system

Abstract

The system according to the embodiment aims to efficiently adapt literary works into films. [Solution] A system according to an embodiment includes an analysis unit, an extraction unit, and a generation unit. The analysis unit analyzes literary text. The extraction unit extracts character, setting, and story elements based on the data analyzed by the analysis unit. The generation unit automatically generates a scenario and setting for a film adaptation based on the elements extracted by the extraction unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, adapting a literary work into a film required a great deal of time and effort.

[0005] The system according to the embodiment aims to efficiently adapt literary works into films. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an extraction unit, and a generation unit. The analysis unit analyzes a literary text. The extraction unit extracts character, setting, and story elements based on the data analyzed by the analysis unit. The generation unit automatically generates a scenario and setting for a film adaptation based on the elements extracted by the extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently adapt literary works into films. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to streamline the process of adapting literature (such as classical Japanese literature or novels) for film, thereby producing high-quality films. This system analyzes literary texts, extracts character, setting, and story elements, and automatically generates a screenplay and setting for the film. For example, the system analyzes literary texts and extracts elements such as the names and personalities of characters, the location of the story, and the plot development. Next, based on the extracted elements, the system automatically generates character appearances and personalities, setting descriptions, and plot development, and compiles them into a film script. Furthermore, the system suggests direction, cinematography, and music selection for the film adaptation, thereby improving the quality of the visualization. This streamlines the filmmaking process and enables the production of high-quality films. For example, the system analyzes literary texts, extracts character, setting, and story elements, and automatically generates a screenplay and setting for the film, thereby significantly reducing the effort required for script creation. The system also suggests direction, filming techniques, and music selection to improve the quality of the film, making the filmmaking process more efficient and enabling the creation of high-quality films.

[0029] The film adaptation support system according to the embodiment includes an analysis unit, an extraction unit, and a generation unit. The analysis unit analyzes a literary text. For example, the analysis unit uses text mining technology to analyze the literary text. The analysis unit can also analyze the content of the text using natural language processing technology. The analysis unit can also analyze the grammar and vocabulary of the text using AI. For example, the analysis unit uses text mining technology to extract important keywords from the text. The analysis unit can also analyze the grammatical structure of the text using natural language processing technology. The analysis unit can also understand the content of the text and extract important information using AI. The extraction unit extracts elements of characters, settings, and story based on the data analyzed by the analysis unit. For example, the extraction unit uses AI to extract elements such as character names and personalities, the setting of the story, and story development from the text. The extraction unit can also extract important elements from the text using natural language processing technology. The extraction unit can also extract important information from the text using text mining technology. For example, the extraction unit uses AI to extract character appearances and personalities from the text. The extraction unit can also use natural language processing technology to extract locations where the story is set from the text. The extraction unit can also use text mining technology to extract story developments from the text. The generation unit automatically generates a scenario and setting for a film based on the elements extracted by the extraction unit. The generation unit can automatically generate character appearances and personalities, descriptions of the setting, and story developments, for example, using AI. The generation unit can also automatically generate a scenario using natural language processing technology. The generation unit can also automatically generate a scenario using text mining technology. For example, the generation unit can automatically generate character appearances and personalities using AI. The generation unit can also automatically generate descriptions of the setting using natural language processing technology. The generation unit can also automatically generate a story development using text mining technology.As a result, the film adaptation support system of the embodiment analyzes literary texts, extracts elements of characters, settings, and stories, and automatically generates scenarios and settings for film adaptation, thereby streamlining the film production process and producing high-quality films.

[0030] The generation unit can automatically generate a character's appearance, personality, setting description, and story development. The generation unit can automatically generate a character's appearance using, for example, AI. For example, the generation unit can automatically generate a character's hairstyle, clothing, body shape, etc. The generation unit can also automatically generate a character's personality using AI. For example, the generation unit can automatically generate a character's personality traits and behavior patterns. The generation unit can also automatically generate a setting description using AI. For example, the generation unit can automatically generate a setting description of the setting and details of the buildings. The generation unit can also automatically generate a story development using AI. For example, the generation unit can automatically generate plot progression and climax settings. This allows the generation unit to automatically generate a character's appearance, personality, setting description, story development, etc., thereby reducing the effort required to create a scenario. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can have a generation AI generate a character's appearance and personality.

[0031] The film adaptation support system includes a suggestion unit that suggests direction, filming techniques, and music selection. The suggestion unit suggests direction, filming techniques, and music selection. The suggestion unit, for example, uses AI to suggest optimal camera angles and lighting settings for each scene. The suggestion unit can also use AI to suggest optimal background music selection for each scene. For example, the suggestion unit suggests optimal camera angles and lighting settings depending on the content of the scene. The suggestion unit can also suggest optimal background music selection depending on the atmosphere of the scene. In this way, the suggestion unit can improve the quality of the visualization by suggesting direction, filming techniques, and music selection. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can cause AI to suggest camera angles and lighting settings for each scene.

[0032] The suggestion unit can suggest optimal camera angles and lighting settings for each scene. The suggestion unit, for example, suggests optimal camera angles for each scene. For example, the suggestion unit suggests camera angles such as bird's-eye views and close-ups depending on the content of the scene. The suggestion unit can also suggest optimal lighting settings for each scene. For example, the suggestion unit suggests lighting settings such as light intensity and shadow creation depending on the atmosphere of the scene. In this way, the suggestion unit can improve the quality of the video by suggesting optimal camera angles and lighting settings for each scene. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can cause AI to suggest camera angles and lighting settings for each scene.

[0033] The suggestion unit can suggest music selections. For example, the suggestion unit suggests optimal music selections for each scene. For example, the suggestion unit suggests a method for selecting music that suits a scene depending on the content of the scene. The suggestion unit can also suggest optimal music selections depending on the atmosphere of the scene. For example, the suggestion unit suggests fast-paced music or emotional ballads depending on the flow of the scene. In this way, the suggestion unit can improve the atmosphere of the video by suggesting music selections. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can cause AI to suggest music selections for each scene.

[0034] The analysis unit can change the analysis method depending on the genre of the literary text. The analysis unit, for example, switches the analysis algorithm depending on the genre of the literary text. For example, in the case of classical Japanese, an analysis algorithm corresponding to classical grammar and vocabulary is used. In addition, in the case of modern novels, an analysis algorithm corresponding to modern grammar and vocabulary can be used. In addition, in the case of poetry, an analysis algorithm that takes rhythm and rhyme into consideration can be used. In this way, the analysis unit can improve the analysis accuracy by switching the analysis algorithm depending on the genre of the literary text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to switch the analysis algorithm depending on the genre.

