System

Abstract

An object of the system according to the embodiment is to maintain and improve motivation by converting daily work and tasks into games.SOLUTION: A system according to an embodiment includes a quest setting unit, a point awarding unit, and a progress tracking unit. The quest setting unit sets a goal or a task of the user as a quest. The point awarding component awards points according to the accomplishment of the quest set by the quest setting component. The progress tracking unit tracks the progress of the quest set by the quest setting unit in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to maintain and improve motivation for daily work and tasks.

[0005] The system according to the embodiment aims to turn everyday work and tasks into games, thereby maintaining and improving motivation. [Means for solving the problem]

[0006] The system according to the embodiment includes a quest setting unit, a point awarding unit, and a progress tracking unit. The quest setting unit sets a user's goal or task as a quest. The point awarding unit awards points according to the achievement of the quest set by the quest setting unit. The progress tracking unit tracks the progress of the quest set by the quest setting unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can turn everyday work and tasks into games, thereby maintaining and improving motivation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The gamification platform according to an embodiment of the present invention is a system that allows users to set daily tasks and routines as game quests and receive rewards for each achievement, thereby maintaining user motivation and promoting career growth.

[0029] A gamification platform according to an embodiment includes a quest setting unit, a point awarding unit, and a progress tracking unit. The quest setting unit sets a user's goals or tasks as quests. For example, quests may include the progress of a work project, daily household chores, exercise, etc. The point awarding unit awards points according to the completion of the quest. For example, a small number of points may be awarded for an easy task, and a large number of points may be awarded for a difficult task. The progress tracking unit tracks the progress of the quest in real time. For example, when a user completes a quest, the platform automatically updates the progress and awards points. This allows the gamification platform according to an embodiment to help users achieve their goals and maintain their motivation.

[0030] The quest setting unit can use generation AI to analyze the user's past behavioral history and performance data, and automatically generate the optimal quest. For example, the quest setting unit uses generation AI to analyze the user's past behavioral history and performance data, and automatically generate the optimal quest for each individual user. For example, it adjusts the content of the next quest based on the difficulty and time required for tasks previously completed. This allows the user to be provided with the optimal quest and maintain their motivation.

[0031] The quest setting unit can automatically evaluate the user's skill level based on the degree of quest achievement and adjust the difficulty of the next quest. The quest setting unit, for example, builds a system that automatically evaluates the user's skill level based on the degree of quest achievement. For example, a user with a high degree of achievement can be offered a quest with a high level of difficulty. This allows the user to be provided with quests that match their skill level and maintain their motivation.

[0032] The quest setting unit can introduce a joint quest for a team or group to promote cooperative play. The quest setting unit, for example, sets a joint quest for a team or group to build a system that promotes cooperative play. For example, the quest can be set as a task that multiple users must accomplish in cooperation. This can promote cooperative play in a team or group and improve motivation.

[0033] The quest setting unit diversifies the types of quests and can apply them to fields other than work, such as health management or hobby activities. The quest setting unit, for example, diversifies the types of quests and builds a system that can be applied to fields other than work, such as health management or hobby activities. For example, tasks such as exercise or reading can be set as quests. This allows quests to be applied to fields other than work, such as health management or hobby activities, and can improve the user's motivation.

[0034] The quest setting unit can increase the types of rewards that users can choose from depending on the completion of a quest, and provide rewards that meet individual needs. For example, the quest setting unit builds a system that increases the types of rewards that users can choose from depending on the completion of a quest. For example, a variety of rewards such as gift cards, merchandise, and special offers can be provided. This makes it possible to provide rewards that meet the needs of users and increase their motivation.

[0035] The progress tracking unit can use the generation AI to analyze the user's progress data in real time and automatically generate feedback according to the level of achievement. The progress tracking unit, for example, uses the generation AI to build a system that analyzes the user's progress data in real time and automatically generates feedback according to the level of achievement. For example, it can send encouraging messages according to the progress of a quest. This allows the user to receive feedback according to their progress in real time and maintain their motivation.

