system

Abstract

The system according to this embodiment aims to automatically generate and play background music in accordance with scenes in an electronic comic. [Solution] The system according to the embodiment comprises a character recognition unit, a determination unit, a generation unit, and a playback unit. The character recognition unit recognizes the dialogue as characters. The determination unit determines the scene based on the dialogue recognized by the character recognition unit. The generation unit generates background music (BGM) based on the scene information determined by the determination unit. The playback unit plays the BGM generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is impossible to automatically generate and play BGM according to the scene when reading an electronic comic, and there is a limit to improving the user experience.

[0005] The system according to the embodiment aims to automatically generate and play BGM according to the scene of an electronic comic.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a character recognition unit, a determination unit, a generation unit, and a playback unit. The character recognition unit recognizes the characters in the dialogue. The determination unit determines the scene based on the dialogue recognized by the character recognition unit. The generation unit generates background music (BGM) based on the scene information determined by the determination unit. The playback unit plays the BGM generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate and play background music in accordance with scenes in an electronic comic. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An electronic comic app according to an embodiment of the present invention is a system that improves the user experience by having an AI select and play background music (BGM) according to the scene. When a user starts reading an electronic comic, the AI ​​determines the scene. Next, based on the determined scene, the AI ​​selects and plays appropriate BGM. This mechanism allows the user to have a more immersive reading experience. Furthermore, by giving the app unique functionality, it secures a market advantage. First, when a user starts reading an electronic comic, the AI ​​determines the scene. At this time, the AI ​​recognizes the dialogue and classifies the scene into categories such as everyday conversation, battle, and despair. It also estimates the time period and location from the manga's introduction and synopsis. For example, it selects calm BGM for everyday conversation scenes and tense BGM for battle scenes. Next, based on the determined scene, the AI ​​selects and plays appropriate BGM. The AI ​​generates BGM that does not feel out of place from the scene information and the setting information of the manga's time period and location. For example, it generates Japanese-style BGM for a manga set in the Edo period and science fiction-style BGM for a manga set in the future. This mechanism allows the user to have a more immersive reading experience. For example, playing tense background music during a battle scene draws the user into that scene. Conversely, playing calming background music during everyday conversation scenes allows users to enjoy reading in a relaxed mood. Furthermore, adding unique features to the app can secure a market advantage. For instance, offering an automatic background music playback function, which other e-comic apps lack, can attract user interest and increase the number of users. Since background music can potentially increase the user base by 10%, increased sales can also be expected. In this way, by utilizing AI to automatically play background music appropriate to the scene, e-comic apps can improve the user experience and secure a market advantage. This allows e-comic apps to select background music according to the scene, thereby enhancing the user experience.

[0029] The electronic comic application according to this embodiment comprises a character recognition unit, a determination unit, a generation unit, and a playback unit. The character recognition unit recognizes the characters in the dialogue. The character recognition unit recognizes the dialogue using, for example, OCR (optical character recognition) technology. The character recognition unit can also use recognition technology using deep learning. For example, the character recognition unit can recognize handwritten dialogue with high accuracy using a deep learning model. Furthermore, the character recognition unit can also analyze the font and style of the dialogue. For example, the character recognition unit can analyze bold and italicized characters to identify emphasized dialogue. The determination unit determines the scene based on the dialogue recognized by the character recognition unit. For example, the determination unit analyzes the content of the dialogue and classifies scenes such as everyday conversation, battle, and despair. Furthermore, the determination unit can also estimate the time period and location from the manga's introduction and synopsis. For example, the determination unit uses text analysis technology to analyze the manga's introduction and synopsis and estimate the time period and location. The generation unit generates background music (BGM) based on scene information determined by the judgment unit. The generation unit generates BGM suitable for the scene using, for example, a music generation algorithm. The generation unit can also generate BGM from scene information and setting information for the time period and location of the manga. For example, the generation unit generates Japanese-style BGM for a manga set in the Edo period and science fiction-style BGM for a manga set in the future. The playback unit plays the BGM generated by the generation unit. The playback unit plays the BGM while adjusting the sound quality according to the playback device, for example. The playback unit may also include a customization unit that allows the user to customize the BGM. For example, the playback unit may include an adjustment unit that allows the user to adjust the volume and playback timing of the BGM. As a result, the electronic comic app according to the embodiment can select BGM according to the scene and improve the user experience.

[0030] The character recognition unit recognizes the characters in the dialogue. For example, it uses OCR (Optical Character Recognition) technology to recognize the dialogue. Specifically, OCR technology involves detecting characters within an image and converting them into digital text. This allows for high-precision recognition of both printed and handwritten characters. The character recognition unit can also utilize deep learning-based recognition technology. Deep learning models are trained using large amounts of data and can recognize handwritten dialogue and special fonts with high accuracy. For example, even when handwritten dialogue is included, the deep learning model learns its features and can accurately recognize the characters. Furthermore, the character recognition unit can analyze the font and style of the dialogue. For example, it can analyze bold and italicized characters to identify emphasized dialogue. This allows for accurate understanding of the emotion and emphasis of the dialogue. By combining these functions, the character recognition unit can recognize and analyze manga dialogue with high accuracy. This enables subsequent processing units to operate based on accurate information.

[0031] The judgment unit determines the scene based on the dialogue recognized by the character recognition unit. For example, the judgment unit analyzes the content of the dialogue and classifies scenes such as everyday conversation, battle, and despair. Specifically, it uses natural language processing technology to analyze the content of the dialogue and identify emotions and tone. For example, it can analyze keywords and phrases contained in the dialogue and classify scenes based on them. The judgment unit can also estimate the time period and location from the manga's introduction and synopsis. For example, it uses text analysis technology to analyze the manga's introduction and synopsis and estimate the time period and location. This allows it to accurately grasp the background information of the scene. Furthermore, the judgment unit can determine the scene by considering not only the emotions and tone of the dialogue, but also the relationships between characters and the progress of the story. For example, it analyzes the dialogue and actions between characters and identifies the type of scene based on that. This allows the judgment unit to perform more accurate scene determination and provide information for subsequent processing units to perform appropriate actions.