[0035] The analysis unit can improve analysis accuracy by taking into account the text's writing style and historical background. For example, if the text's writing style is classical, the analysis unit performs analysis by taking into account the grammar and vocabulary of that era. For example, the analysis unit uses an analysis algorithm that corresponds to the classical writing style. Furthermore, if the historical background is the Warring States period, the analysis unit can also perform analysis by taking into account the historical background of that period. For example, the analysis unit uses an analysis algorithm that corresponds to the historical background of the Warring States period. Furthermore, if the writing style is modern, the analysis unit can also perform analysis by taking into account modern culture and social background. For example, the analysis unit uses an analysis algorithm that corresponds to a modern writing style. In this way, the analysis unit can improve analysis accuracy by taking into account the text's writing style and historical background. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to switch analysis algorithms depending on the writing style and historical background.

[0036] The analysis unit can divide the analysis steps according to the length of the text. For example, the analysis unit divides the analysis stages according to the length of the text. For example, in the case of a short story, the entire work is analyzed at once. In addition, in the case of a full-length novel, analysis can be performed chapter by chapter to extract elements step by step. In addition, in the case of a poem, analysis can be performed line by line to grasp the overall theme. In this way, the analysis unit can perform efficient analysis by dividing the analysis stages according to the length of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI perform the division of the analysis stages according to the length of the text.

[0037] The analysis unit can change the analysis method depending on the language of the text. The analysis unit, for example, switches the analysis algorithm depending on the language of the text. For example, in the case of English text, an analysis algorithm corresponding to English grammar and vocabulary is used. In addition, in the case of Japanese text, an analysis algorithm corresponding to Japanese grammar and vocabulary can also be used. In addition, in the case of multilingual text, a combination of analysis algorithms corresponding to each language can be used. In this way, the analysis unit can improve the analysis accuracy by switching the analysis algorithm depending on the language of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to switch the analysis algorithm depending on the language.

[0038] The analysis unit can estimate the intention of the author of the text and perform analysis based on that. The analysis unit, for example, estimates the intention of the author of the text and performs analysis based on that. For example, the analysis unit estimates the message the author wants to convey and performs analysis based on that message. The analysis unit can also estimate the author's writing style and perform analysis based on that style. The analysis unit can also take into account background information about the author and perform analysis based on that information. In this way, the analysis unit can improve the accuracy of analysis by taking into account the intention of the author of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI perform the estimation of the author's intention.

[0039] The analysis unit can adjust the analysis results taking into account the readership of the text. The analysis unit, for example, customizes the analysis results taking into account the readership of the text. For example, for text aimed at young people, the analysis unit can provide concise and easy-to-understand analysis results. For text aimed at experts, the analysis unit can provide detailed and specialized analysis results. For text aimed at general readers, the analysis unit can provide balanced analysis results. In this way, the analysis unit can provide more appropriate analysis results by taking into account the readership of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI customize the analysis results according to the readership.

[0040] The extraction unit can prioritize extracting important scenes and lines from the text. For example, the extraction unit prioritizes extracting important scenes and lines from the text. For example, the extraction unit prioritizes extracting climax scenes and important turning points. The extraction unit can also prioritize extracting lines that symbolize the theme of the story. The extraction unit can also prioritize extracting scenes that show character growth and change. In this way, the extraction unit can prioritize extracting important scenes and lines from the text, thereby ensuring that important elements are incorporated into the film adaptation. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can have AI extract important scenes and lines.

[0041] The extraction unit can improve the accuracy of extraction by taking into account the relationships between characters. The extraction unit improves the accuracy of extraction by taking into account, for example, the relationships between characters. For example, the extraction unit may focus on the relationship between the main character and a major supporting character during extraction. The extraction unit may also prioritize extracting scenes of characters in an antagonistic relationship. The extraction unit may also prioritize extracting scenes of characters in a romantic relationship. In this way, the extraction unit can extract elements with higher accuracy by taking into account the relationships between characters. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit may cause AI to perform element extraction taking into account the relationships between characters.

[0042] The extraction unit can perform extraction based on geographical information of the setting. The extraction unit performs extraction, for example, taking into consideration the geographical information of the setting. For example, the extraction unit prioritizes extraction of locations that are the main setting of the story. The extraction unit can also prioritize extraction of geographically important locations (e.g., battlefields, cities). The extraction unit can also extract settings that change as the story progresses. In this way, the extraction unit can accurately reflect changes in the setting as the story progresses by taking into consideration the geographical information of the setting. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can cause AI to perform element extraction based on geographical information.

[0043] The extraction unit can perform extraction based on the cultural background of the text. For example, the extraction unit performs extraction while taking into account the cultural background of the text. For example, the extraction unit preferentially extracts scenes related to the cultural background of the story. The extraction unit can also extract culturally significant events and rituals. The extraction unit can also extract cultural elements that influence the progression of the story. In this way, the extraction unit can accurately reflect the cultural elements that influence the progression of the story by taking into account the cultural background of the text. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can cause AI to extract elements based on the cultural background.

[0044] The extraction unit can determine elements to extract based on the theme of the text. The extraction unit, for example, selects elements to extract based on the theme of the text. For example, the extraction unit prioritizes extracting scenes related to the main theme of the story. The extraction unit can also extract characters and settings that symbolize the theme. The extraction unit can also extract lines and events related to the theme. In this way, the extraction unit can accurately reflect the main theme of the story by selecting elements to extract based on the theme of the text. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can cause AI to perform element extraction based on the theme.

[0045] The extraction unit can analyze the structure of the text to improve the accuracy of extraction. For example, the extraction unit analyzes the structure of the text to improve the accuracy of extraction. For example, the extraction unit analyzes the structure of a story to extract important scenes. The extraction unit can also extract elements that change as the story progresses. The extraction unit can also extract the climax or turning point of the story. In this way, the extraction unit can accurately extract important scenes and elements of the story by analyzing the structure of the text. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can cause AI to extract elements by analyzing the structure of the text.

[0046] The generation unit can generate a scenario taking into consideration the growth and changes of a character. The generation unit generates a scenario taking into consideration, for example, the growth and changes of a character. For example, the generation unit generates a scenario that emphasizes the growth process of the protagonist. The generation unit can also generate a scenario that reflects changes in the relationships between characters. The generation unit can also generate a scenario that depicts internal changes in a character. In this way, the generation unit can generate a scenario with more depth by taking into consideration the growth and changes of a character. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to generate a scenario that takes into consideration the growth and changes of a character.

