System

Abstract

An object of the system according to the embodiment is to make it possible to recognize aging as an enjoyable challenge.SOLUTION: A system according to an embodiment includes a level setter, a quest suggester, an achievement recognizer, and a reward granter. The level setting unit sets a level according to the age of the user. The quest proposal unit proposes a quest such as a new skill, hobby, or travel on the basis of the level set by the level setting unit. The achievement recognition unit recognizes achievement of the quest proposed by the quest proposal unit. The reward granting component grants a badge or reward on the basis of the accomplishment of the quest recognized by the accomplishment recognition component.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] With conventional technology, it was difficult to see aging as a fun challenge.

[0005] The system according to the embodiment aims to make aging a fun challenge. [Means for solving the problem]

[0006] The system according to the embodiment includes a level setting unit, a quest suggestion unit, an achievement recognition unit, and a reward granting unit. The level setting unit sets a level according to the user's age. The quest suggestion unit suggests quests such as new skills, hobbies, and travel based on the level set by the level setting unit. The achievement recognition unit recognizes the achievement of the quest suggested by the quest suggestion unit. The reward granting unit grants badges and rewards based on the achievement of the quest recognized by the achievement recognition unit. [Effects of the Invention]

[0007] Systems according to embodiments can make aging seem like a fun challenge. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 LifeLeveler AI system, an embodiment of the present invention, is a system that helps users view aging as a fun challenge. The system aims to complete "quests" such as new skills, hobbies, and travel, as "levels" that correspond to the user's age. This allows the LifeLeveler AI system to view aging as a fun challenge, allowing users to enjoy self-improvement and new experiences.

[0029] The LifeLeveler AI system according to the embodiment includes a level setting unit, a quest suggestion unit, an achievement recognition unit, and a reward granting unit. The level setting unit sets a level according to the user's age. For example, the generation AI automatically sets an appropriate level based on the user's age. The quest suggestion unit suggests quests for new skills, hobbies, travel, etc. based on the level set by the level setting unit. For example, the generation AI analyzes the user's past achievements and interests and suggests appropriate quests. The achievement recognition unit recognizes the completion of quests suggested by the quest suggestion unit. For example, the generation AI tracks whether the user has completed a quest and recognizes the completion status. The reward granting unit grants badges and rewards based on the completion of quests recognized by the achievement recognition unit. For example, the generation AI automatically grants appropriate badges and rewards when the user completes a quest. This enables the LifeLeveler AI system according to the embodiment to set a level, suggest quests, recognize achievements, and grant rewards according to the user's age.

[0030] The level setting unit can set a level according to the user's age, taking into account the user's health condition and fitness level. For example, the level setting unit periodically monitors the user's health condition and sets a level according to the user's age based on the data. For example, the level setting unit collects data such as heart rate, blood pressure, and weight, and determines an appropriate level according to the user's health condition. This makes it possible to set a level that takes into account the user's health condition and fitness level.

[0031] The level setting unit can set a level according to the user's age, taking into account the user's social role. For example, if the user is a parent, the level setting unit sets the level taking into account the user's experience and responsibilities in raising a child. For example, the level is adjusted based on the child's age and the number of years of child-rearing experience. This makes it possible to set a level that takes into account the user's social role.

[0032] The quest suggestion unit can analyze the user's past failures and suggest quests to overcome them. For example, the quest suggestion unit stores the user's past failures in a database and suggests quests to overcome them based on that data. For example, it can set a quest to reattempt a project that failed in the past. This makes it possible to suggest quests that take the user's past failures into consideration.

[0033] The quest suggestion unit can consider the interests of the user's friends and family and suggest quests that can be completed together. For example, the quest suggestion unit stores the interests of the user's friends and family in a database and suggests quests that can be completed together based on that data. For example, it can suggest trips or events that the whole family can participate in. This makes it possible to suggest quests that take into account the interests of the user's friends and family.

