System

Abstract

An object of the system according to the embodiment is to provide a novel that allows a reader to enjoy a different conclusion each time the reader reads the novel.SOLUTION: A system according to an embodiment includes a plot generation unit, an outcome generation unit, and a selective expansion unit. The plot generation unit generates a plot. The conclusion generation unit generates a conclusion based on the plot generated by the plot generation unit. A selection development part develops the story based on the selection of the reader.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 the problem that the ending of a novel is fixed, preventing readers from enjoying different endings.

[0005] The system according to the embodiment aims to provide a novel that allows readers to enjoy a different ending each time they read it. [Means for solving the problem]

[0006] The system according to the embodiment includes a plot generation unit, an ending generation unit, and a selection development unit. The plot generation unit generates a plot. The ending generation unit generates an ending based on the plot generated by the plot generation unit. The selection development unit develops the story based on the reader's selection. [Effects of the Invention]

[0007] The system according to the embodiment can provide a novel that allows readers to enjoy a different ending each time they read it. [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 new type of book according to an embodiment of the present invention is a system that uses generative AI to change the ending of a novel each time it is read. This system allows readers to enjoy a different ending each time they read it. This allows the new type of book to provide readers with a new experience.

[0029] A new type of book according to an embodiment includes a plot generation unit, an ending generation unit, and a choice development unit. The plot generation unit generates a plot using a generation AI. For example, the generation AI generates multiple different plots based on prompts and instructions input by a user. The generation AI can generate plots using a text generation AI such as GPT-3 or BERT. The ending generation unit generates an ending based on the plot generated by the plot generation unit. For example, the generation AI can generate different endings for the same plot. The generation AI can generate an ending based on the theme of the story or the fate of the characters. The choice development unit develops the story based on the reader's choice. For example, if the reader selects "the protagonist takes the left path," the generation AI progresses the story based on that choice. The generation AI can customize the story according to the reader's choice. This allows the new type of book according to an embodiment to have different endings each time the reader reads it. For example, the reader can enjoy an ending in which the protagonist finds treasure the first time they read it, and an ending in which the protagonist reunites with his friends the next time they read it.

[0030] The plot generation unit can generate plots optimized for individual readers based on the reader's past selection history. For example, the plot generation unit uses a generation AI to analyze the reader's past selection history and generate plots that the reader tends to prefer. For example, a reader who has made many adventure-related choices in the past will be provided with a plot that has an enhanced adventure element. The plot generation unit also uses a generation AI to generate plots that reflect the reader's preferences and interests based on the reader's selection history. For example, a new plot is created taking into account the patterns of characters and scenarios that the reader has selected in the past. The plot generation unit also uses a generation AI to learn the reader's selection history and generate new plots that the reader has not yet experienced. For example, a fresh experience can be provided by prioritizing scenarios that have not been selected in the past. This makes it possible to provide plots that suit the reader's preferences.

[0031] The plot generation unit can analyze the reader's reading speed or time spent on a page and generate a plot that matches the tempo. For example, the plot generation unit uses a generation AI to analyze the reader's reading speed and generate a plot that matches the tempo. For example, a fast-paced scenario is provided for a speed-reading reader. The plot generation unit also uses the generation AI to generate a plot that matches the tempo based on the reader's time spent on a page. For example, a scenario that includes detailed descriptions is provided for a reader who spends a long time on a page. The plot generation unit also uses the generation AI to learn the reader's reading speed and time spent on a page and generate a plot that progresses at an optimal tempo. For example, the development of the scenario is adjusted to match the reader's pace. This makes it possible to provide a plot that matches the reader's reading speed.

[0032] The plot generation unit can generate cross-genre plots that combine different genres. For example, the generation AI in the plot generation unit generates a plot that combines mystery and fantasy. For example, it provides a scenario with a mystery-solving theme that takes place in a magical world. The plot generation unit also generates plots that combine different genres. For example, it provides a scenario that combines romance and action. The plot generation unit also generates cross-genre plots. For example, it provides a scenario that combines science fiction and horror. This makes it possible to provide plots that combine different genres.

[0033] The plot generation unit can generate a plot based on background music or environmental sounds selected by the reader. In the plot generation unit, for example, the generation AI generates a plot based on the background music selected by the reader. For example, a reader who selects classical music is provided with an elegant scenario. In addition, the plot generation unit generates a plot based on the environmental sounds selected by the reader. For example, a reader who selects the sound of rain is provided with a rainy day scenario. In addition, the plot generation unit analyzes the reader's selection of music or environmental sounds and generates a plot that matches it. For example, a reader who selects the sound of nature is provided with a nature-themed scenario. In this way, a plot that matches the music or environmental sounds selected by the reader can be provided.