[0036] The progress tracking unit can monitor the user's biometric data and provide advice according to the user's health condition. The progress tracking unit, for example, monitors the user's biometric data and builds a system that provides advice according to the user's health condition when tracking the user's progress. For example, the progress tracking unit can suggest relaxation techniques based on the user's heart rate and stress level. This allows the user to receive advice according to the user's health condition and maintain motivation.

[0037] The progress tracking unit can implement a community function that allows users to share progress data with other users and encourage each other. The progress tracking unit, for example, builds a system that implements a community function that allows users to share progress data with other users and encourage each other. For example, a bulletin board for sharing progress status is provided. This allows users to share progress data with each other and encourage each other, thereby maintaining motivation.

[0038] The progress tracking unit can visualize the progress data and make it intuitively understandable with graphs and charts. The progress tracking unit, for example, builds a system that visualizes the progress data and makes it intuitively understandable with graphs and charts. For example, the progress of a quest can be displayed in a line graph. This makes it possible to visualize the progress data and make it intuitively understandable, thereby maintaining the user's motivation.

[0039] The quest setting unit can use a generation AI to automatically customize game elements according to the user's play style and preferences. The quest setting unit, for example, uses a generation AI to build a system that automatically customizes game elements according to the user's play style and preferences. For example, it sets quests based on the user's preferred game genre and difficulty level. This provides game elements according to the user's play style and preferences, making it possible to maintain motivation.

[0040] The quest setting unit can provide special events and challenges to users according to their level of achievement in the game. The quest setting unit, for example, builds a system that provides special events and challenges to users according to their level of achievement in the game. For example, a limited event is held for users who complete a specific quest. This allows users to be provided with special events and challenges according to their level of achievement, thereby maintaining their motivation.

[0041] The quest setting unit can introduce storytelling into game elements, allowing the user to progress through the quest as part of a story. For example, the quest setting unit introduces storytelling into game elements, and builds a system in which the user progresses through the quest as part of a story. For example, the story unfolds as the quest progresses. This allows the user to maintain motivation by progressing through the quest as part of a story.

[0042] The quest setting unit can increase the variety of avatars and items that users can select depending on their level of achievement in the game. The quest setting unit, for example, builds a system that increases the variety of avatars and items that users can select depending on their level of achievement in the game. For example, a limited item is provided to users who complete a specific quest. This increases the variety of avatars and items depending on the user's level of achievement, making it possible to maintain motivation.

[0043] The quest setting unit can use generation AI to analyze the user's career data and suggest the optimal skill-up quest. The quest setting unit, for example, uses generation AI to analyze the user's career data and build a system that suggests the optimal skill-up quest. For example, the quest is set based on past work experience and skill set. This makes it possible to suggest the optimal skill-up quest based on the user's career data and promote career growth.

[0044] The quest setting unit can automatically match mentors and coaching services to users according to their career growth. The quest setting unit, for example, builds a system that automatically matches mentors and coaching services to users according to their career growth. For example, it suggests mentors with specific skills. This makes it possible to provide appropriate mentors and coaching services according to the user's career growth and promote their career growth.

[0045] The quest setting unit can provide specialized workshops and seminars that the user can participate in according to the growth of his / her career. The quest setting unit, for example, builds a system that provides specialized workshops and seminars that the user can participate in according to the growth of his / her career. For example, the quest setting unit suggests a workshop for acquiring a specific skill. This makes it possible to provide specialized workshops and seminars according to the growth of the user's career and promote the growth of the user's career.

[0046] The quest setting unit can introduce a community function that allows users to share career data with other users and encourage each other. The quest setting unit, for example, builds a system that introduces a community function that allows users to share career data with other users and encourage each other. For example, a bulletin board is provided for sharing career progress. This allows users to share career data with each other and encourage each other, thereby maintaining motivation.

[0047] The quest setting unit can use a generation AI to automatically generate avatar customization options according to the user's preferences. For example, the quest setting unit uses a generation AI to build a system that automatically generates avatar customization options according to the user's preferences. For example, the quest setting unit suggests the appearance of the avatar based on the user's preferred style and color. This provides avatar customization options according to the user's preferences, and can maintain motivation.

[0048] The quest setting unit can provide special rewards and benefits to users according to the level-up of their avatars. The quest setting unit, for example, builds a system that provides special rewards and benefits to users according to the level-up of their avatars. For example, limited items are provided to users who reach a specific level. This allows users to maintain their motivation by providing special rewards and benefits according to the level-up of their avatars.

