system

Abstract

The system according to this embodiment aims to provide progress management, encouragement, and feedback for achieving goals. [Solution] The system according to the embodiment comprises a goal setting unit, a progress reporting unit, an encouragement unit, and a feedback unit. The goal setting unit sets goals. The progress reporting unit reports progress based on the goals set by the goal setting unit. The encouragement unit provides encouragement or advice based on the progress reported by the progress reporting unit. The feedback unit provides feedback after the goals have been achieved based on the advice provided by the encouragement unit.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, progress management, encouragement, and feedback for achieving a goal are not sufficiently carried out, and there is room for improvement.

[0005] ​​​​​​​The system according to this embodiment comprises a goal setting unit, a progress reporting unit, an encouragement unit, and a feedback unit. The goal setting unit sets goals. The progress reporting unit reports progress based on the goals set by the goal setting unit. The encouragement unit provides encouragement or advice based on the progress reported by the progress reporting unit. The feedback unit provides feedback after the goals have been achieved based on the advice provided by the encouragement unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide progress management, encouragement, and feedback for achieving goals. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system [0000090] 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The goal achievement support system according to an embodiment of the present invention is a system centered on an application that utilizes a generating AI to support users in achieving goals they have set. In this goal achievement support system, the user decides on the content of their goals in consultation with the AI, and the application presents methods for achieving those goals. The user reports their progress on the application, and based on the report, the application provides encouragement, advice, and adjustments to the timeframe to help them achieve their goals. Upon achieving a goal, the application analyzes what went well and what went wrong, and provides feedback. In addition, achievement points are awarded, which can be exchanged for electronic payment points. By using this service, more people will be able to achieve their goals, and this will become a successful experience, allowing them to live more fulfilling lives. For example, the user decides on the content of their goals in consultation with the AI. For example, when setting goals such as dieting or obtaining a qualification, the AI ​​provides appropriate advice. Next, the application presents methods for achieving the set goals. For example, in the case of dieting, it presents meal management and exercise plans. In the case of obtaining a qualification, it presents study schedules and selection of reference books. The user reports their progress on the application. For example, they report their daily meals, exercise records, and study progress. Based on the report, the system provides encouragement, advice, and adjustments to the timeframe to help users achieve their goals. For example, if progress is behind schedule, the AI ​​will send encouraging messages and provide advice to help users achieve their goals. It can also adjust the timeframe for achieving the goals as needed. Once a goal is achieved, the system analyzes what went well and what didn't, and provides feedback. For example, in the case of weight loss, it provides feedback on successful meal management methods and exercise plans. In the case of obtaining a qualification, it provides feedback on effective learning methods and reference books. Users are also awarded achievement points, which can be exchanged for electronic payment points. This provides users with an incentive to achieve their goals. By using this service, more people can achieve their goals, and this will become a positive experience, leading to more fulfilling lives. In this way, the goal achievement support system can consistently provide support to help users achieve the goals they set.

[0029] The goal achievement support system according to this embodiment comprises a goal setting unit, a progress reporting unit, an encouragement unit, and a feedback unit. The goal setting unit provides AI advice when the user sets a goal. For example, when the user sets a goal such as dieting or obtaining a qualification, the AI ​​can provide appropriate advice. The progress reporting unit allows the user to report their progress. For example, the user can report their daily meals, exercise records, and learning progress. The encouragement unit provides encouragement and advice based on the reported content. For example, if progress is behind schedule, the AI ​​can send an encouraging message or provide advice for achieving the goal. It can also change the goal achievement period as needed. The feedback unit analyzes the good and bad points after the goal has been achieved and provides feedback. For example, in the case of dieting, it can provide feedback on successful meal management methods and exercise plans. In the case of obtaining a qualification, it can provide feedback on effective learning methods and reference books. The feedback unit can also award achievement points, which can be exchanged for electronic payment points. This allows the user to obtain an incentive for achieving their goal. Thus, the goal achievement support system according to this embodiment can consistently provide support for the user to achieve the goals they have set.

[0030] The goal-setting unit allows AI to provide advice when users set goals. For example, when a user sets goals such as dieting or obtaining a qualification, the AI ​​can provide appropriate advice. For example, the AI ​​can analyze the user's past data and current situation to suggest the optimal goal setting. The AI ​​can also provide specific steps and plans for the user to achieve their goals. For example, in the case of dieting, the AI ​​can suggest meal management and exercise plans. In the case of obtaining a qualification, the AI ​​can suggest study schedules and selection of reference books. This makes goal setting easier by providing the user with appropriate advice. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the goal-setting unit can input user input data into a generative AI, which can then suggest the optimal goal setting.

[0031] The progress reporting unit allows users to report their progress. For example, users can report their daily meals, exercise records, and learning progress. For example, users can report their progress on the app. The progress reporting unit can automatically analyze the user's reports and understand their progress. For example, the progress reporting unit can analyze the user's reports using text analysis technology to understand their progress. The progress reporting unit can also visually display the user's reports as graphs and charts. For example, the progress reporting unit can display the user's progress as graphs and charts, providing it in a visually easy-to-understand format. This allows users to understand their progress toward achieving their goals by reporting their progress. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress reporting unit can input the user's reports into a generative AI, which can then analyze the progress.

[0032] The encouragement unit can provide encouragement or advice based on the report. For example, if progress is behind schedule, the encouragement unit can use AI to send encouraging messages or provide advice for achieving goals. For example, the AI ​​can analyze the user's progress and provide appropriate encouragement or advice. The encouragement unit can also change the goal achievement period as needed. For example, if progress is behind schedule, the AI ​​can suggest extending the goal achievement period. This helps maintain user motivation by providing encouragement and advice tailored to the report. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or without a generative AI. For example, the encouragement unit can input the user's progress into a generative AI, which can then provide appropriate encouragement or advice.

[0033] The encouragement unit can change the timeframe for achieving the goal. For example, if progress is behind schedule, the encouragement unit can suggest that the AI ​​extend the timeframe for achieving the goal. For instance, the AI ​​can analyze the user's progress and, if it determines that extending the timeframe is appropriate, suggest an extension to the user. The encouragement unit can also shorten the timeframe for achieving the goal if progress is on track. For example, the AI ​​can analyze the user's progress and, if it determines that shortening the timeframe is appropriate, suggest a shortening to the user. This allows for flexible adjustment of the timeframe for achieving the goal, enabling support tailored to the user's situation. Some or all of the above-described processes in the encouragement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the encouragement unit can input the user's progress into a generating AI, which can then suggest an appropriate change to the timeframe.

[0034] The feedback unit can analyze the good and bad points after the goal has been achieved and provide feedback. For example, in the case of dieting, the feedback unit can provide feedback on successful meal management methods and exercise plans. For example, the AI ​​can analyze the user's meal management methods and exercise plans and provide feedback on what was successful and what needs improvement. In the case of obtaining a qualification, the feedback unit can provide feedback on effective learning methods and reference books. For example, the AI ​​can analyze the user's learning methods and reference books and provide feedback on what was effective and what needs improvement. By providing feedback after the goal has been achieved, information that will be useful for setting and achieving the next goal can be provided. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input the user's goal achievement data into a generative AI, and the generative AI can provide appropriate feedback.