[0034] The ending generation unit can prioritize generating endings that the reader has not yet experienced based on the reader's past reading history. In the ending generation unit, for example, the generation AI analyzes the reader's past reading history and generates endings that the reader has not yet experienced. For example, it prioritizes providing endings that have not been chosen in the past. In addition, the ending generation unit generates a new ending based on the reader's reading history by the generation AI. For example, it provides a scenario that the reader has not yet seen. In addition, the ending generation unit learns the reader's history and generates an ending that the reader has not yet experienced. For example, it provides a scenario that differs from endings chosen in the past. This makes it possible to provide an ending that the reader has not yet experienced.

[0035] The ending generation unit can generate an ending based on the actions and personality of the character selected by the reader. In the ending generation unit, for example, the generation AI generates an ending based on the actions of the character selected by the reader. For example, a heroic ending is provided for a character who chose brave actions. In addition, the ending generation unit generates an ending based on the personality of the character selected by the reader. For example, a moving ending is provided for a character with a kind personality. In addition, the ending generation unit analyzes the actions and personality of the character and generates an ending that matches it. For example, an ending that uses wisdom is provided for a character who chose wise actions. In this way, an ending that matches the reader's choice can be provided.

[0036] The ending generation unit can generate an ending that incorporates different cultures and historical backgrounds. For example, the generation AI in the ending generation unit generates an ending that incorporates different cultures. For example, it provides a scenario that reflects traditional Japanese culture. The ending generation unit also generates an ending based on historical background. For example, it provides a scenario that reflects the history of medieval Europe. The ending generation unit also generates an ending that combines different cultures and historical backgrounds. For example, it provides a scenario that combines Asian and European cultures. This makes it possible to provide an ending that reflects different cultures and historical backgrounds.

[0037] The ending generation unit can generate an ending based on visual effects and animations selected by the reader. For example, in the ending generation unit, the generation AI generates an ending based on visual effects selected by the reader. For example, if a reader selects fantasy-style effects, a magical ending is provided. In addition, in the ending generation unit, the generation AI generates an ending based on animations selected by the reader. For example, if a reader selects action animations, an action-packed ending is provided. In addition, in the ending generation unit, the generation AI analyzes the selection of visual effects and animations and generates an ending that matches them. For example, if a reader selects horror effects, a terrifying ending is provided. In this way, an ending that reflects the visual effects and animations selected by the reader can be provided.

[0038] The selection development unit can analyze the reader's selection history and develop the story based on the selection patterns. In the selection development unit, for example, a generation AI analyzes the reader's selection history and develops the story based on the selection patterns. For example, it provides a new scenario taking into account past choices. In addition, the selection development unit allows the generation AI to develop the story based on the reader's selection patterns. For example, it provides a scenario with enhanced adventure elements for a reader who enjoys adventure. In addition, the selection development unit allows the generation AI to learn the selection history and develop a story that matches the selection patterns. For example, it provides a scenario that reflects past choices. This makes it possible to provide a story that matches the reader's selection patterns.

[0039] The selection development section can develop a story based on the actions and personality of the character selected by the reader. In the selection development section, for example, the generation AI develops a story based on the actions of the character selected by the reader. For example, a heroic scenario is provided for a character who chooses brave actions. In the selection development section, the generation AI also develops a story based on the personality of the character selected by the reader. For example, a moving scenario is provided for a character with a kind personality. In the selection development section, the generation AI also analyzes the character's actions and personality and develops a story that suits them. For example, a scenario that uses wisdom is provided for a character who chooses wise actions. This makes it possible to provide a story that suits the reader's choices.

[0040] The selection development unit can generate story developments that are suitable for readers of different languages ​​and cultural spheres. In the selection development unit, for example, the generation AI develops stories that are suitable for different languages. For example, it provides scenarios in multiple languages ​​such as English, French, and Chinese. The selection development unit also generates stories that are suitable for readers of different cultural spheres. For example, it provides a scenario that reflects Japanese culture for Japanese readers. The selection development unit also develops a suitable story based on data from different languages ​​and cultural spheres. For example, it provides a scenario that combines Asian and European cultures. This makes it possible to provide stories that are suitable for different languages ​​and cultural spheres.

