System

Abstract

An object of the system according to the embodiment is to provide a story experience customized based on the interest and preference of the user.SOLUTION: A system according to an embodiment includes an interest analysis unit, a story generation unit, an interactive experience unit, a feedback collection unit, and a format providing unit. The interest analysis unit analyzes the interest and preference of the user. The story generating section generates a story customized on the basis of the user's interest and preference analyzed by the interest analyzing section. The interactive experience portion provides an interactive experience in which the user can select options for the progression of the narrative. A feedback collector collects feedback from the user to continuously improve the quality of the story. A format provider provides the generated story in a plurality of formats.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 technologies have difficulty providing a narrative experience tailored to a user's interests and preferences, and there is room for improvement in realizing a personalized experience.

[0005] The system according to the embodiment aims to provide a narrative experience that is customized based on the user's interests and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes an interest analysis unit, a story generation unit, an interactive experience unit, a feedback collection unit, and a format provision unit. The interest analysis unit analyzes a user's interests and preferences. The story generation unit generates a customized story based on the user's interests and preferences analyzed by the interest analysis unit. The interactive experience unit provides an interactive experience in which the user can select options for progressing through the story. The feedback collection unit collects feedback from the user and continuously improves the quality of the story. The format provision unit provides the generated story in multiple formats. [Effects of the Invention]

[0007] An embodiment of the system can provide a customized narrative experience based on a user's interests and preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A story experience system according to an embodiment of the present invention is a system that provides a story customized based on a user's interests and preferences. In this system, a generation AI analyzes the user's interests and preferences, generates a story based on the analysis, and provides an interactive experience. This allows the story experience system to provide a story individually customized for the user, resulting in a deeper sense of immersion.

[0029] A story experience system according to an embodiment includes an interest analysis unit, a story generation unit, an interactive experience unit, a feedback collection unit, and a format provision unit. The interest analysis unit analyzes a user's interests and preferences. For example, the user inputs information such as their favorite genre, characters, and themes. The interest analysis unit can also perform analysis based on the user's past behavioral data and survey results. The story generation unit generates a customized story based on the user's interests and preferences analyzed by the interest analysis unit. For example, if a user inputs keywords such as "fantasy," "adventure," or "dragon," the generation AI generates a story that includes these elements. The interactive experience unit provides an interactive experience in which the user can select options for the progression of the story. For example, the AI ​​may present the user with options such as "Choose the next action the protagonist should take" during the story. The feedback collection unit collects user feedback and continuously improves the quality of the story. For example, if a user provides feedback such as "I want more action" about a particular part of the story, the feedback is reflected in the next story generation. The format provision unit provides the generated story in various formats. For example, the story can be provided not only in text format but also in audiobook format or visual novel format, thereby enabling the story experience system according to the embodiment to provide a story customized based on the user's interests and preferences, and to increase the user's engagement through an interactive experience.

[0030] The interest analysis unit analyzes the user's past reading history and viewing history to understand the user's interests and preferences in more detail. The interest analysis unit, for example, collects a history of books the user has read and movies and dramas they have watched in the past and analyzes this data. For example, it identifies preferences for specific genres and authors. The interest analysis unit also analyzes common themes and character trends based on the user's reading history and viewing history. For example, it clarifies preferences for genres such as fantasy and mystery. The interest analysis unit also analyzes the user's past reading history and viewing history to identify elements that the user is particularly interested in (e.g., adventure, romance, suspense). This makes it possible to provide more personalized stories. By analyzing the user's past reading history and viewing history, it is possible to understand the user's preferences in more detail.

[0031] The interest analysis unit can analyze a user's social media activities and posted content to collect information that complements the user's interests and preferences. For example, the interest analysis unit analyzes the content of a user's social media posts to identify the user's interests and preferences. For example, the interest analysis unit understands preferences based on articles and comments that the user frequently shares. The interest analysis unit also analyzes actions such as "likes" and "shares" on social media to identify themes and topics that the user is interested in. For example, it analyzes reactions to specific movies or books. The interest analysis unit also analyzes information on the user's followers and following accounts to complement the user's interests and preferences. For example, it infers preferences based on accounts with the same hobbies or interests. In this way, by analyzing the user's social media activities and posted content, it is possible to collect information that complements the user's interests and preferences.