[0049] The quest setting unit can reflect the user's real-life goals and achievements in the avatar customization options. For example, the quest setting unit builds a system that reflects the user's real-life goals and achievements in the avatar customization options. For example, the quest setting unit provides special equipment to a user who achieves a specific goal. This allows the user's real-life achievements to be reflected in the avatar customization, thereby maintaining motivation.

[0050] The quest setting unit can introduce a collaborative element with other users into the customization of an avatar, enabling joint customization. For example, the quest setting unit introduces a collaborative element with other users into the customization of an avatar, building a system that enables joint customization. For example, customizing an avatar together with team members. This allows users to cooperate with each other in customizing their avatars, thereby maintaining motivation.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The quest setting unit can use the user's geographical location information to suggest quests that are unique to the region. For example, if the user is in a particular city, a quest touring the city's tourist attractions can be set. It can also suggest quests to participate in local events and festivals. It is also possible to set quests that allow users to earn points by using local stores and services. This allows users to enjoy quests that take advantage of the region's characteristics and contributes to revitalizing the local economy.

[0053] The quest setting unit can suggest new skills or activities that the user has never attempted before based on the user's past quest completion history. For example, if the user has completed many exercise-related quests, the unit can suggest new quests such as cooking or handicrafts. Also, if the user has high skills in a specific field, the unit can set a quest that utilizes those skills to support other users. Furthermore, by suggesting new hobbies and activities that the user may be interested in, the unit can broaden the scope of the user's life.

[0054] The quest setting unit can suggest educational quests that suit the user's learning style. For example, for a user who prefers visual learning, quests using videos or infographics can be suggested. For a user who prefers auditory learning, quests using podcasts or audiobooks can be set. Furthermore, for a user who prefers practical learning, it is also possible to suggest project-based quests that allow hands-on learning. This makes it possible to provide educational quests that suit the user's learning style and maximize learning effectiveness.

[0055] The quest setting unit can suggest customized quests that match the user's hobbies and interests. For example, if the user is interested in music, a quest to practice an instrument or to compose a new song can be suggested. If the user is interested in cooking, a quest to try a new recipe or to participate in a cooking contest can be set. Furthermore, if the user is interested in outdoor activities, a quest to go hiking or camping can be suggested. This makes it possible to provide customized quests that match the user's hobbies and interests and increase motivation.

[0056] The quest setting unit can suggest quests according to the user's life stage. For example, for a student user, quests related to academics or exam preparation can be suggested. For a new working user, quests related to improving skills at work or networking can be set. Furthermore, for a user raising children, quests related to childcare or household management can be suggested. In this way, quests according to the user's life stage can be provided, and goal achievement at each life stage can be supported.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The quest setting unit sets the user's goals and tasks as quests. For example, progress on a work project, daily household chores, exercise, etc. can be set as quests. Step 2: The point awarding unit awards points according to the completion of the quest, for example, a small number of points for an easy task and a large number of points for a difficult task. Step 3: The progress tracker tracks the progress of the quest in real time. For example, when a user completes a quest, the platform automatically updates the progress and awards points.

[0059] (Example 2) The gamification platform according to an embodiment of the present invention is a system that allows users to set daily tasks and routines as game quests and receive rewards for each achievement, thereby maintaining user motivation and promoting career growth.

[0060] A gamification platform according to an embodiment includes a quest setting unit, a point awarding unit, and a progress tracking unit. The quest setting unit sets a user's goals or tasks as quests. For example, quests may include the progress of a work project, daily household chores, exercise, etc. The point awarding unit awards points according to the completion of the quest. For example, a small number of points may be awarded for an easy task, and a large number of points may be awarded for a difficult task. The progress tracking unit tracks the progress of the quest in real time. For example, when a user completes a quest, the platform automatically updates the progress and awards points. This allows the gamification platform according to an embodiment to help users achieve their goals and maintain their motivation.