[0041] The selection development unit can generate a story development that incorporates interactive elements selected by the reader. For example, the generation AI in the selection development unit develops a story that incorporates a mini-game selected by the reader. For example, it provides a scenario in which the story progresses by clearing a mini-game during the adventure. The selection development unit also generates a story that incorporates a quiz selected by the reader. For example, it provides a story in which the next scenario develops by answering a quiz correctly. The selection development unit also develops a story in which the generation AI incorporates interactive elements. For example, it provides a scenario in which mini-games and quizzes are inserted depending on the reader's selection. This makes it possible to provide a story that incorporates interactive elements.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The plot generation unit can generate plots optimized for individual readers based on the reader's past selection history. For example, the plot generation unit uses a generation AI to analyze the reader's past selection history and generate plots that the reader tends to prefer. For example, a reader who has made many adventure-related choices in the past will be provided with a plot that has an enhanced adventure element. The plot generation unit also uses a generation AI to generate plots that reflect the reader's preferences and interests based on the reader's selection history. For example, a new plot is created taking into account the patterns of characters and scenarios that the reader has selected in the past. The plot generation unit also uses a generation AI to learn the reader's selection history and generate new plots that the reader has not yet experienced. For example, a fresh experience can be provided by prioritizing scenarios that have not been selected in the past. This makes it possible to provide plots that suit the reader's preferences.

[0044] The plot generation unit can analyze the reader's reading speed or time spent on a page and generate a plot that matches the tempo. For example, the plot generation unit uses a generation AI to analyze the reader's reading speed and generate a plot that matches the tempo. For example, a fast-paced scenario is provided for a speed-reading reader. The plot generation unit also uses the generation AI to generate a plot that matches the tempo based on the reader's time spent on a page. For example, a scenario that includes detailed descriptions is provided for a reader who spends a long time on a page. The plot generation unit also uses the generation AI to learn the reader's reading speed and time spent on a page and generate a plot that progresses at an optimal tempo. For example, the development of the scenario is adjusted to match the reader's pace. This makes it possible to provide a plot that matches the reader's reading speed.

[0045] The plot generation unit can generate cross-genre plots that combine different genres. For example, the generation AI in the plot generation unit generates a plot that combines mystery and fantasy. For example, it provides a scenario with a mystery-solving theme that takes place in a magical world. The plot generation unit also generates plots that combine different genres. For example, it provides a scenario that combines romance and action. The plot generation unit also generates cross-genre plots. For example, it provides a scenario that combines science fiction and horror. This makes it possible to provide plots that combine different genres.

[0046] The plot generation unit can generate a plot based on background music or environmental sounds selected by the reader. In the plot generation unit, for example, the generation AI generates a plot based on the background music selected by the reader. For example, a reader who selects classical music is provided with an elegant scenario. In addition, the plot generation unit generates a plot based on the environmental sounds selected by the reader. For example, a reader who selects the sound of rain is provided with a rainy day scenario. In addition, the plot generation unit analyzes the reader's selection of music or environmental sounds and generates a plot that matches it. For example, a reader who selects the sound of nature is provided with a nature-themed scenario. In this way, a plot that matches the music or environmental sounds selected by the reader can be provided.

[0047] The ending generation unit can prioritize generating endings that the reader has not yet experienced based on the reader's past reading history. In the ending generation unit, for example, the generation AI analyzes the reader's past reading history and generates endings that the reader has not yet experienced. For example, it prioritizes providing endings that have not been chosen in the past. In addition, the ending generation unit generates a new ending based on the reader's reading history by the generation AI. For example, it provides a scenario that the reader has not yet seen. In addition, the ending generation unit learns the reader's history and generates an ending that the reader has not yet experienced. For example, it provides a scenario that differs from endings chosen in the past. This makes it possible to provide an ending that the reader has not yet experienced.

[0048] The ending generation unit can generate an ending based on the actions and personality of the character selected by the reader. In the ending generation unit, for example, the generation AI generates an ending based on the actions of the character selected by the reader. For example, a heroic ending is provided for a character who chose brave actions. In addition, the ending generation unit generates an ending based on the personality of the character selected by the reader. For example, a moving ending is provided for a character with a kind personality. In addition, the ending generation unit analyzes the actions and personality of the character and generates an ending that matches it. For example, an ending that uses wisdom is provided for a character who chose wise actions. In this way, an ending that matches the reader's choice can be provided.

[0049] The ending generation unit can generate an ending that incorporates different cultures and historical backgrounds. For example, the generation AI in the ending generation unit generates an ending that incorporates different cultures. For example, it provides a scenario that reflects traditional Japanese culture. The ending generation unit also generates an ending based on historical background. For example, it provides a scenario that reflects the history of medieval Europe. The ending generation unit also generates an ending that combines different cultures and historical backgrounds. For example, it provides a scenario that combines Asian and European cultures. This makes it possible to provide an ending that reflects different cultures and historical backgrounds.