[0047] The generation unit can generate a setting taking into account the season and time of day of the setting. The generation unit generates a setting taking into account, for example, the season and time of day of the setting. For example, the generation unit generates a setting that reflects the scenery and climate of each season. The generation unit can also generate a setting that reflects changes in light and depictions of shadows for each time of day. The generation unit can also generate a setting that reflects the clothing and behavior of characters according to the season and time of day. In this way, the generation unit can generate a more realistic setting by taking into account the season and time of day of the setting. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to generate a setting that takes into account the season and time of day.

[0048] The generation unit can generate a scenario taking into account the foreshadowing and climax of the story. The generation unit generates a scenario taking into account, for example, the foreshadowing and climax of the story. For example, the generation unit generates a scenario in which the foreshadowing of the story is effectively arranged. The generation unit can also generate a scenario that increases tension towards the climax. The generation unit can also generate a scenario in which the foreshadowing is resolved and the story is resolved at the climax. In this way, the generation unit can generate a scenario with more tension by taking into account the foreshadowing and climax of the story. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to generate a scenario that takes into account the foreshadowing and climax.

[0049] The generation unit can create a scenario by combining elements of different genres. The generation unit, for example, generates a scenario by combining elements of different genres. For example, the generation unit generates a scenario that combines action and romance. The generation unit can also generate a scenario that combines comedy and horror. The generation unit can also generate a scenario that combines fantasy and suspense. In this way, the generation unit can generate a wider variety of scenarios by combining elements of different genres. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may cause AI to generate a scenario that combines elements of different genres.

[0050] The generation unit can generate settings incorporating different cultures and historical backgrounds. The generation unit can generate settings incorporating different cultures and historical backgrounds, for example. For example, the generation unit can generate a setting incorporating ancient Egyptian culture. The generation unit can also generate a setting set in a futuristic cyberpunk world. The generation unit can also generate a setting that reflects medieval European culture. In this way, the generation unit can generate a wider variety of settings by incorporating different cultures and historical backgrounds. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or can be performed without using AI. For example, the generation unit can cause AI to generate settings incorporating different cultures and historical backgrounds.

[0051] The generation unit can adjust the scenario by reflecting the user's past feedback. The generation unit, for example, customizes the scenario by reflecting the user's past feedback. For example, the generation unit generates a new scenario by incorporating elements of scenarios that the user liked in the past. The generation unit can also generate a scenario that emphasizes specific characters or settings based on the user's past feedback. The generation unit can also adjust the development of the story by reflecting the user's past feedback. In this way, the generation unit can generate a scenario that is more suitable for the user by reflecting the user's past feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to generate a scenario that reflects the user's past feedback.

[0052] The suggestion unit can suggest camera angles taking into account the tension and emotion of a scene. The suggestion unit, for example, suggests camera angles taking into account the tension and emotion of a scene. For example, the suggestion unit suggests close-ups or low angles for tense scenes. The suggestion unit can also suggest wide-angle lenses or high angles for emotional scenes. The suggestion unit can also suggest dynamic camera work for action scenes. In this way, the suggestion unit can suggest more effective camera angles by taking into account the tension and emotion of a scene. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can cause AI to suggest camera angles taking into account the tension and emotion of a scene.

[0053] The suggestion unit can suggest lighting arrangements according to the atmosphere of the scene. The suggestion unit, for example, suggests lighting settings according to the atmosphere of the scene. For example, the suggestion unit suggests low-illumination lighting for a scene with a dark atmosphere. The suggestion unit can also suggest lighting that makes use of natural light for a scene with a bright atmosphere. The suggestion unit can also suggest lighting that uses soft light for a romantic scene. In this way, the suggestion unit can suggest more effective lighting by suggesting lighting settings according to the atmosphere of the scene. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause AI to suggest lighting settings according to the atmosphere of the scene.

[0054] The suggestion unit can suggest music selections according to the flow of scenes. The suggestion unit, for example, suggests music selections according to the flow of scenes. For example, the suggestion unit can suggest fast-paced music for action scenes. The suggestion unit can also suggest emotionally enhancing ballads for moving scenes. The suggestion unit can also suggest light and fun music for comedy scenes. In this way, the suggestion unit can suggest more effective music by suggesting music selections according to the flow of scenes. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can cause AI to suggest music selections according to the flow of scenes.

[0055] The suggestion unit can propose a combination of different shooting methods. For example, the suggestion unit proposes a combination of different shooting techniques. For example, the suggestion unit proposes a combination of drone shooting and ground shooting. The suggestion unit can also propose a combination of slow motion and time lapse. The suggestion unit can also propose a combination of a handheld camera and a fixed camera. This allows the suggestion unit to propose a wider variety of performances by combining different shooting techniques. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause AI to execute a proposal that combines different shooting techniques.

[0056] The suggestion unit can suggest selections by combining different music genres. The suggestion unit, for example, suggests song selections by combining different music genres. For example, the suggestion unit suggests a song selection that combines classical music and rock music. The suggestion unit can also suggest a song selection that combines jazz and electronica. The suggestion unit can also suggest a song selection that combines pop and folk music. In this way, the suggestion unit can suggest a wider variety of music by combining different music genres. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause AI to suggest song selections that combine different music genres.

[0057] The suggestion unit can adjust the suggestion content by reflecting the user's past feedback. The suggestion unit, for example, customizes the suggestion content by reflecting the user's past feedback. For example, the suggestion unit makes a suggestion by incorporating a production style that the user has previously preferred. The suggestion unit can also suggest specific camera angles and lighting settings based on the user's past feedback. The suggestion unit can also suggest music selections by reflecting the user's past feedback. In this way, the suggestion unit can make suggestions that are more suitable for the user by reflecting the user's past feedback. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can cause AI to execute suggestions that reflect the user's past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The analysis unit can change the analysis method depending on the genre of the literary text. For example, in the case of classical Japanese, an analysis algorithm corresponding to classical grammar and vocabulary is used. In addition, in the case of modern novels, an analysis algorithm corresponding to modern grammar and vocabulary can be used. In addition, in the case of poetry, an analysis algorithm that takes rhythm and rhyme into consideration can be used. In this way, the analysis unit can improve the analysis accuracy by switching the analysis algorithm depending on the genre of the literary text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to switch the analysis algorithm depending on the genre.

[0060] The analysis unit can improve analysis accuracy by taking into account the text's writing style and historical background. For example, if the text's writing style is classical, the analysis is performed by taking into account the grammar and vocabulary of that era. For example, the analysis unit uses an analysis algorithm that corresponds to the classical writing style. Furthermore, if the historical background is the Warring States period, the analysis unit can also perform analysis by taking into account the historical background of that period. For example, the analysis unit uses an analysis algorithm that corresponds to the historical background of the Warring States period. Furthermore, if the writing style is modern, the analysis unit can also perform analysis by taking into account modern culture and social background. For example, the analysis unit uses an analysis algorithm that corresponds to a modern writing style. In this way, the analysis unit can improve analysis accuracy by taking into account the text's writing style and historical background. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to switch analysis algorithms depending on the writing style and historical background.