[0032] The generation unit generates background music (BGM) based on scene information determined by the judgment unit. For example, the generation unit generates BGM suitable for the scene using a music generation algorithm. Specifically, it adjusts the tempo, melody, and rhythm of the music based on the scene information to create BGM that is optimal for the scene. The generation unit can also generate BGM from scene information and the setting information of the manga, such as the time period and location. For example, the generation unit will generate Japanese-style BGM for a manga set in the Edo period, and sci-fi-style BGM for a manga set in the future. This can further enhance the atmosphere and emotions of the scene. Furthermore, the generation unit can use AI to learn the user's preferences and past viewing history, and provide BGM that is optimal for each individual user. For example, it can learn the patterns of BGM that the user has liked to listen to in the past and generate new BGM based on that. In this way, the generation unit can individually optimize the user experience and provide a more satisfying service.

[0033] The playback unit plays the background music (BGM) generated by the generation unit. The playback unit plays the BGM while adjusting the sound quality according to the playback device. Specifically, it adjusts the acoustic characteristics to provide optimal sound quality for different devices such as smartphones, tablets, and PCs. The playback unit can also include a customization unit that allows the user to customize the BGM. For example, the playback unit can include an adjustment unit that allows the user to adjust the volume and playback timing of the BGM. This allows the user to customize the BGM to their liking and enjoy a more personalized experience. Furthermore, the playback unit can also have a function to seamlessly integrate with other audio content while playing the BGM. For example, it can play dialogue audio and BGM simultaneously and automatically adjust the volume balance between the two to provide a user-friendly audio environment. In this way, the playback unit can provide the user with a high-quality, customizable audio experience, maximizing the appeal of digital comics.

[0034] The judgment unit can estimate the time period and location from the manga's introduction and synopsis. For example, the judgment unit uses text analysis technology to analyze the manga's introduction and synopsis and estimate the time period and location. For example, the judgment unit can use text analysis technology to estimate the setting of the manga, such as the Edo period or the future, from the manga's introduction and synopsis. The judgment unit can also estimate the time period and location from the manga's introduction and synopsis using a database referencing method. For example, the judgment unit refers to information registered in a database to estimate the time period and location from the manga's introduction and synopsis. This improves the accuracy of scene determination.

[0035] The generation unit can generate background music (BGM) from scene information and the setting information of the manga's time period and location. For example, the generation unit uses a music generation algorithm to generate BGM appropriate for the scene. For instance, based on the scene information, the generation unit generates tense BGM for tense battle scenes and calm BGM for calm everyday conversation scenes. Furthermore, based on the setting information of the manga's time period and location, the generation unit can generate Japanese-style BGM for manga set in the Edo period and sci-fi-style BGM for manga set in the future. For example, the generation unit can refer to a music database that matches the historical context to generate appropriate BGM. This allows for the generation of BGM that is optimal for each scene.

[0036] The playback unit may include a customization unit that allows the user to customize the background music (BGM). For example, the playback unit may allow the user to adjust the BGM volume and playback timing through a user interface. For instance, the playback unit could provide a slider to adjust the BGM volume, allowing the user to set their preferred volume. It could also provide an option to adjust the BGM playback timing, allowing the user to change the BGM playback timing to suit the scene. This enables the user to customize the BGM.

[0037] The playback unit may include an adjustment unit for adjusting the volume and playback timing of the background music (BGM). For example, the playback unit can automatically adjust the BGM volume using a volume adjustment algorithm. For instance, the playback unit can adjust the BGM volume according to the tension of the scene, increasing the volume in tense scenes and decreasing it in calm scenes. The playback unit can also adjust the playback timing of the BGM using a playback timing setting method. For example, the playback unit can start playing the BGM at the beginning of a scene and stop it at the end of the scene. This allows for adjustment of both the BGM volume and playback timing.

[0038] The character recognition unit can analyze the font and style of recognized characters to determine the atmosphere of a scene in more detail. For example, the character recognition unit can analyze the font of recognized characters using font recognition technology. For example, the character recognition unit can analyze bold and italicized characters to identify emphasized dialogue. The character recognition unit can also analyze handwritten-style fonts to determine emotional scenes. For example, the character recognition unit can analyze handwritten-style fonts to identify emotional scenes. Furthermore, the character recognition unit can analyze specific styles of characters to determine the tension or relaxation of a scene. For example, the character recognition unit can analyze specific styles of characters to determine the atmosphere of a scene in detail. This allows for a detailed determination of the atmosphere of a scene.

[0039] The character recognition unit can analyze the frequency and patterns of recognized characters to extract features of specific scenes or characters. For example, the character recognition unit can analyze the frequency of recognized characters using a frequency analysis algorithm. For instance, it can analyze words frequently used by a specific character to extract the character's characteristics. The character recognition unit can also analyze the patterns of recognized characters using pattern recognition technology. For example, it can analyze phrases repeated in a specific scene to extract the scene's features. Furthermore, the character recognition unit can analyze the frequency of words expressing specific emotions to extract the emotional features of a scene. For example, it can analyze the frequency of words expressing emotions to identify the emotional features of a scene. This allows for the extraction of features of scenes and characters.

[0040] The judgment unit can analyze the characters' facial expressions and actions during scene determination to perform a detailed classification of the scene. For example, the judgment unit can analyze the characters' facial expressions using facial recognition technology. For instance, it can analyze a character's smile and classify it as a happy scene. The judgment unit can also analyze the characters' actions using motion analysis algorithms. For example, it can analyze a character's angry expression and classify it as a tense scene. Furthermore, the judgment unit can analyze a character's crying face and classify it as an emotional scene. For example, it can analyze a character's crying face and classify it as an emotional scene. This enables a detailed classification of scenes.

[0041] The judgment unit can analyze background sounds and sound effects during scene detection to determine the atmosphere of a scene in more detail. For example, the judgment unit can analyze background sounds using acoustic analysis technology. For instance, if the background sounds are quiet, the judgment unit will determine it to be a calm scene. The judgment unit can also analyze sound effects using a sound effect recognition algorithm. For example, if the sound effects are intense, the judgment unit will determine it to be a tense scene. Furthermore, if the background sounds are lively, the judgment unit can also determine it to be a fun scene. For example, if the background sounds are lively, the judgment unit will determine it to be a fun scene. This allows for a detailed determination of the atmosphere of a scene.