[0050] The ending generation unit can generate an ending based on visual effects and animations selected by the reader. For example, in the ending generation unit, the generation AI generates an ending based on visual effects selected by the reader. For example, if a reader selects fantasy-style effects, a magical ending is provided. In addition, in the ending generation unit, the generation AI generates an ending based on animations selected by the reader. For example, if a reader selects action animations, an action-packed ending is provided. In addition, in the ending generation unit, the generation AI analyzes the selection of visual effects and animations and generates an ending that matches them. For example, if a reader selects horror effects, a terrifying ending is provided. In this way, an ending that reflects the visual effects and animations selected by the reader can be provided.

[0051] The selection development unit can analyze the reader's selection history and develop the story based on the selection patterns. In the selection development unit, for example, a generation AI analyzes the reader's selection history and develops the story based on the selection patterns. For example, it provides a new scenario taking into account past choices. In addition, the selection development unit allows the generation AI to develop the story based on the reader's selection patterns. For example, it provides a scenario with enhanced adventure elements for a reader who enjoys adventure. In addition, the selection development unit allows the generation AI to learn the selection history and develop a story that matches the selection patterns. For example, it provides a scenario that reflects past choices. This makes it possible to provide a story that matches the reader's selection patterns.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The plot generator uses a generation AI to generate plots. For example, the generation AI generates multiple different plots based on prompts and instructions entered by the user. The generation AI can be a text generation AI such as GPT-3 or BERT. Step 2: The ending generator generates an ending based on the plot generated by the plot generator. For example, the generator can generate different endings for the same plot, and generate endings based on the theme of the story and the fates of the characters. Step 3: The choice development section develops the story based on the reader's choice. For example, if the reader selects "The protagonist chooses the left path," the generation AI will progress the story based on that choice. The generation AI can customize the story according to the reader's choice.

[0054] (Example 2) A new type of book according to an embodiment of the present invention is a system that uses generative AI to change the ending of a novel each time it is read. This system allows readers to enjoy a different ending each time they read it. This allows the new type of book to provide readers with a new experience.

[0055] A new type of book according to an embodiment includes a plot generation unit, an ending generation unit, and a choice development unit. The plot generation unit generates a plot using a generation AI. For example, the generation AI generates multiple different plots based on prompts and instructions input by a user. The generation AI can generate plots using a text generation AI such as GPT-3 or BERT. The ending generation unit generates an ending based on the plot generated by the plot generation unit. For example, the generation AI can generate different endings for the same plot. The generation AI can generate an ending based on the theme of the story or the fate of the characters. The choice development unit develops the story based on the reader's choice. For example, if the reader selects "the protagonist takes the left path," the generation AI progresses the story based on that choice. The generation AI can customize the story according to the reader's choice. This allows the new type of book according to an embodiment to have different endings each time the reader reads it. For example, the reader can enjoy an ending in which the protagonist finds treasure the first time they read it, and an ending in which the protagonist reunites with his friends the next time they read it.

[0056] The plot generation unit can generate plots optimized for individual readers based on the reader's past selection history. For example, the plot generation unit uses a generation AI to analyze the reader's past selection history and generate plots that the reader tends to prefer. For example, a reader who has made many adventure-related choices in the past will be provided with a plot that has an enhanced adventure element. The plot generation unit also uses a generation AI to generate plots that reflect the reader's preferences and interests based on the reader's selection history. For example, a new plot is created taking into account the patterns of characters and scenarios that the reader has selected in the past. The plot generation unit also uses a generation AI to learn the reader's selection history and generate new plots that the reader has not yet experienced. For example, a fresh experience can be provided by prioritizing scenarios that have not been selected in the past. This makes it possible to provide plots that suit the reader's preferences.

[0057] The plot generation unit can analyze the reader's emotional state in real time and generate a plot that corresponds to the emotion. In the plot generation unit, for example, the generation AI analyzes the reader's emotional state in real time and generates a plot that corresponds to the emotion. For example, if the reader is excited, it provides a plot that includes many action scenes. In addition, the plot generation unit generates a plot that matches the emotion based on the reader's emotional state, with the generation AI providing a calm scenario if the reader is relaxed. In addition, the plot generation unit collects the reader's emotional data and generates a plot that corresponds to the emotion. For example, if the reader is sad, it provides an emotional scenario. In this way, it is possible to provide a plot that corresponds to the reader's emotions.

[0058] The plot generation unit can analyze the reader's reading speed or time spent on a page and generate a plot that matches the tempo. For example, the plot generation unit uses a generation AI to analyze the reader's reading speed and generate a plot that matches the tempo. For example, a fast-paced scenario is provided for a speed-reading reader. The plot generation unit also uses the generation AI to generate a plot that matches the tempo based on the reader's time spent on a page. For example, a scenario that includes detailed descriptions is provided for a reader who spends a long time on a page. The plot generation unit also uses the generation AI to learn the reader's reading speed and time spent on a page and generate a plot that progresses at an optimal tempo. For example, the development of the scenario is adjusted to match the reader's pace. This makes it possible to provide a plot that matches the reader's reading speed.

