System

Abstract

An object of the system according to the embodiment is to generate a specific task for achieving a goal and to manage progress.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The reception unit receives a target. The analysis unit analyzes the target input by the reception unit. The generation unit generates a task based on the target analyzed by the analysis unit. The providing unit provides the task generated by the generating unit in the form of a quest. The monitoring unit monitors progress of the quest provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to generate specific tasks for achieving goals and to manage progress.

[0005] The system according to the embodiment aims to generate specific tasks for achieving a goal and manage the progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, a providing unit, and a monitoring unit. The receiving unit inputs a goal. The analyzing unit analyzes the goal input by the receiving unit. The generating unit generates a task based on the goal analyzed by the analyzing unit. The providing unit provides the task generated by the generating unit in the form of a quest. The monitoring unit monitors the progress of the quest provided by the providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate specific tasks for achieving a goal and manage the progress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A support system according to an embodiment of the present invention provides support to users to help them achieve their desired appearance or goals. When a user inputs a goal, the AI ​​breaks the goal down into specific tasks and presents them in the form of quests. By completing these quests, the user can progress toward the goal. For example, if a user inputs a goal such as "I want to have a healthy body," the AI ​​analyzes the goal and breaks it down into specific tasks. Tasks such as "exercise 30 minutes daily" and "eat a balanced diet" are generated. These tasks are presented in the form of quests, and the user reports their progress as they complete the quests. Completing quests increases the user's level, similar to a role-playing game, and the user's character moves closer to their ideal appearance. For example, completing exercise quests improves the character's physical strength and makes them look healthier. Furthermore, the AI ​​monitors the user's progress and adjusts tasks as necessary. For example, if the user is unable to continue exercising, the AI ​​simplifies the task or suggests an alternative method. This allows the user to move forward toward their goal without straining themselves. This allows the support system to significantly advance the user's goal. For example, by the time a character reaches Level 100, the user will also be much closer to achieving the goals and dreams they had envisioned.

[0029] The support system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The reception unit receives a user's input of a goal. The goal input by the user may include, but is not limited to, a short-term goal, a long-term goal, a quantitative goal, and a qualitative goal. The reception unit may receive the goal by, for example, text input, voice input, image input, or the like. The analysis unit analyzes the goal input by the reception unit. The analysis may be performed by, for example, but is not limited to, data analysis, text analysis, or sentiment analysis. For example, the analysis unit may analyze the content of the goal using natural language processing technology and extract important keywords. The analysis unit may also analyze the user's sentiment using sentiment analysis technology. The generation unit generates a task based on the goal analyzed by the analysis unit. The task may be generated in the form of, for example, a simple task, a complex task, a step-by-step task, or the like, but is not limited to, the above. For example, the generation unit may generate a task optimal for the goal using a machine learning algorithm. The generation unit may also generate a task using a rule-based algorithm. The providing unit provides the task generated by the generating unit in a quest format. The quest format may be, for example, a game format, a scenario format, a challenge format, or the like, but is not limited to these examples. For example, the providing unit notifies the user of the content of the quest by a text message or a voice message. The providing unit can also display the progress of the quest in real time. The monitoring unit monitors the progress of the quest provided by the providing unit. The progress is measured by, for example, an achievement rate, the number of completed tasks, the passage of time, or other methods, but is not limited to these examples. For example, the monitoring unit checks whether the user has completed the quest and records the progress. The monitoring unit can also adjust the task according to the user's progress. This enables the support system according to the embodiment to efficiently analyze the user's goal, generate tasks, provide quests, and monitor progress.

[0030] The monitoring unit includes an adjustment unit that adjusts tasks according to the user's progress. The adjustment unit adjusts the tasks according to the user's progress. Methods of adjusting the tasks include, but are not limited to, adjusting the difficulty of the tasks and changing the order of the tasks. For example, the adjustment unit may lower the difficulty of a task if the user is unable to continue the task. The adjustment unit may also increase the difficulty of the next task if the user completes the task. The adjustment unit may also change the order of the tasks according to the user's progress. For example, the adjustment unit may present the next task after the user completes a specific task. This allows the user to smoothly progress toward their goal by adjusting the tasks according to their progress. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit may input user progress data into AI, which then adjusts the tasks.

[0031] The reception unit can analyze the user's past goal input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also analyze patterns of goals that the user has previously input and suggest the optimal input method. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. In this way, the optimal input method can be provided by analyzing the user's past goal input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into AI, which can select the optimal input method.

[0032] The reception unit can filter goals based on the user's current living situation and areas of interest when the goal is input. For example, the reception unit can preferentially suggest goals related to a project the user is currently working on. The reception unit can also suggest appropriate goals based on the user's current living situation (work, family, etc.). The reception unit can also filter goals based on the user's areas of interest (hobbies, interests, etc.). In this way, more appropriate goals can be suggested by filtering goals based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's living situation data and area of ​​interest data into AI, which can then filter the goals.

[0033] The reception unit can select the optimal input means depending on the user's input method when inputting a target. For example, if the user prefers voice input, the reception unit can prioritize voice input. Also, if the user prefers text input, the reception unit can prioritize text input. Also, if the user prefers image input, the reception unit can prioritize image input. This makes it easier to input a target by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's input method data to AI, which can select the optimal input means.

[0034] When inputting a goal, the reception unit can prioritize inputting highly relevant goals by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize inputting goals related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting goals related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting goals that can be achieved at home. In this way, highly relevant goals can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into AI, which can select highly relevant goals.

[0035] When a goal is input, the reception unit can analyze the user's social media activity and input related goals. For example, the reception unit can automatically input goals shared by the user on social media. The reception unit can also analyze the user's social media activity and input related goals. The reception unit can also input related goals by referring to the activity of the user's friends on social media. In this way, related goals can be input by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which can select related goals.

[0036] The reception unit can customize the input method by reflecting the user's past feedback when inputting a target. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially provide a specific input method based on the user's past feedback. The reception unit can also analyze the user's feedback and customize the input method. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into AI, which can select the optimal input method.

[0037] During goal analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the goal. For example, the analysis unit performs a detailed analysis on a goal with high importance. The analysis unit can also perform a simplified analysis on a goal with low importance. The analysis unit can also adjust the priority of the analysis according to the importance of the goal. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the goal. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input goal importance data into AI, which can then adjust the level of detail of the analysis.

[0038] When analyzing goals, the analysis unit can apply different analysis algorithms depending on the category of the goal. For example, the analysis unit can apply a health data analysis algorithm to health-related goals. The analysis unit can also apply a learning data analysis algorithm to learning-related goals. The analysis unit can also apply a work data analysis algorithm to work-related goals. In this way, by applying different analysis algorithms depending on the category of the goal, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input goal category data into AI, which can select an appropriate analysis algorithm.