[0032] When analyzing a user's interests and preferences, the interest analysis unit can also accept voice input and image input, and utilize multimodal information for the analysis. For example, the interest analysis unit analyzes information input by voice by the user to identify the user's interests and preferences. For example, if the user describes their favorite genres or characters by voice, that information is utilized for the analysis. The interest analysis unit also accepts image input and analyzes images of the user's favorite characters or scenes. For example, preferences are identified from images uploaded by the user. The interest analysis unit also integrates voice input and image input with text data to build a system that analyzes preferences based on multimodal information. For example, information obtained from both voice and images is utilized for the analysis. This enables more detailed preference analysis by utilizing voice input and image input for the analysis.

[0033] The interest analysis unit can compare the preferences of users of different age groups and cultural spheres and perform preference analysis from a global perspective. The interest analysis unit, for example, compares the preferences of users of different age groups and analyzes the differences in preferences by age. For example, it identifies the differences in preferences between young people and middle-aged and elderly people. The interest analysis unit also compares the preferences of users of different cultural spheres and analyzes the differences in preferences by culture. For example, it identifies the differences in preferences between Asian and Western users. The interest analysis unit also builds a system that comprehensively analyzes the preferences of users of different age groups and cultural spheres from a global perspective. For example, it grasps the preference trends by region and reflects them in the content of the story. This makes it possible to analyze preferences from a global perspective by comparing the preferences of different age groups and cultural spheres.

[0034] The story generation unit can provide a more personalized story by reflecting the user's real-life events and experiences. For example, the story generation unit collects the user's real-life events and experiences as input data, and the generation AI reflects them in the story. For example, the story generation unit incorporates places the user has traveled to and events the user has experienced as story elements. The story generation unit also generates a personalized story using the generation AI based on the user's past experiences and memories. For example, the user recreates specific events or occurrences in the story. The story generation unit also builds a system that provides more realistic and relatable stories by reflecting the user's real-life events and experiences. For example, it incorporates moving events that the user has experienced into the story. This allows the system to provide a more personalized story by reflecting the user's real-life events and experiences.

[0035] The story generation unit can imitate the style of a user's favorite authors and works. For example, the story generation unit collects the user's favorite authors and the style of their works as input data, and the generation AI imitates them. For example, it incorporates the writing style and storytelling techniques of a particular author. The story generation unit also analyzes the elements of the user's favorite works, and the generation AI reflects them in the story. For example, it imitates the characters and settings of a particular work. The story generation unit also builds a system in which the generation AI imitates the style of a user's favorite authors and works to suit the user's preferences. For example, it incorporates the characteristic expressions of the user's favorite authors into the story. In this way, by imitating the style of a user's favorite authors and works, it is possible to provide a story that is more suited to the user's preferences.

[0036] The format providing unit can convert the story generated by the generation AI into different media formats. For example, the format providing unit builds a system that converts the story generated by the generation AI into a manga format. For example, it divides story scenes into manga frames and draws characters and backgrounds. The format providing unit also develops a system that converts the story generated by the generation AI into an anime format. For example, it animates story scenes and reproduces character movements. The format providing unit also converts into different media formats, allowing users to enjoy the story in a variety of ways. For example, it converts a text-format story into a visual novel format. In this way, by converting into different media formats, users can enjoy the story in a variety of ways.

[0037] The format provider can combine music and sound effects selected by the user with the story generated by the generation AI to provide a more immersive experience. For example, the format provider builds a system that combines music and sound effects selected by the user with the story generated by the generation AI. For example, it plays appropriate music and sound effects to match the scenes in the story. The format provider also provides a more immersive experience by incorporating the music and sound effects selected by the user into the development of the story. For example, it adds powerful music to action scenes. The format provider also develops a system that allows the user to become more immersed in the story by combining music and sound effects. For example, it adds music that enhances emotions to moving scenes. In this way, the combination of music and sound effects allows the user to become more immersed in the story.

[0038] The interactive experience unit can provide multiple endings, where there are multiple story endings, depending on the user's choices. The interactive experience unit, for example, builds a system in which a generation AI generates multiple story endings depending on the user's choices. For example, different endings are prepared depending on the user's choices. The interactive experience unit also provides multiple endings, where the development of the story changes based on the user's choices. For example, the ending of the story changes depending on the action chosen by the user. Furthermore, by providing multiple endings, the interactive experience unit allows the user to become more deeply involved in the progression of the story. For example, by trying different choices, the user can enjoy multiple endings. This allows the ending of the story to change depending on the user's choices, providing a deeper level of involvement.