[0034] The achievement recognition unit can customize the types of badges and rewards according to the user's level of achievement. For example, the achievement recognition unit monitors the user's level of achievement in real time and customizes the types of badges and rewards based on that data. For example, a special badge can be awarded to a user with a high level of achievement. This makes it possible to customize badges and rewards according to the user's level of achievement.

[0035] The achievement recognition unit can analyze the user's achievement history and suggest a badge collection based on a specific theme. For example, the achievement recognition unit stores the user's achievement history in a database and suggests a badge collection based on a specific theme based on that data. For example, it suggests a collection of travel-related badges. This makes it possible to suggest a badge collection based on the user's achievement history.

[0036] The reward granting unit can utilize the user's social network to provide badges and rewards that can be shared with friends and family. For example, the reward granting unit builds a system that provides badges and rewards that can be shared with friends and family based on the user's social network. For example, a badge can be set for a quest that is completed by the entire family. This makes it possible to provide badges and rewards that utilize the user's social network.

[0037] The reward granting unit can provide professional rewards based on the user's evaluation at work or school. The reward granting unit, for example, builds a system that provides professional rewards based on the user's evaluation at work or school. For example, rewards are set according to achievements at work. This makes it possible to provide rewards based on the user's evaluation at work or school.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The LifeLeveler AI system can also analyze a user's past travel history to suggest countries and regions they have not yet visited. For example, it can create a list of unvisited locations based on data from countries and regions the user has visited in the past and suggest them as interesting quests. This allows users to enjoy new cultures and scenery. It can also provide historical and cultural information about the suggested travel destinations, deepening the user's knowledge. It can also suggest activities and events at the destination, further enhancing travel planning.

[0040] The LifeLeveler AI system can also monitor a user's eating habits and nutritional status and suggest healthy meal plans. For example, it can create a balanced meal plan based on the user's eating history and nutritional data and suggest it as a quest. This makes it easier for users to maintain a healthy diet. If a user is lacking in certain nutrients, it can also suggest ingredients and recipes to supplement them. It can also automatically generate a shopping list based on the meal plan, helping users to purchase ingredients efficiently.

[0041] The LifeLeveler AI system can also suggest quests that will lead to career advancement, taking into account the user's occupation and career path. For example, it analyzes the user's current occupation and skill set and sets quests to acquire the skills and qualifications necessary for career advancement. This allows users to proactively develop their careers. It also provides information on the latest industry trends and technologies, helping users stay up-to-date with the latest knowledge. It can also suggest quests that encourage participation in networking events and seminars, helping users expand their network.

[0042] The LifeLeveler AI system can also suggest relevant communities and events based on a user's hobbies and interests. For example, if a user has a specific hobby, it can suggest online communities and offline events related to that hobby, making it easier for users to interact with people who share the same hobbies. It can also provide information about hobby-related workshops and seminars, increasing opportunities for users to learn new skills. It can also recommend books and articles based on the user's interests to help deepen their knowledge.

[0043] The LifeLeveler AI system can also analyze a user's learning history and suggest quests to set new learning goals. For example, it can suggest what a user should learn next based on the skills and knowledge they have previously acquired. This allows users to continuously improve themselves. It can also recommend online courses and learning materials, providing an environment where users can learn efficiently. Furthermore, it can monitor learning progress and provide appropriate feedback to maintain user motivation.

[0044] The LifeLeveler AI system can also monitor users' sleep patterns and suggest quests to promote quality sleep. For example, it can suggest optimal bedtimes and wake-up times based on the user's sleep data, helping users develop regular sleep habits. It also provides advice on improving the sleep environment to help users get a good night's sleep. It can also suggest relaxing music and meditation quests to help users fall asleep more easily.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The level setting unit sets the level according to the user's age. For example, the generation AI automatically sets an appropriate level based on the user's age. Step 2: The quest suggestion unit suggests quests such as new skills, hobbies, travel, etc. based on the level set by the level setting unit. For example, the generation AI analyzes the user's past achievements and interests and suggests suitable quests. Step 3: The achievement recognition unit recognizes the achievement of the quest proposed by the quest suggestion unit. For example, the generation AI tracks whether the user has achieved the quest and recognizes the achievement status. Step 4: The reward granting unit grants badges and rewards based on the achievement of the quest recognized by the achievement recognition unit. For example, the generation AI automatically grants appropriate badges and rewards when the user completes a quest.

