System

Abstract

An object of the system according to the embodiment is to generate an original novel based on a genre and a style desired by a user.SOLUTION: A system includes a genre selection unit, a name input unit, and a generation unit. The genre selection unit selects a genre and style of a novel desired by the user. The name input unit inputs the name of the user or a person close to the user. The generation unit generates an original novel based on the information input by the genre selection unit and the name input unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to generate original novels based on the genre and style desired by the user.

[0005] The system according to the embodiment aims to generate an original novel based on the genre and style desired by the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a genre selection unit, a name input unit, and a generation unit. The genre selection unit selects the genre and style of the novel desired by the user. The name input unit inputs the names of the user and people close to the user. The generation unit generates an original novel based on the information input by the genre selection unit and the name input unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an original novel based on the genre and style desired by the user. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The original novel creation system according to an embodiment of the present invention is a system that creates an original novel by allowing a user to select a desired genre and style of novel and to use the user's own name or the names of people close to them as characters. This allows the user to enjoy an original novel with the user or people close to them as characters.

[0029] An original novel creation system according to an embodiment includes a genre selection unit, a name input unit, and a creation unit. The genre selection unit selects a genre and style of novel desired by the user. For example, the genre selection unit can select genres such as mystery, fantasy, and romance. The genre selection unit can also select styles such as serious, comedy, and dramatic. The name input unit inputs the names of the user and close friends. For example, the name input unit can input the user's own name, or the names of friends and family. The name input unit can also input a full name or a nickname. The creation unit creates an original novel based on the information input by the genre selection unit and the name input unit. For example, the creation unit uses a creation AI to automatically create a plot, character settings, and story development for the novel based on the selected genre and style. The creation unit also sets the input names as characters and uses those names in the novel. This allows the original novel creation system according to an embodiment to create an original novel in a genre and style desired by the user. For example, the user can create an adventure novel with themselves as the main character or a moving story based on memories with friends.

[0030] The genre selection unit can suggest the most suitable genre and style based on the user's past reading history and ratings. For example, the generation AI analyzes the user's past reading history to identify the most frequently read genre and the style that has received high ratings. For example, if the user has read many mystery novels in the past, the generation AI will suggest the mystery genre. The genre selection unit can also suggest the most suitable genre and style based on the user's ratings. For example, the generation AI will suggest a style based on a style that the user has given a high rating. This makes it possible to suggest the most suitable genre and style based on the user's past reading history and ratings.

[0031] The genre selection unit can generate a hybrid novel that combines multiple genres and styles according to the user's selection. For example, the generation AI of the genre selection unit combines multiple genres and styles selected by the user to generate a hybrid novel. For example, it generates a novel that combines mystery and fantasy. The genre selection unit can also combine multiple genres and styles according to the user's selection. For example, it generates a novel that combines romance and science fiction. This makes it possible to generate a hybrid novel that combines multiple genres and styles according to the user's selection.

[0032] The name input unit can explain the origin and meaning of a name entered by the user within the novel. For example, the generation AI can automatically search for the origin and meaning of the name entered by the user and explain that information within the novel. For example, the origin and meaning of the name "Taro" can be introduced within the novel. The name input unit can also research the origin and meaning of a name and explain it within the novel. For example, it can explain the historical background and cultural meaning of the name. This allows the origin and meaning of the name entered by the user to be explained within the novel.

[0033] The name input unit can add episodes and background related to the name entered by the user based on the name. For example, the name input unit uses a generation AI to automatically generate episodes and background related to the name entered by the user and incorporate them into the novel. For example, an adventure episode related to the name "Taro" can be added. The name input unit can also research episodes and background related to the name and add them to the novel. For example, an episode based on family history or personal experience can be added. This allows episodes and background related to the name entered by the user to be added to the novel.

[0034] The generation unit can suggest optimal plots and story developments based on the user's past reading history and ratings. For example, the generation AI analyzes the user's past reading history to identify the most frequently read plots and highly rated story developments. For example, if the user has read many mystery novels in the past, the generation AI will suggest mystery plots. The generation unit can also suggest optimal plots and story developments based on the user's ratings. For example, the generation AI will suggest story developments based on story developments that the user has given high ratings to. This makes it possible to suggest optimal plots and story developments based on the user's past reading history and ratings.