[0039] The interactive experience unit can add a function that allows a user to refer to the selection results of other users when selecting an option. The interactive experience unit, for example, builds a system that displays the selection results of other users in real time when a user is selecting an option. For example, it displays the selection rate for each option. The interactive experience unit also allows a user to refer to the selection results of other users, allowing the user to select an option based on more information. For example, it displays comments and ratings for each option. The interactive experience unit also enhances the sense of community by adding a function that allows a user to refer to the selection results of other users when selecting an option. For example, it displays the popularity of each option. This allows a user to refer to the selection results of other users, allowing the user to select an option based on more information.

[0040] The interactive experience unit can provide an interactive story experience in a multiplayer mode in which multiple users can participate simultaneously. The interactive experience unit, for example, builds a system that provides an interactive story experience in a multiplayer mode in which multiple users can participate simultaneously. For example, users cooperate to progress the story. The interactive experience unit also promotes communication between users by providing an interactive story experience in the multiplayer mode. For example, a chat function is added. The interactive experience unit also increases user involvement in the progression of the story by providing a multiplayer mode in which multiple users can participate simultaneously. For example, users discuss and decide on options. This allows multiple users to participate simultaneously, promoting communication between users and increasing their involvement in the progression of the story.

[0041] The interactive experience unit can provide visual and audio guidance related to options when the user selects an option. The interactive experience unit, for example, builds a system that provides visual and audio guidance related to options when the user selects an option. For example, it plays image images and explanatory audio for each option. The interactive experience unit also provides visual and audio guidance to make it easier for the user to understand the options. For example, it displays illustrations or videos that visually show the content of the options. The interactive experience unit also enhances the appeal of the options by providing visual and audio guidance when the user selects an option. For example, it displays animations that recreate scenes for each option. In this way, providing visual and audio guidance makes it easier for the user to understand the options.

[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 story experience system can further include a health analysis unit that analyzes the user's health data. For example, it can collect the user's heart rate and sleep data and adjust the content and pace of the story. If the user is feeling stressed, it can provide a relaxing story, and conversely, if the user is energetic, it can provide an action-packed story. The health analysis unit can also provide health advice and information within the story based on the user's health condition. For example, it can add scenes in the story in which characters practice healthy lifestyle habits. This makes it possible to provide a personalized story according to the user's health condition.

[0044] The story experience system can further include a learning analysis unit that analyzes the user's learning history. For example, it can collect information about what the user has learned in the past and topics that interest them and incorporate educational elements into the story. If the user is interested in a particular historical event, it can set that event as the background of the story. The learning analysis unit can also adjust the way the story progresses to suit the user's learning style. For example, it can provide a story in visual novel format to users who prefer visual learning, and in audiobook format to users who prefer auditory learning. This makes it possible to provide a personalized story based on the user's learning history.

[0045] The story experience system can further include a location information analysis unit that analyzes the user's geographical location information. For example, it can collect information about the user's current location and places the user has visited in the past and include those places in the story. If the user likes to travel, it can provide a story set in a city or country the user has visited. The location information analysis unit can also introduce local culture and customs in the story based on the user's location information. For example, if the user is in Japan, it can incorporate traditional Japanese festivals and food culture into the story. This makes it possible to provide a personalized story based on the user's geographical location information.

[0046] The story experience system may further include an interest analysis unit that analyzes the user's hobbies and special skills. For example, information about the user's hobbies, such as sports, art, and music, may be collected and those elements may be incorporated into the story. If the user's hobby is music, music-related scenes and characters may be added to the story. The interest analysis unit may also adjust the development of the story based on the user's special skills. For example, if the user is good at cooking, a cooking-related episode may be added to the story. This makes it possible to provide a personalized story based on the user's hobbies and special skills.

[0047] The story experience system may further include a family analysis unit that analyzes information about the user's family structure and pets. For example, the user can input information about their family and pets and incorporate that information into the story. If the user values ​​time spent with their family, a scene depicting the bond between the family members can be added to the story. The family analysis unit can also set characters in the story based on the user's family structure. For example, if the user has children, an episode depicting the parent-child relationship can be added to the story. This makes it possible to provide a personalized story based on the user's family structure and pets.

