system

Abstract

The system according to the embodiment aims to enable users to easily create original games that suit their preferences. [Solution] A system according to an embodiment includes a character selection unit, a story generation unit, a game element designation unit, a generation unit, a point acquisition unit, and a location information utilization unit. The character selection unit selects a character. The story generation unit generates a story based on the character selected by the character selection unit. The game element designation unit designates game elements based on the story generated by the story generation unit. The generation unit generates a game based on the game elements designated by the game element designation unit. The point acquisition unit provides points based on the completion of events in the game generated by the generation unit. The location information utilization unit generates a game that utilizes location information based on the points provided by the point acquisition unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to easily create original games that suit their preferences.

[0005] The system according to the embodiment aims to enable users to easily create original games that suit their preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a character selection unit, a story generation unit, a game element designation unit, a generation unit, a point acquisition unit, and a location information utilization unit. The character selection unit selects a character. The story generation unit generates a story based on the character selected by the character selection unit. The game element designation unit designates game elements based on the story generated by the story generation unit. The generation unit generates a game based on the game elements designated by the game element designation unit. The point acquisition unit provides points based on the completion of an event in the game generated by the generation unit. The location information utilization unit generates a game using location information based on the points provided by the point acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create original games that suit their preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The original game generation system according to an embodiment of the present invention generates an original game by selecting a source material, such as a manga, anime, movie, novel, or song. This system allows users to select a protagonist, heroine, or other character, and the story unfolds from the perspective of the selected character. Furthermore, users can specify the game type and elements (e.g., puzzles, mystery solving, training, etc.) of the event, and the AI ​​generates content in which their favorite character strives for their ideal scenario or goal. Unlike Mario Maker, this system does not generate games based on a specific worldview; it can also incorporate multiple elements, such as other manga and novels, or original characters and worldviews created by the user (multimodal AI). The smartphone app is free to play (with ads), with optional features available for purchase. In addition, users can earn points (e.g., points from an electronic payment system) by clearing in-game events, stimulating the economy. Furthermore, it can generate games that use location information (e.g., games like Pokémon GO and Pikmin), and big data on location information can be collected, anonymized, and statistically analyzed for use. For example, a user can select a specific manga, choose a character from that manga, and develop a story. The user specifies game elements such as puzzles and riddles, and the generation AI generates a game based on those elements. When an event is cleared in the generated game, points are provided, and the user can use those points to enjoy a game that utilizes location information. This allows the original game generation system to generate a story based on the characters selected by the user, specify game elements, generate a game, provide points, and generate a game that utilizes location information.

[0029] An original game generation system according to an embodiment includes a character selection unit, a story generation unit, a game element designation unit, a generation unit, a point acquisition unit, and a location information utilization unit. The character selection unit accepts character selection from a user. The character selection unit, for example, provides an interface for the user to select a character from a selection list. The story generation unit generates a story based on the selected character. The story generation unit, for example, uses a generation AI to build a story based on the character's characteristics and background. The game element designation unit accepts designation of game elements from a user. The game element designation unit provides an interface for the user to select game elements such as puzzles, puzzle solving, and character development. The generation unit generates a game based on the designated game elements using the generation AI. For example, the generation AI generates a game scenario and events based on the user's designation. The point acquisition unit provides points based on the completion of in-game events. For example, the point acquisition unit awards points when a user completes a specific in-game event. The location information utilization unit generates a game using location information and acquires, anonymizes, and statistically analyzes big data on location information. The location information utilization unit generates game content based on the user's location information, collects location information data, and statistically analyzes it, allowing the original game creation system according to the embodiment to generate a story based on the user's selection, specify game elements, create a game, provide points, and create a game that utilizes location information.

[0030] The character selection unit can accept a character selection from a user. For example, the character selection unit provides an interface for the user to select a character from options. When the user selects a character from the options, the character selection unit can display detailed information and an image of the character. The character selection unit can also suggest related stories and events based on the character selected by the user. This allows the user to select a character that suits their preferences and enjoy the game.

[0031] The story generation unit can generate a story based on the selected character. The story generation unit, for example, uses a generation AI to construct a story based on the character's characteristics and background. The generation AI can generate character actions and dialogue using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI determines the development of the story by taking into account the character's personality and past events. The story generation unit can also adjust the story's branching and ending according to the user's selection. This allows the user to experience a unique story based on the selected character.

[0032] The game element designation unit can accept designation of game elements from the user. The game element designation unit provides an interface for the user to select game elements such as puzzles, puzzle solving, and training. The game element designation unit can suggest related mini-games and events based on the game elements selected by the user. The game element designation unit can also adjust the difficulty level and rewards of the game according to the game elements selected by the user. This allows the user to select game elements that suit their preferences and enjoy the game.

[0033] The generation unit can use a generation AI to generate a game based on specified game elements. For example, the generation AI generates a game scenario and events based on user specifications. The generation AI can generate a game story and character actions using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates mini-games such as puzzles, mystery solving, and training based on game elements selected by the user. The generation unit can also adjust the game difficulty and rewards based on the content of the game generated by the generation AI. This allows users to enjoy games that suit their preferences.

[0034] The point acquisition unit can provide points based on the completion of an event in the game. For example, the point acquisition unit can grant points when a user completes a specific event in the game. The point acquisition unit can manage the points acquired by the user and display the point usage history. The point acquisition unit also allows the user to use the points acquired to purchase items and benefits in the game. This allows the user to earn points by playing the game and use the points to further enjoy the game.

[0035] The location information utilization unit can generate a game that uses location information, acquire big data of location information, anonymize the data, and compile statistics. The location information utilization unit can generate game content based on, for example, the user's location information. The location information utilization unit can set up a game so that an event occurs when the user moves to a specific location. The location information utilization unit can also collect, anonymize, and statistically analyze the user's location data. This allows the location information utilization unit to understand the user's movement patterns and behavioral tendencies and optimize the game content. Furthermore, the location information utilization unit can provide the user with game events and rewards related to specific locations based on the location information data. This allows the user to enjoy games that utilize location information.

[0036] The character selection unit can analyze the user's past character selection history and present optimal character candidates. For example, the character selection unit stores the user's past character selection history in a database and presents optimal character candidates using an analysis algorithm. For example, it can preferentially present characters that the user has frequently selected in the past. It can also preferentially present characters from a specific genre based on the user's past selection history. It can also analyze the user's past selection history and present highly relevant characters. In this way, the character selection unit can present optimal character candidates based on the user's past selection history, thereby improving user satisfaction.