[0035] The feedback unit can award achievement points and exchange them for electronic payment points. For example, the feedback unit can award achievement points when a user achieves a goal. For example, the AI ​​can analyze the user's goal achievement status and calculate the achievement points. The feedback unit can also exchange achievement points for electronic payment points. For example, the AI ​​can automatically perform the procedure of exchanging the user's achievement points for electronic payment points. This increases the user's incentive by awarding achievement points and exchanging them for electronic payment points. Some or all of the above processing in the feedback unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the feedback unit can input the user's achievement point data into a generating AI, which can then perform the appropriate point exchange procedure.

[0036] The goal-setting unit can analyze the user's past goal-setting history and propose the optimal goal-setting method. For example, the goal-setting unit can propose new goals based on the user's past successful goal-setting methods. For example, the AI ​​can analyze the user's past goal-setting history, extract successful goal-setting methods, and propose new goals based on them. It can also advise the user to avoid goal-setting methods that have failed in the past. For example, the AI ​​can analyze the user's past goal-setting history, identify failed goal-setting methods, and advise against them. Furthermore, it can analyze the user's past goal achievement rate and propose realistic goals. For example, the AI ​​can analyze the user's past goal achievement rate and propose realistic goals. In this way, by analyzing the past goal-setting history, the optimal goal-setting method can be proposed to the user. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the goal-setting unit can input the user's past goal-setting history data into a generative AI, which can then propose the optimal goal-setting method.

[0037] The goal-setting unit can customize goals based on the user's current lifestyle and areas of interest when setting goals. For example, if the user is busy, the goal-setting unit can suggest goals that can be achieved in a short period of time. For example, the AI ​​can analyze the user's lifestyle and suggest goals that can be achieved in a short period of time for a busy user. Also, if the user is interested in health, the goal-setting unit can suggest health-related goals. For example, the AI ​​can analyze the user's areas of interest and suggest health-related goals for a user who is interested in health. Furthermore, if the user wants to learn a new skill, the goal-setting unit can suggest goals related to that skill. For example, the AI ​​can analyze the user's areas of interest and suggest goals related to a new skill for a user who is interested in that skill. This increases the likelihood of achieving goals by setting goals that are tailored to the user's lifestyle and areas of interest. Some or all of the above-described processes in the goal-setting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the goal-setting unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then suggest appropriate goals.

[0038] The goal-setting unit can suggest highly relevant goals when setting goals, taking into account the user's geographical location information. For example, if the user lives in an urban area, the goal-setting unit can suggest goals that are easily achievable in an urban area. For example, the AI ​​can analyze the user's geographical location information and suggest goals that are easily achievable in an urban area for a user living in an urban area. Also, if the user lives in a natural environment, the goal-setting unit can suggest goals related to outdoor activities. For example, the AI ​​can analyze the user's geographical location information and suggest goals related to outdoor activities for a user living in a natural environment. Furthermore, if the user frequently visits a particular region, the goal-setting unit can suggest goals that can be achieved in that region. For example, the AI ​​can analyze the user's geographical location information and suggest goals that can be achieved in a particular region for a user who frequently visits that region. In this way, by considering geographical location information, goals that are easily achievable for the user can be suggested. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the goal-setting unit can input the user's geographical location information data into a generative AI, and the generative AI can suggest appropriate goals.

[0039] The goal-setting unit can analyze a user's social media activity and suggest relevant goals when setting goals. For example, if a user makes many posts about fitness, the goal-setting unit can suggest fitness-related goals. For example, the AI ​​can analyze a user's social media activity and suggest fitness-related goals to a user who makes many fitness-related posts. It can also suggest travel-related goals if a user makes many travel-related posts. For example, the AI ​​can analyze a user's social media activity and suggest travel-related goals to a user who makes many travel-related posts. Furthermore, if a user makes many cooking-related posts, it can suggest cooking-related goals. For example, the AI ​​can analyze a user's social media activity and suggest cooking-related goals to a user who makes many cooking-related posts. In this way, by analyzing social media activity, goals based on the user's interests can be suggested. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the goal-setting unit can input the user's social media activity data into a generative AI, which can then suggest appropriate goals.

[0040] The progress reporting unit can suggest the optimal reporting method by referring to the user's past reporting history when a progress report is submitted. For example, the progress reporting unit can suggest a new reporting method based on the user's past successful reporting methods. For example, the AI ​​can analyze the user's past reporting history, extract successful reporting methods, and suggest a new reporting method based on them. It can also advise the user to avoid reporting methods that have failed in the past. For example, the AI ​​can analyze the user's past reporting history, identify failed reporting methods, and advise against them. Furthermore, it can analyze the user's past reporting history and suggest a realistic reporting method. For example, the AI ​​can analyze the user's past reporting history and suggest a realistic reporting method. This allows the optimal reporting method to be suggested to the user by referring to past reporting history. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress reporting unit can input the user's past reporting history data into a generative AI, which can then suggest the optimal reporting method.

[0041] The progress reporting unit can customize the content of progress reports based on the user's current living situation. For example, if the user is busy, the progress reporting unit can provide a simple report. For example, the AI ​​can analyze the user's living situation and provide a simple report for busy users. It can also provide health-related reports if the user is interested in health. For example, the AI ​​can analyze the user's areas of interest and provide health-related reports for users who are interested in health. Furthermore, if the user wants to learn a new skill, it can provide reports related to that skill. For example, the AI ​​can analyze the user's areas of interest and provide reports related to a new skill for users who are interested in that skill. This reduces the burden of reporting by providing reports that are tailored to the user's living situation. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress reporting unit can input data on the user's living situation and areas of interest into a generative AI, which can then provide appropriate report content.

[0042] The progress reporting unit can adjust the report content when reporting progress, taking into account the user's geographical location information. For example, if the user lives in an urban area, the progress reporting unit can provide report content that is easily achievable in an urban area. For example, the AI ​​can analyze the user's geographical location information and provide report content that is easily achievable in an urban area for users living in urban areas. Also, if the user lives in a natural environment, the progress reporting unit can provide report content related to outdoor activities. For example, the AI ​​can analyze the user's geographical location information and provide report content related to outdoor activities for users living in a natural environment. Furthermore, if the user frequently visits a particular area, the progress reporting unit can provide report content that is achievable in that area. For example, the AI ​​can analyze the user's geographical location information and provide report content that is achievable in that area for users who frequently visit a particular area. In this way, by taking geographical location information into consideration, the progress reporting unit can provide report content that is easily achievable for the user. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the progress reporting unit can input the user's geographical location information data into a generative AI, and the generative AI can provide appropriate report content.

[0043] The progress reporting unit can analyze the user's social media activity and suggest relevant report content when providing progress reports. For example, if a user makes many fitness-related posts, the progress reporting unit can provide fitness-related report content. For example, the AI ​​can analyze a user's social media activity and provide fitness-related report content to users who make many fitness-related posts. Similarly, if a user makes many travel-related posts, the unit can provide travel-related report content. For example, the AI ​​can analyze a user's social media activity and provide travel-related report content to users who make many travel-related posts. Furthermore, if a user makes many cooking-related posts, the unit can provide cooking-related report content. For example, the AI ​​can analyze a user's social media activity and provide cooking-related report content to users who make many cooking-related posts. This allows for the provision of reports based on user interests by analyzing social media activity. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress reporting unit can input user social media activity data into a generative AI, which can then provide appropriate report content.