[0042] The generation unit can analyze the tempo and rhythm of a scene when generating background music (BGM) and create the most suitable BGM for that scene. For example, the generation unit can analyze the tempo of a scene using a tempo analysis algorithm. For example, if the tempo of the scene is fast, the generation unit will generate fast-paced BGM. The generation unit can also analyze the rhythm of a scene using rhythm recognition technology. For example, if the rhythm of the scene is slow, the generation unit will generate slow-paced BGM. Furthermore, if the tempo of the scene changes, the generation unit can also generate BGM to match the tempo. For example, if the tempo of the scene changes, the generation unit will generate BGM to match the tempo. This allows for the generation of BGM that is optimal for each scene.

[0043] The generation unit can analyze the scene's colors and light intensity during BGM generation to create the most suitable BGM for that scene. For example, the generation unit can analyze the scene's colors using color analysis technology. For instance, if the scene's colors are bright, the generation unit will generate bright BGM. The generation unit can also analyze the scene's light intensity using a light intensity recognition algorithm. For example, if the scene's colors are dark, the generation unit will generate dark BGM. Furthermore, if the scene's light intensity changes, the generation unit can generate BGM to match the light intensity. For example, if the scene's light intensity changes, the generation unit will generate BGM to match the light intensity. This allows for the generation of BGM that is optimal for each scene.

[0044] The playback unit analyzes the acoustic characteristics of the user's device during background music (BGM) playback and can reproduce the music with optimal sound quality. For example, the playback unit analyzes the acoustic characteristics of the user's device using acoustic analysis technology. For instance, if the user's device has high-quality speakers, the playback unit will play the BGM with high-quality sound. The playback unit can also analyze the acoustic characteristics of the user's device using a device characteristic recognition algorithm. For example, if the user's device is using earphones, the playback unit will play the BGM with sound quality optimized for earphones. Furthermore, if the user's device has low-quality speakers, the playback unit can adjust the sound quality before playing the BGM. For example, the playback unit adjusts the sound quality before playing the BGM. This allows the BGM to be played with optimal sound quality for the user's device.

[0045] The playback unit can analyze the user's ambient noise during background music (BGM) playback and play it at the optimal volume for that environment. For example, the playback unit can analyze the user's ambient noise using ambient noise analysis technology. For instance, if the user is in a quiet environment, the playback unit will play the BGM at a low volume. The playback unit can also analyze the user's ambient noise using a noise recognition algorithm. For example, if the user is in a noisy environment, the playback unit will play the BGM at a high volume. Furthermore, if the user is in a moderately noisy environment, the playback unit can play the BGM at an appropriate volume. For example, the playback unit will play the BGM at an appropriate volume. This allows the BGM to be played at the optimal volume for the user's environment.

[0046] The customization unit can suggest the optimal options by referring to the user's past customization history during the customization process. For example, the customization unit can refer to the user's past customization history using the structure of a history database. For instance, it can suggest the optimal options based on the customization options the user has previously selected. Furthermore, the customization unit can refer to the user's past customization history using a reference algorithm. For example, it can suggest options suitable for a specific scenario based on the user's past customization history. In addition, the customization unit can analyze the user's customization history and suggest the optimal options. This allows it to suggest the optimal options based on the user's past customization history.

[0047] The adjustment unit can suggest optimal settings by referring to the user's past adjustment history during adjustment. For example, the adjustment unit can refer to the user's past adjustment history using the structure of a history database. For instance, it can suggest the optimal volume based on the volume settings the user has previously selected. The adjustment unit can also refer to the user's past adjustment history using a reference algorithm. For example, it can suggest playback timing suitable for a specific scene based on the user's past adjustment history. Furthermore, the adjustment unit can analyze the user's adjustment history and suggest optimal settings. For example, it can analyze the user's adjustment history and suggest optimal settings. This allows the adjustment unit to suggest optimal settings based on the user's past adjustment history.

[0048] The adjustment unit can provide optimal settings during adjustment, taking into account the acoustic characteristics of the user's device. For example, the adjustment unit can analyze the acoustic characteristics of the user's device using acoustic characteristic analysis technology. For instance, if the user's device is equipped with high-quality speakers, the adjustment unit can provide settings for high-quality playback. The adjustment unit can also analyze the acoustic characteristics of the user's device using a device characteristic recognition algorithm. For example, if the user's device uses earphones, the adjustment unit can provide settings optimized for earphones. Furthermore, if the user's device is equipped with low-quality speakers, the adjustment unit can provide settings to adjust the sound quality for playback. For example, the adjustment unit can provide settings to adjust the sound quality for playback. This allows the system to provide optimal settings based on the acoustic characteristics of the user's device.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] Electronic comic apps can analyze a user's reading speed and dynamically adjust the timing of scene transitions. For example, fast readers can experience quick scene transitions for a fast-paced reading experience, while slower readers can enjoy a more leisurely reading experience. Furthermore, the app can adjust the tempo of the background music (BGM) according to the user's reading speed. For instance, fast-paced BGM can be played for fast readers, and slow-paced BGM for slow readers. This allows for the provision of an optimal reading experience tailored to each user's reading speed.

[0051] Electronic comic apps can refer to a user's past reading history and customize background music (BGM) based on their favorite scenes and characters. For example, if a user likes scenes featuring a particular character, that specific BGM can be set for those scenes. Similarly, if a user prefers a specific scene type (e.g., battle scenes), BGM suited to that scene type will be prioritized. Furthermore, the app can analyze the user's past reading history, extract preferred BGM patterns, and apply them to future reading sessions. This allows for a personalized reading experience tailored to the user's preferences.

[0052] The digital comic app can monitor the user's device's battery level and adjust the background music (BGM) playback accordingly. For example, if the battery level is low, the BGM volume will be lowered. If the battery level is sufficient, the BGM will play at a normal volume. Furthermore, if the battery level is very low, the BGM playback can be paused to conserve battery power. This allows the app to provide the optimal BGM playback method based on the user's device's battery level.

[0053] Electronic comic apps can use the user's device location information to customize background music (BGM) based on that location. For example, if the user is at home, relaxing BGM can be played. If the user is out and about, upbeat BGM can be played. Furthermore, if the user is in a specific location (e.g., a cafe), BGM appropriate for that location can be played. This allows for the provision of the optimal BGM playback method based on the user's location.