[0035] The generation unit can generate multiple endings according to the user's selection, allowing the user to choose from them. For example, the generation unit generates multiple endings using a generation AI based on the genre or style selected by the user, allowing the user to choose from them. For example, in the case of a mystery novel, the generation unit generates multiple endings with different culprits. The generation unit can also generate multiple endings according to the user's selection. For example, different endings such as a happy ending and a bitter ending are generated. In this way, multiple endings are generated according to the user's selection, allowing the user to choose from them.

[0036] The generation unit can display the novel generation process to the user in real time, allowing the user to make corrections or additions along the way. For example, the generation unit provides an interface in which the generation AI displays the novel generation process in real time and allows the user to make corrections or additions along the way. For example, changes to the plot or additions to characters are made in real time. The generation unit can also allow the user to make corrections or additions while checking the novel generation process. For example, changing the development of the story or adding a new character. This allows the user to check the novel generation process in real time and make corrections or additions.

[0037] The generation unit can convert the generated novel into different media based on the genre and style selected by the user. For example, the generation unit builds a system that converts a novel generated by the generation AI into a manga or anime based on the genre and style selected by the user. For example, a fantasy novel can be turned into an anime. The generation unit can also convert the generated novel into different media. For example, a mystery novel can be turned into a manga. This allows the generated novel to be converted into different media.

[0038] The generation unit can analyze the works of the author being studied and automatically filter them to prevent copyright issues from arising. For example, the generation unit constructs a system in which the generation AI analyzes the works of the author being studied and automatically filters them to prevent copyright issues from arising. For example, it automatically detects parts that pose a risk of copyright infringement. The generation unit can also filter works to prevent copyright issues from arising. For example, it uses a text comparison algorithm to exclude parts that pose a risk of copyright infringement. This makes it possible to analyze the works of the author being studied and automatically filter them to prevent copyright issues from arising.

[0039] The generation unit can propose a marketing plan for promoting the author's work when signing a contract with the author being trained. For example, the generation unit builds a system in which the generative AI automatically generates and proposes a marketing plan for promoting the author's work when signing a contract with the author being trained. For example, it proposes plans for social media and advertising campaigns. The generation unit can also propose marketing plans. For example, it proposes an analysis of the target market and a plan for promotional activities. This makes it possible to propose a marketing plan for promoting the author's work.

[0040] The generation unit can propose a plan to expand an author's work into other media when signing a contract with the author being trained. For example, the generation unit builds a system in which, when the generation AI enters into a contract with the author being trained, it automatically generates and proposes a plan to expand an author's work into other media. For example, it proposes a plan to adapt the work into a movie or a TV drama. The generation unit can also propose plans to expand the work into other media. For example, it proposes a plan to adapt the work into a game or an anime. This makes it possible to propose plans to expand an author's work into other media.

[0041] The generation unit can also refer to past interviews and essays of a famous novelist when learning the writing style of that author. For example, when the generation AI learns the writing style of a famous novelist, the generation unit automatically collects the author's past interviews and essays and uses them as learning data. For example, it learns the author's creative process and thoughts. The generation unit can also refer to interviews and essays. For example, it refers to the author's past publications and online articles. This makes it possible to refer to past interviews and essays when learning the writing style of a famous novelist.

[0042] When learning the writing style of a famous novelist, the generation unit can focus on learning the parts of that author's works that are particularly highly rated. For example, when the generation AI learns the writing style of a famous novelist, it automatically identifies and focuses on learning the parts of that author's works that are particularly highly rated. For example, it learns scenes and chapters that are highly rated by readers. The generation unit can also focus on learning the parts that are highly rated. For example, it identifies the parts that are highly rated based on critics' reviews and award history. This makes it possible to focus on learning the parts that are particularly rated when learning the writing style of a famous novelist.

[0043] When the generation unit learns the writing style of a famous novelist, it can generate spin-off works based on the works of that author. For example, the generation unit constructs a system in which the generation AI learns the writing style of a famous novelist and automatically generates spin-off works based on the works of that author. For example, it generates a story from a different perspective of a popular character. The generation unit can also generate spin-off works. For example, it generates a new story based on the characters and settings of the original work. This makes it possible to generate spin-off works based on the works of a famous novelist.