[0044] The encouragement unit can analyze the user's past responses to select the optimal method for providing encouragement and advice. For example, the encouragement unit can suggest new methods of encouragement based on methods of encouragement that the user has received favorably in the past. For example, the AI ​​can analyze the user's past responses, extract methods of encouragement that were received favorably, and suggest new methods of encouragement based on that. It can also advise the user to avoid methods of advice that they have received negatively in the past. For example, the AI ​​can analyze the user's past responses, identify methods of advice that were received negatively, and advise the user to avoid them. Furthermore, it can analyze the user's past responses and suggest the most effective method of encouragement. For example, the AI ​​can analyze the user's past responses and suggest the most effective method of encouragement. In this way, by analyzing past responses, the encouragement unit can provide the user with the most appropriate encouragement and advice. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or without a generative AI. For example, the encouragement unit can input the user's past response data into a generative AI, which can then provide the most appropriate encouragement and advice.

[0045] The encouragement unit can customize the content of encouragement and advice based on the user's current living situation. For example, if the user is busy, the encouragement unit can provide simple and easy-to-implement advice. For example, the AI ​​can analyze the user's living situation and provide simple and easy-to-implement advice to busy users. It can also provide health-related advice if the user is interested in health. For example, the AI ​​can analyze the user's areas of interest and provide health-related advice to users who are interested in health. Furthermore, if the user wants to learn a new skill, it can provide advice related to that skill. For example, the AI ​​can analyze the user's areas of interest and provide advice related to a new skill to users who are interested in that skill. This makes it possible to provide more effective support by providing encouragement and advice tailored to the user's living situation. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the encouragement unit can input data on the user's living situation and areas of interest into a generative AI, which can then provide appropriate encouragement and advice.

[0046] The encouragement unit can adjust the content of encouragement and advice by considering the user's geographical location. For example, if the user lives in an urban area, the encouragement unit can provide advice that is easily achievable in an urban area. For example, the AI ​​can analyze the user's geographical location and provide advice that is easily achievable in an urban area for users living in urban areas. Also, if the user lives in a natural environment, it can provide advice related to outdoor activities. For example, the AI ​​can analyze the user's geographical location and provide advice related to outdoor activities for users living in a natural environment. Furthermore, if the user frequently visits a particular area, it can provide advice that is achievable in that area. For example, the AI ​​can analyze the user's geographical location and provide advice that is achievable in a particular area for users who frequently visit that area. In this way, by considering geographical location, the encouragement unit can provide encouragement and advice that is easily achievable for the user. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the encouragement unit can input the user's geographical location data into a generative AI, and the generative AI can provide appropriate advice.

[0047] The encouragement unit can analyze a user's social media activity and suggest relevant content when providing encouragement and advice. For example, if a user posts a lot about fitness, the encouragement unit can provide fitness-related advice. For example, the AI ​​can analyze a user's social media activity and provide fitness-related advice to users who post a lot about fitness. Similarly, if a user posts a lot about travel, the encouragement unit can provide travel-related advice. For example, the AI ​​can analyze a user's social media activity and provide travel-related advice to users who post a lot about travel. Furthermore, if a user posts a lot about cooking, the encouragement unit can provide cooking-related advice. For example, the AI ​​can analyze a user's social media activity and provide cooking-related advice to users who post a lot about cooking. In this way, by analyzing social media activity, encouragement and advice can be provided based on the user's interests. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the encouragement unit can input the user's social media activity data into a generative AI, which can then provide appropriate advice.

[0048] The feedback unit can analyze the user's past goal achievement history to select the optimal feedback method when providing feedback. For example, the feedback unit can propose a new feedback method based on feedback methods that the user has received favorably in the past. For example, the AI ​​can analyze the user's past goal achievement history, extract feedback methods that were received favorably, and propose a new feedback method based on that. It can also advise the user to avoid feedback methods that they have received negatively in the past. For example, the AI ​​can analyze the user's past goal achievement history, identify feedback methods that were received negatively, and advise the user to avoid them. Furthermore, it can analyze the user's past goal achievement history and propose the most effective feedback method. For example, the AI ​​can analyze the user's past goal achievement history and propose the most effective feedback method. In this way, by analyzing the past goal achievement history, the feedback unit can provide the user with the most optimal feedback. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the user's past goal achievement history data into a generative AI, which can then provide the optimal feedback.

[0049] The feedback unit can customize the content of the feedback based on the user's current lifestyle. For example, if the user is busy, the feedback unit can provide simple and easy-to-implement feedback. For example, the AI ​​can analyze the user's lifestyle and provide simple and easy-to-implement feedback to busy users. It can also provide health-related feedback if the user is interested in health. For example, the AI ​​can analyze the user's areas of interest and provide health-related feedback to users who are interested in health. Furthermore, if the user wants to learn a new skill, it can provide feedback related to that skill. For example, the AI ​​can analyze the user's areas of interest and provide feedback related to a new skill to users who are interested in that skill. This allows for more effective support by providing feedback tailored to the user's lifestyle. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then provide appropriate feedback.

[0050] The feedback unit can adjust the content of the feedback given, taking into account the user's geographical location information. For example, if the user lives in an urban area, the feedback unit can provide feedback that is easily achievable in an urban environment. For example, the AI ​​can analyze the user's geographical location information and provide feedback that is easily achievable in an urban environment to a user living in an urban area. Also, if the user lives in a natural environment, the feedback unit can provide feedback related to outdoor activities. For example, the AI ​​can analyze the user's geographical location information and provide feedback related to outdoor activities to a user living in a natural environment. Furthermore, if the user frequently visits a particular area, the feedback unit can provide feedback that is achievable in that area. For example, the AI ​​can analyze the user's geographical location information and provide feedback that is achievable in that area to a user who frequently visits a particular area. In this way, by taking geographical location information into consideration, feedback that is easily achievable for the user can be provided. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback unit can input the user's geographical location information data into a generative AI, and the generative AI can provide appropriate feedback.

[0051] The feedback unit can analyze a user's social media activity and suggest relevant content when providing feedback. For example, if a user makes many fitness-related posts, the feedback unit can provide fitness-related feedback. For example, the AI ​​can analyze a user's social media activity and provide fitness-related feedback to users who make many fitness-related posts. Similarly, if a user makes many travel-related posts, the feedback unit can provide travel-related feedback. For example, the AI ​​can analyze a user's social media activity and provide travel-related feedback to users who make many travel-related posts. Furthermore, if a user makes many cooking-related posts, the feedback unit can provide cooking-related feedback. For example, the AI ​​can analyze a user's social media activity and provide cooking-related feedback to users who make many cooking-related posts. This allows for the provision of feedback based on the user's interests by analyzing social media activity. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input user social media activity data into a generative AI, which can then provide appropriate feedback.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The goal achievement support system can also include a reward management unit. This unit manages rewards for users upon achieving their goals and provides them at the appropriate time. For example, if a user achieves a weight loss goal, the reward management unit can offer coupons for health-related products or services. Similarly, if a user achieves a certification goal, the unit can offer discounts on learning-related books or online courses. Furthermore, the reward management unit can provide customized rewards tailored to the user's preferences. For instance, if a user is interested in travel, travel-related perks can be offered. This provides users with an incentive to achieve their goals and makes it easier for them to maintain motivation.

[0054] The goal achievement support system can also include a community collaboration section. This section provides features that allow users to share information and encourage each other in order to achieve their goals. For example, a user with a weight loss goal can share information about diet and exercise with other users who also have weight loss goals, and encourage each other. Similarly, a user with a qualification acquisition goal can share their learning progress and information about study materials, and offer each other advice. Furthermore, the community collaboration section allows users to create groups focused on achieving their goals and engage in collaborative activities. This helps users maintain motivation towards achieving their goals without feeling isolated.