[0048] The story experience system can further include a career analysis unit that analyzes information about the user's occupation and career. For example, the user can input information about their current occupation and past career, and incorporate those elements into the story. If the user is a medical professional, medical episodes can be added to the story. The career analysis unit can also set a story theme based on the user's occupation. For example, if the user is an engineer, the story can develop a story about technological innovations and inventions. This makes it possible to provide a personalized story based on the user's occupation and career.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The interest analysis unit analyzes the user's interests and preferences. For example, the user inputs information such as their favorite genres, characters, and themes. The interest analysis unit can also perform analysis based on the user's past behavioral data and survey results. Step 2: The story generation unit generates a customized story based on the user's interests and preferences analyzed by the interest analysis unit. For example, if the user enters keywords such as "fantasy," "adventure," or "dragon," the generation AI will generate a story that includes these elements. Step 3: The interactive experience section provides an interactive experience in which the user can choose options for the progression of the story. For example, in the middle of the story, the user may be presented with options such as, "Please choose the next action the protagonist should take." Step 4: The feedback collector collects user feedback and continuously improves the quality of the story. For example, if a user provides feedback such as "I want more action" about a particular part of the story, this feedback will be reflected in the next story generation. Step 5: The format provider provides the generated story in various formats, such as not only a text story but also an audiobook or visual novel.

[0051] (Example 2) A story experience system according to an embodiment of the present invention is a system that provides a story customized based on a user's interests and preferences. In this system, a generation AI analyzes the user's interests and preferences, generates a story based on the analysis, and provides an interactive experience. This allows the story experience system to provide a story individually customized for the user, resulting in a deeper sense of immersion.

[0052] A story experience system according to an embodiment includes an interest analysis unit, a story generation unit, an interactive experience unit, a feedback collection unit, and a format provision unit. The interest analysis unit analyzes a user's interests and preferences. For example, the user inputs information such as their favorite genre, characters, and themes. The interest analysis unit can also perform analysis based on the user's past behavioral data and survey results. The story generation unit generates a customized story based on the user's interests and preferences analyzed by the interest analysis unit. For example, if a user inputs keywords such as "fantasy," "adventure," or "dragon," the generation AI generates a story that includes these elements. The interactive experience unit provides an interactive experience in which the user can select options for the progression of the story. For example, the AI ​​may present the user with options such as "Choose the next action the protagonist should take" during the story. The feedback collection unit collects user feedback and continuously improves the quality of the story. For example, if a user provides feedback such as "I want more action" about a particular part of the story, the feedback is reflected in the next story generation. The format provision unit provides the generated story in various formats. For example, the story can be provided not only in text format but also in audiobook format or visual novel format, thereby enabling the story experience system according to the embodiment to provide a story customized based on the user's interests and preferences, and to increase the user's engagement through an interactive experience.

[0053] The interest analysis unit analyzes the user's past reading history and viewing history to understand the user's interests and preferences in more detail. The interest analysis unit, for example, collects a history of books the user has read and movies and dramas they have watched in the past and analyzes this data. For example, it identifies preferences for specific genres and authors. The interest analysis unit also analyzes common themes and character trends based on the user's reading history and viewing history. For example, it clarifies preferences for genres such as fantasy and mystery. The interest analysis unit also analyzes the user's past reading history and viewing history to identify elements that the user is particularly interested in (e.g., adventure, romance, suspense). This makes it possible to provide more personalized stories. By analyzing the user's past reading history and viewing history, it is possible to understand the user's preferences in more detail.

[0054] The interest analysis unit can analyze a user's social media activities and posted content to collect information that complements the user's interests and preferences. For example, the interest analysis unit analyzes the content of a user's social media posts to identify the user's interests and preferences. For example, the interest analysis unit understands preferences based on articles and comments that the user frequently shares. The interest analysis unit also analyzes actions such as "likes" and "shares" on social media to identify themes and topics that the user is interested in. For example, it analyzes reactions to specific movies or books. The interest analysis unit also analyzes information on the user's followers and following accounts to complement the user's interests and preferences. For example, it infers preferences based on accounts with the same hobbies or interests. In this way, by analyzing the user's social media activities and posted content, it is possible to collect information that complements the user's interests and preferences.

