System

Abstract

An object of a system according to an embodiment is to automatically generate a Mandala chart and support goal achievement.SOLUTION: A system includes a Mandarin chart generation part, a progress management part, and a feedback provision part. A Mandala chart generation part automatically generates a Mandala chart on the basis of a target and related information inputted from a user. A progress management part manages the progress situation to the target set by the user in real time based on the Mandarin chart generated by the Mandarin chart generation part. The feedback providing unit provides effective feedback based on the progress status managed by the progress management unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the drawback of making it difficult to clearly define the path to achieving a goal, as creating and managing a mandala chart by hand is cumbersome.

[0005] The system according to the embodiment aims to automatically generate a mandala chart and support goal achievement. [Means for solving the problem]

[0006] The system according to the embodiment includes a mandala chart generation unit, a progress management unit, and a feedback provision unit. The mandala chart generation unit automatically generates a mandala chart based on goals and related information input by a user. The progress management unit manages in real time the progress status toward the goals set by the user based on the mandala chart generated by the mandala chart generation unit. The feedback provision unit provides effective feedback based on the progress status managed by the progress management unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a mandala chart and assist in achieving goals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The goal achievement support system according to the embodiment of the present invention is a system that uses a generation AI to automatically generate a Mandala chart and support goal achievement, thereby effectively supporting the user in achieving their goal.

[0029] A goal achievement support system according to an embodiment includes a mandala chart generation unit, a progress management unit, and a feedback provision unit. The mandara chart generation unit automatically generates a mandara chart based on a goal and related information input by a user. For example, if a user inputs a goal such as "become successful in sports," the generation AI presents specific steps and sub-goals necessary to achieve the goal in a chart format. Furthermore, if a user sets a goal such as "become fluent in English," the generation AI can suggest appropriate learning materials and online courses. The progress management unit manages the progress of the user toward the goal in real time based on the mandara chart generated by the mandara chart generation unit. For example, if a user sets a sub-goal such as "exercise one hour every day," the generation AI tracks the progress and evaluates the degree of achievement. The progress management unit also allows the user to check the progress and revise the goal or plan as necessary. The feedback provision unit provides effective feedback based on the progress managed by the progress management unit. For example, if the user is making steady progress toward achieving their goal, the generation AI may send an encouraging message such as "keep it up." On the other hand, if progress is lagging, the system provides specific advice such as "Focus on the next step." This allows the goal achievement support system according to the embodiment to effectively support users in achieving their goals. For example, when an athlete creates a training plan, the generation AI automatically generates a mandala chart, manages progress, and provides effective feedback to support goal achievement. Furthermore, when a learner is acquiring a new skill, the generation AI can suggest appropriate resources and enable interaction with other learners through a community function, thereby enhancing learning effectiveness.

[0030] The Mandala chart generation unit can retrieve the user's past goal achievement history from a database, analyze the goal achievement history to extract success patterns, and reflect the success patterns in the Mandala chart. For example, the generation AI in the Mandala chart generation unit retrieves the user's past goal achievement history from a database and analyzes successful goals and how they were achieved. For example, based on data from users who have previously achieved "completed a marathon," it can suggest effective steps for users with similar goals. The generation AI can also extract success patterns and generate a new Mandara chart based on them. This makes it possible to utilize past success patterns to generate more effective Mandara charts.

[0031] The Mandala chart generation unit can analyze the user's personality diagnostic test and behavioral pattern data to generate an individually optimized Mandala chart. For example, the generation AI in the Mandala chart generation unit analyzes the user's personality diagnostic test and behavioral pattern data to generate an individually optimized Mandala chart. For example, the generation AI can suggest a chart that includes many tasks that can be done alone to an introverted user. The generation AI can also suggest a chart that includes many tasks that can be done in cooperation with others to an extroverted user. This makes it possible to provide a Mandara chart that is optimized based on the user's personality and behavioral patterns.

[0032] The Mandala chart generation unit can collect success stories from a database in different fields and propose new Mandala chart templates that match the user's goals. For example, the generation AI can collect success stories from a database in different fields and propose new Mandala chart templates that match the user's goals. For example, based on success stories in the business field, it can generate the optimal chart for a user aiming to start a business. It can also propose the optimal training plan for an athlete based on success stories in the sports field. In this way, it is possible to utilize success stories from different fields to provide the optimal Mandala chart for the user.

