system

Abstract

An object of the system according to the embodiment is to reconstruct a story based on a user's experience and memory and generate the story as an RPG game.SOLUTION: A system according to an embodiment includes a data upload unit, a story generation unit, and a game generation unit. The data upload unit uploads data of a diary, a resume, and a photograph taken by the user with a smartphone. The story generation unit reconstructs the life of the user as a story based on the data uploaded by the data upload unit. The game generation unit generates an RPG game based on the story generated by the story generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have limited means for effectively recreating and sharing personal experiences and memories with others, leaving room for improvement.

[0005] The system according to the embodiment aims to reconstruct a story based on the user's experiences and memories and generate it as an RPG game. [Means for solving the problem]

[0006] The system according to the embodiment includes a data upload unit, a story generation unit, and a game generation unit. The data upload unit uploads data such as a user's diary, resume, and photos taken with a smartphone. The story generation unit reconstructs the user's life as a story based on the data uploaded by the data upload unit. The game generation unit generates an RPG game based on the story generated by the story generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reconstruct a story based on the user's experiences and memories and generate it as an RPG game. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The RPG game generation system according to an embodiment of the present invention allows users to upload data such as diary entries, resumes, and photos taken with their smartphones. Based on this data, a generation AI reconstructs the user's life story and generates an RPG game. This allows the RPG game generation system to generate an RPG game based on the user's experiences, which the user can play themselves or sell to others. It can also be broadcast as a digest at the user's funeral.

[0029] An RPG game generation system according to an embodiment includes a data upload unit, a story generation unit, and a game generation unit. The data upload unit uploads data such as a user's diary, resume, and photos taken with a smartphone. For example, a diary records daily events and emotions, and a resume lists educational and work history. Photos taken with a smartphone capture images of trips and events. The story generation unit reconstructs the user's life as a story based on the data uploaded by the data upload unit. For example, the story generation unit extracts important events and emotional changes from the diary, identifies educational and work history from the resume, and recreates trips and events from the photos. The generation AI generates a story based on prompts containing instructions from the user regarding what the generation AI wants the user to do. The game generation unit generates an RPG game based on the story generated by the story generation unit. For example, the game generation unit sets important events in the user's life as quests and recreates places and people the user has experienced as characters and stages in the game. The generation AI creates a game scenario based on the flow of the story, allowing the player to progress through the game according to the scenario. This allows the RPG game generation system to generate an RPG game based on the user's experiences.

[0030] The data upload unit automatically tags uploaded data, allowing it to be classified and organized. For example, when a user uploads a diary, resume, or photos, the generation AI automatically tags each piece of data. For example, tags such as "emotion," "event," and "place" are added based on the contents of the diary, and tags such as "educational background" and "work history" are added to resumes. The generation AI also uses image recognition technology to tag uploaded photos. For example, it recognizes the people and places in the photo and adds tags such as "family," "travel," and "event." The generation AI also analyzes data uploaded by the user and automatically adds relevant tags. For example, it extracts tags such as "emotion," "event," and "place" from the contents of the diary and classifies the data. This allows data to be automatically classified and organized.

[0031] The data upload unit can automatically collect external data related to uploaded data and complement the data. For example, when a user uploads a diary, the data upload unit's generation AI automatically collects related news articles and social media posts to complement the diary content. For example, it displays news articles related to a specific event. In addition, when an uploaded resume is submitted, the data upload unit's generation AI collects related industry news and trend information to complement the resume content. For example, it displays industry news related to a specific work history. In addition, when a user uploads a photo, the data upload unit's generation AI collects related external data to complement the photo content. For example, it displays tourist information about a travel destination or event details. This allows data to be complemented automatically.

[0032] The data upload unit can automatically evaluate the quality of uploaded data and filter out low-quality data. For example, when a user uploads a diary, the generation AI automatically evaluates the quality of the data and filters out low-quality diary entries. For example, it excludes diary entries with unclear content. In addition, the data upload unit has the generation AI evaluate the quality of uploaded resumes and filter out low-quality resumes. For example, it excludes resumes with insufficient information. In addition, the data upload unit has the generation AI evaluate the quality of photos uploaded by the user and filter out low-quality photos. For example, it excludes photos with low resolution or blurry photos. This makes it possible to automatically filter out low-quality data.

[0033] The story generation unit can provide prompts to reflect the user's values ​​or beliefs. For example, when the generation AI generates a user's life story, the story generation unit provides prompts to reflect the user's values ​​and beliefs. For example, it asks a question such as, "What values ​​do you value most in your life?" The story generation unit also provides prompts to reflect the user's values ​​and beliefs, and generates a story based on the user's answers. For example, it asks a question such as, "Tell me what you believe and what you value." The story generation unit also provides prompts to reflect the user's values ​​and beliefs, and generates a story based on the user's answers. For example, it asks a question such as, "What event has had the greatest impact on your life?" This makes it possible to generate a story that reflects the user's values ​​and beliefs.

[0034] The story generation unit can incorporate interviews with the user's friends or family to reflect a more multifaceted perspective. For example, when the generation AI generates the user's life story, the story generation unit incorporates interviews with the user's friends and family. For example, it asks friends and family questions such as, "What is the most memorable event in the user's life?" The story generation unit also incorporates interviews with the user's friends and family, and the generation AI generates a story that reflects a more multifaceted perspective. For example, it asks questions such as, "Please tell us what kind of person the user is." The story generation unit also incorporates interviews with the user's friends and family, and the generation AI generates the user's life story. For example, it asks questions such as, "What is the most moving moment in the user's life?" This makes it possible to generate a story that reflects a multifaceted perspective.