[0055] The interest analysis unit uses the emotion estimation function to analyze the emotional response to information entered by the user, enabling more accurate preference analysis. The interest analysis unit, for example, analyzes the emotional response to information entered by the user in real time to identify preferences. For example, if the user expresses positive emotions toward a particular genre, that genre is prioritized. The interest analysis unit also uses the emotion estimation function to calculate an emotion score for keywords or phrases entered by the user and reflect this in the preference analysis. For example, elements with a high emotion score are emphasized. The interest analysis unit also builds a system that analyzes preferences in more detail based on the user's emotional response data. For example, if the user expresses strong emotions toward a particular character or theme, a story containing that element is provided. In this way, the emotion estimation function can improve the accuracy of user preference analysis.

[0056] When analyzing a user's interests and preferences, the interest analysis unit can also accept voice input and image input, and utilize multimodal information for the analysis. For example, the interest analysis unit analyzes information input by voice by the user to identify the user's interests and preferences. For example, if the user describes their favorite genres or characters by voice, that information is utilized for the analysis. The interest analysis unit also accepts image input and analyzes images of the user's favorite characters or scenes. For example, preferences are identified from images uploaded by the user. The interest analysis unit also integrates voice input and image input with text data to build a system that analyzes preferences based on multimodal information. For example, information obtained from both voice and images is utilized for the analysis. This enables more detailed preference analysis by utilizing voice input and image input for the analysis.

[0057] The interest analysis unit can compare the preferences of users of different age groups and cultural spheres and perform preference analysis from a global perspective. The interest analysis unit, for example, compares the preferences of users of different age groups and analyzes the differences in preferences by age. For example, it identifies the differences in preferences between young people and middle-aged and elderly people. The interest analysis unit also compares the preferences of users of different cultural spheres and analyzes the differences in preferences by culture. For example, it identifies the differences in preferences between Asian and Western users. The interest analysis unit also builds a system that comprehensively analyzes the preferences of users of different age groups and cultural spheres from a global perspective. For example, it grasps the preference trends by region and reflects them in the content of the story. This makes it possible to analyze preferences from a global perspective by comparing the preferences of different age groups and cultural spheres.

[0058] The interest analysis unit can use the emotion estimation function to suggest new genres and themes that the user may be interested in. For example, the interest analysis unit uses the emotion estimation function to build a system that suggests new genres and themes that the user may be interested in. For example, it recommends new genres based on the user's emotion score. The interest analysis unit also analyzes the user's emotional response data to identify new themes that the user may be interested in. For example, it suggests themes that include elements that the user expresses positive emotions in. The interest analysis unit also uses the emotion estimation function to suggest new genres and themes that the user has not yet tried. For example, it recommends new story elements based on the user's preferences. In this way, the emotion estimation function can be used to suggest new genres and themes that the user may be interested in.

[0059] The story generation unit can provide a more personalized story by reflecting the user's real-life events and experiences. For example, the story generation unit collects the user's real-life events and experiences as input data, and the generation AI reflects them in the story. For example, the story generation unit incorporates places the user has traveled to and events the user has experienced as story elements. The story generation unit also generates a personalized story using the generation AI based on the user's past experiences and memories. For example, the user recreates specific events or occurrences in the story. The story generation unit also builds a system that provides more realistic and relatable stories by reflecting the user's real-life events and experiences. For example, it incorporates moving events that the user has experienced into the story. This allows the system to provide a more personalized story by reflecting the user's real-life events and experiences.

[0060] The story generation unit can imitate the style of a user's favorite authors and works. For example, the story generation unit collects the user's favorite authors and the style of their works as input data, and the generation AI imitates them. For example, it incorporates the writing style and storytelling techniques of a particular author. The story generation unit also analyzes the elements of the user's favorite works, and the generation AI reflects them in the story. For example, it imitates the characters and settings of a particular work. The story generation unit also builds a system in which the generation AI imitates the style of a user's favorite authors and works to suit the user's preferences. For example, it incorporates the characteristic expressions of the user's favorite authors into the story. In this way, by imitating the style of a user's favorite authors and works, it is possible to provide a story that is more suited to the user's preferences.