[0044] When learning the writing style of a famous novelist, the generation unit can suggest character goods and related products based on the works of that author. For example, the generation unit constructs a system in which the generation AI learns the writing style of a famous novelist and automatically generates and suggests character goods and related products based on the works of that author. For example, it suggests figurines and posters. The generation unit can also suggest character goods and related products. For example, it suggests apparel and accessories. This makes it possible to suggest character goods and related products based on the works of a famous novelist.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The original novel generation system can further include a voice input unit. The voice input unit allows the user to input the genre and name by voice. For example, if the user vocally instructs, "It's a mystery novel, and the main character's name is Taro," the system will recognize that information and generate the novel. The voice input unit can also allow the user to give additional instructions as the story progresses. For example, the user can vocally input instructions such as, "In the next scene, Taro will corner the culprit." This allows the user to operate the novel generation without using their hands.

[0047] The original novel creation system can further include an illustration creation unit. The illustration creation unit automatically creates illustrations that correspond to scenes in the created novel. For example, an illustration depicting a brave protagonist is created for an adventure scene, and an illustration depicting a moving moment is created for a romance scene. The illustration creation unit can also allow users to request illustrations of specific scenes or characters. For example, an illustration is created in response to a request such as "Draw an illustration of a scene where Taro fights a dragon." This allows users to enjoy the experience visually.

[0048] The original novel generation system can also analyze the user's reading speed and adjust the development of the novel accordingly. For example, if the user reads quickly, the system can speed up the story's development to provide a reading material with a good tempo. On the other hand, if the user reads slowly, the system can provide a reading material with depth by increasing detailed descriptions and descriptions of the characters' inner thoughts. This allows the system to generate an optimal novel according to the user's reading speed.

[0049] The original novel generation system can also provide related historical background and cultural information based on the genre and style selected by the user. For example, in the case of a historical novel, it provides the historical background and cultural information of the era, while in the case of a fantasy novel, it details the setting and culture of the fictional world. The system can also allow users to request specific information. For example, information is provided in response to a request such as "I want to know more about the lives of knights in medieval Europe." This allows users to deepen their related knowledge while reading the novel.

[0050] The original novel creation system may further include a function that allows users to share the novels they create with other users and receive feedback. For example, a user may post the novel they create on an online platform and receive comments and ratings from other users. The novel may also be revised based on the feedback. For example, if feedback such as "this character's behavior is unnatural" is received, the character's behavior may be revised. This allows users to improve their novels through interactions with other users.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The genre selection section allows the user to select the genre and style of the novel they want. For example, they can select genres such as mystery, fantasy, romance, and styles such as serious, comedy, and dramatic. Step 2: The name input section allows you to input the name of the user or someone close to you. For example, you can input your own name, the names of your friends and family, your full name, or a nickname. Step 3: The generator generates an original novel based on the information entered by the genre selection unit and name input unit. For example, it uses a generation AI to automatically create the plot, character settings, and story development of the novel based on the selected genre and style, and sets the entered names as characters.

[0053] (Example 2) The original novel creation system according to an embodiment of the present invention is a system that creates an original novel by allowing a user to select a desired genre and style of novel and to use the user's own name or the names of people close to them as characters. This allows the user to enjoy an original novel with the user or people close to them as characters.

[0054] An original novel creation system according to an embodiment includes a genre selection unit, a name input unit, and a creation unit. The genre selection unit selects a genre and style of novel desired by the user. For example, the genre selection unit can select genres such as mystery, fantasy, and romance. The genre selection unit can also select styles such as serious, comedy, and dramatic. The name input unit inputs the names of the user and close friends. For example, the name input unit can input the user's own name, or the names of friends and family. The name input unit can also input a full name or a nickname. The creation unit creates an original novel based on the information input by the genre selection unit and the name input unit. For example, the creation unit uses a creation AI to automatically create a plot, character settings, and story development for the novel based on the selected genre and style. The creation unit also sets the input names as characters and uses those names in the novel. This allows the original novel creation system according to an embodiment to create an original novel in a genre and style desired by the user. For example, the user can create an adventure novel with themselves as the main character or a moving story based on memories with friends.