[0061] The analysis unit can divide the analysis steps according to the length of the text. For example, in the case of a short story, the entire work is analyzed at once. In the case of a full-length novel, analysis can be performed chapter by chapter to extract elements step by step. In the case of a poem, analysis can be performed line by line to grasp the overall theme. This allows the analysis unit to perform efficient analysis by dividing the analysis steps according to the length of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI perform the division of the analysis steps according to the length of the text.

[0062] The extraction unit can prioritize extracting important scenes and lines from the text. For example, the extraction unit can prioritize extracting climax scenes and important turning points. The extraction unit can also prioritize extracting lines that symbolize the theme of the story. The extraction unit can also prioritize extracting scenes that show character growth and change. In this way, the extraction unit can prioritize extracting important scenes and lines from the text, ensuring that important elements are incorporated into the film adaptation. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can have AI extract important scenes and lines.

[0063] The extraction unit can improve the accuracy of extraction by taking into account the relationships between characters. For example, the extraction unit may focus on the relationship between the main character and a major supporting character during extraction. The extraction unit may also prioritize extracting scenes of characters in an antagonistic relationship. The extraction unit may also prioritize extracting scenes of characters in a romantic relationship. In this way, the extraction unit can extract elements with higher accuracy by taking into account the relationships between characters. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit may cause AI to perform element extraction taking into account the relationships between characters.

[0064] The extraction unit can perform extraction based on geographical information of the setting. For example, the extraction unit prioritizes extraction of locations that are the main setting of the story. The extraction unit can also prioritize extraction of geographically important locations (e.g., battlefields, cities). The extraction unit can also extract settings that change as the story progresses. In this way, the extraction unit can accurately reflect changes in the setting as the story progresses by taking into account the geographical information of the setting. Some or all of the above-mentioned processing in the extraction unit may be performed using, or without, AI, for example. For example, the extraction unit can cause AI to perform element extraction based on geographical information.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The analysis unit analyzes the literary text. The analysis unit uses text mining technology, natural language processing technology, and AI to analyze the grammar and vocabulary of the text and extract important keywords and information. Step 2: The extraction unit extracts elements of characters, settings, and stories based on the data analyzed by the analysis unit. The extraction unit uses AI, natural language processing technology, and text mining technology to extract elements such as character names and personalities, the setting of the story, and story development. Step 3: The generation unit automatically generates a screenplay and setting for the film based on the elements extracted by the extraction unit. The generation unit uses AI, natural language processing, and text mining technologies to automatically generate the character appearances and personalities, setting descriptions, story development, and more.

[0067] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to streamline the process of adapting literature (such as classical Japanese literature or novels) for film, thereby producing high-quality films. This system analyzes literary texts, extracts character, setting, and story elements, and automatically generates a screenplay and setting for the film. For example, the system analyzes literary texts and extracts elements such as the names and personalities of characters, the location of the story, and the plot development. Next, based on the extracted elements, the system automatically generates character appearances and personalities, setting descriptions, and plot development, and compiles them into a film script. Furthermore, the system suggests direction, cinematography, and music selection for the film adaptation, thereby improving the quality of the visualization. This streamlines the filmmaking process and enables the production of high-quality films. For example, the system analyzes literary texts, extracts character, setting, and story elements, and automatically generates a screenplay and setting for the film, thereby significantly reducing the effort required for script creation. The system also suggests direction, filming techniques, and music selection to improve the quality of the film, making the filmmaking process more efficient and enabling the creation of high-quality films.

[0068] The film adaptation support system according to the embodiment includes an analysis unit, an extraction unit, and a generation unit. The analysis unit analyzes a literary text. For example, the analysis unit uses text mining technology to analyze the literary text. The analysis unit can also analyze the content of the text using natural language processing technology. The analysis unit can also analyze the grammar and vocabulary of the text using AI. For example, the analysis unit uses text mining technology to extract important keywords from the text. The analysis unit can also analyze the grammatical structure of the text using natural language processing technology. The analysis unit can also understand the content of the text and extract important information using AI. The extraction unit extracts elements of characters, settings, and story based on the data analyzed by the analysis unit. For example, the extraction unit uses AI to extract elements such as character names and personalities, the setting of the story, and story development from the text. The extraction unit can also extract important elements from the text using natural language processing technology. The extraction unit can also extract important information from the text using text mining technology. For example, the extraction unit uses AI to extract character appearances and personalities from the text. The extraction unit can also use natural language processing technology to extract locations where the story is set from the text. The extraction unit can also use text mining technology to extract story developments from the text. The generation unit automatically generates a scenario and setting for a film based on the elements extracted by the extraction unit. The generation unit can automatically generate character appearances and personalities, descriptions of the setting, and story developments, for example, using AI. The generation unit can also automatically generate a scenario using natural language processing technology. The generation unit can also automatically generate a scenario using text mining technology. For example, the generation unit can automatically generate character appearances and personalities using AI. The generation unit can also automatically generate descriptions of the setting using natural language processing technology. The generation unit can also automatically generate a story development using text mining technology.As a result, the film adaptation support system of the embodiment analyzes literary texts, extracts elements of characters, settings, and stories, and automatically generates scenarios and settings for film adaptation, thereby streamlining the film production process and producing high-quality films.

[0069] The generation unit can automatically generate a character's appearance, personality, setting description, and story development. The generation unit can automatically generate a character's appearance using, for example, AI. For example, the generation unit can automatically generate a character's hairstyle, clothing, body shape, etc. The generation unit can also automatically generate a character's personality using AI. For example, the generation unit can automatically generate a character's personality traits and behavior patterns. The generation unit can also automatically generate a setting description using AI. For example, the generation unit can automatically generate a setting description of the setting and details of the buildings. The generation unit can also automatically generate a story development using AI. For example, the generation unit can automatically generate plot progression and climax settings. This allows the generation unit to automatically generate a character's appearance, personality, setting description, story development, etc., thereby reducing the effort required to create a scenario. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can have a generation AI generate a character's appearance and personality.

[0070] The film adaptation support system includes a suggestion unit that suggests direction, filming techniques, and music selection. The suggestion unit suggests direction, filming techniques, and music selection. The suggestion unit, for example, uses AI to suggest optimal camera angles and lighting settings for each scene. The suggestion unit can also use AI to suggest optimal background music selection for each scene. For example, the suggestion unit suggests optimal camera angles and lighting settings depending on the content of the scene. The suggestion unit can also suggest optimal background music selection depending on the atmosphere of the scene. In this way, the suggestion unit can improve the quality of the visualization by suggesting direction, filming techniques, and music selection. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can cause AI to suggest camera angles and lighting settings for each scene.