[0047] (Example 2) The LifeLeveler AI system, an embodiment of the present invention, is a system that helps users view aging as a fun challenge. The system aims to complete "quests" such as new skills, hobbies, and travel, as "levels" that correspond to the user's age. This allows the LifeLeveler AI system to view aging as a fun challenge, allowing users to enjoy self-improvement and new experiences.

[0048] The LifeLeveler AI system according to the embodiment includes a level setting unit, a quest suggestion unit, an achievement recognition unit, and a reward granting unit. The level setting unit sets a level according to the user's age. For example, the generation AI automatically sets an appropriate level based on the user's age. The quest suggestion unit suggests quests for new skills, hobbies, travel, etc. based on the level set by the level setting unit. For example, the generation AI analyzes the user's past achievements and interests and suggests appropriate quests. The achievement recognition unit recognizes the completion of quests suggested by the quest suggestion unit. For example, the generation AI tracks whether the user has completed a quest and recognizes the completion status. The reward granting unit grants badges and rewards based on the completion of quests recognized by the achievement recognition unit. For example, the generation AI automatically grants appropriate badges and rewards when the user completes a quest. This enables the LifeLeveler AI system according to the embodiment to set a level, suggest quests, recognize achievements, and grant rewards according to the user's age.

[0049] The level setting unit can set a level according to the user's age, taking into account the user's health condition and fitness level. For example, the level setting unit periodically monitors the user's health condition and sets a level according to the user's age based on the data. For example, the level setting unit collects data such as heart rate, blood pressure, and weight, and determines an appropriate level according to the user's health condition. This makes it possible to set a level that takes into account the user's health condition and fitness level.

[0050] The level setting unit can set a level according to the user's age, taking into account the user's social role. For example, if the user is a parent, the level setting unit sets the level taking into account the user's experience and responsibilities in raising a child. For example, the level is adjusted based on the child's age and the number of years of child-rearing experience. This makes it possible to set a level that takes into account the user's social role.

[0051] The level setting unit can use the emotion estimation function to analyze the user's emotional state and set a level that elicits positive emotions. For example, the level setting unit monitors the user's emotional state in real time and sets a level that elicits positive emotions. For example, if the user is feeling stressed, the level setting unit suggests a quest that will help them relax. This makes it possible to set a level that takes the user's emotional state into consideration.

[0052] The quest suggestion unit can analyze the user's past failures and suggest quests to overcome them. For example, the quest suggestion unit stores the user's past failures in a database and suggests quests to overcome them based on that data. For example, it can set a quest to reattempt a project that failed in the past. This makes it possible to suggest quests that take the user's past failures into consideration.

[0053] The quest suggestion unit can consider the interests of the user's friends and family and suggest quests that can be completed together. For example, the quest suggestion unit stores the interests of the user's friends and family in a database and suggests quests that can be completed together based on that data. For example, it can suggest trips or events that the whole family can participate in. This makes it possible to suggest quests that take into account the interests of the user's friends and family.

[0054] The quest suggestion unit can use the emotion estimation function to suggest quests that will evoke the most positive emotions in the user. For example, the quest suggestion unit uses the emotion estimation function to build a system that suggests quests that will evoke the most positive emotions in the user. For example, the quest suggestion unit selects the most appropriate quest based on the user's emotion data. This makes it possible to suggest quests that take the user's emotional state into consideration.

[0055] The achievement recognition unit can customize the types of badges and rewards according to the user's level of achievement. For example, the achievement recognition unit monitors the user's level of achievement in real time and customizes the types of badges and rewards based on that data. For example, a special badge can be awarded to a user with a high level of achievement. This makes it possible to customize badges and rewards according to the user's level of achievement.