[0054] The e-comic app can monitor the user's device usage and adjust the background music (BGM) playback accordingly. For example, if the user is using other apps simultaneously, the BGM volume will be lowered. Conversely, if the user is using only the e-comic app, the BGM will play at a normal volume. Furthermore, if the user is using the device for an extended period, the BGM playback can be paused to reduce the load on the device. This allows the app to provide the optimal BGM playback method based on the user's device usage.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The character recognition unit recognizes the text. For example, it can use OCR (optical character recognition) technology or deep learning-based recognition technology to analyze handwritten text, fonts, and styles. This allows it to identify bold and italicized characters and recognize emphasized text. Step 2: The judgment unit determines the scene based on the dialogue recognized by the character recognition unit. For example, it analyzes the content of the dialogue and classifies scenes into categories such as everyday conversation, combat, and despair. It can also estimate the time period and location from the manga's introduction and synopsis. Step 3: The generation unit generates background music (BGM) based on the scene information determined by the judgment unit. For example, it can generate BGM suitable for the scene using a music generation algorithm. It can also generate BGM from scene information or the setting information of the manga's time period and location. For example, it can generate Japanese-style BGM for a manga set in the Edo period, and sci-fi-style BGM for a manga set in the future. Step 4: The playback unit plays the background music (BGM) generated by the generation unit. For example, it plays the BGM while adjusting the sound quality according to the playback device. It may also include a customization unit for the user to customize the BGM, and an adjustment unit to adjust the volume and playback timing.

[0057] (Example of form 2) An electronic comic app according to an embodiment of the present invention is a system that improves the user experience by having an AI select and play background music (BGM) according to the scene. When a user starts reading an electronic comic, the AI ​​determines the scene. Next, based on the determined scene, the AI ​​selects and plays appropriate BGM. This mechanism allows the user to have a more immersive reading experience. Furthermore, by giving the app unique functionality, it secures a market advantage. First, when a user starts reading an electronic comic, the AI ​​determines the scene. At this time, the AI ​​recognizes the dialogue and classifies the scene into categories such as everyday conversation, battle, and despair. It also estimates the time period and location from the manga's introduction and synopsis. For example, it selects calm BGM for everyday conversation scenes and tense BGM for battle scenes. Next, based on the determined scene, the AI ​​selects and plays appropriate BGM. The AI ​​generates BGM that does not feel out of place from the scene information and the setting information of the manga's time period and location. For example, it generates Japanese-style BGM for a manga set in the Edo period and science fiction-style BGM for a manga set in the future. This mechanism allows the user to have a more immersive reading experience. For example, playing tense background music during a battle scene draws the user into that scene. Conversely, playing calming background music during everyday conversation scenes allows users to enjoy reading in a relaxed mood. Furthermore, adding unique features to the app can secure a market advantage. For instance, offering an automatic background music playback function, which other e-comic apps lack, can attract user interest and increase the number of users. Since background music can potentially increase the user base by 10%, increased sales can also be expected. In this way, by utilizing AI to automatically play background music appropriate to the scene, e-comic apps can improve the user experience and secure a market advantage. This allows e-comic apps to select background music according to the scene, thereby enhancing the user experience.

[0058] The electronic comic application according to this embodiment comprises a character recognition unit, a determination unit, a generation unit, and a playback unit. The character recognition unit recognizes the characters in the dialogue. The character recognition unit recognizes the dialogue using, for example, OCR (optical character recognition) technology. The character recognition unit can also use recognition technology using deep learning. For example, the character recognition unit can recognize handwritten dialogue with high accuracy using a deep learning model. Furthermore, the character recognition unit can also analyze the font and style of the dialogue. For example, the character recognition unit can analyze bold and italicized characters to identify emphasized dialogue. The determination unit determines the scene based on the dialogue recognized by the character recognition unit. For example, the determination unit analyzes the content of the dialogue and classifies scenes such as everyday conversation, battle, and despair. Furthermore, the determination unit can also estimate the time period and location from the manga's introduction and synopsis. For example, the determination unit uses text analysis technology to analyze the manga's introduction and synopsis and estimate the time period and location. The generation unit generates background music (BGM) based on scene information determined by the judgment unit. The generation unit generates BGM suitable for the scene using, for example, a music generation algorithm. The generation unit can also generate BGM from scene information and setting information for the time period and location of the manga. For example, the generation unit generates Japanese-style BGM for a manga set in the Edo period and science fiction-style BGM for a manga set in the future. The playback unit plays the BGM generated by the generation unit. The playback unit plays the BGM while adjusting the sound quality according to the playback device, for example. The playback unit may also include a customization unit that allows the user to customize the BGM. For example, the playback unit may include an adjustment unit that allows the user to adjust the volume and playback timing of the BGM. As a result, the electronic comic app according to the embodiment can select BGM according to the scene and improve the user experience.

[0059] The character recognition unit recognizes the characters in the dialogue. For example, it uses OCR (Optical Character Recognition) technology to recognize the dialogue. Specifically, OCR technology involves detecting characters within an image and converting them into digital text. This allows for high-precision recognition of both printed and handwritten characters. The character recognition unit can also utilize deep learning-based recognition technology. Deep learning models are trained using large amounts of data and can recognize handwritten dialogue and special fonts with high accuracy. For example, even when handwritten dialogue is included, the deep learning model learns its features and can accurately recognize the characters. Furthermore, the character recognition unit can analyze the font and style of the dialogue. For example, it can analyze bold and italicized characters to identify emphasized dialogue. This allows for accurate understanding of the emotion and emphasis of the dialogue. By combining these functions, the character recognition unit can recognize and analyze manga dialogue with high accuracy. This enables subsequent processing units to operate based on accurate information.

[0060] The judgment unit determines the scene based on the dialogue recognized by the character recognition unit. For example, the judgment unit analyzes the content of the dialogue and classifies scenes such as everyday conversation, battle, and despair. Specifically, it uses natural language processing technology to analyze the content of the dialogue and identify emotions and tone. For example, it can analyze keywords and phrases contained in the dialogue and classify scenes based on them. The judgment unit can also estimate the time period and location from the manga's introduction and synopsis. For example, it uses text analysis technology to analyze the manga's introduction and synopsis and estimate the time period and location. This allows it to accurately grasp the background information of the scene. Furthermore, the judgment unit can determine the scene by considering not only the emotions and tone of the dialogue, but also the relationships between characters and the progress of the story. For example, it analyzes the dialogue and actions between characters and identifies the type of scene based on that. This allows the judgment unit to perform more accurate scene determination and provide information for subsequent processing units to perform appropriate actions.