[0061] The quest setting unit can use generation AI to analyze the user's past behavioral history and performance data, and automatically generate the optimal quest. For example, the quest setting unit uses generation AI to analyze the user's past behavioral history and performance data, and automatically generate the optimal quest for each individual user. For example, it adjusts the content of the next quest based on the difficulty and time required for tasks previously completed. This allows the user to be provided with the optimal quest and maintain their motivation.

[0062] The quest setting unit can automatically evaluate the user's skill level based on the degree of quest achievement and adjust the difficulty of the next quest. The quest setting unit, for example, builds a system that automatically evaluates the user's skill level based on the degree of quest achievement. For example, a user with a high degree of achievement can be offered a quest with a high level of difficulty. This allows the user to be provided with quests that match their skill level and maintain their motivation.

[0063] The quest setting unit uses the emotion estimation function to suggest quests that correspond to the user's emotional state, thereby eliciting positive emotions. For example, the quest setting unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest quests that elicit positive emotions. For example, if the user is feeling stressed, the quest setting unit suggests a task that will help the user relax. This makes it possible to provide quests that correspond to the user's emotional state and elicit positive emotions.

[0064] The quest setting unit can introduce a joint quest for a team or group to promote cooperative play. The quest setting unit, for example, sets a joint quest for a team or group to build a system that promotes cooperative play. For example, the quest can be set as a task that multiple users must accomplish in cooperation. This can promote cooperative play in a team or group and improve motivation.

[0065] The quest setting unit diversifies the types of quests and can apply them to fields other than work, such as health management or hobby activities. The quest setting unit, for example, diversifies the types of quests and builds a system that can be applied to fields other than work, such as health management or hobby activities. For example, tasks such as exercise or reading can be set as quests. This allows quests to be applied to fields other than work, such as health management or hobby activities, and can improve the user's motivation.

[0066] The quest setting unit can increase the types of rewards that users can choose from depending on the completion of a quest, and provide rewards that meet individual needs. For example, the quest setting unit builds a system that increases the types of rewards that users can choose from depending on the completion of a quest. For example, a variety of rewards such as gift cards, merchandise, and special offers can be provided. This makes it possible to provide rewards that meet the needs of users and increase their motivation.

[0067] The progress tracking unit can use the generation AI to analyze the user's progress data in real time and automatically generate feedback according to the level of achievement. The progress tracking unit, for example, uses the generation AI to build a system that analyzes the user's progress data in real time and automatically generates feedback according to the level of achievement. For example, it can send encouraging messages according to the progress of a quest. This allows the user to receive feedback according to their progress in real time and maintain their motivation.

[0068] The progress tracking unit can monitor the user's biometric data and provide advice according to the user's health condition. The progress tracking unit, for example, monitors the user's biometric data and builds a system that provides advice according to the user's health condition when tracking the user's progress. For example, the progress tracking unit can suggest relaxation techniques based on the user's heart rate and stress level. This allows the user to receive advice according to the user's health condition and maintain motivation.

[0069] The progress tracking unit uses the emotion estimation function to track the user's emotional state in real time, and can make suggestions to increase motivation if a negative emotion is detected. The progress tracking unit, for example, builds a system that uses the emotion estimation function to track the user's emotional state in real time, and makes suggestions to increase motivation if a negative emotion is detected. For example, it sends encouraging messages. This makes it possible to make suggestions according to the user's emotional state and maintain motivation.

[0070] The progress tracking unit can implement a community function that allows users to share progress data with other users and encourage each other. The progress tracking unit, for example, builds a system that implements a community function that allows users to share progress data with other users and encourage each other. For example, a bulletin board for sharing progress status is provided. This allows users to share progress data with each other and encourage each other, thereby maintaining motivation.

[0071] The progress tracking unit can visualize the progress data and make it intuitively understandable with graphs and charts. The progress tracking unit, for example, builds a system that visualizes the progress data and makes it intuitively understandable with graphs and charts. For example, the progress of a quest can be displayed in a line graph. This makes it possible to visualize the progress data and make it intuitively understandable, thereby maintaining the user's motivation.

[0072] The progress tracking unit can use the emotion estimation function to analyze the user's emotional response to the progress data and reinforce positive feedback. The progress tracking unit, for example, uses the emotion estimation function to analyze the user's emotional response to the progress data and builds a system that reinforces positive feedback. For example, a praising message can be sent when a high level of achievement is achieved. This reinforces positive feedback according to the user's emotional response and maintains motivation.