[0039] During target analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into AI, which can improve the accuracy of the analysis.

[0040] During goal analysis, the analysis unit can determine the priority of analysis based on the submission date of the goal. For example, the analysis unit prioritizes analysis of goals with an upcoming submission deadline. The analysis unit can also postpone analysis of goals with a distant submission deadline. The analysis unit can also adjust the analysis schedule according to the submission date. This allows for efficient analysis by determining the priority of analysis based on the submission date of the goal. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of goals into AI, which can then determine the priority of analysis.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the goals during target analysis. For example, the analysis unit prioritizes analysis of highly relevant goals. The analysis unit can also postpone analysis of less relevant goals. The analysis unit can also adjust the order of analysis according to the relevance of the goals. By adjusting the order of analysis based on the relevance of the goals, efficient analysis can be performed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input goal relevance data into AI, which can then adjust the order of analysis.

[0042] During target analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can provide an analysis that uses a lot of technical terms to a user with high levels of expertise. The analysis unit can also provide an analysis in simpler terms to a user with low levels of expertise. The analysis unit can also adjust the level of detail of the analysis according to the user's level of expertise. This allows for more appropriate analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then adjust the use of technical terms in the analysis.

[0043] The generation unit can adjust the level of detail of the task based on the importance of the goal when generating the task. For example, the generation unit generates a detailed task for a goal with high importance. The generation unit can also generate a simplified task for a goal with low importance. The generation unit can also adjust the priority of the task according to the importance of the goal. As a result, efficient task generation can be achieved by adjusting the level of detail of the task based on the importance of the goal. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input goal importance data into AI, which can then adjust the level of detail of the task.

[0044] When generating a task, the generation unit can apply different generation algorithms depending on the category of the goal. For example, the generation unit can apply a health task generation algorithm to a health-related goal. The generation unit can also apply a learning task generation algorithm to a learning-related goal. The generation unit can also apply a work task generation algorithm to a work-related goal. In this way, by applying different generation algorithms depending on the category of the goal, more appropriate tasks are generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input goal category data into AI, which can select an appropriate generation algorithm.

[0045] When generating a task, the generation unit can improve the accuracy of generation by referring to the user's past task generation results. The generation unit, for example, adjusts the generation algorithm based on the user's past task generation results. The generation unit can also extract specific patterns from the user's past task generation results to improve the generation accuracy. The generation unit can also adjust the level of detail of the generation by referring to the user's past task generation results. In this way, the accuracy of generation is improved by referring to the user's past task generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past task data into AI, which can improve the accuracy of generation.

[0046] When generating tasks, the generation unit can determine the priority of the tasks based on the submission time of the goals. For example, the generation unit can generate tasks with priority for goals with an upcoming submission deadline. The generation unit can also postpone generating tasks for goals with a distant submission deadline. The generation unit can also adjust the task schedule according to the submission time. This allows efficient task generation by determining the priority of tasks based on the submission time of the goals. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission time of goals into AI, which can then determine the priority of the tasks.

[0047] The generation unit can adjust the order of tasks based on the relevance of the goals when generating tasks. For example, the generation unit can generate tasks with priority for highly relevant goals. The generation unit can also postpone generating tasks for less relevant goals. The generation unit can also adjust the order of tasks according to the relevance of the goals. As a result, efficient task generation can be achieved by adjusting the order of tasks based on the relevance of the goals. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input goal relevance data into AI, which can then adjust the order of tasks.

[0048] When generating a task, the generation unit can adjust the use of technical terminology in the task according to the user's level of expertise. For example, the generation unit generates a task that uses a lot of technical terminology for a user with high technical expertise. The generation unit can also generate a task in simple language for a user with low technical expertise. The generation unit can also adjust the level of detail of the task according to the user's level of expertise. In this way, a more appropriate task is generated by adjusting the use of technical terminology in the task according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology in the task.

[0049] When providing a quest, the providing unit can adjust the level of detail provided based on the importance of the task. For example, the providing unit can provide a detailed quest for a task with high importance. The providing unit can also provide a simplified quest for a task with low importance. The providing unit can also adjust the priority of the quest according to the importance of the task. In this way, by adjusting the level of detail provided based on the importance of the task, efficient quest provision can be performed. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input task importance data to AI, and the AI ​​can adjust the level of detail provided.

[0050] When providing a quest, the providing unit can apply different providing algorithms depending on the task category. For example, the providing unit can apply a health quest providing algorithm to a health-related task. The providing unit can also apply a learning quest providing algorithm to a learning-related task. The providing unit can also apply a work quest providing algorithm to a work-related task. In this way, by applying different providing algorithms depending on the task category, more appropriate quests are provided. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input task category data into AI, which can select an appropriate providing algorithm.

[0051] When providing a quest, the providing unit can improve the accuracy of the quest provision by referring to the user's past quest provision results. The providing unit, for example, adjusts the provision algorithm based on the user's past quest provision results. The providing unit can also extract specific patterns from the user's past quest provision results to improve the accuracy of the quest provision. The providing unit can also adjust the level of detail of the provision by referring to the user's past quest provision results. In this way, the accuracy of the provision is improved by referring to the user's past quest provision results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past quest data into AI, which can improve the accuracy of the provision.

[0052] When providing a quest, the providing unit can determine the priority of the quest based on the submission time of the task. For example, the providing unit can provide a quest preferentially for a task with an upcoming submission deadline. The providing unit can also provide a quest later for a task with a distant submission deadline. The providing unit can also adjust the quest schedule according to the submission time. In this way, by determining the priority of the quest based on the submission time of the task, efficient quest provision can be performed. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input task submission time data into AI, and the AI ​​can determine the priority of the quest.

[0053] When providing a quest, the providing unit can adjust the order of the quests based on the relevance of the tasks. For example, the providing unit can provide a quest preferentially to a highly relevant task. The providing unit can also provide a quest later to a less relevant task. The providing unit can also adjust the order of the quests according to the relevance of the tasks. In this way, by adjusting the order of the quests based on the relevance of the tasks, quests can be provided efficiently. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input task relevance data into AI, which can then adjust the order of the quests.

[0054] When providing a quest, the providing unit can adjust the use of technical terminology in the quest according to the user's level of expertise. For example, the providing unit can provide a quest that uses a lot of technical terminology to a user with high technical expertise. The providing unit can also provide a quest in simple language to a user with low technical expertise. The providing unit can also adjust the level of detail of the quest according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the quest according to the user's level of expertise, a more appropriate quest is provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology in the quest.

[0055] The monitoring unit can adjust the level of monitoring detail based on the importance of the task during progress monitoring. For example, the monitoring unit performs detailed progress monitoring for tasks with high importance. The monitoring unit can also perform simplified progress monitoring for tasks with low importance. The monitoring unit can also adjust the priority of progress monitoring according to the importance of the task. This allows for efficient progress monitoring by adjusting the level of monitoring detail based on the importance of the task. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI, for example. For example, the monitoring unit can input task importance data into AI, which can then adjust the level of monitoring detail.