[0033] The Mandala chart generation unit can collect the latest research and trend information related to the user's goals and generate a Mandara chart based on that information. For example, the generation AI can collect the latest research and trend information related to the user's goals and generate a Mandara chart based on that information. For example, it can propose a training plan that incorporates the latest fitness trends. It can also propose a study plan that incorporates the latest learning methods. This makes it possible to provide a Mandara chart that incorporates the latest research and trend information.

[0034] The progress management unit can analyze the user's progress data in detail, identify the cause of the stagnation in progress, and propose improvement measures based on the cause. For example, the generation AI can analyze the user's progress data in detail and identify the cause of the stagnation in progress. For example, if the frequency of training has decreased, the AI ​​can identify the cause and propose improvement measures. The progress management unit also allows the user to check their progress and revise their goals and plans as necessary. This makes it possible to identify the cause of the stagnation in progress and propose improvement measures.

[0035] The progress management unit can increase motivation to achieve goals by comparing the user's progress with other users and providing a benchmark. For example, the generation AI compares the user's progress with other users and provides a benchmark. For example, the progress of other users with the same goal can be displayed to increase motivation. The progress management unit also allows the user to check their progress and revise their goals and plans as necessary. This allows comparison with other users to increase motivation to achieve goals.

[0036] The progress management unit can provide a visual dashboard for progress management, allowing the user to intuitively grasp the progress. For example, the generation AI provides a visual dashboard for progress management, allowing the user to intuitively grasp the progress. For example, the progress status is visually displayed using graphs and charts. The progress management unit also allows the user to check the progress and revise goals and plans as necessary. This allows the user to intuitively grasp the progress.

[0037] The progress management unit can send personalized reminders and notifications to the user based on the progress data. For example, the generation AI sends personalized reminders and notifications to the user based on the progress data. For example, it sends a reminder when training time is approaching. The progress management unit also allows the user to check their progress and revise their goals and plans as necessary. This makes it possible to send personalized reminders and notifications to the user.

[0038] The feedback providing unit can retrieve the user's feedback history from a database, analyze the feedback history to identify the most effective feedback pattern, and provide the feedback pattern. For example, the generation AI can retrieve the user's feedback history from a database and identify the most effective feedback pattern. For example, the generation AI can re-provide feedback to which the user previously responded positively. The generation AI can also analyze the feedback history and develop an algorithm for providing optimal feedback to the user. This makes it possible to provide the most effective feedback based on the user's feedback history.

[0039] The feedback providing unit can collect external data related to the user's goal achievement and provide specific advice based on the external data. For example, the generation AI can collect external data related to the user's goal achievement and provide specific advice. For example, the generation AI can suggest the optimal training method for the user based on industry best practices. The generation AI can also analyze the external data and develop an algorithm for providing specific advice to the user. This allows the generation AI to provide specific advice based on the external data.

[0040] The feedback providing unit can provide feedback in a format that suits the user's learning style and preferences. For example, when the generation AI provides feedback, the feedback providing unit provides it in a format that suits the user's learning style and preferences. For example, feedback can be provided in video format to a visual learner. Feedback can also be provided in audio format to an auditory learner. In this way, feedback can be provided in a format that suits the user's learning style and preferences.

[0041] The feedback providing unit can share the content of the feedback with other users, promoting knowledge sharing throughout the community. For example, the generation AI can share the content of the feedback with other users, promoting knowledge sharing throughout the community. For example, by sharing success stories, it can provide information that is useful to other users. The generation AI can also analyze the content of the feedback and develop an algorithm for promoting knowledge sharing throughout the community. In this way, sharing the content of the feedback can promote knowledge sharing throughout the community.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The goal achievement support system can also acquire the user's health data and adjust the goal achievement plan based on the user's health condition. For example, it can analyze the user's sleep data and suggest tasks that prioritize rest if the user is not getting enough rest. It can also make adjustments to avoid excessive exercise based on the user's exercise data. This allows it to provide the user with an optimal goal achievement plan based on their health condition.

[0044] The goal achievement support system can also analyze the user's social media activity and provide information that will help them achieve their goals. For example, it can extract useful advice from posts by influencers the user follows. It can also suggest relevant communities and groups based on the user's interests. This allows the system to utilize the user's social media activity to support goal achievement.