[0055] The goal achievement support system can also include a health management section. This section monitors the user's health status and provides health management advice to help them achieve their goals. For example, for a user with a weight loss goal, it can record daily meals and exercise levels to monitor their health. It can also provide advice to users with qualification acquisition goals on stress management and improving sleep quality. Furthermore, the health management section can analyze the user's health data and provide individually customized health management plans. This allows users to achieve their goals while maintaining their health.

[0056] The goal achievement support system can also include a reminder function. This function provides reminders to help users avoid forgetting important tasks and events related to achieving their goals. For example, it can remind users with weight loss goals about meal times and exercise times. It can also remind users with certification goals about study schedules and exam dates. Furthermore, the reminder function can adjust the timing of reminders to match the user's schedule. This allows users to efficiently manage tasks related to achieving their goals.

[0057] The goal achievement support system can also include a data visualization unit. This unit provides a function to visually display the user's progress toward achieving their goals. For example, for a user with a weight loss goal, it can display changes in weight and exercise levels in graphs and charts. Similarly, for a user with a qualification acquisition goal, it can visually display their learning progress and exam results. Furthermore, the data visualization unit can provide infographics and dashboards to help users maintain their motivation toward achieving their goals. This allows users to grasp their progress at a glance and maintain their motivation toward achieving their goals.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The goal-setting section provides AI-powered advice to users as they set their goals. For example, when a user sets goals such as dieting or obtaining a qualification, the AI ​​provides appropriate advice. Step 2: The progress reporting section allows users to report their progress. For example, users can report their daily meals, exercise records, and learning progress. Step 3: The encouragement team provides encouragement and advice based on the report. For example, if progress is behind schedule, the AI ​​will send an encouraging message or provide advice to help achieve the goal. It can also change the timeframe for achieving the goal as needed. Step 4: The feedback department analyzes what went well and what didn't after the goal was achieved and provides feedback. For example, in the case of dieting, they can provide feedback on successful meal management methods and exercise plans. In the case of obtaining a qualification, they can provide feedback on effective study methods and reference books. The feedback department also awards achievement points, which can be exchanged for electronic payment points.

[0060] (Example of form 2) The goal achievement support system according to an embodiment of the present invention is a system centered on an application that utilizes a generating AI to support users in achieving goals they have set. In this goal achievement support system, the user decides on the content of their goals in consultation with the AI, and the application presents methods for achieving those goals. The user reports their progress on the application, and based on the report, the application provides encouragement, advice, and adjustments to the timeframe to help them achieve their goals. Upon achieving a goal, the application analyzes what went well and what went wrong, and provides feedback. In addition, achievement points are awarded, which can be exchanged for electronic payment points. By using this service, more people will be able to achieve their goals, and this will become a successful experience, allowing them to live more fulfilling lives. For example, the user decides on the content of their goals in consultation with the AI. For example, when setting goals such as dieting or obtaining a qualification, the AI ​​provides appropriate advice. Next, the application presents methods for achieving the set goals. For example, in the case of dieting, it presents meal management and exercise plans. In the case of obtaining a qualification, it presents study schedules and selection of reference books. The user reports their progress on the application. For example, they report their daily meals, exercise records, and study progress. Based on the report, the system provides encouragement, advice, and adjustments to the timeframe to help users achieve their goals. For example, if progress is behind schedule, the AI ​​will send encouraging messages and provide advice to help users achieve their goals. It can also adjust the timeframe for achieving the goals as needed. Once a goal is achieved, the system analyzes what went well and what didn't, and provides feedback. For example, in the case of weight loss, it provides feedback on successful meal management methods and exercise plans. In the case of obtaining a qualification, it provides feedback on effective learning methods and reference books. Users are also awarded achievement points, which can be exchanged for electronic payment points. This provides users with an incentive to achieve their goals. By using this service, more people can achieve their goals, and this will become a positive experience, leading to more fulfilling lives. In this way, the goal achievement support system can consistently provide support to help users achieve the goals they set.

[0061] The goal achievement support system according to this embodiment comprises a goal setting unit, a progress reporting unit, an encouragement unit, and a feedback unit. The goal setting unit provides AI advice when the user sets a goal. For example, when the user sets a goal such as dieting or obtaining a qualification, the AI ​​can provide appropriate advice. The progress reporting unit allows the user to report their progress. For example, the user can report their daily meals, exercise records, and learning progress. The encouragement unit provides encouragement and advice based on the reported content. For example, if progress is behind schedule, the AI ​​can send an encouraging message or provide advice for achieving the goal. It can also change the goal achievement period as needed. The feedback unit analyzes the good and bad points after the goal has been achieved and provides feedback. For example, in the case of dieting, it can provide feedback on successful meal management methods and exercise plans. In the case of obtaining a qualification, it can provide feedback on effective learning methods and reference books. The feedback unit can also award achievement points, which can be exchanged for electronic payment points. This allows the user to obtain an incentive for achieving their goal. Thus, the goal achievement support system according to this embodiment can consistently provide support for the user to achieve the goals they have set.

[0062] The goal-setting unit allows AI to provide advice when users set goals. For example, when a user sets goals such as dieting or obtaining a qualification, the AI ​​can provide appropriate advice. For example, the AI ​​can analyze the user's past data and current situation to suggest the optimal goal setting. The AI ​​can also provide specific steps and plans for the user to achieve their goals. For example, in the case of dieting, the AI ​​can suggest meal management and exercise plans. In the case of obtaining a qualification, the AI ​​can suggest study schedules and selection of reference books. This makes goal setting easier by providing the user with appropriate advice. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the goal-setting unit can input user input data into a generative AI, which can then suggest the optimal goal setting.

[0063] The progress reporting unit allows users to report their progress. For example, users can report their daily meals, exercise records, and learning progress. For example, users can report their progress on the app. The progress reporting unit can automatically analyze the user's reports and understand their progress. For example, the progress reporting unit can analyze the user's reports using text analysis technology to understand their progress. The progress reporting unit can also visually display the user's reports as graphs and charts. For example, the progress reporting unit can display the user's progress as graphs and charts, providing it in a visually easy-to-understand format. This allows users to understand their progress toward achieving their goals by reporting their progress. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress reporting unit can input the user's reports into a generative AI, which can then analyze the progress.

[0064] The encouragement unit can provide encouragement or advice based on the report. For example, if progress is behind schedule, the encouragement unit can use AI to send encouraging messages or provide advice for achieving goals. For example, the AI ​​can analyze the user's progress and provide appropriate encouragement or advice. The encouragement unit can also change the goal achievement period as needed. For example, if progress is behind schedule, the AI ​​can suggest extending the goal achievement period. This helps maintain user motivation by providing encouragement and advice tailored to the report. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or without a generative AI. For example, the encouragement unit can input the user's progress into a generative AI, which can then provide appropriate encouragement or advice.

[0065] The encouragement unit can change the timeframe for achieving the goal. For example, if progress is behind schedule, the encouragement unit can suggest that the AI ​​extend the timeframe for achieving the goal. For instance, the AI ​​can analyze the user's progress and, if it determines that extending the timeframe is appropriate, suggest an extension to the user. The encouragement unit can also shorten the timeframe for achieving the goal if progress is on track. For example, the AI ​​can analyze the user's progress and, if it determines that shortening the timeframe is appropriate, suggest a shortening to the user. This allows for flexible adjustment of the timeframe for achieving the goal, enabling support tailored to the user's situation. Some or all of the above-described processes in the encouragement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the encouragement unit can input the user's progress into a generating AI, which can then suggest an appropriate change to the timeframe.