[0056] The achievement recognition unit can analyze the user's achievement history and suggest a badge collection based on a specific theme. For example, the achievement recognition unit stores the user's achievement history in a database and suggests a badge collection based on a specific theme based on that data. For example, it suggests a collection of travel-related badges. This makes it possible to suggest a badge collection based on the user's achievement history.

[0057] The achievement recognition unit can use the emotion estimation function to provide badges and rewards that give the user the most joy. The achievement recognition unit, for example, uses the emotion estimation function to build a system that provides badges and rewards that give the user the most joy. For example, the achievement recognition unit selects the most appropriate badge based on the user's emotion data. This makes it possible to provide badges and rewards that take the user's emotional state into consideration.

[0058] The reward granting unit can utilize the user's social network to provide badges and rewards that can be shared with friends and family. For example, the reward granting unit builds a system that provides badges and rewards that can be shared with friends and family based on the user's social network. For example, a badge can be set for a quest that is completed by the entire family. This makes it possible to provide badges and rewards that utilize the user's social network.

[0059] The reward granting unit can provide professional rewards based on the user's evaluation at work or school. The reward granting unit, for example, builds a system that provides professional rewards based on the user's evaluation at work or school. For example, rewards are set according to achievements at work. This makes it possible to provide rewards based on the user's evaluation at work or school.

[0060] The reward granting unit can use the emotion estimation function to adjust the reward that will most satisfy the user in real time. The reward granting unit, for example, uses the emotion estimation function to build a system that adjusts the reward that will most satisfy the user in real time. For example, the reward content is dynamically changed based on the user's emotion data. This makes it possible to adjust the reward in real time, taking into account the user's emotional state.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The LifeLeveler AI system can also analyze a user's past travel history to suggest countries and regions they have not yet visited. For example, it can create a list of unvisited locations based on data from countries and regions the user has visited in the past and suggest them as interesting quests. This allows users to enjoy new cultures and scenery. It can also provide historical and cultural information about the suggested travel destinations, deepening the user's knowledge. It can also suggest activities and events at the destination, further enhancing travel planning.

[0063] The LifeLeveler AI system can also monitor a user's eating habits and nutritional status and suggest healthy meal plans. For example, it can create a balanced meal plan based on the user's eating history and nutritional data and suggest it as a quest. This makes it easier for users to maintain a healthy diet. If a user is lacking in certain nutrients, it can also suggest ingredients and recipes to supplement them. It can also automatically generate a shopping list based on the meal plan, helping users to purchase ingredients efficiently.

[0064] The LifeLeveler AI system can also suggest quests that will lead to career advancement, taking into account the user's occupation and career path. For example, it analyzes the user's current occupation and skill set and sets quests to acquire the skills and qualifications necessary for career advancement. This allows users to proactively develop their careers. It also provides information on the latest industry trends and technologies, helping users stay up-to-date with the latest knowledge. It can also suggest quests that encourage participation in networking events and seminars, helping users expand their network.

[0065] The LifeLeveler AI system can also analyze the user's emotional state and suggest quests that will reduce stress and lead to relaxation. For example, if the user is feeling stressed, it can suggest relaxing yoga or meditation quests, helping the user maintain a balance between mind and body. It can also suggest relaxing activities such as nature walks or art therapy. Furthermore, it can customize and individually suggest optimal relaxation methods based on the user's emotional data.

[0066] The LifeLeveler AI system can also analyze a user's emotional state and suggest music or movies that will elicit positive emotions. For example, if a user is feeling down, it can suggest uplifting music or movies, helping them to feel more refreshed. It can also re-suggest content that previously elicited positive emotions based on the user's emotional data. It also provides information on new music and movie releases, helping users to always enjoy the latest entertainment.