[0045] The goal achievement support system can also analyze the user's past failures, identify the causes of failure, and propose improvements. For example, for a user who has failed at dieting in the past, the system can analyze the causes of failure and propose a new approach to succeed. It can also help users set more realistic goals based on lessons learned from past failures. This allows the system to support goal achievement by making use of past failures.

[0046] The goal achievement support system can also incorporate elements that make the goal achievement plan more enjoyable by taking into account the user's hobbies and interests. For example, if the user likes music, it can suggest tasks that can be done while listening to music. Also, if the user likes games, it can provide a goal achievement plan that incorporates game elements. This allows the system to support goal achievement in an enjoyable way based on the user's hobbies and interests.

[0047] The goal achievement support system can also take into account the user's lifestyle rhythm and suggest tasks at the optimal timing. For example, if a user has a morning rhythm, it can suggest tasks that require concentration in the morning. It can also suggest tasks that are suitable for the evening hours to a night owl. This makes it possible to provide an optimal goal achievement plan that matches the user's lifestyle rhythm.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The Mandala Chart Generator automatically generates a Mandala Chart based on the goal and related information entered by the user. For example, if a user enters the goal "to succeed in sports," the system will present the specific steps and sub-goals necessary to achieve that goal in chart form. Similarly, if a user sets the goal "to become fluent in English," the system can suggest appropriate learning materials and online courses. Step 2: The progress management unit manages the progress of the user's set goals in real time based on the mandala chart generated by the mandala chart generation unit. For example, if the user sets a sub-goal of "exercise one hour of training every day," the generation AI tracks the progress and evaluates the degree of achievement. The progress management unit also allows the user to check their progress and revise their goals and plans as necessary. Step 3: The feedback provider provides effective feedback based on the progress managed by the progress manager. For example, if the user is making steady progress toward achieving their goal, the generator AI will send an encouraging message such as "Keep it up." On the other hand, if progress is lagging behind, the generator AI will provide specific advice such as "Focus on the next step."

[0050] (Example 2) The goal achievement support system according to the embodiment of the present invention is a system that uses a generation AI to automatically generate a Mandala chart and support goal achievement, thereby effectively supporting the user in achieving their goal.

[0051] A goal achievement support system according to an embodiment includes a mandala chart generation unit, a progress management unit, and a feedback provision unit. The mandara chart generation unit automatically generates a mandara chart based on a goal and related information input by a user. For example, if a user inputs a goal such as "become successful in sports," the generation AI presents specific steps and sub-goals necessary to achieve the goal in a chart format. Furthermore, if a user sets a goal such as "become fluent in English," the generation AI can suggest appropriate learning materials and online courses. The progress management unit manages the progress of the user toward the goal in real time based on the mandara chart generated by the mandara chart generation unit. For example, if a user sets a sub-goal such as "exercise one hour every day," the generation AI tracks the progress and evaluates the degree of achievement. The progress management unit also allows the user to check the progress and revise the goal or plan as necessary. The feedback provision unit provides effective feedback based on the progress managed by the progress management unit. For example, if the user is making steady progress toward achieving their goal, the generation AI may send an encouraging message such as "keep it up." On the other hand, if progress is lagging, the system provides specific advice such as "Focus on the next step." This allows the goal achievement support system according to the embodiment to effectively support users in achieving their goals. For example, when an athlete creates a training plan, the generation AI automatically generates a mandala chart, manages progress, and provides effective feedback to support goal achievement. Furthermore, when a learner is acquiring a new skill, the generation AI can suggest appropriate resources and enable interaction with other learners through a community function, thereby enhancing learning effectiveness.

[0052] The Mandala chart generation unit can retrieve the user's past goal achievement history from a database, analyze the goal achievement history to extract success patterns, and reflect the success patterns in the Mandala chart. For example, the generation AI in the Mandala chart generation unit retrieves the user's past goal achievement history from a database and analyzes successful goals and how they were achieved. For example, based on data from users who have previously achieved "completed a marathon," it can suggest effective steps for users with similar goals. The generation AI can also extract success patterns and generate a new Mandara chart based on them. This makes it possible to utilize past success patterns to generate more effective Mandara charts.