[0071] The suggestion unit can suggest optimal camera angles and lighting settings for each scene. The suggestion unit, for example, suggests optimal camera angles for each scene. For example, the suggestion unit suggests camera angles such as bird's-eye views and close-ups depending on the content of the scene. The suggestion unit can also suggest optimal lighting settings for each scene. For example, the suggestion unit suggests lighting settings such as light intensity and shadow creation depending on the atmosphere of the scene. In this way, the suggestion unit can improve the quality of the video by suggesting optimal camera angles and lighting settings for each scene. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can cause AI to suggest camera angles and lighting settings for each scene.

[0072] The suggestion unit can suggest music selections. For example, the suggestion unit suggests optimal music selections for each scene. For example, the suggestion unit suggests a method for selecting music that suits a scene depending on the content of the scene. The suggestion unit can also suggest optimal music selections depending on the atmosphere of the scene. For example, the suggestion unit suggests fast-paced music or emotional ballads depending on the flow of the scene. In this way, the suggestion unit can improve the atmosphere of the video by suggesting music selections. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can cause AI to suggest music selections for each scene.

[0073] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user's emotions. For example, if the user is excited, the analysis can be detailed, analyzing the character's detailed characterization and the details of the setting. Alternatively, if the user is relaxed, the analysis can focus on the overall story flow and the main characters. Alternatively, if the user is tired, the analysis can be brief, extracting only the main elements. This allows the analysis unit to provide more appropriate analysis results by adjusting the depth of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the user's emotion estimation.

[0074] The analysis unit can change the analysis method depending on the genre of the literary text. The analysis unit, for example, switches the analysis algorithm depending on the genre of the literary text. For example, in the case of classical Japanese, an analysis algorithm corresponding to classical grammar and vocabulary is used. In addition, in the case of modern novels, an analysis algorithm corresponding to modern grammar and vocabulary can be used. In addition, in the case of poetry, an analysis algorithm that takes rhythm and rhyme into consideration can be used. In this way, the analysis unit can improve the analysis accuracy by switching the analysis algorithm depending on the genre of the literary text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to switch the analysis algorithm depending on the genre.

[0075] The analysis unit can improve analysis accuracy by taking into account the text's writing style and historical background. For example, if the text's writing style is classical, the analysis unit performs analysis by taking into account the grammar and vocabulary of that era. For example, the analysis unit uses an analysis algorithm that corresponds to the classical writing style. Furthermore, if the historical background is the Warring States period, the analysis unit can also perform analysis by taking into account the historical background of that period. For example, the analysis unit uses an analysis algorithm that corresponds to the historical background of the Warring States period. Furthermore, if the writing style is modern, the analysis unit can also perform analysis by taking into account modern culture and social background. For example, the analysis unit uses an analysis algorithm that corresponds to a modern writing style. In this way, the analysis unit can improve analysis accuracy by taking into account the text's writing style and historical background. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to switch analysis algorithms depending on the writing style and historical background.

[0076] The analysis unit can divide the analysis steps according to the length of the text. For example, the analysis unit divides the analysis stages according to the length of the text. For example, in the case of a short story, the entire work is analyzed at once. In addition, in the case of a full-length novel, analysis can be performed chapter by chapter to extract elements step by step. In addition, in the case of a poem, analysis can be performed line by line to grasp the overall theme. In this way, the analysis unit can perform efficient analysis by dividing the analysis stages according to the length of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI perform the division of the analysis stages according to the length of the text.

[0077] The analysis unit can estimate the user's emotions and change the display format of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can display detailed analysis results in a visually rich manner. If the user is relaxed, the analysis unit can display concise and easy-to-read analysis results. If the user is tired, the analysis unit can display analysis results that emphasize only the main points. This allows the analysis unit to provide more appropriate analysis results by adjusting the display method of the analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can cause an AI to estimate the user's emotions.

[0078] The analysis unit can change the analysis method depending on the language of the text. The analysis unit, for example, switches the analysis algorithm depending on the language of the text. For example, in the case of English text, an analysis algorithm corresponding to English grammar and vocabulary is used. In addition, in the case of Japanese text, an analysis algorithm corresponding to Japanese grammar and vocabulary can also be used. In addition, in the case of multilingual text, a combination of analysis algorithms corresponding to each language can be used. In this way, the analysis unit can improve the analysis accuracy by switching the analysis algorithm depending on the language of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to switch the analysis algorithm depending on the language.

[0079] The analysis unit can estimate the intention of the author of the text and perform analysis based on that. The analysis unit, for example, estimates the intention of the author of the text and performs analysis based on that. For example, the analysis unit estimates the message the author wants to convey and performs analysis based on that message. The analysis unit can also estimate the author's writing style and perform analysis based on that style. The analysis unit can also take into account background information about the author and perform analysis based on that information. In this way, the analysis unit can improve the accuracy of analysis by taking into account the intention of the author of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI perform the estimation of the author's intention.

[0080] The analysis unit can adjust the analysis results taking into account the readership of the text. The analysis unit, for example, customizes the analysis results taking into account the readership of the text. For example, for text aimed at young people, the analysis unit can provide concise and easy-to-understand analysis results. For text aimed at experts, the analysis unit can provide detailed and specialized analysis results. For text aimed at general readers, the analysis unit can provide balanced analysis results. In this way, the analysis unit can provide more appropriate analysis results by taking into account the readership of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI customize the analysis results according to the readership.

[0081] The extraction unit can estimate the user's emotions and determine the order of elements to be extracted based on the estimated user emotions. For example, the extraction unit can estimate the user's emotions and determine the priority of elements to be extracted based on the estimated user emotions. For example, if the user is excited, action scenes and moving scenes can be preferentially extracted. Also, if the user is relaxed, the extraction unit can prioritize the overall story flow and extract elements. Also, if the user is tired, the extraction unit can prioritize the extraction of main characters and setting elements. This allows the extraction unit to extract more appropriate elements by determining the priority of elements to be extracted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can cause an AI to estimate the user's emotions.

[0082] The extraction unit can prioritize extracting important scenes and lines from the text. For example, the extraction unit prioritizes extracting important scenes and lines from the text. For example, the extraction unit prioritizes extracting climax scenes and important turning points. The extraction unit can also prioritize extracting lines that symbolize the theme of the story. The extraction unit can also prioritize extracting scenes that show character growth and change. In this way, the extraction unit can prioritize extracting important scenes and lines from the text, thereby ensuring that important elements are incorporated into the film adaptation. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can have AI extract important scenes and lines.