[0035] The story generation unit can incorporate different cultures and historical backgrounds to generate stories that promote intercultural exchange. For example, when the generation AI generates a user's life story, the story generation unit incorporates different cultures and historical backgrounds. For example, it generates a story that reflects the culture and history of the user's ancestors. Furthermore, the story generation unit generates stories that incorporate different cultures and historical backgrounds to promote intercultural exchange. For example, it adds an episode in which the user interacts with people from a different culture. Furthermore, when the generation AI generates a user's life story, the story generation unit incorporates different cultures and historical backgrounds to generate stories that promote intercultural exchange. For example, it adds an episode in which the user experiences events from a different era. In this way, it is possible to generate stories that promote intercultural exchange.

[0036] The story generation unit can incorporate fictional elements to enhance the entertainment value. For example, the story generation unit incorporates fictional elements when the generation AI generates the user's life story. For example, it adds an episode in which the user experiences a fictional adventure or a fantasy world. Furthermore, the story generation unit generates a story that incorporates fictional elements to enhance the entertainment value. For example, it adds an episode in which the user plays the role of a superhero. Furthermore, the story generation unit incorporates fictional elements when the generation AI generates the user's life story to enhance the entertainment value. For example, it adds an episode in which the user adventures in a futuristic world. This makes it possible to generate a story with enhanced entertainment value.

[0037] The game generation unit can recreate important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit uses a generation AI to recreate important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit recreates choices such as where to go to school or where to work in the game, allowing the player to experience different stories by choosing different choices. The game generation unit also recreates important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit recreates important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit recreates important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit recreates important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit recreates important choices in the user's life in the game, allowing the player to try different choices.

[0038] The game generation unit can emphasize emotional highlights in the user's life and create moving scenes. For example, the generation AI in the game generation unit can emphasize emotional highlights in the user's life and create moving scenes. For example, moving moments such as weddings and the birth of a child can be recreated in the game, allowing the player to experience those emotions. The game generation unit can also emphasize emotional highlights in the user's life and create moving scenes. For example, moving moments such as graduations and farewells to loved ones can be recreated in the game, allowing the player to experience those emotions. The game generation unit can also emphasize emotional highlights in the user's life and create moving scenes. For example, moving moments such as the realization of dreams and overcoming difficulties can be recreated in the game, allowing the player to experience those emotions. In this way, moving scenes can be created.

[0039] The game generation unit can convert events in the user's life into a fantasy or science fiction setting and generate games of different genres. For example, the generation AI of the game generation unit converts events in the user's life into a fantasy setting and generates games of different genres. For example, an important event in the user's life is recreated as a fantasy RPG incorporating elements of magic and adventure. The game generation unit can also convert events in the user's life into a science fiction setting and generate games of different genres. For example, an important event in the user's life is recreated as a science fiction RPG incorporating elements of future technology and space exploration. The game generation unit can also convert events in the user's life into a fantasy or science fiction setting and generate games of different genres. For example, an important event in the user's life is recreated as an adventure in an alternate world or a futuristic world. This allows games of different genres to be generated.

[0040] The game generation unit can combine the life stories of multiple users to generate a multiplayer game. For example, the generation AI of the game generation unit combines the life stories of multiple users to generate a multiplayer game. For example, important events in the lives of multiple users are recreated in a single game, and players cooperate to complete a quest. The game generation unit also combines the life stories of multiple users to generate a multiplayer game. For example, users interact with each other in the game as different characters to achieve a common goal. The game generation unit also combines the life stories of multiple users to generate a multiplayer game. For example, multiple players cooperate to complete a quest based on important events in the users' lives. This makes it possible to generate a multiplayer game.

[0041] The game generation unit can introduce multiple endings in which the story branches depending on the choices made by the player when playing the generated RPG game. The game generation unit, for example, introduces multiple endings in the generated RPG game, so that the story branches depending on the choices made by the player. For example, different endings can be reached by the player choosing different choices. The game generation unit also introduces multiple endings in which the story branches depending on the choices made by the player. For example, different endings or epilogues are prepared depending on the choices made by the player. The game generation unit also introduces multiple endings in the generated RPG game, so that the story branches depending on the choices made by the player. For example, the fates of different characters or the development of the story changes depending on the choices made by the player. This makes it possible to introduce multiple endings in which the story branches depending on the choices made by the player.

[0042] The game generation unit can introduce a system in which character growth or skills change depending on the player's actions when playing the generated RPG game. The game generation unit, for example, introduces a system in which character growth or skills change into the generated RPG game. For example, the character's abilities and skills change depending on the player's actions and choices. The game generation unit also introduces a system in which character growth or skills change depending on the player's actions. For example, character growth differs depending on the results of quests and battles selected by the player. The game generation unit also introduces a system in which character growth or skills change into the generated RPG game. For example, the character's skill tree changes depending on the player's choices and actions, allowing different play styles to be enjoyed. This makes it possible to introduce a system in which character growth or skills change depending on the player's actions.

[0043] The game generation unit can provide a customization function that allows a purchaser to add their own life story when selling the generated RPG game to another person. The game generation unit, for example, provides a customization function that allows a purchaser to add their own life story when selling the generated RPG game to another person. For example, the purchaser can add their own experiences and events into the game. The game generation unit also provides a customization function that allows a purchaser to add their own life story. For example, the purchaser can upload their own photos and diary entries and reflect them in the game. The game generation unit also provides a customization function that allows a purchaser to add their own life story when selling the generated RPG game to another person. For example, the purchaser can reflect their own choices and actions in the game to create an original story. This makes it possible to provide a customization function that allows a purchaser to add their own life story.

[0044] When selling the generated RPG game to others, the game generation unit can introduce social functions that allow players to interact with other players within the game. The game generation unit, for example, introduces social functions into the generated RPG game to allow players to interact with other players within the game. For example, it adds a chat function or a friend list. The game generation unit also introduces social functions that allow players to interact with other players within the game. For example, it adds a function that allows players to cooperate with each other to complete quests or compete against each other. The game generation unit also introduces social functions into the generated RPG game to allow players to interact with other players within the game. For example, it adds a function that allows players to exchange items with each other and send messages. This allows the introduction of social functions that allow players to interact with other players.