[0037] When selecting a character, the character selection unit can filter character candidates based on the user's current interests or concerns. For example, the character selection unit identifies the user's current interests or concerns from survey results or behavioral data, and filters character candidates based on that information. For example, it can prioritize presenting characters from genres in which the user is currently interested. It can also prioritize presenting characters from anime or movies that the user has recently watched. It can also prioritize presenting characters related to themes in which the user is currently interested. In this way, the character selection unit can present optimal character candidates based on the user's current interests and concerns, thereby improving user satisfaction.

[0038] When selecting a character, the character selection unit can prioritize presenting highly relevant characters based on the user's geographical location information. The character selection unit, for example, obtains the user's geographical location information from GPS data or a location information service and filters character candidates based on that information. For example, if the user is in a specific area, characters related to that area can be prioritized. Also, if the user is traveling, characters related to the travel destination can be prioritized. Furthermore, if the user is participating in a specific event, characters related to that event can be prioritized. In this way, the character selection unit can present optimal character candidates based on the user's geographical location information, thereby improving user satisfaction.

[0039] When selecting a character, the character selection unit can present related characters based on the user's social media activity. The character selection unit, for example, analyzes the user's social media activity and filters character candidates based on that information. For example, the character selection unit can preferentially present characters that the user frequently mentions on social media. It can also preferentially present characters related to accounts the user follows on social media. It can also preferentially present characters related to communities the user participates in on social media. In this way, the character selection unit can present optimal character candidates based on the user's social media activity, thereby improving user satisfaction.

[0040] The story generation unit can adjust the details of the story based on the characteristics of the selected character when generating the story. For example, the story generation unit obtains the characteristics of the selected character from a database and adjusts the details of the story based on the information. For example, if the selected character has a brave personality, an adventurous story can be generated. Also, if the selected character has an intelligent personality, a story containing many puzzle-solving elements can be generated. Furthermore, if the selected character has an emotional personality, a story with an emotional development can be generated. In this way, the story generation unit can adjust the details of the story based on the characteristics of the selected character and improve user satisfaction.

[0041] When generating a story, the story generation unit can generate an appropriate story by referring to the user's past story selection history. The story generation unit, for example, stores the user's past story selection history in a database and generates an optimal story using an analysis algorithm. For example, the story generation unit can generate an optimal story based on story patterns that the user has previously preferred. It can also generate stories of a specific genre preferentially based on the user's past selection history. It can also analyze the user's past selection history and generate highly relevant stories. In this way, the story generation unit can generate an optimal story based on the user's past story selection history, thereby improving user satisfaction.

[0042] The story generation unit can adjust the order of stories based on the relationships between selected characters when generating a story. The story generation unit, for example, obtains the relationships between the selected characters from a database and adjusts the order of stories based on that information. For example, if the selected characters are best friends, a story with a friendship theme can be generated. Also, if the selected characters are rivals, a story with a competition theme can be generated. Furthermore, if the selected characters are lovers, a story with a romance theme can be generated. In this way, the story generation unit can adjust the order of stories based on the relationships between the selected characters and improve user satisfaction.

[0043] When generating a story, the story generation unit can generate a related story based on the user's social media activity. The story generation unit, for example, analyzes the user's social media activity and generates a story based on that information. For example, the story generation unit can generate a story based on a theme that the user frequently mentions on social media. It can also generate a story related to accounts that the user follows on social media. It can also generate a story related to a community that the user participates in on social media. In this way, the story generation unit can generate an optimal story based on the user's social media activity and improve user satisfaction.

[0044] When specifying a game element, the game element designation unit can analyze the user's past game element selection history and present appropriate game elements. The game element designation unit, for example, stores the user's past game element selection history in a database and presents optimal game elements using an analysis algorithm. For example, the game element designation unit can preferentially present game elements that the user has frequently selected in the past. It can also preferentially present game elements of a specific genre based on the user's past selection history. It can also analyze the user's past selection history and present highly relevant game elements. In this way, the game element designation unit can present optimal game elements based on the user's past game element selection history, thereby improving user satisfaction.

[0045] When specifying game elements, the game element designation unit can filter game elements based on the user's current interests and concerns. The game element designation unit, for example, identifies the user's current interests and concerns from survey results or behavioral data, and filters game elements based on that information. For example, it can preferentially present game elements in genres in which the user is currently interested. It can also preferentially present elements of games that the user has recently played. It can also preferentially present game elements related to themes in which the user is currently interested. In this way, the game element designation unit can present optimal game elements based on the user's current interests and concerns, thereby improving user satisfaction.

[0046] When specifying game elements, the game element designation unit can prioritize presenting highly relevant game elements by taking into account the user's geographical location information. The game element designation unit, for example, acquires the user's geographical location information from GPS data or a location information service and filters game elements based on that information. For example, if the user is in a specific area, game elements related to that area can be prioritized. Also, if the user is traveling, game elements related to the travel destination can be prioritized. Furthermore, if the user is participating in a specific event, game elements related to that event can be prioritized. In this way, the game element designation unit can present optimal game elements based on the user's geographical location information, thereby improving user satisfaction.

[0047] When specifying game elements, the game element designation unit can present relevant game elements based on the user's social media activity. The game element designation unit, for example, analyzes the user's social media activity and filters game elements based on that information. For example, the game element designation unit can preferentially present game elements that the user frequently mentions on social media. It can also preferentially present game elements related to accounts the user follows on social media. It can also preferentially present game elements related to communities the user participates in on social media. In this way, the game element designation unit can present optimal game elements based on the user's social media activity and improve user satisfaction.

[0048] The generation unit can adjust the details of the game based on the characteristics of the specified game elements when generating the game. For example, the generation unit obtains the characteristics of the specified game elements from a database and adjusts the details of the game based on that information. For example, if the specified game element is a puzzle, the generation unit can adjust the difficulty level to generate a game. Also, if the specified game element is an action game, the generation unit can adjust the sense of speed to generate a game. Furthermore, if the specified game element is development, the generation unit can strengthen the growth element to generate a game. In this way, the generation unit can adjust the details of the game based on the characteristics of the specified game elements and improve user satisfaction.

[0049] When generating a game, the generation unit can generate an appropriate game by referring to the user's past game play history. The generation unit, for example, stores the user's past game play history in a database and generates an optimal game using an analysis algorithm. For example, the generation unit can generate an optimal game based on the user's past favorite game patterns. It can also preferentially generate games of a specific genre based on the user's past play history. It can also analyze the user's past play history and generate highly relevant games. In this way, the generation unit can generate an optimal game based on the user's past game play history, thereby improving user satisfaction.

[0050] The generation unit can adjust the order of the game based on the relationships between specified game elements when generating a game. The generation unit, for example, obtains the relationships between the specified game elements from a database and adjusts the order of the game based on that information. For example, if the specified game elements are consecutive, the generation unit can adjust the order to generate a game. Also, if the specified game elements are independent, the generation unit can generate a game by randomizing the order. Furthermore, if the specified game elements are related, the generation unit can adjust the order taking the relationships into consideration. In this way, the generation unit can adjust the order of the game based on the relationships between the specified game elements, thereby improving user satisfaction.

