System

Abstract

To provide a system that keeps users motivated continuously to achieve goals they set.SOLUTION: A system according to the embodiment comprises a goal setting unit, a message generation unit and a notification unit. The goal setting unit receives a goal input from a user. The message generation unit generates an encouraging / supporting message or famous words based on the goal accepted by the goal setting unit. The notification unit notifies the user of the message generated by the message generation unit at a set time.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the disadvantage that it is difficult for users to continually maintain their motivation to achieve the goals they have set.

[0005] The system according to the embodiment aims to continuously maintain the motivation of the user toward the goals that he or she has set. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal setting unit, a message generating unit, and a notification unit. The goal setting unit receives a goal input from a user. The message generating unit generates an encouraging / supportive message or a word of wisdom based on the goal received by the goal setting unit. The notification unit notifies the user of the message generated by the message generating unit at a set time. Effect of the Invention

[0007] The system according to the embodiment can continuously maintain the motivation of the user toward the goal that the user has set. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) In the motivation maintenance system according to the embodiment of the present invention, an AI generates encouraging and supportive messages and quotes according to the goals set by the user, and notifies the user of the generated messages at the set time like a daily calendar. This motivation maintenance system generates messages based on the goals set by the user and notifies the user at the set time, so that the user can frequently touch the goals, and can maintain motivation and manage tasks. In addition, the user can share messages that he likes with other users, and can encourage users who have similar goals. For example, the user sets his own goals. For example, he inputs specific goals such as "continue to exercise for one hour every day" or "lose 5 kg in one month". This information is input to the AI. Next, the AI ​​generates encouraging and supportive messages and quotes according to the user's goals. For example, messages such as "Let's exercise hard today!" and "Success is a step-by-step accumulation" are generated. The AI ​​generates appropriate messages according to the user's goals and progress. The generated messages are notified at the time set by the user. For example, it is possible to set it so that the message "Let's do our best today!" is notified at 7:00 a.m. every morning. This allows the user to touch the goals every day and maintain motivation. Furthermore, users can share messages they like with other users. For example, users with similar goals can encourage each other through social media or messaging apps. This allows users to support each other and encourage each other to achieve their goals. This service is intended to support users in the process of working toward their goals and help them maintain their motivation and manage their tasks. The messages generated by the AI ​​are customized according to the user's goals and progress, so that optimal support can be provided to each individual user. This allows the motivation maintenance system to generate messages based on the user's goals and notify them at the set time, thereby maintaining the user's motivation and supporting task management.

[0029] The motivation maintenance system according to the embodiment includes a goal setting unit, a message generating unit, and a notification unit. The goal setting unit accepts a goal input from the user. The goal input from the user includes, but is not limited to, a short-term goal, a long-term goal, and a numerical goal. For example, the goal setting unit accepts a user's input of a goal such as "continue to exercise for one hour every day." The goal setting unit also accepts a user's input of a goal such as "lose 5 kg in one month." The message generating unit uses a generation AI to generate an encouraging / supportive message or a quote based on the goal accepted by the goal setting unit. The generated messages include, but are not limited to, messages such as "Let's exercise hard today too!" and "Success is a step-by-step accumulation." For example, the generation AI uses a text generation AI (for example, LLM) to generate a message that matches the user's goal. The message generating unit can also generate a message taking into account the user's progress and past data. For example, the generation AI generates an appropriate message based on the user's past goal achievement status and progress data. The notification unit notifies the user of the message generated by the message generation unit at a set time. The timing of the notification includes, for example, a time specified by the user or a time recommended by the system, but is not limited to such examples. For example, the notification unit can be set to notify the message "Let's do our best today too!" at 7:00 a.m. every morning. The notification unit can also estimate the user's emotions and adjust the timing of the notification based on the estimated user's emotions. For example, the notification unit can notify the user immediately when the user is relaxed, and can postpone the notification when the user is busy. Thereby, the motivation maintenance system according to the embodiment can maintain the user's motivation and support task management by generating a message based on the user's goal and notifying the user at a set time. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the timing of the notification using an AI model that inputs the generated message into an AI model and outputs the optimal notification timing.