[0055] The genre selection unit can suggest the most suitable genre and style based on the user's past reading history and ratings. For example, the generation AI analyzes the user's past reading history to identify the most frequently read genre and the style that has received high ratings. For example, if the user has read many mystery novels in the past, the generation AI will suggest the mystery genre. The genre selection unit can also suggest the most suitable genre and style based on the user's ratings. For example, the generation AI will suggest a style based on a style that the user has given a high rating. This makes it possible to suggest the most suitable genre and style based on the user's past reading history and ratings.

[0056] The genre selection unit can generate a hybrid novel that combines multiple genres and styles according to the user's selection. For example, the generation AI of the genre selection unit combines multiple genres and styles selected by the user to generate a hybrid novel. For example, it generates a novel that combines mystery and fantasy. The genre selection unit can also combine multiple genres and styles according to the user's selection. For example, it generates a novel that combines romance and science fiction. This makes it possible to generate a hybrid novel that combines multiple genres and styles according to the user's selection.

[0057] The genre selection unit can use the emotion estimation function to suggest a genre or style that is most suitable for the user's current emotional state. For example, the genre selection unit uses the emotion estimation function to analyze the user's current emotional state and suggest a genre or style that is most suitable for that emotion. For example, if the user wants to relax, comedy or romance is suggested. The genre selection unit can also suggest an optimal genre or style based on the user's emotional state. For example, if the user is excited, action or thriller is suggested. In this way, it is possible to suggest a genre or style that is most suitable for the user's current emotional state.

[0058] The name input unit can explain the origin and meaning of a name entered by the user within the novel. For example, the generation AI can automatically search for the origin and meaning of the name entered by the user and explain that information within the novel. For example, the origin and meaning of the name "Taro" can be introduced within the novel. The name input unit can also research the origin and meaning of a name and explain it within the novel. For example, it can explain the historical background and cultural meaning of the name. This allows the origin and meaning of the name entered by the user to be explained within the novel.

[0059] The name input unit can add episodes and background related to the name entered by the user based on the name. For example, the name input unit uses a generation AI to automatically generate episodes and background related to the name entered by the user and incorporate them into the novel. For example, an adventure episode related to the name "Taro" can be added. The name input unit can also research episodes and background related to the name and add them to the novel. For example, an episode based on family history or personal experience can be added. This allows episodes and background related to the name entered by the user to be added to the novel.

[0060] The name input unit can use the emotion estimation function to set the character's personality and behavior based on the emotional reaction to the name input by the user. The name input unit, for example, uses the emotion estimation function to analyze the emotional reaction to the name input by the user and sets the character's personality and behavior based on the results. For example, based on a positive reaction to the name "Taro," it sets a brave personality. The name input unit can also set the character's personality and behavior based on the emotional reaction. For example, based on a negative reaction, it sets an introverted personality. In this way, the character's personality and behavior can be set based on the emotional reaction to the name input by the user.

[0061] The generation unit can suggest optimal plots and story developments based on the user's past reading history and ratings. For example, the generation AI analyzes the user's past reading history to identify the most frequently read plots and highly rated story developments. For example, if the user has read many mystery novels in the past, the generation AI will suggest mystery plots. The generation unit can also suggest optimal plots and story developments based on the user's ratings. For example, the generation AI will suggest story developments based on story developments that the user has given high ratings to. This makes it possible to suggest optimal plots and story developments based on the user's past reading history and ratings.

[0062] The generation unit can generate multiple endings according to the user's selection, allowing the user to choose from them. For example, the generation unit generates multiple endings using a generation AI based on the genre or style selected by the user, allowing the user to choose from them. For example, in the case of a mystery novel, the generation unit generates multiple endings with different culprits. The generation unit can also generate multiple endings according to the user's selection. For example, different endings such as a happy ending and a bitter ending are generated. In this way, multiple endings are generated according to the user's selection, allowing the user to choose from them.

[0063] The generation unit can use the emotion estimation function to adjust the story development in real time according to the user's emotional state. For example, the generation unit uses the emotion estimation function to analyze the user's current emotional state and adjust the story development in real time to best suit that emotion. For example, if the user wants to relax, the generation unit suggests a calm development. The generation unit can also adjust the story development in real time based on the user's emotional state. For example, if the user is excited, the generation unit suggests a tense development. This makes it possible to adjust the story development in real time according to the user's emotional state.