[0066] The feedback unit can analyze the good and bad points after the goal has been achieved and provide feedback. For example, in the case of dieting, the feedback unit can provide feedback on successful meal management methods and exercise plans. For example, the AI ​​can analyze the user's meal management methods and exercise plans and provide feedback on what was successful and what needs improvement. In the case of obtaining a qualification, the feedback unit can provide feedback on effective learning methods and reference books. For example, the AI ​​can analyze the user's learning methods and reference books and provide feedback on what was effective and what needs improvement. By providing feedback after the goal has been achieved, information that will be useful for setting and achieving the next goal can be provided. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input the user's goal achievement data into a generative AI, and the generative AI can provide appropriate feedback.

[0067] The feedback unit can award achievement points and exchange them for electronic payment points. For example, the feedback unit can award achievement points when a user achieves a goal. For example, the AI ​​can analyze the user's goal achievement status and calculate the achievement points. The feedback unit can also exchange achievement points for electronic payment points. For example, the AI ​​can automatically perform the procedure of exchanging the user's achievement points for electronic payment points. This increases the user's incentive by awarding achievement points and exchanging them for electronic payment points. Some or all of the above processing in the feedback unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the feedback unit can input the user's achievement point data into a generating AI, which can then perform the appropriate point exchange procedure.

[0068] The goal-setting unit can estimate the user's emotions and adjust goal-setting advice based on the estimated emotions. For example, if the user is feeling stressed, the goal-setting unit can suggest easy and achievable goals. For example, the AI ​​can analyze the user's emotions and suggest goals to reduce stress. If the user is relaxed, the goal-setting unit can also suggest challenging goals. For example, the AI ​​can analyze the user's emotions and suggest goals that are achievable in a relaxed state. Furthermore, if the user is feeling anxious, the AI ​​can suggest goals that can be achieved in stages. For example, the AI ​​can analyze the user's emotions and suggest stages of goals to reduce anxiety. This allows for more appropriate goal setting by providing advice tailored to the user's emotions. Some or all of the above-described processes in the goal-setting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the goal-setting unit can input user emotion data into a generative AI, which can then provide appropriate goal-setting advice.

[0069] The goal-setting unit can analyze the user's past goal-setting history and propose the optimal goal-setting method. For example, the goal-setting unit can propose new goals based on the user's past successful goal-setting methods. For example, the AI ​​can analyze the user's past goal-setting history, extract successful goal-setting methods, and propose new goals based on them. It can also advise the user to avoid goal-setting methods that have failed in the past. For example, the AI ​​can analyze the user's past goal-setting history, identify failed goal-setting methods, and advise against them. Furthermore, it can analyze the user's past goal achievement rate and propose realistic goals. For example, the AI ​​can analyze the user's past goal achievement rate and propose realistic goals. In this way, by analyzing the past goal-setting history, the optimal goal-setting method can be proposed to the user. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the goal-setting unit can input the user's past goal-setting history data into a generative AI, which can then propose the optimal goal-setting method.

[0070] The goal-setting unit can customize goals based on the user's current lifestyle and areas of interest when setting goals. For example, if the user is busy, the goal-setting unit can suggest goals that can be achieved in a short period of time. For example, the AI ​​can analyze the user's lifestyle and suggest goals that can be achieved in a short period of time for a busy user. Also, if the user is interested in health, the goal-setting unit can suggest health-related goals. For example, the AI ​​can analyze the user's areas of interest and suggest health-related goals for a user who is interested in health. Furthermore, if the user wants to learn a new skill, the goal-setting unit can suggest goals related to that skill. For example, the AI ​​can analyze the user's areas of interest and suggest goals related to a new skill for a user who is interested in that skill. This increases the likelihood of achieving goals by setting goals that are tailored to the user's lifestyle and areas of interest. Some or all of the above-described processes in the goal-setting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the goal-setting unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then suggest appropriate goals.

[0071] The goal-setting unit can estimate the user's emotions and determine the priority of goal setting based on the estimated user emotions. For example, if the user is feeling stressed, the goal-setting unit can prioritize goals that promote relaxation. For example, the AI ​​can analyze the user's emotions and prioritize goals that reduce stress. Also, if the user is highly motivated, the goal-setting unit can prioritize challenging goals. For example, the AI ​​can analyze the user's emotions and prioritize challenging goals for highly motivated users. Furthermore, if the user is feeling anxious, the goal-setting unit can prioritize goals that provide a sense of security. For example, the AI ​​can analyze the user's emotions and prioritize goals that reduce anxiety. By setting priorities according to the user's emotions, more effective goal achievement becomes possible. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the goal-setting unit can input user emotion data into a generative AI, which can then determine the appropriate priority of goal setting.

[0072] The goal-setting unit can suggest highly relevant goals when setting goals, taking into account the user's geographical location information. For example, if the user lives in an urban area, the goal-setting unit can suggest goals that are easily achievable in an urban area. For example, the AI ​​can analyze the user's geographical location information and suggest goals that are easily achievable in an urban area for a user living in an urban area. Also, if the user lives in a natural environment, the goal-setting unit can suggest goals related to outdoor activities. For example, the AI ​​can analyze the user's geographical location information and suggest goals related to outdoor activities for a user living in a natural environment. Furthermore, if the user frequently visits a particular region, the goal-setting unit can suggest goals that can be achieved in that region. For example, the AI ​​can analyze the user's geographical location information and suggest goals that can be achieved in a particular region for a user who frequently visits that region. In this way, by considering geographical location information, goals that are easily achievable for the user can be suggested. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the goal-setting unit can input the user's geographical location information data into a generative AI, and the generative AI can suggest appropriate goals.

[0073] The goal-setting unit can analyze a user's social media activity and suggest relevant goals when setting goals. For example, if a user makes many posts about fitness, the goal-setting unit can suggest fitness-related goals. For example, the AI ​​can analyze a user's social media activity and suggest fitness-related goals to a user who makes many fitness-related posts. It can also suggest travel-related goals if a user makes many travel-related posts. For example, the AI ​​can analyze a user's social media activity and suggest travel-related goals to a user who makes many travel-related posts. Furthermore, if a user makes many cooking-related posts, it can suggest cooking-related goals. For example, the AI ​​can analyze a user's social media activity and suggest cooking-related goals to a user who makes many cooking-related posts. In this way, by analyzing social media activity, goals based on the user's interests can be suggested. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the goal-setting unit can input the user's social media activity data into a generative AI, which can then suggest appropriate goals.

[0074] The progress reporting unit can estimate the user's emotions and adjust the progress reporting method based on the estimated user emotions. For example, if the user is feeling stressed, the progress reporting unit can provide a simple reporting method. For example, the AI ​​can analyze the user's emotions and provide a simple reporting method to reduce stress. It can also provide a detailed reporting method if the user is relaxed. For example, the AI ​​can analyze the user's emotions and provide a reporting method that allows for detailed reporting while relaxed. Furthermore, if the user is feeling anxious, it can provide a reporting method that provides reassurance. For example, the AI ​​can analyze the user's emotions and provide a reporting method to reduce anxiety. This reduces the burden of reporting by providing progress reporting methods that are tailored to the user's emotions. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the progress reporting unit can input user emotion data into a generative AI, and the generative AI can provide an appropriate progress reporting method.