[0083] The extraction unit can improve the accuracy of extraction by taking into account the relationships between characters. The extraction unit improves the accuracy of extraction by taking into account, for example, the relationships between characters. For example, the extraction unit may focus on the relationship between the main character and a major supporting character during extraction. The extraction unit may also prioritize extracting scenes of characters in an antagonistic relationship. The extraction unit may also prioritize extracting scenes of characters in a romantic relationship. In this way, the extraction unit can extract elements with higher accuracy by taking into account the relationships between characters. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit may cause AI to perform element extraction taking into account the relationships between characters.

[0084] The extraction unit can perform extraction based on geographical information of the setting. The extraction unit performs extraction, for example, taking into consideration the geographical information of the setting. For example, the extraction unit prioritizes extraction of locations that are the main setting of the story. The extraction unit can also prioritize extraction of geographically important locations (e.g., battlefields, cities). The extraction unit can also extract settings that change as the story progresses. In this way, the extraction unit can accurately reflect changes in the setting as the story progresses by taking into consideration the geographical information of the setting. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can cause AI to perform element extraction based on geographical information.

[0085] The extraction unit can estimate the user's emotion and change the display format of the extraction results based on the estimated user emotion. For example, the extraction unit can estimate the user's emotion and adjust the display method of the extraction results based on the estimated user emotion. For example, if the user is excited, a visually rich display method can be provided. Also, if the user is relaxed, a concise and easy-to-read display method can be provided. Also, if the user is tired, a display method that emphasizes only the main points can be provided. This allows the extraction unit to adjust the display method of the extraction results according to the user's emotion, enabling more appropriate display. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can cause an AI to estimate the user's emotion.

[0086] The extraction unit can perform extraction based on the cultural background of the text. For example, the extraction unit performs extraction while taking into account the cultural background of the text. For example, the extraction unit preferentially extracts scenes related to the cultural background of the story. The extraction unit can also extract culturally significant events and rituals. The extraction unit can also extract cultural elements that influence the progression of the story. In this way, the extraction unit can accurately reflect the cultural elements that influence the progression of the story by taking into account the cultural background of the text. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can cause AI to extract elements based on the cultural background.

[0087] The extraction unit can determine elements to extract based on the theme of the text. The extraction unit, for example, selects elements to extract based on the theme of the text. For example, the extraction unit prioritizes extracting scenes related to the main theme of the story. The extraction unit can also extract characters and settings that symbolize the theme. The extraction unit can also extract lines and events related to the theme. In this way, the extraction unit can accurately reflect the main theme of the story by selecting elements to extract based on the theme of the text. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can cause AI to perform element extraction based on the theme.

[0088] The extraction unit can analyze the structure of the text to improve the accuracy of extraction. For example, the extraction unit analyzes the structure of the text to improve the accuracy of extraction. For example, the extraction unit analyzes the structure of a story to extract important scenes. The extraction unit can also extract elements that change as the story progresses. The extraction unit can also extract the climax or turning point of the story. In this way, the extraction unit can accurately extract important scenes and elements of the story by analyzing the structure of the text. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can cause AI to extract elements by analyzing the structure of the text.

[0089] The generation unit can estimate the user's emotions and adjust the atmosphere of the scenario to be generated based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the tone of the scenario to be generated based on the estimated user's emotions. For example, if the user is excited, the generation unit can generate a scenario that emphasizes action scenes and moving scenes. Also, if the user is relaxed, the generation unit can generate a scenario with a calm tone. Also, if the user is tired, the generation unit can generate a concise and easy-to-understand scenario. This allows the generation unit to generate a more appropriate scenario by adjusting the tone of the scenario according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can cause an AI to estimate the user's emotions.

[0090] The generation unit can generate a scenario taking into consideration the growth and changes of a character. The generation unit generates a scenario taking into consideration, for example, the growth and changes of a character. For example, the generation unit generates a scenario that emphasizes the growth process of the protagonist. The generation unit can also generate a scenario that reflects changes in the relationships between characters. The generation unit can also generate a scenario that depicts internal changes in a character. In this way, the generation unit can generate a scenario with more depth by taking into consideration the growth and changes of a character. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to generate a scenario that takes into consideration the growth and changes of a character.

[0091] The generation unit can generate a setting taking into account the season and time of day of the setting. The generation unit generates a setting taking into account, for example, the season and time of day of the setting. For example, the generation unit generates a setting that reflects the scenery and climate of each season. The generation unit can also generate a setting that reflects changes in light and depictions of shadows for each time of day. The generation unit can also generate a setting that reflects the clothing and behavior of characters according to the season and time of day. In this way, the generation unit can generate a more realistic setting by taking into account the season and time of day of the setting. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to generate a setting that takes into account the season and time of day.

[0092] The generation unit can generate a scenario taking into account the foreshadowing and climax of the story. The generation unit generates a scenario taking into account, for example, the foreshadowing and climax of the story. For example, the generation unit generates a scenario in which the foreshadowing of the story is effectively arranged. The generation unit can also generate a scenario that increases tension towards the climax. The generation unit can also generate a scenario in which the foreshadowing is resolved and the story is resolved at the climax. In this way, the generation unit can generate a scenario with more tension by taking into account the foreshadowing and climax of the story. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to generate a scenario that takes into account the foreshadowing and climax.

[0093] The generation unit can estimate the user's emotion and change the length of the scenario to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the scenario to be generated based on the estimated user's emotion. For example, if the user is excited, the generation unit generates a longer scenario. Also, if the user is relaxed, the generation unit can generate a scenario of appropriate length. Also, if the user is tired, the generation unit can generate a shorter scenario. In this way, the generation unit can generate a more appropriate scenario by adjusting the length of the scenario according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can cause an AI to estimate the user's emotion.

[0094] The generation unit can create a scenario by combining elements of different genres. The generation unit, for example, generates a scenario by combining elements of different genres. For example, the generation unit generates a scenario that combines action and romance. The generation unit can also generate a scenario that combines comedy and horror. The generation unit can also generate a scenario that combines fantasy and suspense. In this way, the generation unit can generate a wider variety of scenarios by combining elements of different genres. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may cause AI to generate a scenario that combines elements of different genres.