[0061] The format providing unit can convert the story generated by the generation AI into different media formats. For example, the format providing unit builds a system that converts the story generated by the generation AI into a manga format. For example, it divides story scenes into manga frames and draws characters and backgrounds. The format providing unit also develops a system that converts the story generated by the generation AI into an anime format. For example, it animates story scenes and reproduces character movements. The format providing unit also converts into different media formats, allowing users to enjoy the story in a variety of ways. For example, it converts a text-format story into a visual novel format. In this way, by converting into different media formats, users can enjoy the story in a variety of ways.

[0062] The format provider can combine music and sound effects selected by the user with the story generated by the generation AI to provide a more immersive experience. For example, the format provider builds a system that combines music and sound effects selected by the user with the story generated by the generation AI. For example, it plays appropriate music and sound effects to match the scenes in the story. The format provider also provides a more immersive experience by incorporating the music and sound effects selected by the user into the development of the story. For example, it adds powerful music to action scenes. The format provider also develops a system that allows the user to become more immersed in the story by combining music and sound effects. For example, it adds music that enhances emotions to moving scenes. In this way, the combination of music and sound effects allows the user to become more immersed in the story.

[0063] The format providing unit can use the emotion estimation function to adjust the details and development of a scene based on the emotion a user feels in a particular scene in a story. For example, the format providing unit uses the emotion estimation function to build a system that analyzes the emotion a user feels in a particular scene in a story in real time. For example, the format providing unit analyzes the user's facial expressions and voice and calculates an emotion score. The format providing unit also adjusts the details and development of a story scene based on the user's emotional response data. For example, if the user is excited, an action scene is added. The format providing unit also uses the emotion estimation function to develop a system that dynamically changes a story scene according to the user's emotional response. For example, if the user is sad, an emotional scene is added. In this way, the emotion estimation function can be used to adjust the details and development of a story scene according to the user's emotion.

[0064] The interactive experience unit can provide multiple endings, where there are multiple story endings, depending on the user's choices. The interactive experience unit, for example, builds a system in which a generation AI generates multiple story endings depending on the user's choices. For example, different endings are prepared depending on the user's choices. The interactive experience unit also provides multiple endings, where the development of the story changes based on the user's choices. For example, the ending of the story changes depending on the action chosen by the user. Furthermore, by providing multiple endings, the interactive experience unit allows the user to become more deeply involved in the progression of the story. For example, by trying different choices, the user can enjoy multiple endings. This allows the ending of the story to change depending on the user's choices, providing a deeper level of involvement.

[0065] The interactive experience unit can add a function that allows a user to refer to the selection results of other users when selecting an option. The interactive experience unit, for example, builds a system that displays the selection results of other users in real time when a user is selecting an option. For example, it displays the selection rate for each option. The interactive experience unit also allows a user to refer to the selection results of other users, allowing the user to select an option based on more information. For example, it displays comments and ratings for each option. The interactive experience unit also enhances the sense of community by adding a function that allows a user to refer to the selection results of other users when selecting an option. For example, it displays the popularity of each option. This allows a user to refer to the selection results of other users, allowing the user to select an option based on more information.

[0066] The interactive experience unit can use the emotion estimation function to analyze the emotional response of the user when selecting an option and adjust the way the options are presented. For example, the interactive experience unit uses the emotion estimation function to build a system that analyzes the emotional response of the user when selecting an option in real time. For example, the interactive experience unit analyzes the user's facial expressions and voice and calculates an emotion score. The interactive experience unit also adjusts the way options are presented based on the user's emotional response data. For example, if the user is nervous, options that will help them relax are presented. The interactive experience unit also uses the emotion estimation function to develop a system that dynamically changes the way options are presented depending on the user's emotional response. For example, if the user is excited, options that require more action are presented. In this way, the emotion estimation function can be used to adjust the way options are presented depending on the user's emotions.

[0067] The interactive experience unit can provide an interactive story experience in a multiplayer mode in which multiple users can participate simultaneously. The interactive experience unit, for example, builds a system that provides an interactive story experience in a multiplayer mode in which multiple users can participate simultaneously. For example, users cooperate to progress the story. The interactive experience unit also promotes communication between users by providing an interactive story experience in the multiplayer mode. For example, a chat function is added. The interactive experience unit also increases user involvement in the progression of the story by providing a multiplayer mode in which multiple users can participate simultaneously. For example, users discuss and decide on options. This allows multiple users to participate simultaneously, promoting communication between users and increasing their involvement in the progression of the story.