[0056] The monitoring unit can apply different monitoring algorithms depending on the task category when monitoring progress. For example, the monitoring unit can apply a health progress monitoring algorithm to health-related tasks. The monitoring unit can also apply a learning progress monitoring algorithm to learning-related tasks. The monitoring unit can also apply a work progress monitoring algorithm to work-related tasks. In this way, by applying different monitoring algorithms depending on the task category, more appropriate progress monitoring can be performed. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or without using AI. For example, the monitoring unit can input task category data into AI, which can select an appropriate monitoring algorithm.

[0057] The monitoring unit can improve the accuracy of progress monitoring by referring to the user's past progress monitoring results. The monitoring unit, for example, adjusts the monitoring algorithm based on the user's past progress monitoring results. The monitoring unit can also extract specific patterns from the user's past progress monitoring results to improve the monitoring accuracy. The monitoring unit can also adjust the level of monitoring detail by referring to the user's past progress monitoring results. This improves the accuracy of monitoring by referring to the user's past progress monitoring results. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past progress data into AI, which can improve the accuracy of monitoring.

[0058] When monitoring progress, the monitoring unit can determine the priority of progress based on the submission time of the task. For example, the monitoring unit prioritizes progress monitoring for tasks with an upcoming submission deadline. The monitoring unit can also postpone progress monitoring for tasks with a distant submission deadline. The monitoring unit can also adjust the progress monitoring schedule according to the submission time. This allows efficient progress monitoring by determining the priority of progress based on the submission time of the task. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input task submission time data into AI, which can then determine the priority of progress.

[0059] The monitoring unit can adjust the order of progress based on the relevance of tasks when monitoring progress. For example, the monitoring unit prioritizes monitoring progress for highly relevant tasks. The monitoring unit can also postpone monitoring progress for less relevant tasks. The monitoring unit can also adjust the order of progress based on the relevance of tasks. This allows for efficient progress monitoring by adjusting the order of progress based on the relevance of tasks. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI, for example. For example, the monitoring unit can input task relevance data into AI, which can then adjust the order of progress.

[0060] The monitoring unit can adjust the use of technical terminology for progress monitoring according to the user's level of expertise during progress monitoring. For example, the monitoring unit can provide progress monitoring that uses a lot of technical terminology for a user with high levels of expertise. The monitoring unit can also provide progress monitoring in simpler terms for a user with low levels of expertise. The monitoring unit can also adjust the level of detail of the progress monitoring according to the user's level of expertise. This allows for more appropriate progress monitoring by adjusting the use of technical terminology for progress according to the user's level of expertise. Some or all of the above-described processing by the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology for progress.

[0061] The adjustment unit can adjust the level of detail of the adjustment based on the importance of the task during task adjustment. For example, the adjustment unit performs detailed adjustment for a task with high importance. The adjustment unit can also perform simplified adjustment for a task with low importance. The adjustment unit can also adjust the priority of the adjustment according to the importance of the task. As a result, efficient task adjustment can be performed by adjusting the level of detail of the adjustment based on the importance of the task. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input task importance data to AI, which can then adjust the level of detail of the adjustment.

[0062] The adjustment unit can apply different adjustment algorithms depending on the task category when adjusting a task. For example, the adjustment unit can apply a health task adjustment algorithm to a health-related task. The adjustment unit can also apply a learning task adjustment algorithm to a learning-related task. The adjustment unit can also apply a work task adjustment algorithm to a work-related task. In this way, by applying different adjustment algorithms depending on the task category, more appropriate task adjustment can be performed. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input task category data into AI, which can select an appropriate adjustment algorithm.

[0063] During task adjustment, the adjustment unit can improve the accuracy of the adjustment by referring to the user's past task adjustment results. The adjustment unit, for example, adjusts the adjustment algorithm based on the user's past task adjustment results. The adjustment unit can also extract specific patterns from the user's past task adjustment results to improve the adjustment accuracy. The adjustment unit can also adjust the level of detail of the adjustment by referring to the user's past task adjustment results. In this way, the accuracy of the adjustment is improved by referring to the user's past task adjustment results. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past task data into AI, which can improve the accuracy of the adjustment.

[0064] When adjusting tasks, the adjustment unit can determine the priority of adjustment based on the submission dates of the tasks. For example, the adjustment unit prioritizes adjustment for tasks with upcoming submission deadlines. The adjustment unit can also postpone adjustment for tasks with distant submission deadlines. The adjustment unit can also adjust the adjustment schedule according to the submission dates. In this way, efficient task adjustment can be performed by determining the priority of adjustment based on the submission dates of the tasks. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input task submission date data into AI, which can then determine the priority of adjustment.

[0065] The adjustment unit can adjust the order of adjustment based on the relevance of the tasks during task adjustment. For example, the adjustment unit prioritizes adjustment of highly relevant tasks. The adjustment unit can also postpone adjustment of less relevant tasks. The adjustment unit can also adjust the order of adjustment according to the relevance of the tasks. As a result, efficient task adjustment can be performed by adjusting the order of adjustment based on the relevance of the tasks. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input task relevance data into AI, which can then adjust the order of adjustment.

[0066] During task adjustment, the adjustment unit can adjust the use of technical terms in the adjustment according to the user's level of expertise. For example, the adjustment unit provides an adjustment that uses a lot of technical terms to a user with high level of expertise. The adjustment unit can also provide an adjustment in simple language to a user with low level of expertise. The adjustment unit can also adjust the level of detail of the adjustment according to the user's level of expertise. This allows for more appropriate task adjustment by adjusting the use of technical terms in the adjustment according to the user's level of expertise. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's level of expertise data into AI, which can then adjust the use of technical terms in the adjustment.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The receiving unit can analyze the user's past goal achievement history and adjust the difficulty of the goal based on the level of achievement. For example, if the user has demonstrated a high level of achievement in the past, the receiving unit can suggest a goal with a high level of difficulty. Alternatively, if the user has demonstrated a low level of achievement in the past, the receiving unit can suggest a goal with a low level of difficulty. Furthermore, the receiving unit can adjust the number of steps for the goal based on the user's level of achievement. This makes it possible to provide an optimal goal based on the user's level of achievement in the past.

[0069] The analysis unit can monitor the user's current health condition and analyze goals based on that data. For example, it can analyze the user's heart rate and sleep data and set goals based on the user's health condition. The analysis unit can also analyze the user's dietary data and suggest goals based on nutritional balance. Furthermore, the analysis unit can analyze the user's exercise data and set goals based on the amount of exercise. This makes it possible to provide optimal goals based on the user's health condition.