[0051] When generating a game, the generation unit can generate a related game based on the user's social media activities. The generation unit, for example, analyzes the user's social media activities and generates a game based on that information. For example, the generation unit can generate a game based on a theme that the user frequently mentions on social media. It can also generate a game related to accounts that the user follows on social media. It can also generate a game related to a community that the user participates in on social media. In this way, the generation unit can generate an optimal game based on the user's social media activities and improve user satisfaction.

[0052] When providing points, the point acquisition unit can adjust the amount of points based on the importance of an event in the game. The point acquisition unit, for example, evaluates the importance of an event in the game and adjusts the amount of points based on that information. For example, a large amount of points can be provided when an important event is cleared. A small amount of points can also be provided when a normal event is cleared. Furthermore, special points can also be provided when a special event is cleared. In this way, the point acquisition unit can adjust the amount of points based on the importance of an event in the game, thereby improving user satisfaction.

[0053] When providing points, the point acquisition unit can provide appropriate points by referring to the user's past point acquisition history. The point acquisition unit, for example, stores the user's past point acquisition history in a database and provides optimal points using an analysis algorithm. For example, if the user has acquired a large number of points in the past, a large number of points can be provided for a similar event. Also, if the user has acquired a small number of points in the past, a small number of points can be provided for a similar event. Furthermore, the point acquisition unit can analyze the user's past point acquisition history and provide optimal points. In this way, the point acquisition unit can provide optimal points based on the user's past point acquisition history, thereby improving user satisfaction.

[0054] When providing points, the point acquisition unit can prioritize providing highly relevant points by taking into account the user's geographical location information. The point acquisition unit, for example, acquires the user's geographical location information from GPS data or a location information service and provides points based on that information. For example, if the user is in a specific area, points related to that area can be provided. Also, if the user is traveling, points related to the travel destination can be provided. Furthermore, if the user is participating in a specific event, points related to the event can be provided. In this way, the point acquisition unit can provide optimal points based on the user's geographical location information, thereby improving user satisfaction.

[0055] When providing points, the point acquisition unit can provide relevant points based on the user's social media activity. The point acquisition unit, for example, analyzes the user's social media activity and provides points based on that information. For example, the point acquisition unit can provide points related to topics that the user frequently mentions on social media. It can also provide points related to accounts that the user follows on social media. It can also provide points related to communities that the user participates in on social media. In this way, the point acquisition unit can provide optimal points based on the user's social media activity and improve user satisfaction.

[0056] When using location information, the location information utilization unit can generate an appropriate game by referring to the user's past location information utilization history. The location information utilization unit, for example, stores the user's past location information utilization history in a database and generates an optimal game using an analysis algorithm. For example, the location information utilization unit can generate an optimal location information game based on places the user has visited in the past. It can also generate a game related to a specific area from the user's past location information utilization history. Furthermore, it can analyze the user's past location information utilization history and generate a highly relevant location information game. In this way, the location information utilization unit can generate an optimal game based on the user's past location information utilization history, thereby improving user satisfaction.

[0057] When using location information, the location information utilization unit can adjust game details based on the user's current geographic location. For example, the location information utilization unit obtains the user's current geographic location from GPS data or a location information service and adjusts game details based on that information. For example, if the user is in a specific area, a game related to that area can be generated. Also, if the user is traveling, a game related to the travel destination can be generated. Furthermore, if the user is participating in a specific event, a game related to that event can be generated. In this way, the location information utilization unit can generate an optimal game based on the user's current geographic location and improve user satisfaction.

[0058] When using location information, the location information utilization unit can prioritize generating highly relevant games by taking into account the user's geographical location information. The location information utilization unit, for example, acquires the user's geographical location information from GPS data or a location information service and generates games based on that information. For example, if the user is in a specific area, it can generate a game related to that area. Also, if the user is traveling, it can generate a game related to the travel destination. Furthermore, if the user is participating in a specific event, it can generate a game related to that event. In this way, the location information utilization unit can generate optimal games based on the user's geographical location information and improve user satisfaction.

[0059] The location information utilization unit can generate a relevant game based on the user's social media activity when using location information. The location information utilization unit, for example, analyzes the user's social media activity and generates a game based on that information. For example, it can generate a location-based game related to a theme that the user frequently mentions on social media. It can also generate a location-based game related to accounts that the user follows on social media. It can also generate a location-based game related to a community that the user participates in on social media. In this way, the location information utilization unit can generate an optimal game based on the user's social media activity and improve user satisfaction.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The original game generation system can further include a health management unit that acquires the user's health data and adjusts the game content based on the user's health condition. For example, the system can acquire the user's heart rate and step count data and provide a relaxing game if the user's heart rate is high, and a game that encourages exercise if the user's step count is low. Furthermore, based on the user's sleep data, the system can provide a game that can be enjoyed in a short time if the user is sleep-deprived, and a game that can be enjoyed for a long time if the user has had enough sleep. This makes it possible to provide an optimal game experience according to the user's health condition.

[0062] The character selection unit can analyze the user's past gameplay data and present character candidates based on the user's play style. For example, it can preferentially present characters with high action skills to a user who likes action games, and preferentially present characters with high intelligence to a user who likes puzzle games. It can also analyze the tendencies of characters selected by the user in the past and suggest characters with similar characteristics. Furthermore, if the user frequently selects a particular character, it can suggest new characters related to that character. This can support the user in selecting a character that suits their play style.

[0063] The game element designation unit can suggest game elements based on the user's current activity level. For example, if the user is exercising, active game elements can be suggested, and if the user is resting, relaxing game elements can be suggested. The game difficulty can also be adjusted according to the user's activity level to provide an appropriate challenge. Furthermore, the game element designation unit can analyze the user's past activity patterns based on the user's activity data and suggest optimal game elements. This allows the user to have an optimal game experience according to their activity level.

[0064] The point acquisition unit can analyze the user's past point usage history and suggest the optimal way to use points. For example, if the user has used points frequently to purchase items in the past, it can suggest benefits related to item purchases, and if the user has used points frequently to participate in in-game events in the past, it can suggest benefits related to event participation. It can also suggest the optimal timing to use points based on the user's point usage history. Furthermore, it can analyze the user's point usage history and suggest new ways to use points. This can optimize the user's point usage and improve satisfaction.

[0065] The character selection unit can suggest characters selected by friends based on the user's social media activity. For example, it can prioritize characters selected by the user's friends on social media, and by selecting the same character as a friend, it can provide a common topic of conversation. It can also suggest characters that friends have given high ratings to. Furthermore, it can analyze the user's social media activity and suggest characters that share interests with friends. This allows the user to select a character they share with their friends and enjoy the game.