[0061] The generation unit generates background music (BGM) based on scene information determined by the judgment unit. For example, the generation unit generates BGM suitable for the scene using a music generation algorithm. Specifically, it adjusts the tempo, melody, and rhythm of the music based on the scene information to create BGM that is optimal for the scene. The generation unit can also generate BGM from scene information and the setting information of the manga, such as the time period and location. For example, the generation unit will generate Japanese-style BGM for a manga set in the Edo period, and sci-fi-style BGM for a manga set in the future. This can further enhance the atmosphere and emotions of the scene. Furthermore, the generation unit can use AI to learn the user's preferences and past viewing history, and provide BGM that is optimal for each individual user. For example, it can learn the patterns of BGM that the user has liked to listen to in the past and generate new BGM based on that. In this way, the generation unit can individually optimize the user experience and provide a more satisfying service.

[0062] The playback unit plays the background music (BGM) generated by the generation unit. The playback unit plays the BGM while adjusting the sound quality according to the playback device. Specifically, it adjusts the acoustic characteristics to provide optimal sound quality for different devices such as smartphones, tablets, and PCs. The playback unit can also include a customization unit that allows the user to customize the BGM. For example, the playback unit can include an adjustment unit that allows the user to adjust the volume and playback timing of the BGM. This allows the user to customize the BGM to their liking and enjoy a more personalized experience. Furthermore, the playback unit can also have a function to seamlessly integrate with other audio content while playing the BGM. For example, it can play dialogue audio and BGM simultaneously and automatically adjust the volume balance between the two to provide a user-friendly audio environment. In this way, the playback unit can provide the user with a high-quality, customizable audio experience, maximizing the appeal of digital comics.

[0063] The judgment unit can estimate the time period and location from the manga's introduction and synopsis. For example, the judgment unit uses text analysis technology to analyze the manga's introduction and synopsis and estimate the time period and location. For example, the judgment unit can use text analysis technology to estimate the setting of the manga, such as the Edo period or the future, from the manga's introduction and synopsis. The judgment unit can also estimate the time period and location from the manga's introduction and synopsis using a database referencing method. For example, the judgment unit refers to information registered in a database to estimate the time period and location from the manga's introduction and synopsis. This improves the accuracy of scene determination.

[0064] The generation unit can generate background music (BGM) from scene information and the setting information of the manga's time period and location. For example, the generation unit uses a music generation algorithm to generate BGM appropriate for the scene. For instance, based on the scene information, the generation unit generates tense BGM for tense battle scenes and calm BGM for calm everyday conversation scenes. Furthermore, based on the setting information of the manga's time period and location, the generation unit can generate Japanese-style BGM for manga set in the Edo period and sci-fi-style BGM for manga set in the future. For example, the generation unit can refer to a music database that matches the historical context to generate appropriate BGM. This allows for the generation of BGM that is optimal for each scene.

[0065] The playback unit may include a customization unit that allows the user to customize the background music (BGM). For example, the playback unit may allow the user to adjust the BGM volume and playback timing through a user interface. For instance, the playback unit could provide a slider to adjust the BGM volume, allowing the user to set their preferred volume. It could also provide an option to adjust the BGM playback timing, allowing the user to change the BGM playback timing to suit the scene. This enables the user to customize the BGM.

[0066] The playback unit may include an adjustment unit for adjusting the volume and playback timing of the background music (BGM). For example, the playback unit can automatically adjust the BGM volume using a volume adjustment algorithm. For instance, the playback unit can adjust the BGM volume according to the tension of the scene, increasing the volume in tense scenes and decreasing it in calm scenes. The playback unit can also adjust the playback timing of the BGM using a playback timing setting method. For example, the playback unit can start playing the BGM at the beginning of a scene and stop it at the end of the scene. This allows for adjustment of both the BGM volume and playback timing.

[0067] The character recognition unit can estimate the user's emotions and dynamically adjust the accuracy of character recognition based on the estimated emotions. For example, the character recognition unit can estimate the user's emotions using facial expression recognition technology. For instance, it can analyze the user's facial expressions captured by a camera to determine whether the user is tense or relaxed. The character recognition unit can also estimate the user's emotions using speech analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the character recognition unit dynamically adjusts the accuracy of character recognition based on the estimated emotions. For example, if the user is tense, the accuracy of character recognition is increased to reduce misrecognition. If the user is relaxed, the accuracy of character recognition is kept normal, prioritizing processing speed. If the user is excited, the accuracy of character recognition is increased to ensure that important lines are recognized. This allows the accuracy of character recognition to be adjusted according to the user's emotions.

[0068] The character recognition unit can analyze the font and style of recognized characters to determine the atmosphere of a scene in more detail. For example, the character recognition unit can analyze the font of recognized characters using font recognition technology. For example, the character recognition unit can analyze bold and italicized characters to identify emphasized dialogue. The character recognition unit can also analyze handwritten-style fonts to determine emotional scenes. For example, the character recognition unit can analyze handwritten-style fonts to identify emotional scenes. Furthermore, the character recognition unit can analyze specific styles of characters to determine the tension or relaxation of a scene. For example, the character recognition unit can analyze specific styles of characters to determine the atmosphere of a scene in detail. This allows for a detailed determination of the atmosphere of a scene.

[0069] The character recognition unit can analyze the frequency and patterns of recognized characters to extract features of specific scenes or characters. For example, the character recognition unit can analyze the frequency of recognized characters using a frequency analysis algorithm. For instance, it can analyze words frequently used by a specific character to extract the character's characteristics. The character recognition unit can also analyze the patterns of recognized characters using pattern recognition technology. For example, it can analyze phrases repeated in a specific scene to extract the scene's features. Furthermore, the character recognition unit can analyze the frequency of words expressing specific emotions to extract the emotional features of a scene. For example, it can analyze the frequency of words expressing emotions to identify the emotional features of a scene. This allows for the extraction of features of scenes and characters.