[0064] The generation unit can display the novel generation process to the user in real time, allowing the user to make corrections or additions along the way. For example, the generation unit provides an interface in which the generation AI displays the novel generation process in real time and allows the user to make corrections or additions along the way. For example, changes to the plot or additions to characters are made in real time. The generation unit can also allow the user to make corrections or additions while checking the novel generation process. For example, changing the development of the story or adding a new character. This allows the user to check the novel generation process in real time and make corrections or additions.

[0065] The generation unit can convert the generated novel into different media based on the genre and style selected by the user. For example, the generation unit builds a system that converts a novel generated by the generation AI into a manga or anime based on the genre and style selected by the user. For example, a fantasy novel can be turned into an anime. The generation unit can also convert the generated novel into different media. For example, a mystery novel can be turned into a manga. This allows the generated novel to be converted into different media.

[0066] The generation unit can collect other users' emotional reactions to the novel generated using the emotion estimation function and reflect the data in the next generation. For example, the generation unit can use the emotion estimation function to collect other users' emotional reactions to the generated novel in real time and reflect the data in the next generation. For example, elements that receive many positive reactions can be incorporated into the next novel. The generation unit can also adjust the content of the next generation based on the emotional reactions of other users. For example, elements that receive many negative reactions can be eliminated. In this way, the emotional reactions of other users to the generated novel can be collected and reflected in the next generation.

[0067] The generation unit can analyze the works of the author being studied and automatically filter them to prevent copyright issues from arising. For example, the generation unit constructs a system in which the generation AI analyzes the works of the author being studied and automatically filters them to prevent copyright issues from arising. For example, it automatically detects parts that pose a risk of copyright infringement. The generation unit can also filter works to prevent copyright issues from arising. For example, it uses a text comparison algorithm to exclude parts that pose a risk of copyright infringement. This makes it possible to analyze the works of the author being studied and automatically filter them to prevent copyright issues from arising.

[0068] The generation unit can use the emotion estimation function to analyze the emotions the writer has toward the contract and adjust the contract contents. The generation unit, for example, uses the emotion estimation function to analyze the emotions the writer has toward the contract in real time and adjust the contract contents based on the results. For example, if the writer is feeling anxious, the contract conditions are relaxed. The generation unit can also adjust the contract contents based on the writer's emotions. For example, if the writer is satisfied, the contract conditions are maintained. In this way, the emotions the writer has toward the contract can be analyzed and the contract contents can be adjusted.

[0069] The generation unit can propose a marketing plan for promoting the author's work when signing a contract with the author being trained. For example, the generation unit builds a system in which the generative AI automatically generates and proposes a marketing plan for promoting the author's work when signing a contract with the author being trained. For example, it proposes plans for social media and advertising campaigns. The generation unit can also propose marketing plans. For example, it proposes an analysis of the target market and a plan for promotional activities. This makes it possible to propose a marketing plan for promoting the author's work.

[0070] The generation unit can propose a plan to expand an author's work into other media when signing a contract with the author being trained. For example, the generation unit builds a system in which, when the generation AI enters into a contract with the author being trained, it automatically generates and proposes a plan to expand an author's work into other media. For example, it proposes a plan to adapt the work into a movie or a TV drama. The generation unit can also propose plans to expand the work into other media. For example, it proposes a plan to adapt the work into a game or an anime. This makes it possible to propose plans to expand an author's work into other media.

[0071] The generation unit can use the emotion estimation function to adjust the contract contents based on the emotions the writer feels about the contract, thereby improving the writer's satisfaction. The generation unit, for example, uses the emotion estimation function to analyze the emotions the writer feels about the contract in real time and adjust the contract contents based on the results. For example, if the writer feels anxious, the contract terms are relaxed. The generation unit can also adjust the contract contents based on the writer's emotions. For example, if the writer is satisfied, the contract terms are maintained. In this way, the contract contents can be adjusted based on the emotions the writer feels about the contract, improving the writer's satisfaction.