[0066] The game element designation unit can suggest game elements that the user has not experienced based on the user's past game element selection history. For example, it can provide a new experience by preferentially presenting game elements that the user has not selected in the past. It can also analyze the user's past selection history to suggest new, highly relevant game elements. Furthermore, it can suggest game elements from different genres based on the trends in game elements selected by the user in the past. This allows the user to experience new game elements and expand the enjoyment of the game.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The character selection unit accepts a character selection from the user, for example, by providing an interface for the user to select a character from options. Step 2: The story generator generates a story based on the selected characters, for example, using a generation AI to build a story based on the characters' characteristics and backgrounds. Step 3: The game element designation unit accepts designation of game elements from the user. For example, it provides an interface for the user to select game elements such as puzzles, mystery solving, and training. Step 4: The generator generates a game based on the specified game elements. For example, the generator AI generates a game scenario and events based on the user's specifications. Step 5: The point acquisition unit provides points based on the completion of an event in the game. For example, points are awarded when the user completes a specific event in the game. Step 6: The location information utilization unit generates a game that utilizes location information, and acquires, anonymizes, and statistically analyzes big data on location information. For example, it generates game content based on the user's location information, collects location data, and statistically analyzes it.

[0069] (Example 2) The original game generation system according to an embodiment of the present invention generates an original game by selecting a source material, such as a manga, anime, movie, novel, or song. This system allows users to select a protagonist, heroine, or other character, and the story unfolds from the perspective of the selected character. Furthermore, users can specify the game type and elements (e.g., puzzles, mystery solving, training, etc.) of the event, and the AI ​​generates content in which their favorite character strives for their ideal scenario or goal. Unlike Mario Maker, this system does not generate games based on a specific worldview; it can also incorporate multiple elements, such as other manga and novels, or original characters and worldviews created by the user (multimodal AI). The smartphone app is free to play (with ads), with optional features available for purchase. In addition, users can earn points (e.g., points from an electronic payment system) by clearing in-game events, stimulating the economy. Furthermore, it can generate games that use location information (e.g., games like Pokémon GO and Pikmin), and big data on location information can be collected, anonymized, and statistically analyzed for use. For example, a user can select a specific manga, choose a character from that manga, and develop a story. The user specifies game elements such as puzzles and riddles, and the generation AI generates a game based on those elements. When an event is cleared in the generated game, points are provided, and the user can use those points to enjoy a game that utilizes location information. This allows the original game generation system to generate a story based on the characters selected by the user, specify game elements, generate a game, provide points, and generate a game that utilizes location information.

[0070] An original game generation system according to an embodiment includes a character selection unit, a story generation unit, a game element designation unit, a generation unit, a point acquisition unit, and a location information utilization unit. The character selection unit accepts character selection from a user. The character selection unit, for example, provides an interface for the user to select a character from a selection list. The story generation unit generates a story based on the selected character. The story generation unit, for example, uses a generation AI to build a story based on the character's characteristics and background. The game element designation unit accepts designation of game elements from a user. The game element designation unit provides an interface for the user to select game elements such as puzzles, puzzle solving, and character development. The generation unit generates a game based on the designated game elements using the generation AI. For example, the generation AI generates a game scenario and events based on the user's designation. The point acquisition unit provides points based on the completion of in-game events. For example, the point acquisition unit awards points when a user completes a specific in-game event. The location information utilization unit generates a game using location information and acquires, anonymizes, and statistically analyzes big data on location information. The location information utilization unit generates game content based on the user's location information, collects location information data, and statistically analyzes it, allowing the original game creation system according to the embodiment to generate a story based on the user's selection, specify game elements, create a game, provide points, and create a game that utilizes location information.

[0071] The character selection unit can accept a character selection from a user. For example, the character selection unit provides an interface for the user to select a character from options. When the user selects a character from the options, the character selection unit can display detailed information and an image of the character. The character selection unit can also suggest related stories and events based on the character selected by the user. This allows the user to select a character that suits their preferences and enjoy the game.

[0072] The story generation unit can generate a story based on the selected character. The story generation unit, for example, uses a generation AI to construct a story based on the character's characteristics and background. The generation AI can generate character actions and dialogue using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI determines the development of the story by taking into account the character's personality and past events. The story generation unit can also adjust the story's branching and ending according to the user's selection. This allows the user to experience a unique story based on the selected character.

[0073] The game element designation unit can accept designation of game elements from the user. The game element designation unit provides an interface for the user to select game elements such as puzzles, puzzle solving, and training. The game element designation unit can suggest related mini-games and events based on the game elements selected by the user. The game element designation unit can also adjust the difficulty level and rewards of the game according to the game elements selected by the user. This allows the user to select game elements that suit their preferences and enjoy the game.

[0074] The generation unit can use a generation AI to generate a game based on specified game elements. For example, the generation AI generates a game scenario and events based on user specifications. The generation AI can generate a game story and character actions using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates mini-games such as puzzles, mystery solving, and training based on game elements selected by the user. The generation unit can also adjust the game difficulty and rewards based on the content of the game generated by the generation AI. This allows users to enjoy games that suit their preferences.

[0075] The point acquisition unit can provide points based on the completion of an event in the game. For example, the point acquisition unit can grant points when a user completes a specific event in the game. The point acquisition unit can manage the points acquired by the user and display the point usage history. The point acquisition unit also allows the user to use the points acquired to purchase items and benefits in the game. This allows the user to earn points by playing the game and use the points to further enjoy the game.

[0076] The location information utilization unit can generate a game that uses location information, acquire big data of location information, anonymize the data, and compile statistics. The location information utilization unit can generate game content based on, for example, the user's location information. The location information utilization unit can set up a game so that an event occurs when the user moves to a specific location. The location information utilization unit can also collect, anonymize, and statistically analyze the user's location data. This allows the location information utilization unit to understand the user's movement patterns and behavioral tendencies and optimize the game content. Furthermore, the location information utilization unit can provide the user with game events and rewards related to specific locations based on the location information data. This allows the user to enjoy games that utilize location information.

[0077] The character selection unit can estimate the user's emotions and present character selection candidates based on the estimated user emotions. The character selection unit, for example, analyzes the user's facial expressions and voice to estimate emotions. The emotion estimation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, characters with high action potential can be presented preferentially. Also, if the user is relaxed, soothing characters can be presented preferentially. Furthermore, if the user is stressed, characters that are useful for relieving stress can be presented preferentially. In this way, the character selection unit can present optimal character candidates based on the user's emotions and improve user satisfaction.