[0073] The quest setting unit can use a generation AI to automatically customize game elements according to the user's play style and preferences. The quest setting unit, for example, uses a generation AI to build a system that automatically customizes game elements according to the user's play style and preferences. For example, it sets quests based on the user's preferred game genre and difficulty level. This provides game elements according to the user's play style and preferences, making it possible to maintain motivation.

[0074] The quest setting unit can provide special events and challenges to users according to their level of achievement in the game. The quest setting unit, for example, builds a system that provides special events and challenges to users according to their level of achievement in the game. For example, a limited event is held for users who complete a specific quest. This allows users to be provided with special events and challenges according to their level of achievement, thereby maintaining their motivation.

[0075] The quest setting unit uses the emotion estimation function to suggest game elements according to the user's emotional state, thereby maintaining motivation. The quest setting unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and builds a system that suggests game elements to maintain motivation. For example, when the user is tired, the quest setting unit suggests a quest that will help the user relax. This makes it possible to provide game elements according to the user's emotional state and maintain motivation.

[0076] The quest setting unit can introduce storytelling into game elements, allowing the user to progress through the quest as part of a story. For example, the quest setting unit introduces storytelling into game elements, and builds a system in which the user progresses through the quest as part of a story. For example, the story unfolds as the quest progresses. This allows the user to maintain motivation by progressing through the quest as part of a story.

[0077] The quest setting unit can increase the variety of avatars and items that users can select depending on their level of achievement in the game. The quest setting unit, for example, builds a system that increases the variety of avatars and items that users can select depending on their level of achievement in the game. For example, a limited item is provided to users who complete a specific quest. This increases the variety of avatars and items depending on the user's level of achievement, making it possible to maintain motivation.

[0078] The quest setting unit uses the emotion estimation function to provide a reward according to the user's emotional state, thereby eliciting positive emotions. The quest setting unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time, and builds a system that provides a reward that elicits positive emotions. For example, when the user is feeling stressed, the quest setting unit provides a reward that helps the user relax. This makes it possible to provide a reward according to the user's emotional state and elicit positive emotions.

[0079] The quest setting unit can use generation AI to analyze the user's career data and suggest the optimal skill-up quest. The quest setting unit, for example, uses generation AI to analyze the user's career data and build a system that suggests the optimal skill-up quest. For example, the quest is set based on past work experience and skill set. This makes it possible to suggest the optimal skill-up quest based on the user's career data and promote career growth.

[0080] The quest setting unit can automatically match mentors and coaching services to users according to their career growth. The quest setting unit, for example, builds a system that automatically matches mentors and coaching services to users according to their career growth. For example, it suggests mentors with specific skills. This makes it possible to provide appropriate mentors and coaching services according to the user's career growth and promote their career growth.

[0081] The quest setting unit uses the emotion estimation function to provide career advice according to the user's emotional state, thereby maintaining motivation. The quest setting unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and builds a system that provides career advice to maintain motivation. For example, when the user is feeling stressed, advice to help the user relax is provided. This allows the user to be provided with career advice according to the user's emotional state and maintain motivation.

[0082] The quest setting unit can provide specialized workshops and seminars that the user can participate in according to the growth of his / her career. The quest setting unit, for example, builds a system that provides specialized workshops and seminars that the user can participate in according to the growth of his / her career. For example, the quest setting unit suggests a workshop for acquiring a specific skill. This makes it possible to provide specialized workshops and seminars according to the growth of the user's career and promote the growth of the user's career.

[0083] The quest setting unit can introduce a community function that allows users to share career data with other users and encourage each other. The quest setting unit, for example, builds a system that introduces a community function that allows users to share career data with other users and encourage each other. For example, a bulletin board is provided for sharing career progress. This allows users to share career data with each other and encourage each other, thereby maintaining motivation.

[0084] The quest setting unit uses the emotion estimation function to set career goals according to the user's emotional state, thereby eliciting positive emotions. The quest setting unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time, and builds a system for setting career goals that elicit positive emotions. For example, when the user is feeling stressed, the quest setting unit sets a goal that will help the user relax. This makes it possible to set career goals according to the user's emotional state and elicit positive emotions.