[0059] The plot generation unit can generate cross-genre plots that combine different genres. For example, the generation AI in the plot generation unit generates a plot that combines mystery and fantasy. For example, it provides a scenario with a mystery-solving theme that takes place in a magical world. The plot generation unit also generates plots that combine different genres. For example, it provides a scenario that combines romance and action. The plot generation unit also generates cross-genre plots. For example, it provides a scenario that combines science fiction and horror. This makes it possible to provide plots that combine different genres.

[0060] The plot generation unit can generate a plot based on background music or environmental sounds selected by the reader. In the plot generation unit, for example, the generation AI generates a plot based on the background music selected by the reader. For example, a reader who selects classical music is provided with an elegant scenario. In addition, the plot generation unit generates a plot based on the environmental sounds selected by the reader. For example, a reader who selects the sound of rain is provided with a rainy day scenario. In addition, the plot generation unit analyzes the reader's selection of music or environmental sounds and generates a plot that matches it. For example, a reader who selects the sound of nature is provided with a nature-themed scenario. In this way, a plot that matches the music or environmental sounds selected by the reader can be provided.

[0061] The plot generation unit can use the emotion estimation function to estimate the reader's emotions and generate a plot that corresponds to the emotions. In the plot generation unit, for example, the generation AI uses the emotion estimation function to estimate the reader's emotions and generates a plot that corresponds to the emotions. For example, if the reader is happy, a scenario with a happy ending is provided. The plot generation unit also estimates the reader's emotions and the generation AI generates a plot that matches the emotions. For example, if the reader is nervous, a thrilling scenario is provided. The plot generation unit also generates a plot that corresponds to the emotions based on the emotion estimation data by the generation AI. For example, if the reader is relaxed, a calm scenario is provided. In this way, a plot that corresponds to the reader's emotions can be provided.

[0062] The ending generation unit can prioritize generating endings that the reader has not yet experienced based on the reader's past reading history. In the ending generation unit, for example, the generation AI analyzes the reader's past reading history and generates endings that the reader has not yet experienced. For example, it prioritizes providing endings that have not been chosen in the past. In addition, the ending generation unit generates a new ending based on the reader's reading history by the generation AI. For example, it provides a scenario that the reader has not yet seen. In addition, the ending generation unit learns the reader's history and generates an ending that the reader has not yet experienced. For example, it provides a scenario that differs from endings chosen in the past. This makes it possible to provide an ending that the reader has not yet experienced.

[0063] The ending generation unit can analyze the reader's real-time emotional state and generate an ending that corresponds to the emotion. In the ending generation unit, for example, the generation AI analyzes the reader's real-time emotional state and generates an ending that corresponds to the emotion. For example, if the reader is moved, an emotional ending is provided. In addition, the ending generation unit generates an ending that matches the emotion based on the reader's emotional state by the generation AI. For example, if the reader is excited, an action-packed ending is provided. In addition, the ending generation unit collects the reader's emotional data and generates an ending that corresponds to the emotion. For example, if the reader is relaxed, a calm ending is provided. In this way, an ending that corresponds to the reader's emotion can be provided.

[0064] The ending generation unit can generate an ending based on the actions and personality of the character selected by the reader. In the ending generation unit, for example, the generation AI generates an ending based on the actions of the character selected by the reader. For example, a heroic ending is provided for a character who chose brave actions. In addition, the ending generation unit generates an ending based on the personality of the character selected by the reader. For example, a moving ending is provided for a character with a kind personality. In addition, the ending generation unit analyzes the actions and personality of the character and generates an ending that matches it. For example, an ending that uses wisdom is provided for a character who chose wise actions. In this way, an ending that matches the reader's choice can be provided.

[0065] The ending generation unit can generate an ending that incorporates different cultures and historical backgrounds. For example, the generation AI in the ending generation unit generates an ending that incorporates different cultures. For example, it provides a scenario that reflects traditional Japanese culture. The ending generation unit also generates an ending based on historical background. For example, it provides a scenario that reflects the history of medieval Europe. The ending generation unit also generates an ending that combines different cultures and historical backgrounds. For example, it provides a scenario that combines Asian and European cultures. This makes it possible to provide an ending that reflects different cultures and historical backgrounds.