[0070] The judgment unit can estimate the user's emotions and dynamically adjust the scene judgment criteria based on the estimated user emotions. For example, the judgment unit can estimate the user's emotions using facial recognition technology. For instance, it can analyze the user's facial expressions captured by a camera to determine whether the user is tense or relaxed. The judgment unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the judgment unit dynamically adjusts the scene judgment criteria based on the estimated user emotions. For example, if the user is tense, the scene judgment criteria are made stricter to prioritize tense scenes. If the user is relaxed, the scene judgment criteria are made looser to prioritize calm scenes. If the user is excited, the scene judgment criteria are adjusted to prioritize emotional scenes. This allows the scene judgment criteria to be adjusted according to the user's emotions.

[0071] The judgment unit can analyze the characters' facial expressions and actions during scene determination to perform a detailed classification of the scene. For example, the judgment unit can analyze the characters' facial expressions using facial recognition technology. For instance, it can analyze a character's smile and classify it as a happy scene. The judgment unit can also analyze the characters' actions using motion analysis algorithms. For example, it can analyze a character's angry expression and classify it as a tense scene. Furthermore, the judgment unit can analyze a character's crying face and classify it as an emotional scene. For example, it can analyze a character's crying face and classify it as an emotional scene. This enables a detailed classification of scenes.

[0072] The judgment unit can analyze background sounds and sound effects during scene detection to determine the atmosphere of a scene in more detail. For example, the judgment unit can analyze background sounds using acoustic analysis technology. For instance, if the background sounds are quiet, the judgment unit will determine it to be a calm scene. The judgment unit can also analyze sound effects using a sound effect recognition algorithm. For example, if the sound effects are intense, the judgment unit will determine it to be a tense scene. Furthermore, if the background sounds are lively, the judgment unit can also determine it to be a fun scene. For example, if the background sounds are lively, the judgment unit will determine it to be a fun scene. This allows for a detailed determination of the atmosphere of a scene.

[0073] The generation unit can estimate the user's emotions and dynamically adjust the BGM generation method based on the estimated user emotions. For example, the generation unit can estimate the user's emotions using facial recognition technology. For example, the generation unit can analyze the user's facial expressions captured by a camera to determine whether the user is relaxed or tense. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the generation unit dynamically adjusts the BGM generation method based on the estimated user emotions. For example, if the user is relaxed, it generates relaxing BGM. If the user is tense, it generates BGM with a sense of urgency. If the user is excited, it generates stimulating BGM. This allows the BGM generation method to be adjusted according to the user's emotions.

[0074] The generation unit can analyze the tempo and rhythm of a scene when generating background music (BGM) and create the most suitable BGM for that scene. For example, the generation unit can analyze the tempo of a scene using a tempo analysis algorithm. For example, if the tempo of the scene is fast, the generation unit will generate fast-paced BGM. The generation unit can also analyze the rhythm of a scene using rhythm recognition technology. For example, if the rhythm of the scene is slow, the generation unit will generate slow-paced BGM. Furthermore, if the tempo of the scene changes, the generation unit can also generate BGM to match the tempo. For example, if the tempo of the scene changes, the generation unit will generate BGM to match the tempo. This allows for the generation of BGM that is optimal for each scene.

[0075] The generation unit can analyze the scene's colors and light intensity during BGM generation to create the most suitable BGM for that scene. For example, the generation unit can analyze the scene's colors using color analysis technology. For instance, if the scene's colors are bright, the generation unit will generate bright BGM. The generation unit can also analyze the scene's light intensity using a light intensity recognition algorithm. For example, if the scene's colors are dark, the generation unit will generate dark BGM. Furthermore, if the scene's light intensity changes, the generation unit can generate BGM to match the light intensity. For example, if the scene's light intensity changes, the generation unit will generate BGM to match the light intensity. This allows for the generation of BGM that is optimal for each scene.

[0076] The playback unit can estimate the user's emotions and dynamically adjust the BGM playback method based on the estimated emotions. For example, the playback unit can estimate the user's emotions using facial recognition technology. For instance, it can analyze the user's facial expressions captured by a camera to determine whether the user is tense or relaxed. The playback unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the playback unit dynamically adjusts the BGM playback method based on the estimated emotions. For example, if the user is tense, the BGM volume is lowered. If the user is relaxed, the BGM volume is set to normal. If the user is excited, the BGM volume is increased. This allows the BGM playback method to be adjusted according to the user's emotions.

[0077] The playback unit analyzes the acoustic characteristics of the user's device during background music (BGM) playback and can reproduce the music with optimal sound quality. For example, the playback unit analyzes the acoustic characteristics of the user's device using acoustic analysis technology. For instance, if the user's device has high-quality speakers, the playback unit will play the BGM with high-quality sound. The playback unit can also analyze the acoustic characteristics of the user's device using a device characteristic recognition algorithm. For example, if the user's device is using earphones, the playback unit will play the BGM with sound quality optimized for earphones. Furthermore, if the user's device has low-quality speakers, the playback unit can adjust the sound quality before playing the BGM. For example, the playback unit adjusts the sound quality before playing the BGM. This allows the BGM to be played with optimal sound quality for the user's device.

[0078] The playback unit can analyze the user's ambient noise during background music (BGM) playback and play it at the optimal volume for that environment. For example, the playback unit can analyze the user's ambient noise using ambient noise analysis technology. For instance, if the user is in a quiet environment, the playback unit will play the BGM at a low volume. The playback unit can also analyze the user's ambient noise using a noise recognition algorithm. For example, if the user is in a noisy environment, the playback unit will play the BGM at a high volume. Furthermore, if the user is in a moderately noisy environment, the playback unit can play the BGM at an appropriate volume. For example, the playback unit will play the BGM at an appropriate volume. This allows the BGM to be played at the optimal volume for the user's environment.

[0079] The customization unit can estimate the user's emotions and dynamically provide customization options based on those emotions. For example, the customization unit can estimate the user's emotions using facial recognition technology. For instance, it can analyze the user's facial expressions captured by a camera to determine whether the user is tense or relaxed. The customization unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the customization unit dynamically provides customization options based on the estimated user emotions. For example, if the user is tense, it provides simple customization options. If the user is relaxed, it provides detailed customization options. If the user is excited, it provides visually stimulating customization options. This allows the customization options to be provided according to the user's emotions.