[0085] The quest setting unit can use a generation AI to automatically generate avatar customization options according to the user's preferences. For example, the quest setting unit uses a generation AI to build a system that automatically generates avatar customization options according to the user's preferences. For example, the quest setting unit suggests the appearance of the avatar based on the user's preferred style and color. This provides avatar customization options according to the user's preferences, and can maintain motivation.

[0086] The quest setting unit can provide special rewards and benefits to users according to the level-up of their avatars. The quest setting unit, for example, builds a system that provides special rewards and benefits to users according to the level-up of their avatars. For example, limited items are provided to users who reach a specific level. This allows users to maintain their motivation by providing special rewards and benefits according to the level-up of their avatars.

[0087] The quest setting unit uses the emotion estimation function to suggest avatar customizations that correspond to the user's emotional state, thereby eliciting positive emotions. The quest setting unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time, and builds a system that suggests avatar customizations that elicit positive emotions. For example, when the user is feeling stressed, the system suggests a relaxing appearance. This makes it possible to suggest avatar customizations that correspond to the user's emotional state, thereby eliciting positive emotions.

[0088] The quest setting unit can reflect the user's real-life goals and achievements in the avatar customization options. For example, the quest setting unit builds a system that reflects the user's real-life goals and achievements in the avatar customization options. For example, the quest setting unit provides special equipment to a user who achieves a specific goal. This allows the user's real-life achievements to be reflected in the avatar customization, thereby maintaining motivation.

[0089] The quest setting unit can introduce a collaborative element with other users into the customization of an avatar, enabling joint customization. For example, the quest setting unit introduces a collaborative element with other users into the customization of an avatar, building a system that enables joint customization. For example, customizing an avatar together with team members. This allows users to cooperate with each other in customizing their avatars, thereby maintaining motivation.

[0090] The quest setting unit uses the emotion estimation function to provide avatar customization options according to the user's emotional state, thereby eliciting positive emotions. The quest setting unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time, and builds a system that provides avatar customization options that elicit positive emotions. For example, when the user is feeling stressed, the system suggests a relaxing appearance. This allows the system to provide avatar customization options according to the user's emotional state, thereby eliciting positive emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The quest setting unit can use the user's geographical location information to suggest quests that are unique to the region. For example, if the user is in a particular city, a quest touring the city's tourist attractions can be set. It can also suggest quests to participate in local events and festivals. It is also possible to set quests that allow users to earn points by using local stores and services. This allows users to enjoy quests that take advantage of the region's characteristics and contributes to revitalizing the local economy.

[0093] The quest setting unit can suggest new skills or activities that the user has never attempted before based on the user's past quest completion history. For example, if the user has completed many exercise-related quests, the unit can suggest new quests such as cooking or handicrafts. Also, if the user has high skills in a specific field, the unit can set a quest that utilizes those skills to support other users. Furthermore, by suggesting new hobbies and activities that the user may be interested in, the unit can broaden the scope of the user's life.

[0094] The quest setting unit can estimate the user's emotional state and suggest quests that provide a relaxing environment for the user. For example, if the user is feeling stressed, a quest to take a walk in nature or meditate can be suggested. If the user is tired, a quest to listen to relaxing music or try aromatherapy can be set. Furthermore, if the user is feeling positive, a fun activity to further enhance that emotion can be suggested. This allows the system to provide quests that correspond to the user's emotional state and improve the user's overall sense of well-being.

[0095] The quest setting unit can estimate the user's emotional state and suggest quests that allow the user to have positive interactions with other users. For example, if the user is feeling lonely, the unit can suggest quests that allow the user to interact with other users through online group chats or video calls. Also, if the user is feeling positive, the unit can suggest quests that allow the user to make social media posts to share those emotions. Furthermore, the unit can suggest quests that allow the user to feel grateful by supporting other users. This can promote positive interactions according to the user's emotional state and improve the user's overall sense of happiness.