[0068] The interactive experience unit can provide visual and audio guidance related to options when the user selects an option. The interactive experience unit, for example, builds a system that provides visual and audio guidance related to options when the user selects an option. For example, it plays image images and explanatory audio for each option. The interactive experience unit also provides visual and audio guidance to make it easier for the user to understand the options. For example, it displays illustrations or videos that visually show the content of the options. The interactive experience unit also enhances the appeal of the options by providing visual and audio guidance when the user selects an option. For example, it displays animations that recreate scenes for each option. In this way, providing visual and audio guidance makes it easier for the user to understand the options.

[0069] The interactive experience unit can use the emotion estimation function to customize the next option based on the emotional reaction of the user when selecting an option. The interactive experience unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reaction of the user when selecting an option in real time. For example, the interactive experience unit analyzes the user's facial expressions and voice and calculates an emotion score. The interactive experience unit also customizes the next option based on the user's emotional reaction data. For example, if the user is excited, options with high action potential are presented. The interactive experience unit also uses the emotion estimation function to develop a system that dynamically changes the next option according to the user's emotional reaction. For example, if the user is relaxed, options that will help them relax are presented. In this way, the emotion estimation function can be used to customize the next option according to the user's emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The story experience system can further include a health analysis unit that analyzes the user's health data. For example, it can collect the user's heart rate and sleep data and adjust the content and pace of the story. If the user is feeling stressed, it can provide a relaxing story, and conversely, if the user is energetic, it can provide an action-packed story. The health analysis unit can also provide health advice and information within the story based on the user's health condition. For example, it can add scenes in the story in which characters practice healthy lifestyle habits. This makes it possible to provide a personalized story according to the user's health condition.

[0072] The story experience system can further include a learning analysis unit that analyzes the user's learning history. For example, it can collect information about what the user has learned in the past and topics that interest them and incorporate educational elements into the story. If the user is interested in a particular historical event, it can set that event as the background of the story. The learning analysis unit can also adjust the way the story progresses to suit the user's learning style. For example, it can provide a story in visual novel format to users who prefer visual learning, and in audiobook format to users who prefer auditory learning. This makes it possible to provide a personalized story based on the user's learning history.

[0073] The story experience system can further include a location information analysis unit that analyzes the user's geographical location information. For example, it can collect information about the user's current location and places the user has visited in the past and include those places in the story. If the user likes to travel, it can provide a story set in a city or country the user has visited. The location information analysis unit can also introduce local culture and customs in the story based on the user's location information. For example, if the user is in Japan, it can incorporate traditional Japanese festivals and food culture into the story. This makes it possible to provide a personalized story based on the user's geographical location information.

[0074] The story experience system can also use the user's emotion estimation function to change the emotions of characters in the story in real time. For example, the emotion estimation function analyzes the user's emotions while the user is reading a story and changes the character's facial expressions and behavior to match those emotions. If the user is sad, a scene in which the character is also sad can be added. The emotion estimation function can also be used to adjust the development of the story according to the user's emotions. For example, if the user is excited, an action scene can be added, and if the user is relaxed, a calm scene can be added. This makes it possible to provide a personalized story that matches the user's emotions.

[0075] The story experience system can further use the user's emotion estimation function to change the music and sound effects in real time during the story. For example, the emotion estimation function can analyze the user's emotions while the user is reading a story and change the music and sound effects to match the emotions. If the user is nervous, tense music can be played, and if the user is relaxed, calm music can be played. The emotion estimation function can also be used to adjust the volume and tempo of the music and sound effects according to the user's emotions. For example, if the user is excited, the tempo of the music can be increased, and if the user is relaxed, the tempo can be decreased. In this way, music and sound effects can be provided that match the user's emotions.

[0076] The story experience system can further use the user's emotion estimation function to change the character's dialogue in real time within the story. For example, while the user is reading the story, the emotion estimation function analyzes the user's emotions and changes the character's dialogue to match those emotions. If the user is happy, the character can add dialogue that expresses similar joy. The emotion estimation function can also adjust the tone and tempo of the dialogue according to the user's emotions. For example, if the user is excited, the tempo of the dialogue can be made faster, and if the user is relaxed, the tempo can be made slower. This makes it possible to provide personalized dialogue according to the user's emotions.