[0030] The goal setting unit accepts goal input from the user. The goal input from the user includes, but is not limited to, short-term goals, long-term goals, and numerical goals, for example. For example, the goal setting unit accepts the user's input of a goal such as "continue to exercise for one hour every day." The goal setting unit also accepts the user's input of a goal such as "lose 5 kg in one month." The goal setting unit provides an interface for the user to input a goal, allowing the user to easily set a goal. For example, the goal setting unit provides a form or template for the user to input a goal through a smartphone app or web application. This allows the user to intuitively set a goal. Furthermore, the goal setting unit saves the goal set by the user and allows the user to refer to it later. For example, the goal setting unit displays a list of goals set by the user in the past, allowing the user to check the progress. The goal setting unit also has a function of recording whether the user has achieved the goal and providing feedback on the achievement status. This allows the user to grasp his / her progress and maintain motivation. Furthermore, the goal setting unit can provide specific advice and guidelines when the user sets a goal. For example, when a user sets a goal of "lose 5 kg in one month," the system can provide appropriate diet and exercise advice and show specific steps to achieve the goal, allowing the user to create a specific action plan to achieve the goal.

[0031] The message generation unit uses the generation AI to generate encouraging and supportive messages and quotes based on the goals accepted by the goal setting unit. Examples of messages that are generated include, but are not limited to, "Let's exercise hard today!" and "Success is a step-by-step process." For example, the generation AI uses a text generation AI (e.g., LLM) to generate a message that matches the user's goal. The message generation unit can also generate a message taking into account the user's progress and past data. For example, the generation AI generates an appropriate message based on the user's past goal achievement status and progress data. The generation AI selects encouraging words and quotes according to the user's goal and progress status, and provides the most effective message for the user. Furthermore, the message generation unit can generate a message taking into account the user's emotions and motivation state. For example, the generation AI generates a particularly encouraging message when the user is working hard to achieve a goal but is feeling less motivated. On the other hand, when the user is smoothly achieving his or her goal, a message that encourages further challenges can be generated. This allows the message generation unit to provide the optimal message according to the user's situation and maintain motivation. Furthermore, the message generator can customize messages based on the user's preferences and interests. For example, if a user likes quotes from a particular celebrity, a message can be generated that includes many quotes from that celebrity. This allows the user to receive a message that is tailored to them and to maintain their motivation more effectively.

[0032] The notification unit notifies the user of the message generated by the message generation unit at a set time. The timing of the notification includes, for example, a time specified by the user or a time recommended by the system, but is not limited to such examples. For example, the notification unit can be set to notify the message "Let's do our best today too!" at 7:00 a.m. every morning. The notification unit can also estimate the user's emotions and adjust the timing of the notification based on the estimated user's emotions. For example, the notification unit can notify the user immediately when the user is relaxed, and can postpone the notification when the user is busy. The notification unit notifies the user of the message through the user's smartphone or wearable device. For example, the message can be delivered to the user by using a push notification of the smartphone or a vibration notification of the wearable device. This allows the user to receive important messages without missing them. Furthermore, the notification unit can learn the user's schedule and daily activity patterns and automatically adjust the optimal notification timing. For example, if the user has a habit of exercising at a specific time every day, the user can be motivated by notifying the user of a message at that time. The notification unit also has a function of collecting user feedback and evaluating the effectiveness of the notification. For example, the system can record changes in a user's behavior and emotions after receiving a notification and use the data to improve the timing and content of notifications. This allows the notification unit to find the most effective notification method for the user and maximize the effectiveness of the motivation maintenance system.