[0072] The generation unit can also refer to past interviews and essays of a famous novelist when learning the writing style of that author. For example, when the generation AI learns the writing style of a famous novelist, the generation unit automatically collects the author's past interviews and essays and uses them as learning data. For example, it learns the author's creative process and thoughts. The generation unit can also refer to interviews and essays. For example, it refers to the author's past publications and online articles. This makes it possible to refer to past interviews and essays when learning the writing style of a famous novelist.

[0073] When learning the writing style of a famous novelist, the generation unit can focus on learning the parts of that author's works that are particularly highly rated. For example, when the generation AI learns the writing style of a famous novelist, it automatically identifies and focuses on learning the parts of that author's works that are particularly highly rated. For example, it learns scenes and chapters that are highly rated by readers. The generation unit can also focus on learning the parts that are highly rated. For example, it identifies the parts that are highly rated based on critics' reviews and award history. This makes it possible to focus on learning the parts that are particularly rated when learning the writing style of a famous novelist.

[0074] The generation unit can use the emotion estimation function to analyze the user's emotional reactions to the writing styles of famous novelists and adjust the learning content. For example, the generation unit can use the emotion estimation function to collect the user's emotional reactions to the writing styles of famous novelists in real time and adjust the learning content based on that data. For example, the generation unit can focus on learning writing styles that receive a lot of positive reactions. The generation unit can also adjust the learning content based on the user's emotional reactions. For example, the generation unit can exclude writing styles that receive a lot of negative reactions. This makes it possible to analyze the user's emotional reactions to the writing styles of famous novelists and adjust the learning content.

[0075] When the generation unit learns the writing style of a famous novelist, it can generate spin-off works based on the works of that author. For example, the generation unit constructs a system in which the generation AI learns the writing style of a famous novelist and automatically generates spin-off works based on the works of that author. For example, it generates a story from a different perspective of a popular character. The generation unit can also generate spin-off works. For example, it generates a new story based on the characters and settings of the original work. This makes it possible to generate spin-off works based on the works of a famous novelist.

[0076] When learning the writing style of a famous novelist, the generation unit can suggest character goods and related products based on the works of that author. For example, the generation unit constructs a system in which the generation AI learns the writing style of a famous novelist and automatically generates and suggests character goods and related products based on the works of that author. For example, it suggests figurines and posters. The generation unit can also suggest character goods and related products. For example, it suggests apparel and accessories. This makes it possible to suggest character goods and related products based on the works of a famous novelist.

[0077] The generation unit can use the emotion estimation function to adjust the content of the collaboration based on the user's emotional reaction to the famous novelist's style of writing. For example, the generation unit uses the emotion estimation function to collect users' emotional reactions to the famous novelist's style of writing in real time and adjust the content of the collaboration based on that data. For example, the generation unit can focus on incorporating styles that receive many positive reactions. The generation unit can also adjust the content of the collaboration based on the user's emotional reaction. For example, the generation unit can exclude styles that receive many negative reactions. This makes it possible to adjust the content of the collaboration based on the user's emotional reaction to the famous novelist's style of writing.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The original novel generation system can further include a voice input unit. The voice input unit allows the user to input the genre and name by voice. For example, if the user vocally instructs, "It's a mystery novel, and the main character's name is Taro," the system will recognize that information and generate the novel. The voice input unit can also allow the user to give additional instructions as the story progresses. For example, the user can vocally input instructions such as, "In the next scene, Taro will corner the culprit." This allows the user to operate the novel generation without using their hands.

[0080] The original novel creation system may further include a background music providing unit. The background music providing unit provides background music according to the genre and style selected by the user. For example, for a mystery novel, it provides tense music, and for a fantasy novel, it provides fantastical music. The background music providing unit may also adjust the music based on the user's emotional state. For example, if the user wants to relax, it provides calm music. This allows the user to enjoy a more immersive experience while reading the novel.

[0081] The original novel creation system can further include an illustration creation unit. The illustration creation unit automatically creates illustrations that correspond to scenes in the created novel. For example, an illustration depicting a brave protagonist is created for an adventure scene, and an illustration depicting a moving moment is created for a romance scene. The illustration creation unit can also allow users to request illustrations of specific scenes or characters. For example, an illustration is created in response to a request such as "Draw an illustration of a scene where Taro fights a dragon." This allows users to enjoy the experience visually.