[0080] The customization unit can suggest the optimal options by referring to the user's past customization history during the customization process. For example, the customization unit can refer to the user's past customization history using the structure of a history database. For instance, it can suggest the optimal options based on the customization options the user has previously selected. Furthermore, the customization unit can refer to the user's past customization history using a reference algorithm. For example, it can suggest options suitable for a specific scenario based on the user's past customization history. In addition, the customization unit can analyze the user's customization history and suggest the optimal options. This allows it to suggest the optimal options based on the user's past customization history.

[0081] The adjustment unit can suggest optimal settings by referring to the user's past adjustment history during adjustment. For example, the adjustment unit can refer to the user's past adjustment history using the structure of a history database. For instance, it can suggest the optimal volume based on the volume settings the user has previously selected. The adjustment unit can also refer to the user's past adjustment history using a reference algorithm. For example, it can suggest playback timing suitable for a specific scene based on the user's past adjustment history. Furthermore, the adjustment unit can analyze the user's adjustment history and suggest optimal settings. For example, it can analyze the user's adjustment history and suggest optimal settings. This allows the adjustment unit to suggest optimal settings based on the user's past adjustment history.

[0082] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, the adjustment unit can estimate the user's emotions using facial recognition technology. For instance, it can analyze the user's facial expressions captured by a camera to determine whether the user is tense or relaxed. The adjustment unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the adjustment unit determines the priority of adjustments based on the estimated emotions. For example, if the user is tense, volume adjustment is prioritized. If the user is relaxed, playback timing adjustment is prioritized. If the user is excited, both volume and playback timing are adjusted. This allows the adjustment priority to be determined according to the user's emotions.

[0083] The adjustment unit can provide optimal settings during adjustment, taking into account the acoustic characteristics of the user's device. For example, the adjustment unit can analyze the acoustic characteristics of the user's device using acoustic characteristic analysis technology. For instance, if the user's device is equipped with high-quality speakers, the adjustment unit can provide settings for high-quality playback. The adjustment unit can also analyze the acoustic characteristics of the user's device using a device characteristic recognition algorithm. For example, if the user's device uses earphones, the adjustment unit can provide settings optimized for earphones. Furthermore, if the user's device is equipped with low-quality speakers, the adjustment unit can provide settings to adjust the sound quality for playback. For example, the adjustment unit can provide settings to adjust the sound quality for playback. This allows the system to provide optimal settings based on the acoustic characteristics of the user's device.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] Electronic comic apps can analyze a user's reading speed and dynamically adjust the timing of scene transitions. For example, fast readers can experience quick scene transitions for a fast-paced reading experience, while slower readers can enjoy a more leisurely reading experience. Furthermore, the app can adjust the tempo of the background music (BGM) according to the user's reading speed. For instance, fast-paced BGM can be played for fast readers, and slow-paced BGM for slow readers. This allows for the provision of an optimal reading experience tailored to each user's reading speed.

[0086] Electronic comic apps can refer to a user's past reading history and customize background music (BGM) based on their favorite scenes and characters. For example, if a user likes scenes featuring a particular character, that specific BGM can be set for those scenes. Similarly, if a user prefers a specific scene type (e.g., battle scenes), BGM suited to that scene type will be prioritized. Furthermore, the app can analyze the user's past reading history, extract preferred BGM patterns, and apply them to future reading sessions. This allows for a personalized reading experience tailored to the user's preferences.

[0087] Electronic comic apps can estimate the user's emotions and dynamically adjust the timing of scene transitions based on those emotions. For example, if the user is tense, scene transitions can be rapid to provide a fast-paced reading experience. If the user is relaxed, scene transitions can be slower to allow for a more leisurely reading experience. Furthermore, the tempo of the background music can also be adjusted according to the user's emotions. For example, fast-paced background music can be played when the user is tense, and slow-paced background music when they are relaxed. This allows for the provision of an optimal reading experience tailored to the user's emotions.

[0088] The digital comic app can monitor the user's device's battery level and adjust the background music (BGM) playback accordingly. For example, if the battery level is low, the BGM volume will be lowered. If the battery level is sufficient, the BGM will play at a normal volume. Furthermore, if the battery level is very low, the BGM playback can be paused to conserve battery power. This allows the app to provide the optimal BGM playback method based on the user's device's battery level.

[0089] The digital comic app can estimate the user's emotions and dynamically adjust the background music (BGM) volume based on those emotions. For example, if the user is nervous, the BGM volume can be lowered. If the user is relaxed, the BGM volume can be played at a normal level. If the user is excited, the BGM volume can be increased. Furthermore, the timing of BGM playback can also be adjusted according to the user's emotions. For example, if the user is nervous, the BGM playback can be delayed, and if the user is relaxed, the BGM playback can be sped up. This allows the app to provide the optimal BGM playback method according to the user's emotions.

[0090] Electronic comic apps can use the user's device location information to customize background music (BGM) based on that location. For example, if the user is at home, relaxing BGM can be played. If the user is out and about, upbeat BGM can be played. Furthermore, if the user is in a specific location (e.g., a cafe), BGM appropriate for that location can be played. This allows for the provision of the optimal BGM playback method based on the user's location.

[0091] The digital comic app can estimate the user's emotions and dynamically change the background music (BGM) of a scene based on those emotions. For example, if the user is feeling sad, the BGM can be changed to something sentimental. If the user is feeling happy, the BGM can be changed to something upbeat. Furthermore, if the user is excited, the BGM can be changed to something stimulating. This allows the app to provide the optimal BGM according to the user's emotions.

[0092] Electronic comic apps can analyze a user's reading history and estimate their emotions towards specific scenes or characters. For example, if a user frequently expresses emotion during scenes featuring a particular character, the app can estimate their feelings towards that character. Similarly, if a user expresses emotion during specific scene types (e.g., battle scenes), the app can estimate their feelings towards those scene types. Furthermore, it can even customize background music based on the user's emotions. This allows for the provision of an optimal reading experience tailored to the user's feelings.

[0093] The e-comic app can monitor the user's device usage and adjust the background music (BGM) playback accordingly. For example, if the user is using other apps simultaneously, the BGM volume will be lowered. Conversely, if the user is using only the e-comic app, the BGM will play at a normal volume. Furthermore, if the user is using the device for an extended period, the BGM playback can be paused to reduce the load on the device. This allows the app to provide the optimal BGM playback method based on the user's device usage.