[0045] The system can edit digest videos to be aired at funerals to emphasize moving moments in the user's life. For example, the system edits digest videos to be aired at funerals to emphasize moving moments in the user's life. For example, moving moments such as weddings and the birth of a child are incorporated into the video. The system also edits digest videos to emphasize moving moments in the user's life and creates digest videos to be aired at funerals. For example, moving moments such as graduations and farewells to loved ones are incorporated into the video. The system also edits digest videos to be aired at funerals to emphasize moving moments in the user's life. For example, moving moments such as the realization of dreams and overcoming difficulties are incorporated into the video. In this way, editing can be performed to emphasize moving moments.

[0046] The system can add messages from the user's friends or family to the digest video to be aired at the funeral, deepening the impact. For example, the system can add messages from the user's friends or family to the digest video to be aired at the funeral. For example, it can incorporate video in which the friends and family share their memories and words of gratitude for the user. The system can also add messages from the user's friends and family to create a digest video to be aired at the funeral. For example, it can incorporate video in which the friends and family share their memories with the user. The system can also add messages from the user's friends and family to the digest video to be aired at the funeral. For example, it can incorporate video in which the friends and family share their words of gratitude and messages of encouragement for the user. In this way, the system can add messages from the friends and family to deepen the impact.

[0047] The system can recreate important events in the user's life using animation or CG in a digest video to be aired at the funeral. For example, the system recreates important events in the user's life using animation in a digest video to be aired at the funeral. For example, important events such as a wedding or the birth of a child are depicted using animation. The system also recreates important events in the user's life using CG to create a digest video to be aired at the funeral. For example, important events such as a graduation ceremony or a farewell to a loved one are depicted using CG. The system also recreates important events in the user's life using animation or CG in a digest video to be aired at the funeral. For example, important events such as the realization of a dream or overcoming difficulties are depicted using animation or CG. In this way, important events can be recreated using animation or CG.

[0048] The system can use music or narration to depict events in the user's life in a digest video to be aired at a funeral. For example, the system uses music to depict events in the user's life in a digest video to be aired at a funeral. For example, important events in the user's life are depicted in video against the backdrop of moving music. The system also uses narration to depict events in the user's life, creating a digest video to be aired at the funeral. For example, a video is created in which a narrator tells the user's life story. The system also uses music and narration to depict events in the user's life in a digest video to be aired at a funeral. For example, a video is created that depicts important events in the user's life by combining moving music and narration. In this way, life events can be depicted with music and narration.

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

[0050] The data upload unit can automatically perform voice recognition on uploaded data and convert the voice data into text. For example, when a user uploads a voice memo, the generation AI automatically converts the voice into text and adds it to the contents of a diary or resume. The data upload unit can also use voice recognition technology to extract important keywords from the uploaded voice data and tag it. For example, it can tag the content of the voice memo with words such as "meeting," "idea," and "emotion." Furthermore, the data upload unit can analyze the voice data and infer the user's emotions and intentions. This allows for effective use of voice data and automatic classification and organization of data.

[0051] The data upload unit can automatically filter uploaded data to protect privacy. For example, when a user uploads a diary or photos, the generation AI automatically detects personal or confidential information and masks or deletes it. The data upload unit can also anonymize uploaded data to protect privacy. For example, personal information such as names and addresses can be anonymized to ensure data security. Furthermore, the data upload unit can provide privacy protection guidelines for data uploaded by users. This allows data to be used while protecting the user's privacy.

[0052] The data upload unit can automatically translate uploaded data and integrate data in different languages. For example, if a user uploads a diary or resume written in different languages, the generation AI automatically translates it and organizes the data in a unified language. The data upload unit can also use the translation function to tag the uploaded data in multiple languages. For example, it can tag the uploaded data in multiple languages, such as "emotion," "event," and "place," based on the diary content. Furthermore, the data upload unit can analyze data in different languages ​​and infer the user's emotions and intentions. This allows data in different languages ​​to be effectively integrated and automatically classified and organized.

[0053] The story generation unit can automatically add visual effects to a user's life story to create a visual presentation. For example, the generation AI analyzes the user's life story and automatically selects visual effects suitable for important scenes and adds them to the story. The story generation unit can also customize visual effects based on the user's emotions to enhance the visual impact of the story. For example, it can adjust the color and movement of effects based on the emotions the user felt in a particular scene. Furthermore, the story generation unit can suggest visual effects according to the user's preferences to enhance the presentation of the story. This allows for visual presentation using visual effects.

[0054] The story generation unit can automatically add narration to a user's life story to deepen understanding of the story. For example, the generation AI analyzes the user's life story and automatically generates narration appropriate for important scenes and adds it to the story. The story generation unit can also customize the narration based on the user's emotions to enhance the emotional impact of the story. For example, it can adjust the tone and content of the narration based on the emotions the user felt in a particular scene. Furthermore, the story generation unit can suggest narration based on the user's preferences to enhance the story's presentation. This allows for a deeper understanding of the story using narration.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The data uploading unit uploads data such as a diary, resume, and photos taken with a smartphone. For example, a diary records daily events and emotions, a resume lists educational and work history, and photos taken with a smartphone show travel and event scenes. Step 2: The story generation unit reconstructs the user's life as a story based on the data uploaded by the data upload unit. For example, it extracts important events and emotional changes from diary entries, understands educational and work history from resume information, and recreates trips and events from photos. The generation AI generates a story based on prompts that include instructions on what the user wants the generation AI to do. Step 3: The game generation unit generates an RPG game based on the story generated by the story generation unit. For example, it may set important events in the user's life as quests, and recreate the places and people the user has experienced as characters and stages in the game. The generation AI creates a game scenario based on the flow of the story, allowing the player to progress through the game according to that scenario.