[0066] The ending generation unit can generate an ending based on visual effects and animations selected by the reader. For example, in the ending generation unit, the generation AI generates an ending based on visual effects selected by the reader. For example, if a reader selects fantasy-style effects, a magical ending is provided. In addition, in the ending generation unit, the generation AI generates an ending based on animations selected by the reader. For example, if a reader selects action animations, an action-packed ending is provided. In addition, in the ending generation unit, the generation AI analyzes the selection of visual effects and animations and generates an ending that matches them. For example, if a reader selects horror effects, a terrifying ending is provided. In this way, an ending that reflects the visual effects and animations selected by the reader can be provided.

[0067] The ending generation unit can use an emotion estimation function to estimate the reader's emotions and generate an ending that matches the emotions. In the ending generation unit, for example, a generation AI uses the emotion estimation function to estimate the reader's emotions and generate an ending that matches the emotions. For example, if the reader is happy, a happy ending is provided. The ending generation unit also estimates the reader's emotions and the generation AI generates an ending that matches the emotions. For example, if the reader is nervous, a thrilling ending is provided. The ending generation unit also generates an ending that matches the emotions based on the emotion estimation data by the generation AI. For example, if the reader is relaxed, a calm ending is provided. In this way, an ending that matches the reader's emotions can be provided.

[0068] The selection development unit can analyze the reader's selection history and develop the story based on the selection patterns. In the selection development unit, for example, a generation AI analyzes the reader's selection history and develops the story based on the selection patterns. For example, it provides a new scenario taking into account past choices. In addition, the selection development unit allows the generation AI to develop the story based on the reader's selection patterns. For example, it provides a scenario with enhanced adventure elements for a reader who enjoys adventure. In addition, the selection development unit allows the generation AI to learn the selection history and develop a story that matches the selection patterns. For example, it provides a scenario that reflects past choices. This makes it possible to provide a story that matches the reader's selection patterns.

[0069] The selection development unit can analyze the reader's real-time emotional state and generate a story development that corresponds to the emotion. In the selection development unit, for example, the generation AI analyzes the reader's real-time emotional state and generates a story development that corresponds to the emotion. For example, if the reader is moved, it will provide an emotional scenario. In addition, the selection development unit allows the generation AI to develop a story that matches the reader's emotion based on the reader's emotional state. For example, if the reader is excited, it will provide an action-packed scenario. In addition, the selection development unit allows the generation AI to collect the reader's emotional data and develop a story that corresponds to the emotion. For example, if the reader is relaxed, it will provide a calm scenario. This makes it possible to provide a story development that corresponds to the reader's emotion.

[0070] The selection development section can develop a story based on the actions and personality of the character selected by the reader. In the selection development section, for example, the generation AI develops a story based on the actions of the character selected by the reader. For example, a heroic scenario is provided for a character who chooses brave actions. In the selection development section, the generation AI also develops a story based on the personality of the character selected by the reader. For example, a moving scenario is provided for a character with a kind personality. In the selection development section, the generation AI also analyzes the character's actions and personality and develops a story that suits them. For example, a scenario that uses wisdom is provided for a character who chooses wise actions. This makes it possible to provide a story that suits the reader's choices.

[0071] The selection development unit can generate story developments that are suitable for readers of different languages ​​and cultural spheres. In the selection development unit, for example, the generation AI develops stories that are suitable for different languages. For example, it provides scenarios in multiple languages ​​such as English, French, and Chinese. The selection development unit also generates stories that are suitable for readers of different cultural spheres. For example, it provides a scenario that reflects Japanese culture for Japanese readers. The selection development unit also develops a suitable story based on data from different languages ​​and cultural spheres. For example, it provides a scenario that combines Asian and European cultures. This makes it possible to provide stories that are suitable for different languages ​​and cultural spheres.

[0072] The selection development unit can generate a story development that incorporates interactive elements selected by the reader. For example, the generation AI in the selection development unit develops a story that incorporates a mini-game selected by the reader. For example, it provides a scenario in which the story progresses by clearing a mini-game during the adventure. The selection development unit also generates a story that incorporates a quiz selected by the reader. For example, it provides a story in which the next scenario develops by answering a quiz correctly. The selection development unit also develops a story in which the generation AI incorporates interactive elements. For example, it provides a scenario in which mini-games and quizzes are inserted depending on the reader's selection. This makes it possible to provide a story that incorporates interactive elements.