[0078] The character selection unit can analyze the user's past character selection history and present optimal character candidates. For example, the character selection unit stores the user's past character selection history in a database and presents optimal character candidates using an analysis algorithm. For example, it can preferentially present characters that the user has frequently selected in the past. It can also preferentially present characters from a specific genre based on the user's past selection history. It can also analyze the user's past selection history and present highly relevant characters. In this way, the character selection unit can present optimal character candidates based on the user's past selection history, thereby improving user satisfaction.

[0079] When selecting a character, the character selection unit can filter character candidates based on the user's current interests or concerns. For example, the character selection unit identifies the user's current interests or concerns from survey results or behavioral data, and filters character candidates based on that information. For example, it can prioritize presenting characters from genres in which the user is currently interested. It can also prioritize presenting characters from anime or movies that the user has recently watched. It can also prioritize presenting characters related to themes in which the user is currently interested. In this way, the character selection unit can present optimal character candidates based on the user's current interests and concerns, thereby improving user satisfaction.

[0080] The character selection unit can estimate the user's emotions and adjust the order of character selection based on the estimated user emotions. The character selection unit, for example, analyzes the user's facial expressions and voice to estimate emotions. The emotion estimation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, a character with a high level of action can be presented first. Also, if the user is relaxed, a soothing character can be presented first. Furthermore, if the user is stressed, a character that helps relieve stress can be presented first. In this way, the character selection unit can adjust the order of character selection based on the user's emotions and improve user satisfaction.

[0081] When selecting a character, the character selection unit can prioritize presenting highly relevant characters based on the user's geographical location information. The character selection unit, for example, obtains the user's geographical location information from GPS data or a location information service and filters character candidates based on that information. For example, if the user is in a specific area, characters related to that area can be prioritized. Also, if the user is traveling, characters related to the travel destination can be prioritized. Furthermore, if the user is participating in a specific event, characters related to that event can be prioritized. In this way, the character selection unit can present optimal character candidates based on the user's geographical location information, thereby improving user satisfaction.

[0082] When selecting a character, the character selection unit can present related characters based on the user's social media activity. The character selection unit, for example, analyzes the user's social media activity and filters character candidates based on that information. For example, the character selection unit can preferentially present characters that the user frequently mentions on social media. It can also preferentially present characters related to accounts the user follows on social media. It can also preferentially present characters related to communities the user participates in on social media. In this way, the character selection unit can present optimal character candidates based on the user's social media activity, thereby improving user satisfaction.

[0083] The story generation unit can estimate the user's emotions and adjust the development of the story based on the estimated user emotions. The story generation unit, for example, analyzes the user's facial expressions and voice to estimate emotions. The emotion estimation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, a story including many action scenes can be generated. Also, if the user is relaxed, a story with a calm development can be generated. Furthermore, if the user is stressed, a story with a development that helps relieve stress can be generated. In this way, the story generation unit can adjust the development of the story based on the user's emotions and improve user satisfaction.

[0084] The story generation unit can adjust the details of the story based on the characteristics of the selected character when generating the story. For example, the story generation unit obtains the characteristics of the selected character from a database and adjusts the details of the story based on the information. For example, if the selected character has a brave personality, an adventurous story can be generated. Also, if the selected character has an intelligent personality, a story containing many puzzle-solving elements can be generated. Furthermore, if the selected character has an emotional personality, a story with an emotional development can be generated. In this way, the story generation unit can adjust the details of the story based on the characteristics of the selected character and improve user satisfaction.

[0085] When generating a story, the story generation unit can generate an appropriate story by referring to the user's past story selection history. The story generation unit, for example, stores the user's past story selection history in a database and generates an optimal story using an analysis algorithm. For example, the story generation unit can generate an optimal story based on story patterns that the user has previously preferred. It can also generate stories of a specific genre preferentially based on the user's past selection history. It can also analyze the user's past selection history and generate highly relevant stories. In this way, the story generation unit can generate an optimal story based on the user's past story selection history, thereby improving user satisfaction.

[0086] The story generation unit can estimate the user's emotions and adjust the length of the story based on the estimated user emotions. The story generation unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. The emotion estimation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is in a hurry, a short and to-the-point story can be generated. Alternatively, if the user is relaxed, a longer story with detailed explanations can be generated. Furthermore, if the user is excited, a story with visually stimulating effects can be generated. In this way, the story generation unit can adjust the length of the story based on the user's emotions and improve user satisfaction.

[0087] The story generation unit can adjust the order of stories based on the relationships between selected characters when generating a story. The story generation unit, for example, obtains the relationships between the selected characters from a database and adjusts the order of stories based on that information. For example, if the selected characters are best friends, a story with a friendship theme can be generated. Also, if the selected characters are rivals, a story with a competition theme can be generated. Furthermore, if the selected characters are lovers, a story with a romance theme can be generated. In this way, the story generation unit can adjust the order of stories based on the relationships between the selected characters and improve user satisfaction.

[0088] When generating a story, the story generation unit can generate a related story based on the user's social media activity. The story generation unit, for example, analyzes the user's social media activity and generates a story based on that information. For example, the story generation unit can generate a story based on a theme that the user frequently mentions on social media. It can also generate a story related to accounts that the user follows on social media. It can also generate a story related to a community that the user participates in on social media. In this way, the story generation unit can generate an optimal story based on the user's social media activity and improve user satisfaction.

[0089] The game element designation unit can estimate the user's emotions and present candidate game elements based on the estimated user emotions. The game element designation unit, for example, analyzes the user's facial expressions and voice to estimate emotions. The emotion designation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, game elements with high action can be presented preferentially. Also, if the user is relaxed, soothing game elements can be presented preferentially. Furthermore, if the user is stressed, game elements that are useful for relieving stress can be presented preferentially. In this way, the game element designation unit can present optimal candidate game elements based on the user's emotions and improve user satisfaction.

[0090] When specifying a game element, the game element designation unit can analyze the user's past game element selection history and present appropriate game elements. The game element designation unit, for example, stores the user's past game element selection history in a database and presents optimal game elements using an analysis algorithm. For example, the game element designation unit can preferentially present game elements that the user has frequently selected in the past. It can also preferentially present game elements of a specific genre based on the user's past selection history. It can also analyze the user's past selection history and present highly relevant game elements. In this way, the game element designation unit can present optimal game elements based on the user's past game element selection history, thereby improving user satisfaction.

[0091] When specifying game elements, the game element designation unit can filter game elements based on the user's current interests and concerns. The game element designation unit, for example, identifies the user's current interests and concerns from survey results or behavioral data, and filters game elements based on that information. For example, it can preferentially present game elements in genres in which the user is currently interested. It can also preferentially present elements of games that the user has recently played. It can also preferentially present game elements related to themes in which the user is currently interested. In this way, the game element designation unit can present optimal game elements based on the user's current interests and concerns, thereby improving user satisfaction.