[0070] The providing unit can analyze the user's past quest completion history and adjust the difficulty of the quest based on the completion rate. For example, if the user has a high completion rate in the past, the providing unit can provide a quest with a high level of difficulty. Alternatively, if the user has a low completion rate in the past, the providing unit can provide a quest with a low level of difficulty. Furthermore, the providing unit can adjust the number of steps in the quest based on the user's completion rate. This makes it possible to provide the optimal quest based on the user's past completion rate.

[0071] The adjustment unit can analyze the user's past task adjustment history and optimize the task adjustment method based on the adjustment effect. For example, it can preferentially apply adjustment methods that have been effective in the past. It can also avoid adjustment methods that have been ineffective in the past. Furthermore, the adjustment unit can suggest new adjustment methods based on the user's past adjustment history. This allows optimal task adjustment to be performed based on the user's past adjustment history.

[0072] The analysis unit can analyze the user's past goal achievement data and optimize the goal analysis method based on the achievement pattern. For example, it can prioritize the analysis of goal achievement patterns that have been successful in the past. It can also avoid goal achievement patterns that have failed in the past. Furthermore, the analysis unit can suggest a new analysis method based on the user's past achievement data. This allows for optimal goal analysis based on the user's past achievement data.

[0073] The providing unit can analyze the user's past quest provision history and optimize the quest provision method based on the effectiveness of the provision. For example, it can prioritize the application of provision methods that have been effective in the past. It can also avoid provision methods that have not been effective in the past. Furthermore, the providing unit can suggest new provision methods based on the user's past provision history. This allows the optimal quest provision to be performed based on the user's past provision history.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The receiving unit receives the user's goal input. The goals input by the user include short-term goals, long-term goals, quantitative goals, qualitative goals, etc. The receiving unit can receive the goal by text input, voice input, image input, etc. Step 2: The analysis unit analyzes the goal input by the reception unit. The analysis is performed using methods such as data analysis, text analysis, and sentiment analysis. For example, the analysis unit analyzes the content of the goal using natural language processing technology and extracts important keywords. The analysis unit can also analyze the user's emotions using sentiment analysis technology. Step 3: The generator generates tasks based on the goals analyzed by the analyzer. The tasks may be simple tasks, complex tasks, step-by-step tasks, or other types of tasks. For example, the generator may use a machine learning algorithm to generate tasks that best fit the goals. Alternatively, the generator may use a rule-based algorithm to generate tasks. Step 4: The providing unit provides the task generated by the generating unit in the form of a quest. The quest may be provided in the form of a game, a scenario, a challenge, or the like. For example, the providing unit may notify the user of the content of the quest by a text message or a voice message. The providing unit may also display the progress of the quest in real time. Step 5: The monitoring unit monitors the progress of the quest provided by the providing unit. The progress is measured by methods such as the completion rate, the number of completed tasks, or the passage of time. For example, the monitoring unit checks whether the user has completed the quest and records the progress. The monitoring unit can also adjust the task according to the user's progress.

[0076] (Example 2) A support system according to an embodiment of the present invention provides support to users to help them achieve their desired appearance or goals. When a user inputs a goal, the AI ​​breaks the goal down into specific tasks and presents them in the form of quests. By completing these quests, the user can progress toward the goal. For example, if a user inputs a goal such as "I want to have a healthy body," the AI ​​analyzes the goal and breaks it down into specific tasks. Tasks such as "exercise 30 minutes daily" and "eat a balanced diet" are generated. These tasks are presented in the form of quests, and the user reports their progress as they complete the quests. Completing quests increases the user's level, similar to a role-playing game, and the user's character moves closer to their ideal appearance. For example, completing exercise quests improves the character's physical strength and makes them look healthier. Furthermore, the AI ​​monitors the user's progress and adjusts tasks as necessary. For example, if the user is unable to continue exercising, the AI ​​simplifies the task or suggests an alternative method. This allows the user to move forward toward their goal without straining themselves. This allows the support system to significantly advance the user's goal. For example, by the time a character reaches Level 100, the user will also be much closer to achieving the goals and dreams they had envisioned.

[0077] The support system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The reception unit receives a user's input of a goal. The goal input by the user may include, but is not limited to, a short-term goal, a long-term goal, a quantitative goal, and a qualitative goal. The reception unit may receive the goal by, for example, text input, voice input, image input, or the like. The analysis unit analyzes the goal input by the reception unit. The analysis may be performed by, for example, but is not limited to, data analysis, text analysis, or sentiment analysis. For example, the analysis unit may analyze the content of the goal using natural language processing technology and extract important keywords. The analysis unit may also analyze the user's sentiment using sentiment analysis technology. The generation unit generates a task based on the goal analyzed by the analysis unit. The task may be generated in the form of, for example, a simple task, a complex task, a step-by-step task, or the like, but is not limited to, the above. For example, the generation unit may generate a task optimal for the goal using a machine learning algorithm. The generation unit may also generate a task using a rule-based algorithm. The providing unit provides the task generated by the generating unit in a quest format. The quest format may be, for example, a game format, a scenario format, a challenge format, or the like, but is not limited to these examples. For example, the providing unit notifies the user of the content of the quest by a text message or a voice message. The providing unit can also display the progress of the quest in real time. The monitoring unit monitors the progress of the quest provided by the providing unit. The progress is measured by, for example, an achievement rate, the number of completed tasks, the passage of time, or other methods, but is not limited to these examples. For example, the monitoring unit checks whether the user has completed the quest and records the progress. The monitoring unit can also adjust the task according to the user's progress. This enables the support system according to the embodiment to efficiently analyze the user's goal, generate tasks, provide quests, and monitor progress.

[0078] The monitoring unit includes an adjustment unit that adjusts tasks according to the user's progress. The adjustment unit adjusts the tasks according to the user's progress. Methods of adjusting the tasks include, but are not limited to, adjusting the difficulty of the tasks and changing the order of the tasks. For example, the adjustment unit may lower the difficulty of a task if the user is unable to continue the task. The adjustment unit may also increase the difficulty of the next task if the user completes the task. The adjustment unit may also change the order of the tasks according to the user's progress. For example, the adjustment unit may present the next task after the user completes a specific task. This allows the user to smoothly progress toward their goal by adjusting the tasks according to their progress. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit may input user progress data into AI, which then adjusts the tasks.

[0079] The reception unit can estimate the user's emotions and adjust the timing of goal input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input goals at a time when the user can relax. Furthermore, if the user is concentrating, the reception unit can also prompt the user to input goals at that time. Furthermore, if the user is tired, the reception unit can also prompt the user to input goals after resting. By adjusting the timing of goal input according to the user's emotions, goals can be input at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the timing of goal input based on the estimation result.

