applied to the onomatopoeia "wazawa!". In this way, by applying different sound effect generation algorithms depending on the type of onomatopoeia, more appropriate sound effects can be generated.

[0157] During playback, the playback unit can apply different playback algorithms depending on the category of the manga. For example, for action manga, an algorithm specialized for playing back movement and battle scenes can be applied. For romance manga, an algorithm specialized for playing back character emotions and relationships can be applied. Furthermore, for comedy manga, an algorithm specialized for playing back humor and gags can be applied. In this way, by applying different playback algorithms depending on the category of the manga, more appropriate playback results can be provided.

[0158] When adjusting sound effects, the sound effect adjustment unit can improve the accuracy of the adjustment by analyzing in detail the type and frequency of use of the sound effects. For example, it can analyze in detail the sound effect "bang" to generate an explosion sound. It can also generate sound effects by prioritizing adjustment of frequently used sound effects. Furthermore, it can improve the accuracy of the adjustment by combining the type and frequency of use of the sound effects. In this way, the accuracy of the adjustment can be improved by analyzing in detail the type and frequency of use of the sound effects.

[0159] The processing flow of the second embodiment will be briefly explained below.

[0160] Step 1: The reception unit inputs the data of the digital comic. The digital comic data includes image data, text data, audio data, etc. For example, the reception unit inputs the digital comic in PDF format or the pages of a comic saved as an image file. Step 2: The analysis unit analyzes the digital comic data entered by the reception unit. The analysis is performed using methods such as image analysis, text analysis, and audio analysis. For example, a generation AI is used to analyze each page of the comic and identify the emotions of the characters and the drawings on the comic. Drawing elements such as onomatopoeia and effect lines are also analyzed and used to generate sound effects. Step 3: The generator generates the reading and sound effects based on the data analyzed by the analyzer. This is done using voice synthesis technology. The generator AI adjusts the tone and tempo of the voice according to the character's emotions, achieving natural reading. Appropriate sound effects are generated based on the onomatopoeia. Step 4: The playback unit plays back the speech and sound effects generated by the generation unit. The playback is performed using a playback device. For example, the generated speech and sound effects are played back using speakers or headphones.

[0161] 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.

[0162] 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.

[0163] 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.

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

[0165] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0166] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The 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.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0182] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0197] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0198] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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."

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Explanation of symbols]

[0233] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for inputting data on electronic comics; an analysis unit that analyzes the digital comic data input by the reception unit; a generator that generates a reading or a sound effect based on the data analyzed by the analyzer; a playback unit that plays back the reading or sound effect generated by the generation unit. A system characterized by:

2. The analysis unit Equipped with an emotion determination unit that determines the emotion of the character 2. The system of claim 1.

3. The analysis unit Equipped with an onomatopoeia analysis unit that analyzes onomatopoeia 2. The system of claim 1.

4. The generation unit A sound effect adjustment unit is provided to adjust the generated sound effects.

2. The system of claim 1.

5. The generation unit Adjust the tone and tempo of the voice according to the character's emotions 2. The system of claim 1.

6. The playback unit Play the generated speech and sound effects 2. The system of claim 1.

7. The reception unit Estimates user emotions and adjusts the timing of accepting digital comic data based on the user's emotions.

2. The system of claim 1.

8. The reception unit When accepting digital comic data, the system analyzes the user's past usage history and selects the appropriate acceptance method.

2. The system of claim 1.

9. The reception unit When accepting digital comic data, filtering is performed based on the user's current interests.

2. The system of claim 1.

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