[0082] The original novel creation system can also use an emotion estimation function to generate character dialogue based on the user's emotions. For example, if the user is feeling sad, the dialogue between characters can be adjusted to be more moving. If the user is excited, the dialogue can be adjusted to be more tense. This allows the system to generate dialogue that matches the user's emotions, allowing the user to enjoy a novel that is easier to empathize with.

[0083] The original novel generation system can also analyze the user's reading speed and adjust the development of the novel accordingly. For example, if the user reads quickly, the system can speed up the story's development to provide a reading material with a good tempo. On the other hand, if the user reads slowly, the system can provide a reading material with depth by increasing detailed descriptions and descriptions of the characters' inner thoughts. This allows the system to generate an optimal novel according to the user's reading speed.

[0084] The original novel creation system can also use an emotion estimation function to generate an ending based on the user's emotions. For example, if the user is feeling happy, it will suggest a happy ending. If the user is looking for an emotional experience, it will suggest an emotional ending. This allows the system to provide an ending that matches the user's emotions and achieve a highly satisfying reading experience.

[0085] The original novel generation system can also provide related historical background and cultural information based on the genre and style selected by the user. For example, in the case of a historical novel, it provides the historical background and cultural information of the era, while in the case of a fantasy novel, it details the setting and culture of the fictional world. The system can also allow users to request specific information. For example, information is provided in response to a request such as "I want to know more about the lives of knights in medieval Europe." This allows users to deepen their related knowledge while reading the novel.

[0086] The original novel generation system can also use emotion estimation to suggest plot developments based on the user's emotions. For example, if the user is looking for a surprise, it can suggest an unexpected development. If the user is looking for a sense of security, it can suggest a stable development. This allows for plot developments that correspond to the user's emotions, resulting in a more personalized reading experience.

[0087] The original novel creation system may further include a function that allows users to share the novels they create with other users and receive feedback. For example, a user may post the novel they create on an online platform and receive comments and ratings from other users. The novel may also be revised based on the feedback. For example, if feedback such as "this character's behavior is unnatural" is received, the character's behavior may be revised. This allows users to improve their novels through interactions with other users.

[0088] The original novel generation system can also use emotion estimation to depict character growth based on the user's emotions. For example, if the user feels hopeful, it will suggest a story in which the character overcomes difficulties and grows. Alternatively, if the user feels anxious, it will depict the character's process of overcoming that anxiety. This allows the system to depict character growth based on the user's emotions, thereby providing a more relatable novel.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The genre selection section allows the user to select the genre and style of the novel they want. For example, they can select genres such as mystery, fantasy, romance, and styles such as serious, comedy, and dramatic. Step 2: The name input section allows you to input the name of the user or someone close to you. For example, you can input your own name, the names of your friends and family, your full name, or a nickname. Step 3: The generator generates an original novel based on the information entered by the genre selection unit and name input unit. For example, it uses a generation AI to automatically create the plot, character settings, and story development of the novel based on the selected genre and style, and sets the entered names as characters.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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 AI 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0117] 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.

[0118] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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 AI 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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 AI 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a genre selection section for selecting the genre and style of novel desired by the user; a name input section for inputting the name of the user or a familiar person; a generation unit that generates an original novel based on the information input by the genre selection unit and the name input unit. A system characterized by:

2. The genre selection unit Suggesting the most suitable genre and style based on the user's past reading history and ratings 2. The system of claim 1.

3. The name input unit Explain the origin and meaning of the name in the novel based on the name entered by the user 2. The system of claim 1.

4. The generation unit Suggesting optimal plots and story developments based on the user's past reading history and ratings 2. The system of claim 1.

5. The genre selection unit Suggesting the genre or style that best suits the user's current emotional state 2. The system of claim 1.

6. The generation unit Adjusting the story development in real time according to the emotional state of the user 2. The system of claim 1.

7. The generation unit Analyze the writer's feelings about the contract and adjust the contract 2. The system of claim 1.

8. The generation unit Analyzing the user's emotional response to the writing style of the famous novelist and adjusting the learning content 2. The system of claim 1.

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