[0080] The reception unit can analyze the user's past goal input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also analyze patterns of goals that the user has previously input and suggest the optimal input method. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. In this way, the optimal input method can be provided by analyzing the user's past goal input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into AI, which can select the optimal input method.

[0081] The reception unit can filter goals based on the user's current living situation and areas of interest when the goal is input. For example, the reception unit can preferentially suggest goals related to a project the user is currently working on. The reception unit can also suggest appropriate goals based on the user's current living situation (work, family, etc.). The reception unit can also filter goals based on the user's areas of interest (hobbies, interests, etc.). In this way, more appropriate goals can be suggested by filtering goals based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's living situation data and area of ​​interest data into AI, which can then filter the goals.

[0082] The reception unit can select the optimal input means depending on the user's input method when inputting a target. For example, if the user prefers voice input, the reception unit can prioritize voice input. Also, if the user prefers text input, the reception unit can prioritize text input. Also, if the user prefers image input, the reception unit can prioritize image input. This makes it easier to input a target by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's input method data to AI, which can select the optimal input means.

[0083] The reception unit can estimate the user's emotions and determine the priority of goals to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize inputting easier goals. Furthermore, if the user is relaxed, the reception unit can prioritize inputting more difficult goals. Furthermore, if the user is in a hurry, the reception unit can prioritize inputting goals that can be achieved in a short time. This allows for more appropriate goals to be input by determining the priority of goals according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI, which then estimates the user's emotions and determines the priority of goals based on the result.

[0084] When inputting a goal, the reception unit can prioritize inputting highly relevant goals by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize inputting goals related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting goals related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting goals that can be achieved at home. In this way, highly relevant goals can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into AI, which can select highly relevant goals.

[0085] When a goal is input, the reception unit can analyze the user's social media activity and input related goals. For example, the reception unit can automatically input goals shared by the user on social media. The reception unit can also analyze the user's social media activity and input related goals. The reception unit can also input related goals by referring to the activity of the user's friends on social media. In this way, related goals can be input by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which can select related goals.

[0086] The reception unit can customize the input method by reflecting the user's past feedback when inputting a target. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially provide a specific input method based on the user's past feedback. The reception unit can also analyze the user's feedback and customize the input method. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into AI, which can select the optimal input method.

[0087] The analysis unit can estimate the user's emotions and adjust the goal analysis method based on the estimated user emotions. For example, the analysis unit can provide a simple analysis method when the user is stressed. The analysis unit can also provide a detailed analysis method when the user is relaxed. The analysis unit can also provide a quick analysis method when the user is in a hurry. This allows for more appropriate analysis by adjusting the goal analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can estimate the emotion and adjust the analysis method based on the result.

[0088] During goal analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the goal. For example, the analysis unit performs a detailed analysis on a goal with high importance. The analysis unit can also perform a simplified analysis on a goal with low importance. The analysis unit can also adjust the priority of the analysis according to the importance of the goal. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the goal. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input goal importance data into AI, which can then adjust the level of detail of the analysis.

[0089] When analyzing goals, the analysis unit can apply different analysis algorithms depending on the category of the goal. For example, the analysis unit can apply a health data analysis algorithm to health-related goals. The analysis unit can also apply a learning data analysis algorithm to learning-related goals. The analysis unit can also apply a work data analysis algorithm to work-related goals. In this way, by applying different analysis algorithms depending on the category of the goal, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input goal category data into AI, which can select an appropriate analysis algorithm.

[0090] During target analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into AI, which can improve the accuracy of the analysis.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide an analysis that is completed in a short time. The analysis unit can also provide a detailed analysis if the user is relaxed. The analysis unit can also provide a quick analysis if the user is in a hurry. This allows for more appropriate analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the length of the analysis based on the results.

[0092] During goal analysis, the analysis unit can determine the priority of analysis based on the submission date of the goal. For example, the analysis unit prioritizes analysis of goals with an upcoming submission deadline. The analysis unit can also postpone analysis of goals with a distant submission deadline. The analysis unit can also adjust the analysis schedule according to the submission date. This allows for efficient analysis by determining the priority of analysis based on the submission date of the goal. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of goals into AI, which can then determine the priority of analysis.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the goals during target analysis. For example, the analysis unit prioritizes analysis of highly relevant goals. The analysis unit can also postpone analysis of less relevant goals. The analysis unit can also adjust the order of analysis according to the relevance of the goals. By adjusting the order of analysis based on the relevance of the goals, efficient analysis can be performed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input goal relevance data into AI, which can then adjust the order of analysis.

[0094] During target analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can provide an analysis that uses a lot of technical terms to a user with high levels of expertise. The analysis unit can also provide an analysis in simpler terms to a user with low levels of expertise. The analysis unit can also adjust the level of detail of the analysis according to the user's level of expertise. This allows for more appropriate analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then adjust the use of technical terms in the analysis.

[0095] The generation unit can estimate the user's emotions and adjust the task generation method based on the estimated user emotions. For example, if the user is stressed, the generation unit can generate a simple task. If the user is relaxed, the generation unit can also generate a detailed task. If the user is in a hurry, the generation unit can also generate a task that can be completed quickly. By adjusting the task generation method according to the user's emotions, a more appropriate task can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the task generation method based on the estimation result.

[0096] The generation unit can adjust the level of detail of the task based on the importance of the goal when generating the task. For example, the generation unit generates a detailed task for a goal with high importance. The generation unit can also generate a simplified task for a goal with low importance. The generation unit can also adjust the priority of the task according to the importance of the goal. As a result, efficient task generation can be achieved by adjusting the level of detail of the task based on the importance of the goal. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input goal importance data into AI, which can then adjust the level of detail of the task.

[0097] When generating a task, the generation unit can apply different generation algorithms depending on the category of the goal. For example, the generation unit can apply a health task generation algorithm to a health-related goal. The generation unit can also apply a learning task generation algorithm to a learning-related goal. The generation unit can also apply a work task generation algorithm to a work-related goal. In this way, by applying different generation algorithms depending on the category of the goal, more appropriate tasks are generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input goal category data into AI, which can select an appropriate generation algorithm.

[0098] When generating a task, the generation unit can improve the accuracy of generation by referring to the user's past task generation results. The generation unit, for example, adjusts the generation algorithm based on the user's past task generation results. The generation unit can also extract specific patterns from the user's past task generation results to improve the generation accuracy. The generation unit can also adjust the level of detail of the generation by referring to the user's past task generation results. In this way, the accuracy of generation is improved by referring to the user's past task generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past task data into AI, which can improve the accuracy of generation.

[0099] The generation unit can estimate the user's emotions and determine the priority of tasks to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can prioritize generating easy tasks. Furthermore, if the user is relaxed, the generation unit can prioritize generating difficult tasks. Furthermore, if the user is in a hurry, the generation unit can prioritize generating tasks that can be completed in a short time. Thus, by determining the priority of tasks according to the user's emotions, more appropriate tasks are generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI, which then estimates the user's emotions and determines the priority of tasks based on the result.