[0073] The selection development unit can use the emotion estimation function to estimate the reader's emotions and generate a story development that corresponds to the emotions. In the selection development unit, for example, the generation AI uses the emotion estimation function to estimate the reader's emotions and develops a story that corresponds to the emotions. For example, if the reader is happy, a scenario with a happy ending is provided. The selection development unit also estimates the reader's emotions and the generation AI develops a story that matches the emotions. For example, if the reader is nervous, a thrilling scenario is provided. The selection development unit also develops a story that corresponds to the emotions based on the emotion estimation data by the generation AI. For example, if the reader is relaxed, a calm scenario is provided. This makes it possible to provide a story development that corresponds to the reader's emotions.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The plot generation unit can generate plots optimized for individual readers based on the reader's past selection history. For example, the plot generation unit uses a generation AI to analyze the reader's past selection history and generate plots that the reader tends to prefer. For example, a reader who has made many adventure-related choices in the past will be provided with a plot that has an enhanced adventure element. The plot generation unit also uses a generation AI to generate plots that reflect the reader's preferences and interests based on the reader's selection history. For example, a new plot is created taking into account the patterns of characters and scenarios that the reader has selected in the past. The plot generation unit also uses a generation AI to learn the reader's selection history and generate new plots that the reader has not yet experienced. For example, a fresh experience can be provided by prioritizing scenarios that have not been selected in the past. This makes it possible to provide plots that suit the reader's preferences.

[0076] The plot generation unit can analyze the reader's emotional state in real time and generate a plot that corresponds to the emotion. In the plot generation unit, for example, the generation AI analyzes the reader's emotional state in real time and generates a plot that corresponds to the emotion. For example, if the reader is excited, it provides a plot that includes many action scenes. In addition, the plot generation unit generates a plot that matches the emotion based on the reader's emotional state, with the generation AI providing a calm scenario if the reader is relaxed. In addition, the plot generation unit collects the reader's emotional data and generates a plot that corresponds to the emotion. For example, if the reader is sad, it provides an emotional scenario. In this way, it is possible to provide a plot that corresponds to the reader's emotions.

[0077] The plot generation unit can analyze the reader's reading speed or time spent on a page and generate a plot that matches the tempo. For example, the plot generation unit uses a generation AI to analyze the reader's reading speed and generate a plot that matches the tempo. For example, a fast-paced scenario is provided for a speed-reading reader. The plot generation unit also uses the generation AI to generate a plot that matches the tempo based on the reader's time spent on a page. For example, a scenario that includes detailed descriptions is provided for a reader who spends a long time on a page. The plot generation unit also uses the generation AI to learn the reader's reading speed and time spent on a page and generate a plot that progresses at an optimal tempo. For example, the development of the scenario is adjusted to match the reader's pace. This makes it possible to provide a plot that matches the reader's reading speed.

[0078] The plot generation unit can generate cross-genre plots that combine different genres. For example, the generation AI in the plot generation unit generates a plot that combines mystery and fantasy. For example, it provides a scenario with a mystery-solving theme that takes place in a magical world. The plot generation unit also generates plots that combine different genres. For example, it provides a scenario that combines romance and action. The plot generation unit also generates cross-genre plots. For example, it provides a scenario that combines science fiction and horror. This makes it possible to provide plots that combine different genres.

[0079] The plot generation unit can generate a plot based on background music or environmental sounds selected by the reader. In the plot generation unit, for example, the generation AI generates a plot based on the background music selected by the reader. For example, a reader who selects classical music is provided with an elegant scenario. In addition, the plot generation unit generates a plot based on the environmental sounds selected by the reader. For example, a reader who selects the sound of rain is provided with a rainy day scenario. In addition, the plot generation unit analyzes the reader's selection of music or environmental sounds and generates a plot that matches it. For example, a reader who selects the sound of nature is provided with a nature-themed scenario. In this way, a plot that matches the music or environmental sounds selected by the reader can be provided.

[0080] The ending generation unit can prioritize generating endings that the reader has not yet experienced based on the reader's past reading history. In the ending generation unit, for example, the generation AI analyzes the reader's past reading history and generates endings that the reader has not yet experienced. For example, it prioritizes providing endings that have not been chosen in the past. In addition, the ending generation unit generates a new ending based on the reader's reading history by the generation AI. For example, it provides a scenario that the reader has not yet seen. In addition, the ending generation unit learns the reader's history and generates an ending that the reader has not yet experienced. For example, it provides a scenario that differs from endings chosen in the past. This makes it possible to provide an ending that the reader has not yet experienced.

[0081] The ending generation unit can analyze the reader's real-time emotional state and generate an ending that corresponds to the emotion. In the ending generation unit, for example, the generation AI analyzes the reader's real-time emotional state and generates an ending that corresponds to the emotion. For example, if the reader is moved, an emotional ending is provided. In addition, the ending generation unit generates an ending that matches the emotion based on the reader's emotional state by the generation AI. For example, if the reader is excited, an action-packed ending is provided. In addition, the ending generation unit collects the reader's emotional data and generates an ending that corresponds to the emotion. For example, if the reader is relaxed, a calm ending is provided. In this way, an ending that corresponds to the reader's emotion can be provided.