[0057] (Example 2) The RPG game generation system according to an embodiment of the present invention allows users to upload data such as diary entries, resumes, and photos taken with their smartphones. Based on this data, a generation AI reconstructs the user's life story and generates an RPG game. This allows the RPG game generation system to generate an RPG game based on the user's experiences, which the user can play themselves or sell to others. It can also be broadcast as a digest at the user's funeral.

[0058] An RPG game generation system according to an embodiment includes a data upload unit, a story generation unit, and a game generation unit. The data upload unit uploads data such as a user's diary, resume, and photos taken with a smartphone. For example, a diary records daily events and emotions, and a resume lists educational and work history. Photos taken with a smartphone capture images of trips and events. The story generation unit reconstructs the user's life as a story based on the data uploaded by the data upload unit. For example, the story generation unit extracts important events and emotional changes from the diary, identifies educational and work history from the resume, and recreates trips and events from the photos. The generation AI generates a story based on prompts containing instructions from the user regarding what the generation AI wants the user to do. The game generation unit generates an RPG game based on the story generated by the story generation unit. For example, the game generation unit sets important events in the user's life as quests and recreates places and people the user has experienced as characters and stages in the game. The generation AI creates a game scenario based on the flow of the story, allowing the player to progress through the game according to the scenario. This allows the RPG game generation system to generate an RPG game based on the user's experiences.

[0059] The data upload unit automatically tags uploaded data, allowing it to be classified and organized. For example, when a user uploads a diary, resume, or photos, the generation AI automatically tags each piece of data. For example, tags such as "emotion," "event," and "place" are added based on the contents of the diary, and tags such as "educational background" and "work history" are added to resumes. The generation AI also uses image recognition technology to tag uploaded photos. For example, it recognizes the people and places in the photo and adds tags such as "family," "travel," and "event." The generation AI also analyzes data uploaded by the user and automatically adds relevant tags. For example, it extracts tags such as "emotion," "event," and "place" from the contents of the diary and classifies the data. This allows data to be automatically classified and organized.

[0060] The data upload unit performs sentiment analysis on uploaded data and visualizes changes in sentiment over time. For example, when a user uploads a diary entry, the data upload unit uses a generation AI to perform sentiment analysis and visualize changes in sentiment over time based on the diary entry's contents. For example, it displays daily changes in sentiment in a graph. The data upload unit also uses a generation AI to perform sentiment analysis on uploaded resumes and visualize changes in sentiment related to work history and educational background. For example, it displays changes in sentiment related to important events such as changing jobs or graduating. The data upload unit also uses a generation AI to perform sentiment analysis on photos uploaded by users and visualize changes in sentiment based on the facial expressions and situations of people in the photos. For example, it extracts changes in sentiment from photos of trips or events. This makes it possible to visualize changes in sentiment over time.

[0061] The data upload unit can use the emotion estimation function to estimate the emotion a user is feeling when uploading data in real time and provide feedback to elicit positive emotions. For example, when a user uploads data, the data upload unit uses the generation AI to estimate the emotion in real time and provide feedback to elicit positive emotions. For example, it displays encouraging messages or positive comments. The data upload unit also uses the emotion estimation function to analyze the emotion a user is feeling when uploading data and provides relaxation music or messages to alleviate negative emotions. For example, it plays relaxing music when the user is feeling stressed. The data upload unit also uses the generation AI to estimate the emotion in real time when a user uploads data and provides interactive feedback to elicit positive emotions. For example, it asks questions or makes suggestions to elicit positive emotions. This makes it possible to provide feedback to elicit positive emotions.

[0062] The data upload unit can automatically collect external data related to uploaded data and complement the data. For example, when a user uploads a diary, the data upload unit's generation AI automatically collects related news articles and social media posts to complement the diary content. For example, it displays news articles related to a specific event. In addition, when an uploaded resume is submitted, the data upload unit's generation AI collects related industry news and trend information to complement the resume content. For example, it displays industry news related to a specific work history. In addition, when a user uploads a photo, the data upload unit's generation AI collects related external data to complement the photo content. For example, it displays tourist information about a travel destination or event details. This allows data to be complemented automatically.

[0063] The data upload unit can automatically evaluate the quality of uploaded data and filter out low-quality data. For example, when a user uploads a diary, the generation AI automatically evaluates the quality of the data and filters out low-quality diary entries. For example, it excludes diary entries with unclear content. In addition, the data upload unit has the generation AI evaluate the quality of uploaded resumes and filter out low-quality resumes. For example, it excludes resumes with insufficient information. In addition, the data upload unit has the generation AI evaluate the quality of photos uploaded by the user and filter out low-quality photos. For example, it excludes photos with low resolution or blurry photos. This makes it possible to automatically filter out low-quality data.

[0064] The data upload unit can use the emotion estimation function to analyze the emotions of a user when uploading data and provide relaxation music or a message to reduce negative emotions. For example, when a user uploads data, the data upload unit uses the generation AI to analyze the emotions and provide relaxation music to reduce negative emotions. For example, relaxing music is played when the user is feeling stressed. The data upload unit also uses the emotion estimation function to analyze the emotions of a user when uploading data and provide positive messages to reduce negative emotions. For example, encouraging words or positive comments are displayed. The data upload unit also uses the generation AI to analyze the emotions of a user when uploading data in real time and provide interactive feedback to reduce negative emotions. For example, relaxation suggestions or positive activities are recommended. This makes it possible to provide relaxation music or a message to reduce negative emotions.