[0095] The generation unit can generate settings incorporating different cultures and historical backgrounds. The generation unit can generate settings incorporating different cultures and historical backgrounds, for example. For example, the generation unit can generate a setting incorporating ancient Egyptian culture. The generation unit can also generate a setting set in a futuristic cyberpunk world. The generation unit can also generate a setting that reflects medieval European culture. In this way, the generation unit can generate a wider variety of settings by incorporating different cultures and historical backgrounds. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or can be performed without using AI. For example, the generation unit can cause AI to generate settings incorporating different cultures and historical backgrounds.

[0096] The generation unit can adjust the scenario by reflecting the user's past feedback. The generation unit, for example, customizes the scenario by reflecting the user's past feedback. For example, the generation unit generates a new scenario by incorporating elements of scenarios that the user liked in the past. The generation unit can also generate a scenario that emphasizes specific characters or settings based on the user's past feedback. The generation unit can also adjust the development of the story by reflecting the user's past feedback. In this way, the generation unit can generate a scenario that is more suitable for the user by reflecting the user's past feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to generate a scenario that reflects the user's past feedback.

[0097] The suggestion unit can estimate the user's emotion and adjust the style of the proposed presentation based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the style of the proposed presentation based on the estimated user's emotion. For example, if the user is excited, the suggestion unit can suggest dynamic camerawork and lighting. Also, if the user is relaxed, the suggestion unit can suggest a gentle presentation style. Also, if the user is tired, the suggestion unit can suggest a simple, highly visible presentation style. This allows the suggestion unit to suggest a more appropriate presentation by adjusting the presentation style according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can cause an AI to estimate the user's emotion.

[0098] The suggestion unit can suggest camera angles taking into account the tension and emotion of a scene. The suggestion unit, for example, suggests camera angles taking into account the tension and emotion of a scene. For example, the suggestion unit suggests close-ups or low angles for tense scenes. The suggestion unit can also suggest wide-angle lenses or high angles for emotional scenes. The suggestion unit can also suggest dynamic camera work for action scenes. In this way, the suggestion unit can suggest more effective camera angles by taking into account the tension and emotion of a scene. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can cause AI to suggest camera angles taking into account the tension and emotion of a scene.

[0099] The suggestion unit can suggest lighting arrangements according to the atmosphere of the scene. The suggestion unit, for example, suggests lighting settings according to the atmosphere of the scene. For example, the suggestion unit suggests low-illumination lighting for a scene with a dark atmosphere. The suggestion unit can also suggest lighting that makes use of natural light for a scene with a bright atmosphere. The suggestion unit can also suggest lighting that uses soft light for a romantic scene. In this way, the suggestion unit can suggest more effective lighting by suggesting lighting settings according to the atmosphere of the scene. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause AI to suggest lighting settings according to the atmosphere of the scene.

[0100] The suggestion unit can suggest music selections according to the flow of scenes. The suggestion unit, for example, suggests music selections according to the flow of scenes. For example, the suggestion unit can suggest fast-paced music for action scenes. The suggestion unit can also suggest emotionally enhancing ballads for moving scenes. The suggestion unit can also suggest light and fun music for comedy scenes. In this way, the suggestion unit can suggest more effective music by suggesting music selections according to the flow of scenes. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can cause AI to suggest music selections according to the flow of scenes.

[0101] The suggestion unit can estimate the user's emotion and adjust the order of the proposed effects based on the estimated user emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the order of the proposed effects based on the estimated user emotion. For example, if the user is excited, an action scene can be suggested first. Also, if the user is relaxed, a calm scene can be suggested first. Also, if the user is tired, an important scene can be suggested first. In this way, the suggestion unit can suggest more appropriate effects by adjusting the order of effects according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, for example, or without an AI. For example, the suggestion unit can cause an AI to estimate the user's emotion.

[0102] The suggestion unit can propose a combination of different shooting methods. For example, the suggestion unit proposes a combination of different shooting techniques. For example, the suggestion unit proposes a combination of drone shooting and ground shooting. The suggestion unit can also propose a combination of slow motion and time lapse. The suggestion unit can also propose a combination of a handheld camera and a fixed camera. This allows the suggestion unit to propose a wider variety of performances by combining different shooting techniques. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause AI to execute a proposal that combines different shooting techniques.

[0103] The suggestion unit can suggest selections by combining different music genres. The suggestion unit, for example, suggests song selections by combining different music genres. For example, the suggestion unit suggests a song selection that combines classical music and rock music. The suggestion unit can also suggest a song selection that combines jazz and electronica. The suggestion unit can also suggest a song selection that combines pop and folk music. In this way, the suggestion unit can suggest a wider variety of music by combining different music genres. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause AI to suggest song selections that combine different music genres.

[0104] The suggestion unit can adjust the suggestion content by reflecting the user's past feedback. The suggestion unit, for example, customizes the suggestion content by reflecting the user's past feedback. For example, the suggestion unit makes a suggestion by incorporating a production style that the user has previously preferred. The suggestion unit can also suggest specific camera angles and lighting settings based on the user's past feedback. The suggestion unit can also suggest music selections by reflecting the user's past feedback. In this way, the suggestion unit can make suggestions that are more suitable for the user by reflecting the user's past feedback. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can cause AI to execute suggestions that reflect the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, extraction unit, generation unit, and suggestion unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes literary text. The extraction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and extracts character, setting, and story elements based on the analyzed data. The generation unit is implemented, for example, by the control unit 46A of the smart device 14 and automatically generates a scenario and setting for film adaptation based on the extracted elements. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests direction, filming techniques, and music selection. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, extraction unit, generation unit, and suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes literary text. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts character, setting, and story elements based on the analyzed data. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214 and automatically generates a scenario and setting for film adaptation based on the extracted elements. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests direction, filming techniques, and music selection. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, generation unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset-type terminal 314 and analyzes literary text. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts character, setting, and story elements based on the analyzed data. The generation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and automatically generates a scenario and setting for film adaptation based on the extracted elements. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests direction, filming techniques, and music selection. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, generation unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes literary text. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts character, setting, and story elements based on the analyzed data. The generation unit is realized, for example, by the control unit 46A of the robot 414 and automatically generates a scenario and setting for film adaptation based on the extracted elements. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests direction, filming techniques, and music selection.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user's emotions. For example, if the user is excited, a detailed analysis can be performed, focusing on the character's detailed characterization and the details of the setting. Alternatively, if the user is relaxed, the analysis can focus on the overall story flow and the main characters. Alternatively, if the user is tired, the analysis can be simplified and extract only the main elements. This allows the analysis unit to provide more appropriate analysis results by adjusting the depth of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can have an AI perform the user's emotion estimation.