[0092] The game element designation unit can estimate the user's emotions and adjust the order of game elements based on the estimated user emotions. The game element designation unit, for example, analyzes the user's facial expressions and voice to estimate emotions. The emotion designation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, game elements with high action can be presented first. Also, if the user is relaxed, soothing game elements can be presented first. Furthermore, if the user is stressed, game elements that help relieve stress can be presented first. In this way, the game element designation unit can adjust the order of game elements based on the user's emotions and improve user satisfaction.

[0093] When specifying game elements, the game element designation unit can prioritize presenting highly relevant game elements by taking into account the user's geographical location information. The game element designation unit, for example, acquires the user's geographical location information from GPS data or a location information service and filters game elements based on that information. For example, if the user is in a specific area, game elements related to that area can be prioritized. Also, if the user is traveling, game elements related to the travel destination can be prioritized. Furthermore, if the user is participating in a specific event, game elements related to that event can be prioritized. In this way, the game element designation unit can present optimal game elements based on the user's geographical location information, thereby improving user satisfaction.

[0094] When specifying game elements, the game element designation unit can present relevant game elements based on the user's social media activity. The game element designation unit, for example, analyzes the user's social media activity and filters game elements based on that information. For example, the game element designation unit can preferentially present game elements that the user frequently mentions on social media. It can also preferentially present game elements related to accounts the user follows on social media. It can also preferentially present game elements related to communities the user participates in on social media. In this way, the game element designation unit can present optimal game elements based on the user's social media activity and improve user satisfaction.

[0095] The generation unit can estimate the user's emotions and adjust the content of the game to be generated based on the estimated user emotions. The generation unit, for example, analyzes the user's facial expressions and voice to estimate emotions. The emotion estimation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, a game with high action can be generated. Also, if the user is relaxed, a soothing game can be generated. Furthermore, if the user is stressed, a game that helps relieve stress can be generated. In this way, the generation unit can adjust the content of the game to be generated based on the user's emotions and improve user satisfaction.

[0096] The generation unit can adjust the details of the game based on the characteristics of the specified game elements when generating the game. For example, the generation unit obtains the characteristics of the specified game elements from a database and adjusts the details of the game based on that information. For example, if the specified game element is a puzzle, the generation unit can adjust the difficulty level to generate a game. Also, if the specified game element is an action game, the generation unit can adjust the sense of speed to generate a game. Furthermore, if the specified game element is development, the generation unit can strengthen the growth element to generate a game. In this way, the generation unit can adjust the details of the game based on the characteristics of the specified game elements and improve user satisfaction.

[0097] When generating a game, the generation unit can generate an appropriate game by referring to the user's past game play history. The generation unit, for example, stores the user's past game play history in a database and generates an optimal game using an analysis algorithm. For example, the generation unit can generate an optimal game based on the user's past favorite game patterns. It can also preferentially generate games of a specific genre based on the user's past play history. It can also analyze the user's past play history and generate highly relevant games. In this way, the generation unit can generate an optimal game based on the user's past game play history, thereby improving user satisfaction.

[0098] The generation unit can estimate the user's emotions and adjust the difficulty level of the game to be generated based on the estimated user's emotions. The generation unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. The emotion estimation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, a game with a high level of difficulty can be generated. Also, if the user is relaxed, a game with a low level of difficulty can be generated. Furthermore, if the user is stressed, a game with a level of difficulty that helps relieve stress can be generated. In this way, the generation unit can adjust the difficulty level of the game to be generated based on the user's emotions and improve user satisfaction.

[0099] The generation unit can adjust the order of the game based on the relationships between specified game elements when generating a game. The generation unit, for example, obtains the relationships between the specified game elements from a database and adjusts the order of the game based on that information. For example, if the specified game elements are consecutive, the generation unit can adjust the order to generate a game. Also, if the specified game elements are independent, the generation unit can generate a game by randomizing the order. Furthermore, if the specified game elements are related, the generation unit can adjust the order taking the relationships into consideration. In this way, the generation unit can adjust the order of the game based on the relationships between the specified game elements, thereby improving user satisfaction.

[0100] When generating a game, the generation unit can generate a related game based on the user's social media activities. The generation unit, for example, analyzes the user's social media activities and generates a game based on that information. For example, the generation unit can generate a game based on a theme that the user frequently mentions on social media. It can also generate a game related to accounts that the user follows on social media. It can also generate a game related to a community that the user participates in on social media. In this way, the generation unit can generate an optimal game based on the user's social media activities and improve user satisfaction.

[0101] The point acquisition unit can estimate the user's emotions and adjust the timing of providing points based on the estimated user emotions. The point acquisition unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. The emotion estimation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, points can be provided at the highlight scenes of the game. Also, if the user is relaxed, points can be provided at the end of the game. Furthermore, if the user is feeling stressed, points can be provided at a timing that helps relieve stress. In this way, the point acquisition unit can adjust the timing of providing points based on the user's emotions and improve user satisfaction.

[0102] When providing points, the point acquisition unit can adjust the amount of points based on the importance of an event in the game. The point acquisition unit, for example, evaluates the importance of an event in the game and adjusts the amount of points based on that information. For example, a large amount of points can be provided when an important event is cleared. A small amount of points can also be provided when a normal event is cleared. Furthermore, special points can also be provided when a special event is cleared. In this way, the point acquisition unit can adjust the amount of points based on the importance of an event in the game, thereby improving user satisfaction.

[0103] When providing points, the point acquisition unit can provide appropriate points by referring to the user's past point acquisition history. The point acquisition unit, for example, stores the user's past point acquisition history in a database and provides optimal points using an analysis algorithm. For example, if the user has acquired a large number of points in the past, a large number of points can be provided for a similar event. Also, if the user has acquired a small number of points in the past, a small number of points can be provided for a similar event. Furthermore, the point acquisition unit can analyze the user's past point acquisition history and provide optimal points. In this way, the point acquisition unit can provide optimal points based on the user's past point acquisition history, thereby improving user satisfaction.

[0104] The point acquisition unit can estimate the user's emotions and adjust the order of point awarding based on the estimated user emotions. The point acquisition unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. The emotion estimation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, points can be awarded after an important event. Alternatively, if the user is relaxed, points can be awarded at the end of a game. Furthermore, if the user is stressed, points can be awarded at a timing that helps relieve stress. In this way, the point acquisition unit can adjust the order of point awarding based on the user's emotions and improve user satisfaction.

[0105] When providing points, the point acquisition unit can prioritize providing highly relevant points by taking into account the user's geographical location information. The point acquisition unit, for example, acquires the user's geographical location information from GPS data or a location information service and provides points based on that information. For example, if the user is in a specific area, points related to that area can be provided. Also, if the user is traveling, points related to the travel destination can be provided. Furthermore, if the user is participating in a specific event, points related to the event can be provided. In this way, the point acquisition unit can provide optimal points based on the user's geographical location information, thereby improving user satisfaction.