[0065] The story generation unit can provide prompts to reflect the user's values ​​or beliefs. For example, when the generation AI generates a user's life story, the story generation unit provides prompts to reflect the user's values ​​and beliefs. For example, it asks a question such as, "What values ​​do you value most in your life?" The story generation unit also provides prompts to reflect the user's values ​​and beliefs, and generates a story based on the user's answers. For example, it asks a question such as, "Tell me what you believe and what you value." The story generation unit also provides prompts to reflect the user's values ​​and beliefs, and generates a story based on the user's answers. For example, it asks a question such as, "What event has had the greatest impact on your life?" This makes it possible to generate a story that reflects the user's values ​​and beliefs.

[0066] The story generation unit can incorporate interviews with the user's friends or family to reflect a more multifaceted perspective. For example, when the generation AI generates the user's life story, the story generation unit incorporates interviews with the user's friends and family. For example, it asks friends and family questions such as, "What is the most memorable event in the user's life?" The story generation unit also incorporates interviews with the user's friends and family, and the generation AI generates a story that reflects a more multifaceted perspective. For example, it asks questions such as, "Please tell us what kind of person the user is." The story generation unit also incorporates interviews with the user's friends and family, and the generation AI generates the user's life story. For example, it asks questions such as, "What is the most moving moment in the user's life?" This makes it possible to generate a story that reflects a multifaceted perspective.

[0067] The story generation unit uses the emotion estimation function to analyze the user's emotional response to the generated story, and can generate a story that is easy to empathize with emotionally. The story generation unit, for example, uses the emotion estimation function to analyze the user's emotional response to a life story generated by the generation AI. For example, it analyzes facial expressions and voice when reading the story and calculates an emotion score. The story generation unit also uses the emotion estimation function to analyze the user's emotional response to the generated life story in real time, and generates a story that is easy to empathize with emotionally. For example, it emphasizes parts with strong positive emotions. The story generation unit also generates a story that is easy to empathize with emotionally based on the user's emotional response data. For example, it emphasizes moving moments and important events to increase the emotional impact of the story. This makes it possible to generate a story that is easy to empathize with emotionally.

[0068] The story generation unit can incorporate different cultures and historical backgrounds to generate stories that promote intercultural exchange. For example, when the generation AI generates a user's life story, the story generation unit incorporates different cultures and historical backgrounds. For example, it generates a story that reflects the culture and history of the user's ancestors. Furthermore, the story generation unit generates stories that incorporate different cultures and historical backgrounds to promote intercultural exchange. For example, it adds an episode in which the user interacts with people from a different culture. Furthermore, when the generation AI generates a user's life story, the story generation unit incorporates different cultures and historical backgrounds to generate stories that promote intercultural exchange. For example, it adds an episode in which the user experiences events from a different era. In this way, it is possible to generate stories that promote intercultural exchange.

[0069] The story generation unit can incorporate fictional elements to enhance the entertainment value. For example, the story generation unit incorporates fictional elements when the generation AI generates the user's life story. For example, it adds an episode in which the user experiences a fictional adventure or a fantasy world. Furthermore, the story generation unit generates a story that incorporates fictional elements to enhance the entertainment value. For example, it adds an episode in which the user plays the role of a superhero. Furthermore, the story generation unit incorporates fictional elements when the generation AI generates the user's life story to enhance the entertainment value. For example, it adds an episode in which the user adventures in a futuristic world. This makes it possible to generate a story with enhanced entertainment value.

[0070] The story generation unit uses the emotion estimation function to collect other users' emotional reactions to the generated story, which can be used to improve the story. The story generation unit, for example, collects other users' emotional reactions to the life story generated by the generation AI. For example, it analyzes facial expressions and voices when reading the story and calculates an emotion score. The story generation unit also uses the emotion estimation function to collect other users' emotional reactions to the generated life story in real time, which can be used to improve the story. For example, it emphasizes parts with strong positive emotions and improves negative parts. Furthermore, the story generation unit uses the generation AI to identify areas for improvement in the story based on the emotional reaction data of other users, and generates a story that is easy to empathize with emotionally. For example, it emphasizes moving moments and important events to increase the emotional impact of the story. This allows the story to be improved based on the emotional reactions of other users.

[0071] The game generation unit can recreate important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit uses a generation AI to recreate important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit recreates choices such as where to go to school or where to work in the game, allowing the player to experience different stories by choosing different choices. The game generation unit also recreates important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit recreates important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit recreates important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit recreates important choices in the user's life in the game, allowing the player to try different choices. For example, the game generation unit recreates important choices in the user's life in the game, allowing the player to try different choices.

[0072] The game generation unit can emphasize emotional highlights in the user's life and create moving scenes. For example, the generation AI in the game generation unit can emphasize emotional highlights in the user's life and create moving scenes. For example, moving moments such as weddings and the birth of a child can be recreated in the game, allowing the player to experience those emotions. The game generation unit can also emphasize emotional highlights in the user's life and create moving scenes. For example, moving moments such as graduations and farewells to loved ones can be recreated in the game, allowing the player to experience those emotions. The game generation unit can also emphasize emotional highlights in the user's life and create moving scenes. For example, moving moments such as the realization of dreams and overcoming difficulties can be recreated in the game, allowing the player to experience those emotions. In this way, moving scenes can be created.

[0073] The game generation unit uses the emotion estimation function to analyze the player's emotional response to the generated RPG game, and can generate a game scenario that is easy to empathize with emotionally. The game generation unit, for example, uses the emotion estimation function to analyze the player's emotional response to the RPG game generated by the generation AI. For example, it analyzes facial expressions and voices during gameplay and calculates an emotion score. The game generation unit also uses the emotion estimation function to analyze the player's emotional response to the generated RPG game in real time, and generates a game scenario that is easy to empathize with emotionally. For example, it emphasizes parts with strong positive emotions and improves negative parts. The game generation unit also generates a game scenario that is easy to empathize with emotionally based on the player's emotional response data. For example, it emphasizes moving moments and important events to increase the emotional impact of the game scenario. This makes it possible to generate a game scenario that is easy to empathize with emotionally.