[0077] The story experience system can further use a user emotion estimation function to reflect environmental changes in the story in real time. For example, the emotion estimation function can analyze the user's emotions while the user is reading a story and change the story environment to match the emotions. If the user is sad, a rainy scene can be added, and if the user is happy, a sunny scene can be added. The emotion estimation function can also be used to adjust environmental sounds and background sounds according to the user's emotions. For example, if the user is relaxed, the sound of birds chirping and wind can be added, and if the user is nervous, the sound of a storm can be added. This makes it possible to provide environmental changes that match the user's emotions.

[0078] The story experience system may further include an interest analysis unit that analyzes the user's hobbies and special skills. For example, information about the user's hobbies, such as sports, art, and music, may be collected and those elements may be incorporated into the story. If the user's hobby is music, music-related scenes and characters may be added to the story. The interest analysis unit may also adjust the development of the story based on the user's special skills. For example, if the user is good at cooking, a cooking-related episode may be added to the story. This makes it possible to provide a personalized story based on the user's hobbies and special skills.

[0079] The story experience system may further include a family analysis unit that analyzes information about the user's family structure and pets. For example, the user can input information about their family and pets and incorporate that information into the story. If the user values ​​time spent with their family, a scene depicting the bond between the family members can be added to the story. The family analysis unit can also set characters in the story based on the user's family structure. For example, if the user has children, an episode depicting the parent-child relationship can be added to the story. This makes it possible to provide a personalized story based on the user's family structure and pets.

[0080] The story experience system can further include a career analysis unit that analyzes information about the user's occupation and career. For example, the user can input information about their current occupation and past career, and incorporate those elements into the story. If the user is a medical professional, medical episodes can be added to the story. The career analysis unit can also set a story theme based on the user's occupation. For example, if the user is an engineer, the story can develop a story about technological innovations and inventions. This makes it possible to provide a personalized story based on the user's occupation and career.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The interest analysis unit analyzes the user's interests and preferences. For example, the user inputs information such as their favorite genres, characters, and themes. The interest analysis unit can also perform analysis based on the user's past behavioral data and survey results. Step 2: The story generation unit generates a customized story based on the user's interests and preferences analyzed by the interest analysis unit. For example, if the user enters keywords such as "fantasy," "adventure," or "dragon," the generation AI will generate a story that includes these elements. Step 3: The interactive experience section provides an interactive experience in which the user can choose options for the progression of the story. For example, in the middle of the story, the user may be presented with options such as, "Please choose the next action the protagonist should take." Step 4: The feedback collector collects user feedback and continuously improves the quality of the story. For example, if a user provides feedback such as "I want more action" about a particular part of the story, this feedback will be reflected in the next story generation. Step 5: The format provider provides the generated story in various formats, such as not only a text story but also an audiobook or visual novel.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an interest analysis unit that analyzes the user's interests and preferences; a story generation unit that generates a customized story based on the user's interests and preferences analyzed by the interest analysis unit; an interactive experience unit that provides an interactive experience in which the user can select options for the progression of the story; a feedback collection unit that collects feedback from the users and continuously improves the quality of the stories; a format providing unit that provides the generated story in multiple formats; A system characterized by:

2. The interest analysis unit Analyzing the user's past reading and viewing history to understand the user's interests and preferences in more detail 2. The system of claim 1.

3. The story generation unit Provide a more personalized story that reflects the user's real-life events and experiences 2. The system of claim 1.

4. The format providing unit Converting the story generated by the generative AI into a different media format 2. The system of claim 1.

5. The interactive experience unit includes: Providing multiple endings to the story depending on the user's choices 2. The system of claim 1.

6. The interest analysis unit Analyzing the emotional response to the information entered by the user to perform more accurate preference analysis 2. The system of claim 1.

7. The story generation unit Analyzing the emotional response of the user while reading the story in real time and adjusting the development of the story 2. The system of claim 1.

8. The interactive experience unit includes: Analyzing the emotional response of the user when selecting an option and adjusting the way the option is presented.

2. The system of claim 1.

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