[0106] When providing points, the point acquisition unit can provide relevant points based on the user's social media activity. The point acquisition unit, for example, analyzes the user's social media activity and provides points based on that information. For example, the point acquisition unit can provide points related to topics that the user frequently mentions on social media. It can also provide points related to accounts that the user follows on social media. It can also provide points related to communities that the user participates in on social media. In this way, the point acquisition unit can provide optimal points based on the user's social media activity and improve user satisfaction.

[0107] The location information utilization unit can estimate the user's emotions and adjust the content of the location information-based game based on the estimated user's emotions. The location information utilization unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. The emotion estimation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, a location information game with high action can be generated. Also, if the user is relaxed, a soothing location information game can be generated. Furthermore, if the user is stressed, a location information game that helps relieve stress can be generated. In this way, the location information utilization unit can adjust the content of the location information-based game based on the user's emotions and improve user satisfaction.

[0108] When using location information, the location information utilization unit can generate an appropriate game by referring to the user's past location information utilization history. The location information utilization unit, for example, stores the user's past location information utilization history in a database and generates an optimal game using an analysis algorithm. For example, the location information utilization unit can generate an optimal location information game based on places the user has visited in the past. It can also generate a game related to a specific area from the user's past location information utilization history. Furthermore, it can analyze the user's past location information utilization history and generate a highly relevant location information game. In this way, the location information utilization unit can generate an optimal game based on the user's past location information utilization history, thereby improving user satisfaction.

[0109] When using location information, the location information utilization unit can adjust game details based on the user's current geographic location. For example, the location information utilization unit obtains the user's current geographic location from GPS data or a location information service and adjusts game details based on that information. For example, if the user is in a specific area, a game related to that area can be generated. Also, if the user is traveling, a game related to the travel destination can be generated. Furthermore, if the user is participating in a specific event, a game related to that event can be generated. In this way, the location information utilization unit can generate an optimal game based on the user's current geographic location and improve user satisfaction.

[0110] The location information utilization unit can estimate the user's emotions and adjust the difficulty level of the location information-based game based on the estimated user's emotions. The location information utilization unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. The emotion estimation algorithm evaluates the user's emotional state using facial expression recognition and voice analysis technology. For example, if the user is excited, a location information game with a high level of difficulty can be generated. Also, if the user is relaxed, a location information game with a low level of difficulty can be generated. Furthermore, if the user is stressed, a location information game with a level of difficulty that helps relieve stress can be generated. In this way, the location information utilization unit can adjust the difficulty level of the location information-based game based on the user's emotions and improve user satisfaction.

[0111] When using location information, the location information utilization unit can prioritize generating highly relevant games by taking into account the user's geographical location information. The location information utilization unit, for example, acquires the user's geographical location information from GPS data or a location information service and generates games based on that information. For example, if the user is in a specific area, it can generate a game related to that area. Also, if the user is traveling, it can generate a game related to the travel destination. Furthermore, if the user is participating in a specific event, it can generate a game related to that event. In this way, the location information utilization unit can generate optimal games based on the user's geographical location information and improve user satisfaction.

[0112] The location information utilization unit can generate a relevant game based on the user's social media activity when using location information. The location information utilization unit, for example, analyzes the user's social media activity and generates a game based on that information. For example, it can generate a location-based game related to a theme that the user frequently mentions on social media. It can also generate a location-based game related to accounts that the user follows on social media. It can also generate a location-based game related to a community that the user participates in on social media. In this way, the location information utilization unit can generate an optimal game based on the user's social media activity and improve user satisfaction. === Hard Collateral 1-1 === Each of the multiple elements, including the character selection unit, story generation unit, game element designation unit, generation unit, point acquisition unit, and location information utilization unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the character selection unit is realized by the reception device 38 of the smart device 14 and provides an interface for the user to select a character from options. The story generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to build a story based on the character's characteristics and background. The game element designation unit is realized by the reception device 38 of the smart device 14 and provides an interface for the user to select game elements such as puzzles, riddles, and character development. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to generate game scenarios and events based on the user's designations. The point acquisition unit is realized by the control unit 46A of the smart device 14 and awards points when the user completes a specific event in the game. The location information utilization unit is realized by the communication I / F 44 of the smart device 14 and the specific processing unit 290 of the data processing device 12, and generates game content based on the user's location information, collects location information data, and statistically analyzes it. === Hard Collateral 1-2 === Each of the multiple elements, including the character selection unit, story generation unit, game element designation unit, generation unit, point acquisition unit, and location information utilization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the character selection unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for the user to select a character from options. The story generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to build a story based on the characteristics and background of the characters. The game element designation unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for the user to select game elements such as puzzles, riddles, and character development. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to generate game scenarios and events based on user designations. The point acquisition unit is realized by the control unit 46A of the smart glasses 214 and awards points when the user completes a specific event in the game. The location information utilization unit is realized by the communication I / F 44 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, and generates game content based on the user's location information, collects location information data, and statistically analyzes it. === Hard Collateral 1-3 === Each of the multiple elements, including the character selection unit, story generation unit, game element designation unit, generation unit, point acquisition unit, and location information utilization unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the character selection unit is realized by the microphone 238 of the headset terminal 314 and provides an interface for the user to select a character from options. The story generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to build a story based on the character's characteristics and background. The game element designation unit is realized by the microphone 238 of the headset terminal 314 and provides an interface for the user to select game elements such as puzzles, riddles, and character development. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to generate game scenarios and events based on user designations. The point acquisition unit is realized by the control unit 46A of the headset terminal 314 and awards points when the user completes a specific event in the game. The location information utilization unit is realized by the communication I / F 44 of the headset-type terminal 314 and the specific processing unit 290 of the data processing device 12, and generates game content based on the user's location information, collects location information data, and statistically analyzes it. === Hard Collateral 1-4 === Each of the multiple elements, including the character selection unit, story generation unit, game element designation unit, generation unit, point acquisition unit, and location information utilization unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the character selection unit is realized by the microphone 238 of the robot 414 and provides an interface for the user to select a character from options. The story generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to build a story based on the character's characteristics and background. The game element designation unit is realized by the microphone 238 of the robot 414 and provides an interface for the user to select game elements such as puzzles, riddles, and character development. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to generate game scenarios and events based on user designations. The point acquisition unit is realized by the control unit 46A of the robot 414 and awards points when the user completes a specific event in the game. The location information utilization unit is realized by the communication I / F 44 of the robot 414 and the specific processing unit 290 of the data processing device 12, and generates game content based on the user's location information, collects location information data, and statistically analyzes it.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] The original game generation system can further include a health management unit that acquires the user's health data and adjusts the game content based on the user's health condition. For example, the system can acquire the user's heart rate and step count data and provide a relaxing game if the user's heart rate is high, and a game that encourages exercise if the user's step count is low. Furthermore, based on the user's sleep data, the system can provide a game that can be enjoyed in a short time if the user is sleep-deprived, and a game that can be enjoyed for a long time if the user has had enough sleep. This makes it possible to provide an optimal game experience according to the user's health condition.