[0074] The game generation unit can convert events in the user's life into a fantasy or science fiction setting and generate games of different genres. For example, the generation AI of the game generation unit converts events in the user's life into a fantasy setting and generates games of different genres. For example, an important event in the user's life is recreated as a fantasy RPG incorporating elements of magic and adventure. The game generation unit can also convert events in the user's life into a science fiction setting and generate games of different genres. For example, an important event in the user's life is recreated as a science fiction RPG incorporating elements of future technology and space exploration. The game generation unit can also convert events in the user's life into a fantasy or science fiction setting and generate games of different genres. For example, an important event in the user's life is recreated as an adventure in an alternate world or a futuristic world. This allows games of different genres to be generated.

[0075] The game generation unit can combine the life stories of multiple users to generate a multiplayer game. For example, the generation AI of the game generation unit combines the life stories of multiple users to generate a multiplayer game. For example, important events in the lives of multiple users are recreated in a single game, and players cooperate to complete a quest. The game generation unit also combines the life stories of multiple users to generate a multiplayer game. For example, users interact with each other in the game as different characters to achieve a common goal. The game generation unit also combines the life stories of multiple users to generate a multiplayer game. For example, multiple players cooperate to complete a quest based on important events in the users' lives. This makes it possible to generate a multiplayer game.

[0076] The game generation unit uses the emotion estimation function to collect other players' emotional reactions to the generated RPG game, which can be used to improve the game. The game generation unit, for example, collects other players' emotional reactions to the RPG game generated by the generation AI. For example, it analyzes facial expressions and voices during gameplay and calculates an emotion score. The game generation unit also uses the emotion estimation function to collect other players' emotional reactions to the generated RPG game in real time, which can be used to improve the game. For example, it emphasizes parts with strong positive emotions and improves negative parts. Furthermore, the game generation unit uses the generation AI to identify areas for improvement in the game based on the emotional reaction data of other players, and generates a game scenario that is easy to empathize with emotionally. For example, it emphasizes moving moments and important events to increase the emotional impact of the game scenario. This allows the game to be improved based on the emotional reactions of other players.

[0077] The game generation unit can introduce multiple endings in which the story branches depending on the choices made by the player when playing the generated RPG game. The game generation unit, for example, introduces multiple endings in the generated RPG game, so that the story branches depending on the choices made by the player. For example, different endings can be reached by the player choosing different choices. The game generation unit also introduces multiple endings in which the story branches depending on the choices made by the player. For example, different endings or epilogues are prepared depending on the choices made by the player. The game generation unit also introduces multiple endings in the generated RPG game, so that the story branches depending on the choices made by the player. For example, the fates of different characters or the development of the story changes depending on the choices made by the player. This makes it possible to introduce multiple endings in which the story branches depending on the choices made by the player.

[0078] The game generation unit can introduce a system in which character growth or skills change depending on the player's actions when playing the generated RPG game. The game generation unit, for example, introduces a system in which character growth or skills change into the generated RPG game. For example, the character's abilities and skills change depending on the player's actions and choices. The game generation unit also introduces a system in which character growth or skills change depending on the player's actions. For example, character growth differs depending on the results of quests and battles selected by the player. The game generation unit also introduces a system in which character growth or skills change into the generated RPG game. For example, the character's skill tree changes depending on the player's choices and actions, allowing different play styles to be enjoyed. This makes it possible to introduce a system in which character growth or skills change depending on the player's actions.

[0079] The game generation unit can use the emotion estimation function to monitor the emotional reactions of the player as he plays the game in real time and provide feedback according to the player's emotions. The game generation unit, for example, uses the emotion estimation function to monitor the emotional reactions of the player as he plays the game in real time. For example, it analyzes the player's facial expressions and voice and calculates an emotion score. The game generation unit also monitors the player's emotional reactions in real time and provides feedback according to the emotions. For example, it plays relaxation music when the player is feeling stressed. The game generation unit also uses the emotion estimation function to monitor the player's emotional reactions in real time and provides interactive feedback according to the emotions. For example, it displays an encouraging message when the player is feeling positive emotions. In this way, it is possible to provide feedback according to the player's emotions.

[0080] The game generation unit can provide a customization function that allows a purchaser to add their own life story when selling the generated RPG game to another person. The game generation unit, for example, provides a customization function that allows a purchaser to add their own life story when selling the generated RPG game to another person. For example, the purchaser can add their own experiences and events into the game. The game generation unit also provides a customization function that allows a purchaser to add their own life story. For example, the purchaser can upload their own photos and diary entries and reflect them in the game. The game generation unit also provides a customization function that allows a purchaser to add their own life story when selling the generated RPG game to another person. For example, the purchaser can reflect their own choices and actions in the game to create an original story. This makes it possible to provide a customization function that allows a purchaser to add their own life story.

[0081] When selling the generated RPG game to others, the game generation unit can introduce social functions that allow players to interact with other players within the game. The game generation unit, for example, introduces social functions into the generated RPG game to allow players to interact with other players within the game. For example, it adds a chat function or a friend list. The game generation unit also introduces social functions that allow players to interact with other players within the game. For example, it adds a function that allows players to cooperate with each other to complete quests or compete against each other. The game generation unit also introduces social functions into the generated RPG game to allow players to interact with other players within the game. For example, it adds a function that allows players to exchange items with each other and send messages. This allows the introduction of social functions that allow players to interact with other players.

[0082] The game generation unit can use the emotion estimation function to analyze emotions felt by game purchasers while playing the game and reflect the results in the development of the next game. The game generation unit, for example, uses the emotion estimation function to analyze emotions felt by game purchasers while playing the game. For example, it analyzes the player's facial expressions and voice and calculates an emotion score. The game generation unit also analyzes emotions felt by game purchasers while playing the game and reflects the data in the development of the next game. For example, it emphasizes parts with strong positive emotions and improves negative parts. The game generation unit also uses the emotion estimation function to analyze emotions felt by game purchasers while playing the game in real time and reflects the data in the development of the next game. For example, it creates a new game scenario based on the player's emotional reactions. This allows the emotions felt by purchasers while playing the game to be reflected in the development of the next game.