[0082] The ending generation unit can generate an ending based on the actions and personality of the character selected by the reader. In the ending generation unit, for example, the generation AI generates an ending based on the actions of the character selected by the reader. For example, a heroic ending is provided for a character who chose brave actions. In addition, the ending generation unit generates an ending based on the personality of the character selected by the reader. For example, a moving ending is provided for a character with a kind personality. In addition, the ending generation unit analyzes the actions and personality of the character and generates an ending that matches it. For example, an ending that uses wisdom is provided for a character who chose wise actions. In this way, an ending that matches the reader's choice can be provided.

[0083] The ending generation unit can generate an ending that incorporates different cultures and historical backgrounds. For example, the generation AI in the ending generation unit generates an ending that incorporates different cultures. For example, it provides a scenario that reflects traditional Japanese culture. The ending generation unit also generates an ending based on historical background. For example, it provides a scenario that reflects the history of medieval Europe. The ending generation unit also generates an ending that combines different cultures and historical backgrounds. For example, it provides a scenario that combines Asian and European cultures. This makes it possible to provide an ending that reflects different cultures and historical backgrounds.

[0084] The ending generation unit can generate an ending based on visual effects and animations selected by the reader. For example, in the ending generation unit, the generation AI generates an ending based on visual effects selected by the reader. For example, if a reader selects fantasy-style effects, a magical ending is provided. In addition, in the ending generation unit, the generation AI generates an ending based on animations selected by the reader. For example, if a reader selects action animations, an action-packed ending is provided. In addition, in the ending generation unit, the generation AI analyzes the selection of visual effects and animations and generates an ending that matches them. For example, if a reader selects horror effects, a terrifying ending is provided. In this way, an ending that reflects the visual effects and animations selected by the reader can be provided.

[0085] The selection development unit can analyze the reader's selection history and develop the story based on the selection patterns. In the selection development unit, for example, a generation AI analyzes the reader's selection history and develops the story based on the selection patterns. For example, it provides a new scenario taking into account past choices. In addition, the selection development unit allows the generation AI to develop the story based on the reader's selection patterns. For example, it provides a scenario with enhanced adventure elements for a reader who enjoys adventure. In addition, the selection development unit allows the generation AI to learn the selection history and develop a story that matches the selection patterns. For example, it provides a scenario that reflects past choices. This makes it possible to provide a story that matches the reader's selection patterns.

[0086] The selection development unit can analyze the reader's real-time emotional state and generate a story development that corresponds to the emotion. In the selection development unit, for example, the generation AI analyzes the reader's real-time emotional state and generates a story development that corresponds to the emotion. For example, if the reader is moved, it will provide an emotional scenario. In addition, the selection development unit allows the generation AI to develop a story that matches the reader's emotion based on the reader's emotional state. For example, if the reader is excited, it will provide an action-packed scenario. In addition, the selection development unit allows the generation AI to collect the reader's emotional data and develop a story that corresponds to the emotion. For example, if the reader is relaxed, it will provide a calm scenario. This makes it possible to provide a story development that corresponds to the reader's emotion.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The plot generator uses a generation AI to generate plots. For example, the generation AI generates multiple different plots based on prompts and instructions entered by the user. The generation AI can be a text generation AI such as GPT-3 or BERT. Step 2: The ending generator generates an ending based on the plot generated by the plot generator. For example, the generator can generate different endings for the same plot, and generate endings based on the theme of the story and the fates of the characters. Step 3: The choice development section develops the story based on the reader's choice. For example, if the reader selects "The protagonist chooses the left path," the generation AI will progress the story based on that choice. The generation AI can customize the story according to the reader's choice.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[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 robot 414, 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 robot 414 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.

[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 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.

[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 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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. It is a system that uses generative AI to change the ending of a novel every time you read it. a plot generator for generating a plot; an ending generation unit that generates an ending based on the plot generated by the plot generation unit; A selection development section that develops the story based on the reader's selection. A system characterized by:

2. The plot generation unit Generates plots optimized for individual readers based on the reader's past selection history 2. The system of claim 1.

3. The plot generation unit Analyzing the emotional state of the reader in real time and generating plots according to the emotions 2. The system of claim 1.

4. The plot generation unit Analyze the reader's reading speed or page dwell time and generate a plot according to the tempo 2. The system of claim 1.

5. The plot generation unit Generate cross-genre plots that combine different genres 2. The system of claim 1.

Images