[0053] The Mandala chart generation unit can analyze the user's personality diagnostic test and behavioral pattern data to generate an individually optimized Mandala chart. For example, the generation AI in the Mandala chart generation unit analyzes the user's personality diagnostic test and behavioral pattern data to generate an individually optimized Mandala chart. For example, the generation AI can suggest a chart that includes many tasks that can be done alone to an introverted user. The generation AI can also suggest a chart that includes many tasks that can be done in cooperation with others to an extroverted user. This makes it possible to provide a Mandara chart that is optimized based on the user's personality and behavioral patterns.

[0054] The mandala chart generation unit can use the emotion estimation function to analyze the user's current emotional state in real time and incorporate elements to increase motivation into the mandala chart. The mandala chart generation unit can, for example, use the emotion estimation function to analyze the user's current emotional state in real time and incorporate elements to increase motivation into the mandala chart. For example, if the user is feeling stressed, it can suggest relaxing tasks. Also, if the user is highly motivated, it can suggest challenging tasks. In this way, it is possible to provide elements to increase motivation according to the user's emotional state.

[0055] The Mandala chart generation unit can collect success stories from a database in different fields and propose new Mandala chart templates that match the user's goals. For example, the generation AI can collect success stories from a database in different fields and propose new Mandala chart templates that match the user's goals. For example, based on success stories in the business field, it can generate the optimal chart for a user aiming to start a business. It can also propose the optimal training plan for an athlete based on success stories in the sports field. In this way, it is possible to utilize success stories from different fields to provide the optimal Mandala chart for the user.

[0056] The Mandala chart generation unit can collect the latest research and trend information related to the user's goals and generate a Mandara chart based on that information. For example, the generation AI can collect the latest research and trend information related to the user's goals and generate a Mandara chart based on that information. For example, it can propose a training plan that incorporates the latest fitness trends. It can also propose a study plan that incorporates the latest learning methods. This makes it possible to provide a Mandara chart that incorporates the latest research and trend information.

[0057] The mandala chart generation unit can use the emotion estimation function to analyze the emotions of the user when entering their goals in real time and make suggestions to bring out positive emotions. The mandala chart generation unit can, for example, use the emotion estimation function to analyze the emotions of the user when entering their goals in real time and make suggestions to bring out positive emotions. For example, if the user is feeling anxious, an encouraging message can be displayed. Also, if the user is confident, it can suggest further challenges. In this way, suggestions to bring out positive emotions can be made according to the user's emotions.

[0058] The progress management unit can analyze the user's progress data in detail, identify the cause of the stagnation in progress, and propose improvement measures based on the cause. For example, the generation AI can analyze the user's progress data in detail and identify the cause of the stagnation in progress. For example, if the frequency of training has decreased, the AI ​​can identify the cause and propose improvement measures. The progress management unit also allows the user to check their progress and revise their goals and plans as necessary. This makes it possible to identify the cause of the stagnation in progress and propose improvement measures.

[0059] The progress management unit can increase motivation to achieve goals by comparing the user's progress with other users and providing a benchmark. For example, the generation AI compares the user's progress with other users and provides a benchmark. For example, the progress of other users with the same goal can be displayed to increase motivation. The progress management unit also allows the user to check their progress and revise their goals and plans as necessary. This allows comparison with other users to increase motivation to achieve goals.

[0060] The progress management unit can use the emotion estimation function to track changes in the user's emotions relative to their progress and provide emotional support based on these changes. The progress management unit can, for example, use the emotion estimation function to track changes in the user's emotions relative to their progress and provide emotional support. For example, if progress is slow, an encouraging message can be sent. Also, if progress is going well, a suggestion for further challenges can be made. This makes it possible to provide support according to changes in the user's emotions.

[0061] The progress management unit can provide a visual dashboard for progress management, allowing the user to intuitively grasp the progress. For example, the generation AI provides a visual dashboard for progress management, allowing the user to intuitively grasp the progress. For example, the progress status is visually displayed using graphs and charts. The progress management unit also allows the user to check the progress and revise goals and plans as necessary. This allows the user to intuitively grasp the progress.

[0062] The progress management unit can send personalized reminders and notifications to the user based on the progress data. For example, the generation AI sends personalized reminders and notifications to the user based on the progress data. For example, it sends a reminder when training time is approaching. The progress management unit also allows the user to check their progress and revise their goals and plans as necessary. This makes it possible to send personalized reminders and notifications to the user.