[0100] When generating tasks, the generation unit can determine the priority of the tasks based on the submission time of the goals. For example, the generation unit can generate tasks with priority for goals with an upcoming submission deadline. The generation unit can also postpone generating tasks for goals with a distant submission deadline. The generation unit can also adjust the task schedule according to the submission time. This allows efficient task generation by determining the priority of tasks based on the submission time of the goals. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission time of goals into AI, which can then determine the priority of the tasks.

[0101] The generation unit can adjust the order of tasks based on the relevance of the goals when generating tasks. For example, the generation unit can generate tasks with priority for highly relevant goals. The generation unit can also postpone generating tasks for less relevant goals. The generation unit can also adjust the order of tasks according to the relevance of the goals. As a result, efficient task generation can be achieved by adjusting the order of tasks based on the relevance of the goals. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input goal relevance data into AI, which can then adjust the order of tasks.

[0102] When generating a task, the generation unit can adjust the use of technical terminology in the task according to the user's level of expertise. For example, the generation unit generates a task that uses a lot of technical terminology for a user with high technical expertise. The generation unit can also generate a task in simple language for a user with low technical expertise. The generation unit can also adjust the level of detail of the task according to the user's level of expertise. In this way, a more appropriate task is generated by adjusting the use of technical terminology in the task according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology in the task.

[0103] The providing unit can estimate the user's emotions and adjust the way in which quests are provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple quest. If the user is relaxed, the providing unit can also provide a detailed quest. If the user is in a hurry, the providing unit can also provide a quest that can be completed quickly. By adjusting the way in which quests are provided according to the user's emotions, more appropriate quests can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the way in which quests are provided based on the result.

[0104] When providing a quest, the providing unit can adjust the level of detail provided based on the importance of the task. For example, the providing unit can provide a detailed quest for a task with high importance. The providing unit can also provide a simplified quest for a task with low importance. The providing unit can also adjust the priority of the quest according to the importance of the task. In this way, by adjusting the level of detail provided based on the importance of the task, efficient quest provision can be performed. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input task importance data to AI, and the AI ​​can adjust the level of detail provided.

[0105] When providing a quest, the providing unit can apply different providing algorithms depending on the task category. For example, the providing unit can apply a health quest providing algorithm to a health-related task. The providing unit can also apply a learning quest providing algorithm to a learning-related task. The providing unit can also apply a work quest providing algorithm to a work-related task. In this way, by applying different providing algorithms depending on the task category, more appropriate quests are provided. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input task category data into AI, which can select an appropriate providing algorithm.

[0106] When providing a quest, the providing unit can improve the accuracy of the quest provision by referring to the user's past quest provision results. The providing unit, for example, adjusts the provision algorithm based on the user's past quest provision results. The providing unit can also extract specific patterns from the user's past quest provision results to improve the accuracy of the quest provision. The providing unit can also adjust the level of detail of the provision by referring to the user's past quest provision results. In this way, the accuracy of the provision is improved by referring to the user's past quest provision results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past quest data into AI, which can improve the accuracy of the provision.

[0107] The providing unit can estimate the user's emotions and determine the priority of quests to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing easy quests. Furthermore, if the user is relaxed, the providing unit can prioritize providing difficult quests. Furthermore, if the user is in a hurry, the providing unit can prioritize quests that can be completed in a short time. By determining the priority of quests according to the user's emotions, more appropriate quests can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and determine the priority of quests based on the result.

[0108] When providing a quest, the providing unit can determine the priority of the quest based on the submission time of the task. For example, the providing unit can provide a quest preferentially for a task with an upcoming submission deadline. The providing unit can also provide a quest later for a task with a distant submission deadline. The providing unit can also adjust the quest schedule according to the submission time. In this way, by determining the priority of the quest based on the submission time of the task, efficient quest provision can be performed. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input task submission time data into AI, and the AI ​​can determine the priority of the quest.

[0109] When providing a quest, the providing unit can adjust the order of the quests based on the relevance of the tasks. For example, the providing unit can provide a quest preferentially to a highly relevant task. The providing unit can also provide a quest later to a less relevant task. The providing unit can also adjust the order of the quests according to the relevance of the tasks. In this way, by adjusting the order of the quests based on the relevance of the tasks, quests can be provided efficiently. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input task relevance data into AI, which can then adjust the order of the quests.

[0110] When providing a quest, the providing unit can adjust the use of technical terminology in the quest according to the user's level of expertise. For example, the providing unit can provide a quest that uses a lot of technical terminology to a user with high technical expertise. The providing unit can also provide a quest in simple language to a user with low technical expertise. The providing unit can also adjust the level of detail of the quest according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the quest according to the user's level of expertise, a more appropriate quest is provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology in the quest.

[0111] The monitoring unit can estimate the user's emotions and adjust the progress monitoring method based on the estimated user emotions. For example, the monitoring unit can provide a simple progress monitoring method when the user is stressed. The monitoring unit can also provide a detailed progress monitoring method when the user is relaxed. The monitoring unit can also provide a quick progress monitoring method when the user is in a hurry. This allows for more appropriate progress monitoring by adjusting the progress monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the progress monitoring method based on the estimation result.

[0112] The monitoring unit can adjust the level of monitoring detail based on the importance of the task during progress monitoring. For example, the monitoring unit performs detailed progress monitoring for tasks with high importance. The monitoring unit can also perform simplified progress monitoring for tasks with low importance. The monitoring unit can also adjust the priority of progress monitoring according to the importance of the task. This allows for efficient progress monitoring by adjusting the level of monitoring detail based on the importance of the task. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI, for example. For example, the monitoring unit can input task importance data into AI, which can then adjust the level of monitoring detail.

[0113] The monitoring unit can apply different monitoring algorithms depending on the task category when monitoring progress. For example, the monitoring unit can apply a health progress monitoring algorithm to health-related tasks. The monitoring unit can also apply a learning progress monitoring algorithm to learning-related tasks. The monitoring unit can also apply a work progress monitoring algorithm to work-related tasks. In this way, by applying different monitoring algorithms depending on the task category, more appropriate progress monitoring can be performed. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or without using AI. For example, the monitoring unit can input task category data into AI, which can select an appropriate monitoring algorithm.

[0114] The monitoring unit can improve the accuracy of progress monitoring by referring to the user's past progress monitoring results. The monitoring unit, for example, adjusts the monitoring algorithm based on the user's past progress monitoring results. The monitoring unit can also extract specific patterns from the user's past progress monitoring results to improve the monitoring accuracy. The monitoring unit can also adjust the level of monitoring detail by referring to the user's past progress monitoring results. This improves the accuracy of monitoring by referring to the user's past progress monitoring results. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past progress data into AI, which can improve the accuracy of monitoring.