[0094] The digital comic app can estimate the user's emotions and dynamically change the background music genre based on those emotions. For example, if the user is relaxed, it can play relaxing genres such as classical music or jazz. If the user is excited, it can play stimulating genres such as rock or electronica. Furthermore, if the user is feeling sad, it can play sentimental genres such as ballads or ambient music. This allows the app to provide the optimal background music genre according to the user's emotions.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The character recognition unit recognizes the text. For example, it can use OCR (optical character recognition) technology or deep learning-based recognition technology to analyze handwritten text, fonts, and styles. This allows it to identify bold and italicized characters and recognize emphasized text. Step 2: The judgment unit determines the scene based on the dialogue recognized by the character recognition unit. For example, it analyzes the content of the dialogue and classifies scenes into categories such as everyday conversation, combat, and despair. It can also estimate the time period and location from the manga's introduction and synopsis. Step 3: The generation unit generates background music (BGM) based on the scene information determined by the judgment unit. For example, it can generate BGM suitable for the scene using a music generation algorithm. It can also generate BGM from scene information or the setting information of the manga's time period and location. For example, it can generate Japanese-style BGM for a manga set in the Edo period, and sci-fi-style BGM for a manga set in the future. Step 4: The playback unit plays the background music (BGM) generated by the generation unit. For example, it plays the BGM while adjusting the sound quality according to the playback device. It may also include a customization unit for the user to customize the BGM, and an adjustment unit to adjust the volume and playback timing.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the character recognition unit, determination unit, generation unit, and playback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the character recognition unit is implemented by the processor 46 of the smart device 14 and recognizes dialogue using OCR technology or a deep learning model. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the scene based on the recognized dialogue. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates background music based on the determined scene information. The playback unit is implemented by the control unit 46A of the smart device 14 and plays the generated background music. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the character recognition unit, determination unit, generation unit, and playback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the character recognition unit is implemented by the processor 46 of the smart glasses 214 and recognizes dialogue using OCR technology or a deep learning model. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the scene based on the recognized dialogue. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates background music based on the determined scene information. The playback unit is implemented by the control unit 46A of the smart glasses 214 and plays the generated background music. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the character recognition unit, determination unit, generation unit, and playback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the character recognition unit is implemented by the processor 46 of the headset terminal 314 and recognizes dialogue using OCR technology or a deep learning model. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the scene based on the recognized dialogue. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates background music based on the determined scene information. The playback unit is implemented by the control unit 46A of the headset terminal 314 and plays the generated background music. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0134] As shown in Figure 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the character recognition unit, determination unit, generation unit, and playback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the character recognition unit is implemented by the processor 46 of the robot 414 and recognizes dialogue using OCR technology or a deep learning model. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the scene based on the recognized dialogue. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates background music (BGM) based on the determined scene information. The playback unit is implemented by the control unit 46A of the robot 414 and plays the generated BGM. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A character recognition unit that recognizes the dialogue as text, A determination unit that determines the scene based on the dialogue recognized by the character recognition unit, A generation unit that generates background music based on scene information determined by the determination unit, The system includes a playback unit that plays the background music generated by the generation unit. A system characterized by the following features. (Note 2) The determination unit, Estimate the time period and location from the manga's introduction and synopsis. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate background music from scene information and the setting information of the manga's time period and location. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned regeneration unit is It features a customization section that allows users to customize the background music. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned regeneration unit is It features an adjustment unit for adjusting the volume and playback timing of background music. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned character recognition unit, It estimates the user's emotions and dynamically adjusts the accuracy of character recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned character recognition unit, The font and style of the recognized characters are analyzed to determine the atmosphere of the scene in more detail. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned character recognition unit, The frequency and patterns of recognized characters are analyzed to extract features of specific scenes or characters. The system described in Appendix 1, characterized by the features described herein. (Note 9) The determination unit, It estimates the user's emotions and dynamically adjusts the scene judgment criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The determination unit, During scene analysis, the character's facial expressions and movements are analyzed to perform a detailed classification of the scene. The system described in Appendix 1, characterized by the features described herein. (Note 11) The determination unit, During scene detection, background sounds and sound effects are analyzed to determine the atmosphere of the scene in more detail. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and dynamically adjusts the BGM generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating background music (BGM), the tempo and rhythm of the scene are analyzed to create the most suitable BGM for that scene. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating background music (BGM), the system analyzes the scene's colors and light intensity to create the most suitable BGM for that scene. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned regeneration unit is It estimates the user's emotions and dynamically adjusts the BGM playback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned regeneration unit is When playing background music, the system analyzes the acoustic characteristics of the user's device and plays it back at the optimal sound quality. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned regeneration unit is When playing background music, the system analyzes the user's ambient sounds and plays the music at the optimal volume for that environment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned customization unit is It estimates the user's emotions and dynamically provides customized options based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 19) The aforementioned customization unit is During customization, the system refers to the user's past customization history to suggest the most suitable options. The system described in Appendix 4, characterized by the features described herein. (Note 20) The adjustment unit is, During adjustments, the system will refer to the user's past adjustment history to suggest the optimal settings. The system described in Appendix 5, characterized by the features described herein. (Note 21) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 22) The adjustment unit is, During adjustment, the system takes into account the acoustic characteristics of the user's device to provide the optimal settings. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A character recognition unit that recognizes the dialogue as text, A determination unit that determines the scene based on the dialogue recognized by the character recognition unit, A generation unit that generates background music based on scene information determined by the determination unit, The system includes a playback unit that plays the background music generated by the generation unit. A system characterized by the following features.

2. The determination unit, Estimate the time period and location from the manga's introduction and synopsis. The system according to feature 1.

3. The generating unit is Generate background music from scene information and the setting information of the manga's time period and location. The system according to feature 1.

4. The aforementioned regeneration unit is It features a customization section that allows users to customize the background music. The system according to feature 1.

5. The aforementioned regeneration unit is It features an adjustment unit for adjusting the volume and playback timing of background music. The system according to feature 1.

6. The aforementioned character recognition unit, It estimates the user's emotions and dynamically adjusts the accuracy of character recognition based on the estimated emotions. The system according to feature 1.

7. The aforementioned character recognition unit, The font and style of the recognized characters are analyzed to determine the atmosphere of the scene in more detail. The system according to feature 1.

8. The aforementioned character recognition unit, The frequency and patterns of recognized characters are analyzed to extract features of specific scenes or characters. The system according to feature 1.

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