[0063] The progress management unit can use the emotion estimation function to analyze the user's emotions in real time during progress management and provide encouragement and advice at the appropriate time. The progress management unit can, for example, use the emotion estimation function to analyze the user's emotions in real time during progress management and provide encouragement and advice at the appropriate time. For example, if the user is feeling down, the progress management unit can send an encouraging message. Also, if the user is feeling confident, the progress management unit can suggest further challenges. This makes it possible to provide encouragement and advice according to the user's emotions.

[0064] The feedback providing unit can retrieve the user's feedback history from a database, analyze the feedback history to identify the most effective feedback pattern, and provide the feedback pattern. For example, the generation AI can retrieve the user's feedback history from a database and identify the most effective feedback pattern. For example, the generation AI can re-provide feedback to which the user previously responded positively. The generation AI can also analyze the feedback history and develop an algorithm for providing optimal feedback to the user. This makes it possible to provide the most effective feedback based on the user's feedback history.

[0065] The feedback providing unit can collect external data related to the user's goal achievement and provide specific advice based on the external data. For example, the generation AI can collect external data related to the user's goal achievement and provide specific advice. For example, the generation AI can suggest the optimal training method for the user based on industry best practices. The generation AI can also analyze the external data and develop an algorithm for providing specific advice to the user. This allows the generation AI to provide specific advice based on the external data.

[0066] The feedback providing unit can use the emotion estimation function to analyze the user's emotional state in real time and provide feedback based on the emotional state to maintain motivation. The feedback providing unit can, for example, use the emotion estimation function to analyze the user's emotional state in real time and provide feedback to maintain motivation. For example, if the user is feeling down, the feedback providing unit can send an encouraging message. Also, if the user is highly motivated, the feedback providing unit can suggest further challenges. In this way, feedback according to the user's emotional state can be provided to maintain motivation.

[0067] The feedback providing unit can provide feedback in a format that suits the user's learning style and preferences. For example, when the generation AI provides feedback, the feedback providing unit provides it in a format that suits the user's learning style and preferences. For example, feedback can be provided in video format to a visual learner. Feedback can also be provided in audio format to an auditory learner. In this way, feedback can be provided in a format that suits the user's learning style and preferences.

[0068] The feedback providing unit can share the content of the feedback with other users, promoting knowledge sharing throughout the community. For example, the generation AI can share the content of the feedback with other users, promoting knowledge sharing throughout the community. For example, by sharing success stories, it can provide information that is useful to other users. The generation AI can also analyze the content of the feedback and develop an algorithm for promoting knowledge sharing throughout the community. In this way, sharing the content of the feedback can promote knowledge sharing throughout the community.

[0069] The feedback providing unit can use the emotion estimation function to analyze the user's emotions in real time when providing feedback and make suggestions to elicit positive emotions. The feedback providing unit can, for example, use the emotion estimation function to analyze the user's emotions in real time when providing feedback and make suggestions to elicit positive emotions. For example, if the user is feeling anxious, the feedback providing unit can send an encouraging message. Also, if the user is confident, the feedback providing unit can suggest further challenges. In this way, suggestions to elicit positive emotions can be made according to the user's emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The goal achievement support system can also acquire the user's health data and adjust the goal achievement plan based on the user's health condition. For example, it can analyze the user's sleep data and suggest tasks that prioritize rest if the user is not getting enough rest. It can also make adjustments to avoid excessive exercise based on the user's exercise data. This allows it to provide the user with an optimal goal achievement plan based on their health condition.

[0072] The goal achievement support system can also analyze the user's social media activity and provide information that will help them achieve their goals. For example, it can extract useful advice from posts by influencers the user follows. It can also suggest relevant communities and groups based on the user's interests. This allows the system to utilize the user's social media activity to support goal achievement.

[0073] The goal achievement support system can also analyze the user's past failures, identify the causes of failure, and propose improvements. For example, for a user who has failed at dieting in the past, the system can analyze the causes of failure and propose a new approach to succeed. It can also help users set more realistic goals based on lessons learned from past failures. This allows the system to support goal achievement by making use of past failures.

[0074] The goal achievement support system can also incorporate elements that make the goal achievement plan more enjoyable by taking into account the user's hobbies and interests. For example, if the user likes music, it can suggest tasks that can be done while listening to music. Also, if the user likes games, it can provide a goal achievement plan that incorporates game elements. This allows the system to support goal achievement in an enjoyable way based on the user's hobbies and interests.