[0115] The monitoring unit can estimate the user's emotions and determine the priority of progress to be monitored based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize monitoring simple progress. Furthermore, if the user is relaxed, the monitoring unit can prioritize monitoring detailed progress. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring progress that can be completed quickly. This allows for more appropriate progress monitoring by determining the priority of progress according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and determine the priority of progress based on the results.

[0116] When monitoring progress, the monitoring unit can determine the priority of progress based on the submission time of the task. For example, the monitoring unit prioritizes progress monitoring for tasks with an upcoming submission deadline. The monitoring unit can also postpone progress monitoring for tasks with a distant submission deadline. The monitoring unit can also adjust the progress monitoring schedule according to the submission time. This allows efficient progress monitoring by determining the priority of progress based on the submission time of the task. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input task submission time data into AI, which can then determine the priority of progress.

[0117] The monitoring unit can adjust the order of progress based on the relevance of tasks when monitoring progress. For example, the monitoring unit prioritizes monitoring progress for highly relevant tasks. The monitoring unit can also postpone monitoring progress for less relevant tasks. The monitoring unit can also adjust the order of progress based on the relevance of tasks. This allows for efficient progress monitoring by adjusting the order of progress based on the relevance of tasks. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI, for example. For example, the monitoring unit can input task relevance data into AI, which can then adjust the order of progress.

[0118] The monitoring unit can adjust the use of technical terminology for progress monitoring according to the user's level of expertise during progress monitoring. For example, the monitoring unit can provide progress monitoring that uses a lot of technical terminology for a user with high levels of expertise. The monitoring unit can also provide progress monitoring in simpler terms for a user with low levels of expertise. The monitoring unit can also adjust the level of detail of the progress monitoring according to the user's level of expertise. This allows for more appropriate progress monitoring by adjusting the use of technical terminology for progress according to the user's level of expertise. Some or all of the above-described processing by the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology for progress.

[0119] The adjustment unit can estimate the user's emotions and adjust the task adjustment method based on the estimated user emotions. For example, the adjustment unit can lower the task difficulty when the user is stressed. The adjustment unit can also increase the task difficulty when the user is relaxed. The adjustment unit can also shorten the task time when the user is in a hurry. This allows for more appropriate task adjustment by adjusting the task adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the adjustment unit can input the user's facial expression data into the generation AI, which can estimate the emotion, and adjust the task adjustment method based on the result.

[0120] The adjustment unit can adjust the level of detail of the adjustment based on the importance of the task during task adjustment. For example, the adjustment unit performs detailed adjustment for a task with high importance. The adjustment unit can also perform simplified adjustment for a task with low importance. The adjustment unit can also adjust the priority of the adjustment according to the importance of the task. As a result, efficient task adjustment can be performed by adjusting the level of detail of the adjustment based on the importance of the task. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input task importance data to AI, which can then adjust the level of detail of the adjustment.

[0121] The adjustment unit can apply different adjustment algorithms depending on the task category when adjusting a task. For example, the adjustment unit can apply a health task adjustment algorithm to a health-related task. The adjustment unit can also apply a learning task adjustment algorithm to a learning-related task. The adjustment unit can also apply a work task adjustment algorithm to a work-related task. In this way, by applying different adjustment algorithms depending on the task category, more appropriate task adjustment can be performed. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input task category data into AI, which can select an appropriate adjustment algorithm.

[0122] During task adjustment, the adjustment unit can improve the accuracy of the adjustment by referring to the user's past task adjustment results. The adjustment unit, for example, adjusts the adjustment algorithm based on the user's past task adjustment results. The adjustment unit can also extract specific patterns from the user's past task adjustment results to improve the adjustment accuracy. The adjustment unit can also adjust the level of detail of the adjustment by referring to the user's past task adjustment results. In this way, the accuracy of the adjustment is improved by referring to the user's past task adjustment results. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past task data into AI, which can improve the accuracy of the adjustment.

[0123] The adjustment unit can estimate the user's emotions and determine the priority of tasks to be adjusted based on the estimated user emotions. For example, when the user is feeling stressed, the adjustment unit can prioritize easy tasks. Furthermore, when the user is relaxed, the adjustment unit can also prioritize difficult tasks. Furthermore, when the user is in a hurry, the adjustment unit can also prioritize tasks that can be completed in a short time. This allows for more appropriate task adjustment by determining the priority of tasks according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit can be performed using, for example, an AI, or without an AI. For example, the adjustment unit can input the user's facial expression data into the generation AI, which can estimate the emotion, and determine the priority of tasks based on the result.

[0124] When adjusting tasks, the adjustment unit can determine the priority of adjustment based on the submission dates of the tasks. For example, the adjustment unit prioritizes adjustment for tasks with upcoming submission deadlines. The adjustment unit can also postpone adjustment for tasks with distant submission deadlines. The adjustment unit can also adjust the adjustment schedule according to the submission dates. In this way, efficient task adjustment can be performed by determining the priority of adjustment based on the submission dates of the tasks. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input task submission date data into AI, which can then determine the priority of adjustment.

[0125] The adjustment unit can adjust the order of adjustment based on the relevance of the tasks during task adjustment. For example, the adjustment unit prioritizes adjustment of highly relevant tasks. The adjustment unit can also postpone adjustment of less relevant tasks. The adjustment unit can also adjust the order of adjustment according to the relevance of the tasks. As a result, efficient task adjustment can be performed by adjusting the order of adjustment based on the relevance of the tasks. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input task relevance data into AI, which can then adjust the order of adjustment.

[0126] During task adjustment, the adjustment unit can adjust the use of technical terms in the adjustment according to the user's level of expertise. For example, the adjustment unit provides an adjustment that uses a lot of technical terms to a user with high level of expertise. The adjustment unit can also provide an adjustment in simple language to a user with low level of expertise. The adjustment unit can also adjust the level of detail of the adjustment according to the user's level of expertise. This allows for more appropriate task adjustment by adjusting the use of technical terms in the adjustment according to the user's level of expertise. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's level of expertise data into AI, which can then adjust the use of technical terms in the adjustment. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and monitoring unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input the user's goal using the touch panel 38A or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input goal. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a task based on the analysis result. The provision unit provides the task in the form of a quest using the display 40A or speaker 40B of the smart device 14. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's progress and adjusts the task as necessary. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and monitoring unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the user's goal using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input goal. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a task based on the analysis result. The provision unit provides the task in the form of a quest using the speaker 240 of the smart glasses 214. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's progress and adjusts the task as necessary. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and monitoring unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input the user's goal using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input goal. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a task based on the analysis result. The provision unit provides the task in the form of a quest using the speaker 240 of the headset-type terminal 314. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's progress and adjusts the task as necessary. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the user's goal using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input goal. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a task based on the analysis result. The provision unit provides the task in the form of a quest using the speaker 240 of the robot 414. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's progress and adjusts the task as necessary.