[0083] The system can edit digest videos to be aired at funerals to emphasize moving moments in the user's life. For example, the system edits digest videos to be aired at funerals to emphasize moving moments in the user's life. For example, moving moments such as weddings and the birth of a child are incorporated into the video. The system also edits digest videos to emphasize moving moments in the user's life and creates digest videos to be aired at funerals. For example, moving moments such as graduations and farewells to loved ones are incorporated into the video. The system also edits digest videos to be aired at funerals to emphasize moving moments in the user's life. For example, moving moments such as the realization of dreams and overcoming difficulties are incorporated into the video. In this way, editing can be performed to emphasize moving moments.

[0084] The system can add messages from the user's friends or family to the digest video to be aired at the funeral, deepening the impact. For example, the system can add messages from the user's friends or family to the digest video to be aired at the funeral. For example, it can incorporate video in which the friends and family share their memories and words of gratitude for the user. The system can also add messages from the user's friends and family to create a digest video to be aired at the funeral. For example, it can incorporate video in which the friends and family share their memories with the user. The system can also add messages from the user's friends and family to the digest video to be aired at the funeral. For example, it can incorporate video in which the friends and family share their words of gratitude and messages of encouragement for the user. In this way, the system can add messages from the friends and family to deepen the impact.

[0085] The system uses the emotion estimation function to analyze the emotional reactions of attendees watching the digest video and can generate videos that are easy to empathize with emotionally. For example, the system uses the emotion estimation function to analyze the emotional reactions of attendees watching the digest video. For example, it analyzes the attendees' facial expressions and voices and calculates an emotion score. The system also analyzes the emotional reactions of attendees watching the digest video in real time and generates videos that are easy to empathize with emotionally. For example, it emphasizes parts with strong positive emotions and improves negative parts. The system also generates digest videos that are easy to empathize with emotionally based on the attendees' emotional reaction data. For example, it emphasizes moving moments and important events to increase the emotional impact of the video. In this way, it is possible to generate videos that are easy to empathize with emotionally based on the attendees' emotional reactions.

[0086] The system can recreate important events in the user's life using animation or CG in a digest video to be aired at the funeral. For example, the system recreates important events in the user's life using animation in a digest video to be aired at the funeral. For example, important events such as a wedding or the birth of a child are depicted using animation. The system also recreates important events in the user's life using CG to create a digest video to be aired at the funeral. For example, important events such as a graduation ceremony or a farewell to a loved one are depicted using CG. The system also recreates important events in the user's life using animation or CG in a digest video to be aired at the funeral. For example, important events such as the realization of a dream or overcoming difficulties are depicted using animation or CG. In this way, important events can be recreated using animation or CG.

[0087] The system can use music or narration to depict events in the user's life in a digest video to be aired at a funeral. For example, the system uses music to depict events in the user's life in a digest video to be aired at a funeral. For example, important events in the user's life are depicted in video against the backdrop of moving music. The system also uses narration to depict events in the user's life, creating a digest video to be aired at the funeral. For example, a video is created in which a narrator tells the user's life story. The system also uses music and narration to depict events in the user's life in a digest video to be aired at a funeral. For example, a video is created that depicts important events in the user's life by combining moving music and narration. In this way, life events can be depicted with music and narration.

[0088] The system uses an emotion estimation function to monitor the emotional reactions of attendees watching the digest video in real time, which can be used to improve the video. For example, the system uses the emotion estimation function to monitor the emotional reactions of attendees watching the digest video in real time. For example, it analyzes the attendees' facial expressions and voices to calculate an emotion score. The system also monitors the emotional reactions of attendees watching the digest video in real time and identifies areas for improvement in the video based on that data. For example, it emphasizes parts with strong positive emotions and improves negative parts. The system also uses the attendees' emotional reaction data to help improve the digest video. For example, it emphasizes moving moments and important events to increase the emotional impact of the video. In this way, the system can monitor the attendees' emotional reactions in real time, which can be used to help improve the video.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The data upload unit can automatically perform voice recognition on uploaded data and convert the voice data into text. For example, when a user uploads a voice memo, the generation AI automatically converts the voice into text and adds it to the contents of a diary or resume. The data upload unit can also use voice recognition technology to extract important keywords from the uploaded voice data and tag it. For example, it can tag the content of the voice memo with words such as "meeting," "idea," and "emotion." Furthermore, the data upload unit can analyze the voice data and infer the user's emotions and intentions. This allows for effective use of voice data and automatic classification and organization of data.

[0091] The data upload unit can automatically filter uploaded data to protect privacy. For example, when a user uploads a diary or photos, the generation AI automatically detects personal or confidential information and masks or deletes it. The data upload unit can also anonymize uploaded data to protect privacy. For example, personal information such as names and addresses can be anonymized to ensure data security. Furthermore, the data upload unit can provide privacy protection guidelines for data uploaded by users. This allows data to be used while protecting the user's privacy.

[0092] The data upload unit can use the emotion estimation function to analyze the emotions of the user when uploading data and provide customized feedback according to the emotions. For example, when the user uploads data, the generation AI analyzes the emotions and displays an encouraging message to elicit positive emotions. The data upload unit also uses the emotion estimation function to monitor the emotions of the user when uploading data in real time and provide relaxation music or messages to alleviate negative emotions. Furthermore, the data upload unit can provide interactive feedback according to the user's emotions, keeping the user's emotions positive. This makes it possible to provide customized feedback according to the user's emotions.