[0096] The quest setting unit can estimate the user's emotional state and suggest quests that will help the user feel grateful. For example, if the user is feeling stressed, the unit can suggest quests to reduce stress by feeling grateful. Specifically, the unit can set a quest to keep a gratitude diary or to write a thank you letter. Furthermore, if the user is feeling positive, the unit can suggest a quest to send a thank you message to other users to further enhance those feelings. This makes it possible to provide quests that elicit gratitude according to the user's emotional state and improve the user's overall sense of happiness.

[0097] The quest setting unit can estimate the user's emotional state and suggest quests that can increase the user's self-esteem. For example, if the user is feeling unsure of themselves, a quest to increase their self-esteem can be suggested. Specifically, a quest to reflect on past successes or a quest to list their strengths can be set. Furthermore, if the user is feeling positive, a quest to take on a new challenge can be suggested to further increase those feelings. This allows for the provision of quests that increase self-esteem according to the user's emotional state, thereby improving the user's overall sense of happiness.

[0098] The quest setting unit can estimate the user's emotional state and suggest activities that will refresh the user. For example, if the user is tired, it can suggest activities that will refresh the user. Specifically, it can set a quest to take a short nap or a quest to do some light stretching. Furthermore, if the user is feeling stressed, it can suggest a quest to listen to relaxing music or a quest to take some deep breaths. This can provide refreshing activities that correspond to the user's emotional state and improve the user's overall sense of well-being.

[0099] The quest setting unit can suggest educational quests that suit the user's learning style. For example, for a user who prefers visual learning, quests using videos or infographics can be suggested. For a user who prefers auditory learning, quests using podcasts or audiobooks can be set. Furthermore, for a user who prefers practical learning, it is also possible to suggest project-based quests that allow hands-on learning. This makes it possible to provide educational quests that suit the user's learning style and maximize learning effectiveness.

[0100] The quest setting unit can suggest customized quests that match the user's hobbies and interests. For example, if the user is interested in music, a quest to practice an instrument or to compose a new song can be suggested. If the user is interested in cooking, a quest to try a new recipe or to participate in a cooking contest can be set. Furthermore, if the user is interested in outdoor activities, a quest to go hiking or camping can be suggested. This makes it possible to provide customized quests that match the user's hobbies and interests and increase motivation.

[0101] The quest setting unit can suggest quests according to the user's life stage. For example, for a student user, quests related to academics or exam preparation can be suggested. For a new working user, quests related to improving skills at work or networking can be set. Furthermore, for a user raising children, quests related to childcare or household management can be suggested. In this way, quests according to the user's life stage can be provided, and goal achievement at each life stage can be supported.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The quest setting unit sets the user's goals and tasks as quests. For example, progress on a work project, daily household chores, exercise, etc. can be set as quests. Step 2: The point awarding unit awards points according to the completion of the quest, for example, a small number of points for an easy task and a large number of points for a difficult task. Step 3: The progress tracker tracks the progress of the quest in real time. For example, when a user completes a quest, the platform automatically updates the progress and awards points.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a quest setting unit that sets a user's goal or task as a quest; a point awarding unit that awards points according to the completion of the quest set by the quest setting unit; a progress tracking unit that tracks the progress of the quest set by the quest setting unit in real time. A system characterized by:

2. The quest setting unit Using emotion estimation function, quests are proposed according to the user's emotional state, eliciting positive emotions.

2. The system of claim 1.

3. The progress tracking unit Using generation AI, the user's progress data is analyzed in real time and feedback is automatically generated according to the level of achievement.

2. The system of claim 1.

4. The quest setting unit Using a generative AI, game elements are automatically customized according to the user's play style and preferences.

2. The system of claim 1.

5. The quest setting unit Using generative AI to analyze the user's career data and suggest optimal skill-up quests 2. The system of claim 1.

6. The progress tracking unit Tracking the user's emotional state in real time using emotion estimation functionality and making motivational suggestions when negative emotions are detected 2. The system of claim 1.

7. The quest setting unit Using an emotion estimation function, career advice is provided according to the user's emotional state, thereby maintaining motivation.

2. The system of claim 1.

8. The quest setting unit Using emotion estimation function, we propose customization of avatar according to the user's emotional state, and elicit positive emotions.

2. The system of claim 1.

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