[0127] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0128] The receiving unit can analyze the user's past goal achievement history and adjust the difficulty of the goal based on the level of achievement. For example, if the user has demonstrated a high level of achievement in the past, the receiving unit can suggest a goal with a high level of difficulty. Alternatively, if the user has demonstrated a low level of achievement in the past, the receiving unit can suggest a goal with a low level of difficulty. Furthermore, the receiving unit can adjust the number of steps for the goal based on the user's level of achievement. This makes it possible to provide an optimal goal based on the user's level of achievement in the past.

[0129] The analysis unit can monitor the user's current health condition and analyze goals based on that data. For example, it can analyze the user's heart rate and sleep data and set goals based on the user's health condition. The analysis unit can also analyze the user's dietary data and suggest goals based on nutritional balance. Furthermore, the analysis unit can analyze the user's exercise data and set goals based on the amount of exercise. This makes it possible to provide optimal goals based on the user's health condition.

[0130] The generation unit can estimate the user's emotions and adjust the task rewards based on the estimated user emotions. For example, if the user is feeling stressed, a high reward can be set for an easy task. Also, if the user is relaxed, a high reward can be set for a difficult task. Furthermore, if the user is in a hurry, a high reward can be set for a task that can be completed quickly. In this way, adjusting the task rewards according to the user's emotions can increase motivation.

[0131] The providing unit can analyze the user's past quest completion history and adjust the difficulty of the quest based on the completion rate. For example, if the user has a high completion rate in the past, the providing unit can provide a quest with a high level of difficulty. Alternatively, if the user has a low completion rate in the past, the providing unit can provide a quest with a low level of difficulty. Furthermore, the providing unit can adjust the number of steps in the quest based on the user's completion rate. This makes it possible to provide the optimal quest based on the user's past completion rate.

[0132] The monitoring unit can estimate the user's emotions and adjust the progress feedback method based on the estimated user's emotions. For example, if the user is feeling stressed, positive feedback can be provided preferentially. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, quick feedback can be provided. In this way, by adjusting the progress feedback method according to the user's emotions, more appropriate feedback can be provided.

[0133] The adjustment unit can analyze the user's past task adjustment history and optimize the task adjustment method based on the adjustment effect. For example, it can preferentially apply adjustment methods that have been effective in the past. It can also avoid adjustment methods that have been ineffective in the past. Furthermore, the adjustment unit can suggest new adjustment methods based on the user's past adjustment history. This allows optimal task adjustment to be performed based on the user's past adjustment history.

[0134] The receiving unit can estimate the user's emotion and customize the target input interface based on the estimated user emotion. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. Alternatively, if the user is relaxed, detailed input options can be provided. Furthermore, if the user is in a hurry, an interface that allows for quick input can be provided. In this way, by customizing the target input interface according to the user's emotion, a more comfortable input experience can be provided.

[0135] The analysis unit can analyze the user's past goal achievement data and optimize the goal analysis method based on the achievement pattern. For example, it can prioritize the analysis of goal achievement patterns that have been successful in the past. It can also avoid goal achievement patterns that have failed in the past. Furthermore, the analysis unit can suggest a new analysis method based on the user's past achievement data. This allows for optimal goal analysis based on the user's past achievement data.

[0136] The generation unit can estimate the user's emotions and adjust the way in which tasks are presented based on the estimated user emotions. For example, if the user is feeling stressed, the task can be presented in a visually appealing manner. If the user is feeling relaxed, the task can be presented in a manner that includes detailed explanations. Furthermore, if the user is in a hurry, the task can be presented in a concise and quickly understandable manner. In this way, by adjusting the way in which tasks are presented according to the user's emotions, more effective task presentation can be achieved.

[0137] The providing unit can analyze the user's past quest provision history and optimize the quest provision method based on the effectiveness of the provision. For example, it can prioritize the application of provision methods that have been effective in the past. It can also avoid provision methods that have not been effective in the past. Furthermore, the providing unit can suggest new provision methods based on the user's past provision history. This allows the optimal quest provision to be performed based on the user's past provision history.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The receiving unit receives the user's goal input. The goals input by the user include short-term goals, long-term goals, quantitative goals, qualitative goals, etc. The receiving unit can receive the goal by text input, voice input, image input, etc. Step 2: The analysis unit analyzes the goal input by the reception unit. The analysis is performed using methods such as data analysis, text analysis, and sentiment analysis. For example, the analysis unit analyzes the content of the goal using natural language processing technology and extracts important keywords. The analysis unit can also analyze the user's emotions using sentiment analysis technology. Step 3: The generator generates tasks based on the goals analyzed by the analyzer. The tasks may be simple tasks, complex tasks, step-by-step tasks, or other types of tasks. For example, the generator may use a machine learning algorithm to generate tasks that best fit the goals. Alternatively, the generator may use a rule-based algorithm to generate tasks. Step 4: The providing unit provides the task generated by the generating unit in the form of a quest. The quest may be provided in the form of a game, a scenario, a challenge, or the like. For example, the providing unit may notify the user of the content of the quest by a text message or a voice message. The providing unit may also display the progress of the quest in real time. Step 5: The monitoring unit monitors the progress of the quest provided by the providing unit. The progress is measured by methods such as the completion rate, the number of completed tasks, or the passage of time. For example, the monitoring unit checks whether the user has completed the quest and records the progress. The monitoring unit can also adjust the task according to the user's progress.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0187] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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."

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Explanation of symbols]

[0212] 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 reception section for inputting goals; an analysis unit that analyzes the target input by the reception unit; a generation unit that generates a task based on the goal analyzed by the analysis unit; a providing unit that provides the task generated by the generating unit in a quest format; a monitoring unit that monitors the progress of the quest provided by the providing unit; Equipped with A system characterized by:

2. The monitoring unit Equipped with an adjustment unit that adjusts tasks according to the user's progress 2. The system of claim 1.

3. The reception unit Estimate the user's emotions and adjust the timing of the target input based on the estimated user emotions.

2. The system of claim 1.

4. The reception unit Analyze the user's past goal entry history and select the optimal entry method 2. The system of claim 1.

5. The reception unit When entering goals, filters based on the user's current life situation and areas of interest 2. The system of claim 1.

6. The reception unit When inputting a target, select the optimal input method according to the user's input method.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and prioritize the goals to be input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit When entering goals, the system takes into account the user's geographic location information to prioritize the most relevant goals.

2. The system of claim 1.

9. The reception unit When entering goals, analyze the user's social media activity and enter related goals.

2. The system of claim 1.

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