[0075] The progress reporting unit can suggest the optimal reporting method by referring to the user's past reporting history when a progress report is submitted. For example, the progress reporting unit can suggest a new reporting method based on the user's past successful reporting methods. For example, the AI ​​can analyze the user's past reporting history, extract successful reporting methods, and suggest a new reporting method based on them. It can also advise the user to avoid reporting methods that have failed in the past. For example, the AI ​​can analyze the user's past reporting history, identify failed reporting methods, and advise against them. Furthermore, it can analyze the user's past reporting history and suggest a realistic reporting method. For example, the AI ​​can analyze the user's past reporting history and suggest a realistic reporting method. This allows the optimal reporting method to be suggested to the user by referring to past reporting history. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress reporting unit can input the user's past reporting history data into a generative AI, which can then suggest the optimal reporting method.

[0076] The progress reporting unit can customize the content of progress reports based on the user's current living situation. For example, if the user is busy, the progress reporting unit can provide a simple report. For example, the AI ​​can analyze the user's living situation and provide a simple report for busy users. It can also provide health-related reports if the user is interested in health. For example, the AI ​​can analyze the user's areas of interest and provide health-related reports for users who are interested in health. Furthermore, if the user wants to learn a new skill, it can provide reports related to that skill. For example, the AI ​​can analyze the user's areas of interest and provide reports related to a new skill for users who are interested in that skill. This reduces the burden of reporting by providing reports that are tailored to the user's living situation. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress reporting unit can input data on the user's living situation and areas of interest into a generative AI, which can then provide appropriate report content.

[0077] The progress reporting unit can estimate the user's emotions and adjust the frequency of progress reports based on the estimated emotions. For example, if the user is feeling stressed, the progress reporting unit can reduce the reporting frequency. For example, the AI ​​can analyze the user's emotions and reduce the reporting frequency to alleviate stress. It can also increase the reporting frequency if the user is relaxed. For example, the AI ​​can analyze the user's emotions and increase the reporting frequency when the user is relaxed. Furthermore, if the user is feeling anxious, the reporting frequency can be adjusted to provide reassurance. For example, the AI ​​can analyze the user's emotions and adjust the reporting frequency to alleviate anxiety. This reduces the burden of reporting by providing a reporting frequency that matches the user's emotions. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress reporting unit can input user emotion data into a generative AI, which can then provide an appropriate reporting frequency.

[0078] The progress reporting unit can adjust the report content when reporting progress, taking into account the user's geographical location information. For example, if the user lives in an urban area, the progress reporting unit can provide report content that is easily achievable in an urban area. For example, the AI ​​can analyze the user's geographical location information and provide report content that is easily achievable in an urban area for users living in urban areas. Also, if the user lives in a natural environment, the progress reporting unit can provide report content related to outdoor activities. For example, the AI ​​can analyze the user's geographical location information and provide report content related to outdoor activities for users living in a natural environment. Furthermore, if the user frequently visits a particular area, the progress reporting unit can provide report content that is achievable in that area. For example, the AI ​​can analyze the user's geographical location information and provide report content that is achievable in that area for users who frequently visit a particular area. In this way, by taking geographical location information into consideration, the progress reporting unit can provide report content that is easily achievable for the user. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the progress reporting unit can input the user's geographical location information data into a generative AI, and the generative AI can provide appropriate report content.

[0079] The progress reporting unit can analyze the user's social media activity and suggest relevant report content when providing progress reports. For example, if a user makes many fitness-related posts, the progress reporting unit can provide fitness-related report content. For example, the AI ​​can analyze a user's social media activity and provide fitness-related report content to users who make many fitness-related posts. Similarly, if a user makes many travel-related posts, the unit can provide travel-related report content. For example, the AI ​​can analyze a user's social media activity and provide travel-related report content to users who make many travel-related posts. Furthermore, if a user makes many cooking-related posts, the unit can provide cooking-related report content. For example, the AI ​​can analyze a user's social media activity and provide cooking-related report content to users who make many cooking-related posts. This allows for the provision of reports based on user interests by analyzing social media activity. Some or all of the above processing in the progress reporting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress reporting unit can input user social media activity data into a generative AI, which can then provide appropriate report content.

[0080] The encouragement unit can estimate the user's emotions and adjust the content of encouragement and advice based on the estimated emotions. For example, if the user is feeling stressed, the encouragement unit can offer gentle words of encouragement. For example, the AI ​​can analyze the user's emotions and provide gentle words to alleviate stress. It can also offer specific advice if the user is relaxed. For example, the AI ​​can analyze the user's emotions and provide specific advice in a relaxed state. Furthermore, if the user is feeling anxious, the encouragement unit can offer reassuring words of encouragement. For example, the AI ​​can analyze the user's emotions and provide reassuring words to alleviate anxiety. This allows for more effective support by providing encouragement and advice tailored to the user's emotions. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or without a generative AI. For example, the encouragement unit can input user emotion data into a generative AI, which can then provide appropriate encouragement and advice.

[0081] The encouragement unit can analyze the user's past responses to select the optimal method for providing encouragement and advice. For example, the encouragement unit can suggest new methods of encouragement based on methods of encouragement that the user has received favorably in the past. For example, the AI ​​can analyze the user's past responses, extract methods of encouragement that were received favorably, and suggest new methods of encouragement based on that. It can also advise the user to avoid methods of advice that they have received negatively in the past. For example, the AI ​​can analyze the user's past responses, identify methods of advice that were received negatively, and advise the user to avoid them. Furthermore, it can analyze the user's past responses and suggest the most effective method of encouragement. For example, the AI ​​can analyze the user's past responses and suggest the most effective method of encouragement. In this way, by analyzing past responses, the encouragement unit can provide the user with the most appropriate encouragement and advice. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or without a generative AI. For example, the encouragement unit can input the user's past response data into a generative AI, which can then provide the most appropriate encouragement and advice.

[0082] The encouragement unit can customize the content of encouragement and advice based on the user's current living situation. For example, if the user is busy, the encouragement unit can provide simple and easy-to-implement advice. For example, the AI ​​can analyze the user's living situation and provide simple and easy-to-implement advice to busy users. It can also provide health-related advice if the user is interested in health. For example, the AI ​​can analyze the user's areas of interest and provide health-related advice to users who are interested in health. Furthermore, if the user wants to learn a new skill, it can provide advice related to that skill. For example, the AI ​​can analyze the user's areas of interest and provide advice related to a new skill to users who are interested in that skill. This makes it possible to provide more effective support by providing encouragement and advice tailored to the user's living situation. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the encouragement unit can input data on the user's living situation and areas of interest into a generative AI, which can then provide appropriate encouragement and advice.

[0083] The encouragement unit can estimate the user's emotions and adjust the timing of encouragement and advice based on the estimated emotions. For example, if the user is feeling stressed, the encouragement unit can offer encouragement at the appropriate time. For example, the AI ​​can analyze the user's emotions and provide encouragement at the right time to alleviate stress. It can also provide advice at the right time if the user is relaxed. For example, the AI ​​can analyze the user's emotions and provide advice at the right time while the user is relaxed. Furthermore, if the user is feeling anxious, the encouragement unit can offer reassurance at the right time. For example, the AI ​​can analyze the user's emotions and provide encouragement at the right time to alleviate anxiety. This allows for more effective support by providing encouragement and advice at the right time according to the user's emotions. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the encouragement unit can input user emotion data into a generative AI, which can then provide encouragement and advice at the appropriate time.