[0075] The goal achievement support system can also take into account the user's lifestyle rhythm and suggest tasks at the optimal timing. For example, if a user has a morning rhythm, it can suggest tasks that require concentration in the morning. It can also suggest tasks that are suitable for the evening hours to a night owl. This makes it possible to provide an optimal goal achievement plan that matches the user's lifestyle rhythm.

[0076] The goal achievement support system can use the emotion estimation function to provide a reward system based on the user's emotional state. For example, when a user achieves a goal, the emotion estimation function can be used to provide a reward that enhances joy and satisfaction. Also, if the user is feeling down, the system can suggest a reward to lift their spirits. This makes it possible to provide a reward system that corresponds to the user's emotional state.

[0077] The goal achievement support system can use the emotion estimation function to provide community support based on the user's emotional state. For example, if a user feels lonely, the emotion estimation function can be used to encourage interaction with other users. Also, if a user loses motivation, the emotion estimation function can be used to send encouraging messages. This allows community support to be provided according to the user's emotional state.

[0078] The goal achievement support system can use the emotion estimation function to provide customized feedback based on the user's emotional state. For example, if the user is feeling stressed, the emotion estimation function can be used to provide advice on how to relax. Also, if the user is highly motivated, the emotion estimation function can be used to suggest further challenges. This allows the system to provide customized feedback according to the user's emotional state.

[0079] The goal achievement support system can use the emotion estimation function to provide personalized learning resources based on the user's emotional state. For example, if the user is feeling anxious, the emotion estimation function can be used to suggest learning resources that provide a sense of security. Alternatively, if the user is feeling confident, the emotion estimation function can be used to suggest challenging learning resources. This allows the system to provide personalized learning resources according to the user's emotional state.

[0080] The goal achievement support system can use the emotion estimation function to evaluate the progress of the user in achieving their goal based on their emotional state. For example, if the user feels a sense of accomplishment, the emotion estimation function can be used to evaluate that emotion and suggest the next step. Also, if the user feels frustrated, the emotion estimation function can be used to evaluate that emotion and send an encouraging message. This allows the system to provide a progress evaluation that corresponds to the user's emotional state.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The Mandala Chart Generator automatically generates a Mandala Chart based on the goal and related information entered by the user. For example, if a user enters the goal "to succeed in sports," the system will present the specific steps and sub-goals necessary to achieve that goal in chart form. Similarly, if a user sets the goal "to become fluent in English," the system can suggest appropriate learning materials and online courses. Step 2: The progress management unit manages the progress of the user's set goals in real time based on the mandala chart generated by the mandala chart generation unit. For example, if the user sets a sub-goal of "exercise one hour of training every day," the generation AI tracks the progress and evaluates the degree of achievement. The progress management unit also allows the user to check their progress and revise their goals and plans as necessary. Step 3: The feedback provider provides effective feedback based on the progress managed by the progress manager. For example, if the user is making steady progress toward achieving their goal, the generator AI will send an encouraging message such as "Keep it up." On the other hand, if progress is lagging behind, the generator AI will provide specific advice such as "Focus on the next step."

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a mandala chart generation unit that automatically generates a mandala chart based on the goals and related information input by the user; a progress management unit that manages in real time the progress status toward the goal set by the user based on the Mandala chart generated by the Mandala chart generation unit; a feedback providing unit that provides effective feedback based on the progress status managed by the progress management unit. A system characterized by:

2. The mandala chart generation unit Analyzes the user's personality test and behavioral pattern data to generate an individually optimized Mandala chart 2. The system of claim 1.

3. The mandala chart generation unit Collecting success stories from a database of different fields and proposing new Mandala chart templates according to the user's goals 2. The system of claim 1.

4. The progress management unit Analyze user progress data in detail, identify causes of stagnation, and propose improvement measures based on those causes.

2. The system of claim 1.

5. The progress management unit Provides a visual dashboard for progress management, allowing users to intuitively grasp progress 2. The system of claim 1.

6. The feedback providing unit: A user's feedback history is obtained from a database, the feedback history is analyzed to identify a most effective feedback pattern, and the feedback pattern is provided.

2. The system of claim 1.

7. The feedback providing unit: Analyzing a user's emotional state in real time and providing feedback based on said emotional state to maintain motivation 2. The system of claim 1.

8. The mandala chart generation unit Analyze the user's current emotional state in real time and incorporate motivational elements into the Mandala chart.

2. The system of claim 1.

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