[0115] The character selection unit can analyze the user's past gameplay data and present character candidates based on the user's play style. For example, it can preferentially present characters with high action skills to a user who likes action games, and preferentially present characters with high intelligence to a user who likes puzzle games. It can also analyze the tendencies of characters selected by the user in the past and suggest characters with similar characteristics. Furthermore, if the user frequently selects a particular character, it can suggest new characters related to that character. This can support the user in selecting a character that suits their play style.

[0116] The story generation unit can estimate the user's emotions and adjust the tempo of the story based on the estimated user emotions. For example, if the user is excited, a fast-paced story can be provided, and if the user is relaxed, a slow-paced story can be provided. Also, if the user is feeling stressed, a story with a tempo that helps relieve stress can be provided. Furthermore, the story branching points can be adjusted according to the user's emotions, and options that match the emotions can be presented. This makes it possible to provide an optimal story experience according to the user's emotions.

[0117] The game element designation unit can suggest game elements based on the user's current activity level. For example, if the user is exercising, active game elements can be suggested, and if the user is resting, relaxing game elements can be suggested. The game difficulty can also be adjusted according to the user's activity level to provide an appropriate challenge. Furthermore, the game element designation unit can analyze the user's past activity patterns based on the user's activity data and suggest optimal game elements. This allows the user to have an optimal game experience according to their activity level.

[0118] The generation unit can estimate the user's emotion and adjust the visual effects of the game based on the estimated user's emotion. For example, if the user is excited, a vivid and dynamic visual effect can be provided, and if the user is relaxed, a calm and soothing visual effect can be provided. Also, if the user is feeling stressed, a visual effect that helps relieve stress can be provided. Furthermore, the color tone and brightness of the game can be adjusted according to the user's emotion to provide a visual experience that matches the emotion. This makes it possible to provide an optimal visual experience according to the user's emotion.

[0119] The point acquisition unit can analyze the user's past point usage history and suggest the optimal way to use points. For example, if the user has used points frequently to purchase items in the past, it can suggest benefits related to item purchases, and if the user has used points frequently to participate in in-game events in the past, it can suggest benefits related to event participation. It can also suggest the optimal timing to use points based on the user's point usage history. Furthermore, it can analyze the user's point usage history and suggest new ways to use points. This can optimize the user's point usage and improve satisfaction.

[0120] The location information utilization unit can estimate the user's emotions and adjust the rewards for games that use location information based on the estimated user's emotions. For example, if the user is excited, the amount of reward can be increased, and if the user is relaxed, the quality of the reward can be improved. Also, if the user is feeling stressed, a reward that helps relieve stress can be provided. Furthermore, the type of reward can be adjusted according to the user's emotions, and a reward that matches the emotion can be provided. This makes it possible to provide an optimal reward experience according to the user's emotions.

[0121] The character selection unit can suggest characters selected by friends based on the user's social media activity. For example, it can prioritize characters selected by the user's friends on social media, and by selecting the same character as a friend, it can provide a common topic of conversation. It can also suggest characters that friends have given high ratings to. Furthermore, it can analyze the user's social media activity and suggest characters that share interests with friends. This allows the user to select a character they share with their friends and enjoy the game.

[0122] The story generation unit can estimate the user's emotions and adjust the music of the story based on the estimated user's emotions. For example, if the user is excited, fast-paced music can be provided, and if the user is relaxed, calm music can be provided. Also, if the user is feeling stressed, music that helps relieve stress can be provided. Furthermore, the volume and tone of the music can be adjusted according to the user's emotions, providing a musical experience that matches the emotions. This makes it possible to provide an optimal musical experience according to the user's emotions.

[0123] The game element designation unit can suggest game elements that the user has not experienced based on the user's past game element selection history. For example, it can provide a new experience by preferentially presenting game elements that the user has not selected in the past. It can also analyze the user's past selection history to suggest new, highly relevant game elements. Furthermore, it can suggest game elements from different genres based on the trends in game elements selected by the user in the past. This allows the user to experience new game elements and expand the enjoyment of the game.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The character selection unit accepts a character selection from the user. For example, it provides an interface for the user to select a character from options. Step 2: The story generator generates a story based on the selected characters, for example, using a generation AI to build a story based on the characters' characteristics and backgrounds. Step 3: The game element designation unit accepts designation of game elements from the user. For example, it provides an interface for the user to select game elements such as puzzles, mystery solving, and training. Step 4: The generator generates a game based on the specified game elements. For example, the generator AI generates a game scenario and events based on the user's specifications. Step 5: The point acquisition unit provides points based on the completion of an event in the game. For example, points are awarded when the user completes a specific event in the game. Step 6: The location information utilization unit generates a game that utilizes location information, and acquires, anonymizes, and statistically analyzes big data on location information. For example, it generates game content based on the user's location information, collects location data, and statistically analyzes it.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0174] 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.

[0175] 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.

[0176] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] [Explanation of symbols]

[0198] 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. Character selection section, a story generation unit that generates a story based on the character selected by the character selection unit; a game element designation unit that designates game elements based on the story generated by the story generation unit; a generation unit that generates a game based on the game elements specified by the game element specification unit; a point acquisition unit that provides points based on completion of an event in the game generated by the generation unit; a location information utilization unit that generates a game using location information based on the points provided by the point acquisition unit; A system characterized by:

2. The character selection unit Accept character selection from the user 2. The system of claim 1.

3. The story generation unit Generate a story based on the characters you select 2. The system of claim 1.

4. The game element designation unit Accepts game element specifications from the user 2. The system of claim 1.

5. The generation unit Generate a game based on specified game elements using generation AI 2. The system of claim 1.

6. The point acquisition unit Awards points based on completing in-game events 2. The system of claim 1.

7. The location information utilization unit Generate location-based games, acquire big data on location information, anonymize it, and compile statistics 2. The system of claim 1.

8. The character selection unit Estimate the user's emotions and present character selection candidates based on the estimated user emotions.

2. The system of claim 1.

9. The character selection unit Analyze the user's past character selection history and present appropriate character candidates 2. The system of claim 1.

10. The character selection unit During character selection, filter character suggestions based on the user's current interests or concerns 2. The system of claim 1.

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