[0084] The encouragement unit can adjust the content of encouragement and advice by considering the user's geographical location. For example, if the user lives in an urban area, the encouragement unit can provide advice that is easily achievable in an urban area. For example, the AI ​​can analyze the user's geographical location and provide advice that is easily achievable in an urban area for users living in urban areas. Also, if the user lives in a natural environment, it can provide advice related to outdoor activities. For example, the AI ​​can analyze the user's geographical location and provide advice related to outdoor activities for users living in a natural environment. Furthermore, if the user frequently visits a particular area, it can provide advice that is achievable in that area. For example, the AI ​​can analyze the user's geographical location and provide advice that is achievable in a particular area for users who frequently visit that area. In this way, by considering geographical location, the encouragement unit can provide encouragement and advice that is easily achievable for the user. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the encouragement unit can input the user's geographical location data into a generative AI, and the generative AI can provide appropriate advice.

[0085] The encouragement unit can analyze a user's social media activity and suggest relevant content when providing encouragement and advice. For example, if a user posts a lot about fitness, the encouragement unit can provide fitness-related advice. For example, the AI ​​can analyze a user's social media activity and provide fitness-related advice to users who post a lot about fitness. Similarly, if a user posts a lot about travel, the encouragement unit can provide travel-related advice. For example, the AI ​​can analyze a user's social media activity and provide travel-related advice to users who post a lot about travel. Furthermore, if a user posts a lot about cooking, the encouragement unit can provide cooking-related advice. For example, the AI ​​can analyze a user's social media activity and provide cooking-related advice to users who post a lot about cooking. In this way, by analyzing social media activity, encouragement and advice can be provided based on the user's interests. Some or all of the above processing in the encouragement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the encouragement unit can input the user's social media activity data into a generative AI, which can then provide appropriate advice.

[0086] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can provide gentle feedback. For example, the AI ​​can analyze the user's emotions and provide gentle words to alleviate stress. It can also provide specific feedback if the user is relaxed. For example, the AI ​​can analyze the user's emotions and provide specific feedback in a relaxed state. Furthermore, if the user is feeling anxious, it can provide reassuring feedback. For example, the AI ​​can analyze the user's emotions and provide reassuring feedback to alleviate anxiety. This allows for more effective support by providing feedback tailored to the user's emotions. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input user emotion data into a generative AI, which can then provide appropriate feedback.

[0087] The feedback unit can analyze the user's past goal achievement history to select the optimal feedback method when providing feedback. For example, the feedback unit can propose a new feedback method based on feedback methods that the user has received favorably in the past. For example, the AI ​​can analyze the user's past goal achievement history, extract feedback methods that were received favorably, and propose a new feedback method based on that. It can also advise the user to avoid feedback methods that they have received negatively in the past. For example, the AI ​​can analyze the user's past goal achievement history, identify feedback methods that were received negatively, and advise the user to avoid them. Furthermore, it can analyze the user's past goal achievement history and propose the most effective feedback method. For example, the AI ​​can analyze the user's past goal achievement history and propose the most effective feedback method. In this way, by analyzing the past goal achievement history, the feedback unit can provide the user with the most optimal feedback. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the user's past goal achievement history data into a generative AI, which can then provide the optimal feedback.

[0088] The feedback unit can customize the content of the feedback based on the user's current lifestyle. For example, if the user is busy, the feedback unit can provide simple and easy-to-implement feedback. For example, the AI ​​can analyze the user's lifestyle and provide simple and easy-to-implement feedback to busy users. It can also provide health-related feedback if the user is interested in health. For example, the AI ​​can analyze the user's areas of interest and provide health-related feedback to users who are interested in health. Furthermore, if the user wants to learn a new skill, it can provide feedback related to that skill. For example, the AI ​​can analyze the user's areas of interest and provide feedback related to a new skill to users who are interested in that skill. This allows for more effective support by providing feedback tailored to the user's lifestyle. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then provide appropriate feedback.

[0089] The feedback unit can estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can provide feedback at an appropriate time. For example, the AI ​​can analyze the user's emotions and provide feedback at the appropriate time to alleviate stress. It can also provide feedback at an appropriate time if the user is relaxed. For example, the AI ​​can analyze the user's emotions and provide feedback at the appropriate time while the user is relaxed. Furthermore, if the user is feeling anxious, it can provide feedback at a time that provides reassurance. For example, the AI ​​can analyze the user's emotions and provide feedback at the appropriate time to alleviate anxiety. This allows for more effective support by providing feedback at a time that matches the user's emotions. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input user emotion data into a generative AI, which can then provide feedback at an appropriate time.

[0090] The feedback unit can adjust the content of the feedback given, taking into account the user's geographical location information. For example, if the user lives in an urban area, the feedback unit can provide feedback that is easily achievable in an urban environment. For example, the AI ​​can analyze the user's geographical location information and provide feedback that is easily achievable in an urban environment to a user living in an urban area. Also, if the user lives in a natural environment, the feedback unit can provide feedback related to outdoor activities. For example, the AI ​​can analyze the user's geographical location information and provide feedback related to outdoor activities to a user living in a natural environment. Furthermore, if the user frequently visits a particular area, the feedback unit can provide feedback that is achievable in that area. For example, the AI ​​can analyze the user's geographical location information and provide feedback that is achievable in that area to a user who frequently visits a particular area. In this way, by taking geographical location information into consideration, feedback that is easily achievable for the user can be provided. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback unit can input the user's geographical location information data into a generative AI, and the generative AI can provide appropriate feedback.

[0091] The feedback unit can analyze a user's social media activity and suggest relevant content when providing feedback. For example, if a user makes many fitness-related posts, the feedback unit can provide fitness-related feedback. For example, the AI ​​can analyze a user's social media activity and provide fitness-related feedback to users who make many fitness-related posts. Similarly, if a user makes many travel-related posts, the feedback unit can provide travel-related feedback. For example, the AI ​​can analyze a user's social media activity and provide travel-related feedback to users who make many travel-related posts. Furthermore, if a user makes many cooking-related posts, the feedback unit can provide cooking-related feedback. For example, the AI ​​can analyze a user's social media activity and provide cooking-related feedback to users who make many cooking-related posts. This allows for the provision of feedback based on the user's interests by analyzing social media activity. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input user social media activity data into a generative AI, which can then provide appropriate feedback. === Hard Collateral 1-1 === Each of the multiple elements described above, including the goal-setting unit, progress reporting unit, encouragement unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the goal-setting unit is implemented by the control unit 46A of the smart device 14, where AI provides advice when the user sets goals. The progress reporting unit is implemented by the control unit 46A of the smart device 14, where the user reports on daily meals, exercise records, and learning progress. The encouragement unit is implemented by the specific processing unit 290 of the data processing unit 12, where encouragement and advice are provided based on the reported content. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes what went well and what didn't after the goal was achieved and provides feedback. === Hard Collateral 1-2 === Each of the multiple elements described above, including the goal-setting unit, progress reporting unit, encouragement unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the goal-setting unit is implemented by the control unit 46A of the smart glasses 214, where the AI ​​provides advice when the user sets goals. The progress reporting unit is implemented by the control unit 46A of the smart glasses 214, where the user reports on daily meals, exercise records, and learning progress. The encouragement unit is implemented by the specific processing unit 290 of the data processing unit 12, where encouragement and advice are provided based on the reported content. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, where the good and bad points are analyzed after the goal is achieved, and feedback is provided. === Hard Collateral 1-3 === Each of the multiple elements described above, including the goal-setting unit, progress reporting unit, encouragement unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the goal-setting unit is implemented by the control unit 46A of the headset terminal 314, where the AI ​​provides advice when the user sets goals. The progress reporting unit is implemented by the control unit 46A of the headset terminal 314, where the user reports on daily meals, exercise records, and learning progress. The encouragement unit is implemented by the specific processing unit 290 of the data processing unit 12, where encouragement and advice are provided based on the reported content. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes what went well and what didn't after the goal was achieved and provides feedback. === Hard Collateral 1-4 === Each of the multiple elements described above, including the goal-setting unit, progress reporting unit, encouragement unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the goal-setting unit is implemented by the control unit 46A of the robot 414, where the AI ​​provides advice when the user sets goals. The progress reporting unit is implemented by the control unit 46A of the robot 414, where the user reports on daily meals, exercise records, and learning progress. The encouragement unit is implemented by the specific processing unit 290 of the data processing unit 12, where encouragement and advice are provided based on the reported content. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, where the good and bad points are analyzed after the goal is achieved, and feedback is provided.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The goal achievement support system can also include a reward management unit. This unit manages rewards for users upon achieving their goals and provides them at the appropriate time. For example, if a user achieves a weight loss goal, the reward management unit can offer coupons for health-related products or services. Similarly, if a user achieves a certification goal, the unit can offer discounts on learning-related books or online courses. Furthermore, the reward management unit can provide customized rewards tailored to the user's preferences. For instance, if a user is interested in travel, travel-related perks can be offered. This provides users with an incentive to achieve their goals and makes it easier for them to maintain motivation.