[0107] The analysis unit can change the analysis method depending on the genre of the literary text. For example, in the case of classical Japanese, an analysis algorithm corresponding to classical grammar and vocabulary is used. In addition, in the case of modern novels, an analysis algorithm corresponding to modern grammar and vocabulary can be used. In addition, in the case of poetry, an analysis algorithm that takes rhythm and rhyme into consideration can be used. In this way, the analysis unit can improve the analysis accuracy by switching the analysis algorithm depending on the genre of the literary text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to switch the analysis algorithm depending on the genre.

[0108] The analysis unit can improve analysis accuracy by taking into account the text's writing style and historical background. For example, if the text's writing style is classical, the analysis is performed by taking into account the grammar and vocabulary of that era. For example, the analysis unit uses an analysis algorithm that corresponds to the classical writing style. Furthermore, if the historical background is the Warring States period, the analysis unit can also perform analysis by taking into account the historical background of that period. For example, the analysis unit uses an analysis algorithm that corresponds to the historical background of the Warring States period. Furthermore, if the writing style is modern, the analysis unit can also perform analysis by taking into account modern culture and social background. For example, the analysis unit uses an analysis algorithm that corresponds to a modern writing style. In this way, the analysis unit can improve analysis accuracy by taking into account the text's writing style and historical background. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to switch analysis algorithms depending on the writing style and historical background.

[0109] The analysis unit can divide the analysis steps according to the length of the text. For example, in the case of a short story, the entire work is analyzed at once. In the case of a full-length novel, analysis can be performed chapter by chapter to extract elements step by step. In the case of a poem, analysis can be performed line by line to grasp the overall theme. This allows the analysis unit to perform efficient analysis by dividing the analysis steps according to the length of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI perform the division of the analysis steps according to the length of the text.

[0110] The analysis unit can estimate the user's emotions and change the display format of the analysis results based on the estimated user emotions. For example, if the user is excited, detailed analysis results can be displayed in a visually rich manner. If the user is relaxed, concise and easy-to-read analysis results can be displayed. If the user is tired, analysis results can be displayed with only the main points emphasized. This allows the analysis unit to provide more appropriate analysis results by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can cause an AI to estimate the user's emotions.

[0111] The extraction unit can estimate the user's emotions and determine the order of elements to be extracted based on the estimated user emotions. For example, if the user is excited, action scenes and moving scenes can be extracted with priority. Also, if the user is relaxed, the extraction unit can prioritize the overall story flow and extract elements related to the main characters and setting. Thus, the extraction unit can extract more appropriate elements by determining the priority of elements to be extracted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the extraction unit can be performed using, for example, an AI, or without an AI. For example, the extraction unit can cause an AI to estimate the user's emotions.

[0112] The extraction unit can prioritize extracting important scenes and lines from the text. For example, the extraction unit can prioritize extracting climax scenes and important turning points. The extraction unit can also prioritize extracting lines that symbolize the theme of the story. The extraction unit can also prioritize extracting scenes that show character growth and change. In this way, the extraction unit can prioritize extracting important scenes and lines from the text, ensuring that important elements are incorporated into the film adaptation. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can have AI extract important scenes and lines.

[0113] The extraction unit can improve the accuracy of extraction by taking into account the relationships between characters. For example, the extraction unit may focus on the relationship between the main character and a major supporting character during extraction. The extraction unit may also prioritize extracting scenes of characters in an antagonistic relationship. The extraction unit may also prioritize extracting scenes of characters in a romantic relationship. In this way, the extraction unit can extract elements with higher accuracy by taking into account the relationships between characters. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit may cause AI to perform element extraction taking into account the relationships between characters.

[0114] The extraction unit can perform extraction based on geographical information of the setting. For example, the extraction unit prioritizes extraction of locations that are the main setting of the story. The extraction unit can also prioritize extraction of geographically important locations (e.g., battlefields, cities). The extraction unit can also extract settings that change as the story progresses. In this way, the extraction unit can accurately reflect changes in the setting as the story progresses by taking into account the geographical information of the setting. Some or all of the above-mentioned processing in the extraction unit may be performed using, or without, AI, for example. For example, the extraction unit can cause AI to perform element extraction based on geographical information.

[0115] The extraction unit can estimate the user's emotion and change the display format of the extraction results based on the estimated user emotion. For example, if the user is excited, a visually rich display method can be provided. Also, if the user is relaxed, a concise and easy-to-read display method can be provided. Also, if the user is tired, a display method that emphasizes only the main points can be provided. This allows the extraction unit to adjust the display method of the extraction results according to the user's emotion, enabling more appropriate display. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the extraction unit can be performed using AI, for example, or without AI. For example, the extraction unit can cause AI to estimate the user's emotion.

[0116] The generation unit can estimate the user's emotions and adjust the atmosphere of the scenario to be generated based on the estimated user's emotions. For example, if the user is excited, the generation unit can generate a scenario that emphasizes action scenes and moving scenes. If the user is relaxed, the generation unit can generate a scenario with a calm tone. If the user is tired, the generation unit can generate a concise and easy-to-understand scenario. This allows the generation unit to generate a more appropriate scenario by adjusting the tone of the scenario according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can cause an AI to estimate the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The analysis unit analyzes the literary text. The analysis unit uses text mining technology, natural language processing technology, and AI to analyze the grammar and vocabulary of the text and extract important keywords and information. Step 2: The extraction unit extracts elements of characters, settings, and stories based on the data analyzed by the analysis unit. The extraction unit uses AI, natural language processing technology, and text mining technology to extract elements such as character names and personalities, the setting of the story, and story development. Step 3: The generation unit automatically generates a screenplay and setting for the film based on the elements extracted by the extraction unit. The generation unit uses AI, natural language processing, and text mining technologies to automatically generate the character appearances and personalities, setting descriptions, story development, and more.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis department that analyzes literary texts; an extraction unit that extracts elements of characters, settings, and stories based on the data analyzed by the analysis unit; a generation unit that automatically generates a scenario and a setting for film adaptation based on the elements extracted by the extraction unit; Equipped with A system characterized by:

2. The generation unit Automatically generate character appearances, personalities, setting descriptions, and story developments 2. The system of claim 1.

3. We have a proposal department that offers suggestions on direction, filming techniques, and music selection.

2. The system of claim 1.

4. The proposal unit Suggesting optimal camera angles and lighting settings for each scene 4. The system of claim 3.

5. The proposal unit Suggest a music selection 4. The system of claim 3.

6. The analysis unit Inferring user emotions and adjusting the level of analysis detail based on the estimated user emotions 2. The system of claim 1.

7. The analysis unit Vary your analysis method depending on the genre of the literary text 2. The system of claim 1.

8. The analysis unit Improve analysis accuracy by taking into account the text's style and historical context 2. The system of claim 1.

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