[0093] The data upload unit can automatically translate uploaded data and integrate data in different languages. For example, if a user uploads a diary or resume written in different languages, the generation AI automatically translates it and organizes the data in a unified language. The data upload unit can also use the translation function to tag the uploaded data in multiple languages. For example, it can tag the uploaded data in multiple languages, such as "emotion," "event," and "place," based on the diary content. Furthermore, the data upload unit can analyze data in different languages ​​and infer the user's emotions and intentions. This allows data in different languages ​​to be effectively integrated and automatically classified and organized.

[0094] The data upload unit uses the emotion estimation function to analyze the emotions of users when uploading data and organize the data based on those emotions. For example, when a user uploads a diary entry, the generation AI analyzes the emotions and prioritizes organizing diary entries with positive emotions. The data upload unit also uses the emotion estimation function to monitor the emotions of users when uploading data in real time and classify the data based on those emotions. Furthermore, the data upload unit suggests a method for organizing data according to the user's emotions, allowing the user to manage their data effectively. This allows data to be organized based on emotions.

[0095] The story generation unit can automatically add music to a user's life story to create an emotional impact. For example, the generation AI analyzes the user's life story and automatically selects music suitable for moving scenes to add to the story. The story generation unit can also customize music based on the user's emotions to enhance the emotional impact of the story. For example, it can adjust the tempo and melody of the music based on the emotions the user felt in a particular scene. Furthermore, the story generation unit can suggest music that suits the user's preferences to enhance the story's presentation. This allows for an emotional presentation using music.

[0096] The story generation unit can automatically add visual effects to a user's life story to create a visual presentation. For example, the generation AI analyzes the user's life story and automatically selects visual effects suitable for important scenes and adds them to the story. The story generation unit can also customize visual effects based on the user's emotions to enhance the visual impact of the story. For example, it can adjust the color and movement of effects based on the emotions the user felt in a particular scene. Furthermore, the story generation unit can suggest visual effects according to the user's preferences to enhance the presentation of the story. This allows for visual presentation using visual effects.

[0097] The story generation unit uses the emotion estimation function to analyze the user's emotional response to the generated story, and can generate a story that is easy to empathize with emotionally. For example, the emotion estimation function is used to analyze the user's emotional response to a life story generated by the generation AI. For example, facial expressions and voice when reading the story are analyzed to calculate an emotion score. The story generation unit also uses the emotion estimation function to analyze the user's emotional response to the generated life story in real time, and generate a story that is easy to empathize with emotionally. For example, parts with strong positive emotions are emphasized. Furthermore, the story generation unit generates a story that is easy to empathize with emotionally based on the user's emotional response data. For example, moving moments and important events are emphasized to increase the emotional impact of the story. This makes it possible to generate a story that is easy to empathize with emotionally.

[0098] The story generation unit can automatically add narration to a user's life story to deepen understanding of the story. For example, the generation AI analyzes the user's life story and automatically generates narration appropriate for important scenes and adds it to the story. The story generation unit can also customize the narration based on the user's emotions to enhance the emotional impact of the story. For example, it can adjust the tone and content of the narration based on the emotions the user felt in a particular scene. Furthermore, the story generation unit can suggest narration based on the user's preferences to enhance the story's presentation. This allows for a deeper understanding of the story using narration.

[0099] The story generation unit uses the emotion estimation function to collect other users' emotional reactions to the generated story, which can be used to improve the story. For example, the generation AI collects other users' emotional reactions to the life story generated. For example, it analyzes facial expressions and voices when reading the story to calculate an emotion score. The story generation unit also uses the emotion estimation function to collect other users' emotional reactions to the generated life story in real time, which can be used to improve the story. For example, it emphasizes parts with strong positive emotions and improves negative parts. Furthermore, the story generation unit uses the generation AI to identify areas for improvement in the story based on the emotional reaction data of other users, generating a story that is easy to empathize with emotionally. For example, it emphasizes moving moments and important events to increase the emotional impact of the story. This allows the story to be improved based on the emotional reactions of other users.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The data uploading unit uploads data such as a diary, resume, and photos taken with a smartphone. For example, a diary records daily events and emotions, a resume lists educational and work history, and photos taken with a smartphone show travel and event scenes. Step 2: The story generation unit reconstructs the user's life as a story based on the data uploaded by the data upload unit. For example, it extracts important events and emotional changes from diary entries, understands educational and work history from resume information, and recreates trips and events from photos. The generation AI generates a story based on prompts that include instructions on what the user wants the generation AI to do. Step 3: The game generation unit generates an RPG game based on the story generated by the story generation unit. For example, it may set important events in the user's life as quests, and recreate the places and people the user has experienced as characters and stages in the game. The generation AI creates a game scenario based on the flow of the story, allowing the player to progress through the game according to that scenario.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

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

Claims

1. A data upload section where users can upload data such as diaries, resumes, and photos taken with their smartphones; a story generation unit that reconstructs the user's life as a story based on the data uploaded by the data upload unit; a game generation unit that generates an RPG game based on the story generated by the story generation unit; A system characterized by:

2. The data upload unit Sentiment analysis is performed on the uploaded data, and changes in sentiment are visualized over time.

2. The system of claim 1.

3. The story generation unit Providing prompts to reflect on the user's values ​​or beliefs 2. The system of claim 1.

4. The game generation unit Analyzing the player's emotional response to the generated RPG game and generating a game scenario that is easy to empathize with emotionally.

2. The system of claim 1.

5. The game generation unit To monitor a player's emotional response in real time while playing a game and provide feedback according to the player's emotions.

2. The system of claim 1.

6. The system comprises: Analyzing the emotional responses of attendees watching digest videos and generating videos that are easy to empathize with emotionally 2. The system of claim 1.

7. The story generation unit Analyzing the user's emotional response to the generated story and generating a story that is likely to resonate emotionally with the user.

2. The system of claim 1.

8. The game generation unit Collecting other players' emotional reactions to the generated RPG game and using the collected information to improve the game 2. The system of claim 1.

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