[0094] The goal achievement support system can also include a community collaboration section. This section provides features that allow users to share information and encourage each other in order to achieve their goals. For example, a user with a weight loss goal can share information about diet and exercise with other users who also have weight loss goals, and encourage each other. Similarly, a user with a qualification acquisition goal can share their learning progress and information about study materials, and offer each other advice. Furthermore, the community collaboration section allows users to create groups focused on achieving their goals and engage in collaborative activities. This helps users maintain motivation towards achieving their goals without feeling isolated.

[0095] The goal achievement support system can also include a health management section. This section monitors the user's health status and provides health management advice to help them achieve their goals. For example, for a user with a weight loss goal, it can record daily meals and exercise levels to monitor their health. It can also provide advice to users with qualification acquisition goals on stress management and improving sleep quality. Furthermore, the health management section can analyze the user's health data and provide individually customized health management plans. This allows users to achieve their goals while maintaining their health.

[0096] The goal achievement support system can also include a reminder function. This function provides reminders to help users avoid forgetting important tasks and events related to achieving their goals. For example, it can remind users with weight loss goals about meal times and exercise times. It can also remind users with certification goals about study schedules and exam dates. Furthermore, the reminder function can adjust the timing of reminders to match the user's schedule. This allows users to efficiently manage tasks related to achieving their goals.

[0097] The goal achievement support system can also include a data visualization unit. This unit provides a function to visually display the user's progress toward achieving their goals. For example, for a user with a weight loss goal, it can display changes in weight and exercise levels in graphs and charts. Similarly, for a user with a qualification acquisition goal, it can visually display their learning progress and exam results. Furthermore, the data visualization unit can provide infographics and dashboards to help users maintain their motivation toward achieving their goals. This allows users to grasp their progress at a glance and maintain their motivation toward achieving their goals.

[0098] The goal achievement support system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions and adjust the support for goal achievement based on those emotions. For example, if the user is feeling stressed, the emotion estimation unit can suggest relaxation methods to reduce stress. Similarly, if the user's motivation is low, the emotion estimation unit can provide encouraging messages to boost motivation. Furthermore, the emotion estimation unit can adjust the goal achievement schedule according to the user's emotions. This allows the user to receive support tailored to their emotions and maintain motivation towards achieving their goals.

[0099] The goal achievement support system can also be equipped with an emotion analysis unit. This unit can analyze the user's emotions in detail and provide feedback in response to changes in those emotions. For example, as a user works towards achieving a goal, the emotion analysis unit can monitor changes in the user's emotions in real time and provide feedback at the appropriate time. Furthermore, the emotion analysis unit can accumulate user emotional data and analyze long-term emotional trends. This allows users to understand their own emotional changes and take appropriate measures to achieve their goals.

[0100] The goal achievement support system can also include an emotion sharing section. This section provides a function that allows users to share their emotions with other users and encourage each other. For example, when a user is working towards achieving a goal, the emotion sharing section can share the user's emotions with other users and send encouraging messages. The emotion sharing section also provides a function that allows users to record their emotions and review them later. This allows users to share their emotions with others and maintain their motivation towards achieving their goals.

[0101] The goal achievement support system can also be equipped with an emotion tracking unit. This unit can continuously track the user's emotions and provide support tailored to changes in those emotions. For example, while a user is working towards achieving a goal, the emotion tracking unit can periodically record the user's emotions and analyze changes in those emotions. Furthermore, based on the user's emotional data, the emotion tracking unit can provide advice and encouraging messages tailored to those emotional changes. This allows the user to understand their own emotional changes and receive appropriate support towards achieving their goals.

[0102] The goal achievement support system can also be equipped with an emotion prediction unit. This unit can predict future emotions based on the user's past emotional data and provide support tailored to those predicted emotions. For example, if the user has experienced periods of high stress in the past, the emotion prediction unit can provide stress reduction advice appropriate to those periods. Similarly, if the user has experienced periods of low motivation in the past, the emotion prediction unit can provide support to improve motivation appropriate to those periods. This allows the user to receive appropriate support in preparation for future emotional changes, enabling them to maintain motivation toward achieving their goals.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The goal-setting section provides AI-powered advice to users as they set their goals. For example, when a user sets goals such as dieting or obtaining a qualification, the AI ​​provides appropriate advice. Step 2: The progress reporting section allows users to report their progress. For example, users can report their daily meals, exercise records, and learning progress. Step 3: The encouragement team provides encouragement and advice based on the report. For example, if progress is behind schedule, the AI ​​will send an encouraging message or provide advice to help achieve the goal. It can also change the timeframe for achieving the goal as needed. Step 4: The feedback department analyzes what went well and what didn't after the goal was achieved and provides feedback. For example, in the case of dieting, they can provide feedback on successful meal management methods and exercise plans. In the case of obtaining a qualification, they can provide feedback on effective study methods and reference books. The feedback department also awards achievement points, which can be exchanged for electronic payment points.

[0105] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0108] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 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.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The goal setting unit sets the objectives, A progress reporting unit reports progress based on the goals set by the aforementioned goal setting unit, An encouragement unit that provides encouragement or advice based on the progress reported by the aforementioned progress reporting unit, A feedback unit provides feedback after the goal has been achieved based on the advice provided by the aforementioned encouragement unit, Equipped with A system characterized by the following features.

2. The aforementioned target setting unit, AI provides advice when users set goals. The system according to feature 1.

3. The aforementioned progress reporting unit, Users report progress The system according to feature 1.

4. The aforementioned encouragement unit is, Provide encouragement or advice based on the report. The system according to feature 1.

5. The aforementioned encouragement unit is, Change the timeframe for achieving the goal. The system according to feature 1.

6. The aforementioned feedback unit is After achieving the goal, analyze the good and bad points and provide feedback. The system according to feature 1.

7. The aforementioned feedback unit is Achievement points will be awarded and exchanged for electronic payment points. The system according to feature 1.

8. The aforementioned target setting unit, It estimates the user's emotions and adjusts goal-setting advice based on those estimated emotions. The system according to feature 1.

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