[0067] The LifeLeveler AI system can also suggest relevant communities and events based on a user's hobbies and interests. For example, if a user has a specific hobby, it can suggest online communities and offline events related to that hobby, making it easier for users to interact with people who share the same hobbies. It can also provide information about hobby-related workshops and seminars, increasing opportunities for users to learn new skills. It can also recommend books and articles based on the user's interests to help deepen their knowledge.

[0068] The LifeLeveler AI system can also analyze a user's emotional state and suggest fitness plans based on their emotions. For example, if a user is feeling energetic, it will suggest high-intensity training, and if they want to relax, it will suggest yoga or stretching. This allows users to perform fitness activities that are tailored to their emotional state. It can also customize optimal exercise duration and frequency based on emotional data to help users maintain their health. It can also provide dietary and nutritional advice based on the fitness plan.

[0069] The LifeLeveler AI system can also analyze a user's learning history and suggest quests to set new learning goals. For example, it can suggest what a user should learn next based on the skills and knowledge they have previously acquired. This allows users to continuously improve themselves. It can also recommend online courses and learning materials, providing an environment where users can learn efficiently. Furthermore, it can monitor learning progress and provide appropriate feedback to maintain user motivation.

[0070] The LifeLeveler AI system can also analyze a user's emotional state and suggest reading lists based on that emotion. For example, if a user wants to relax, it will suggest books with a relaxing effect, and if they are feeling energized, it will suggest books with stimulating content. This allows users to enjoy reading that suits their emotional state. Based on emotional data, it can also suggest books that have previously elicited positive emotions. It also provides information on new book releases, helping users to always enjoy the latest reading experience.

[0071] The LifeLeveler AI system can also monitor users' sleep patterns and suggest quests to promote quality sleep. For example, it can suggest optimal bedtimes and wake-up times based on the user's sleep data, helping users develop regular sleep habits. It also provides advice on improving the sleep environment to help users get a good night's sleep. It can also suggest relaxing music and meditation quests to help users fall asleep more easily.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The level setting unit sets the level according to the user's age. For example, the generation AI automatically sets an appropriate level based on the user's age. Step 2: The quest suggestion unit suggests quests such as new skills, hobbies, travel, etc. based on the level set by the level setting unit. For example, the generation AI analyzes the user's past achievements and interests and suggests suitable quests. Step 3: The achievement recognition unit recognizes the achievement of the quest proposed by the quest suggestion unit. For example, the generation AI tracks whether the user has achieved the quest and recognizes the achievement status. Step 4: The reward granting unit grants badges and rewards based on the achievement of the quest recognized by the achievement recognition unit. For example, the generation AI automatically grants appropriate badges and rewards when the user completes a quest.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The 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.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] 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.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0106] The data processing system 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[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 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.

[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 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).

[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] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 level setting unit that sets a level according to the age of the user; a quest suggestion unit that suggests quests for new skills, hobbies, travel, etc. based on the level set by the level setting unit; an achievement recognition unit that recognizes the achievement of the quest proposed by the quest proposal unit; a reward granting unit that grants a badge or a reward based on the achievement of the quest recognized by the achievement recognition unit. A system characterized by:

2. The level setting unit Analyzing the user's emotional state using an emotion estimation function and setting the level that elicits positive emotions 2. The system of claim 1.

3. The quest suggestion unit: Analyzing the user's past failure experiences and proposing the quest to overcome them 2. The system of claim 1.

4. The achievement recognition unit Customize the type of badge or reward according to the user's achievement level.

2. The system of claim 1.

5. The reward granting unit Leveraging the user's social network to provide the badges and rewards that can be shared with friends and family 2. The system of claim 1.

6. The quest suggestion unit: Using an emotion estimation function, the quest that the user feels the most positive about is suggested.

2. The system of claim 1.

7. The achievement recognition unit Using an emotion estimation function, the badge or reward that the user feels most happy about is provided.

2. The system of claim 1.

8. The reward granting unit Using an emotion estimation function, the reward that the user is most satisfied with is adjusted in real time.

2. The system of claim 1.

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