[0033] The motivation maintenance system includes a progress management unit that manages the progress of the user. The progress management unit manages the progress of the user. The progress includes, for example, the completion status and progress of a task, but is not limited to such examples. For example, the progress management unit records and manages the progress for a goal set by the user. The progress management unit can also visualize the progress of the user so that the user can check his or her own progress. For example, the progress management unit displays the progress of the user as a graph or chart, and provides it in a visually easy-to-understand format. In this way, the progress management unit can manage the progress of the user and support the achievement of the goal. A part or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the progress data of the user into an AI model and perform progress management using the AI ​​model that manages the progress.

[0034] The motivation maintenance system includes a sharing unit that shares the generated message with other users. The sharing unit shares the generated message with other users. The sharing method includes, for example, sharing on an SNS or sharing within a specific group, but is not limited to such examples. For example, the sharing unit can share the generated message through an SNS. The sharing unit can also share the message through a message app. For example, the sharing unit can share the generated message within a specific group through a message app. In this way, the sharing unit can share the generated message with other users and encourage each other. A part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input the generated message into an AI model and select a sharing method using an AI model that outputs an optimal sharing method.

[0035] The motivation maintenance system includes a feedback unit that provides feedback when a goal is achieved. The feedback unit provides feedback when the goal is achieved. The content of the feedback includes, for example, a text message and an evaluation score, but is not limited to such examples. For example, the feedback unit provides a message saying "Congratulations!" when the user achieves the goal. The feedback unit can also provide an evaluation score according to the degree of goal achievement of the user. For example, when the user achieves a goal set by the user, the feedback unit gives a score according to the degree of achievement. In this way, the feedback unit can provide feedback when the goal is achieved and maintain the motivation of the user. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's goal achievement data into an AI model and adjust the content of the feedback using an AI model that outputs optimal feedback.

[0036] The message generating unit can generate a message based on the user's progress and past data. The message generating unit generates a message based on, for example, the user's progress and past data. The past data includes, for example, past goal achievement status and past message content, but is not limited to such examples. For example, the message generating unit analyzes the user's past goal achievement status and generates an appropriate message. The message generating unit can also generate a new message taking into account the user's past message content. For example, the message generating unit generates a new message based on a pattern of messages that the user liked in the past. This allows the message generating unit to provide a more appropriate message based on the user's progress and past data.

[0037] The sharing unit can share the message through an SNS or a messaging app. The sharing unit, for example, shares the message through an SNS or a messaging app. Specific SNSs and messaging apps include, for example, Facebook (registered trademark), X (formerly Twitter (registered trademark)), LINE (registered trademark), and the like, but are not limited to such examples. For example, the sharing unit can share the generated message through Facebook. The sharing unit can also share the generated message through X (formerly Twitter). Furthermore, the sharing unit can share the generated message through LINE. In this way, the sharing unit can share the message through an SNS or a messaging app, and users can encourage each other.

[0038] The goal setting unit can analyze the user's past goal setting history and propose an appropriate goal setting method. The goal setting unit can, for example, analyze the user's past goal setting history and propose an optimal goal setting method. The past goal setting history includes, for example, the type of past goals and achievement status, but is not limited to such examples. For example, the goal setting unit preferentially proposes a goal setting method that the user has been successful in the past. In addition, the goal setting unit can also suggest that the user avoid a goal setting method that has failed in the past. Furthermore, the goal setting unit can also propose a realistic goal setting based on the user's past goal achievement rate. Thereby, the goal setting unit can analyze the user's past goal setting history and set a more realistic goal. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the user's past goal setting data into the generation AI and have the generation AI execute the proposed optimal goal setting method.

[0039] The goal setting unit can filter goals based on the user's current living situation and areas of interest when setting goals. The goal setting unit, for example, filters goals based on the user's current living situation and areas of interest when setting goals. Living situations include, for example, work situations and home situations, but are not limited to such examples. Areas of interest include, for example, hobbies and topics of interest, but are not limited to such examples. For example, the goal setting unit proposes goals that can be achieved in a short period of time if the user is busy. In addition, the goal setting unit can also propose health-related goals if the user is interested in health. Furthermore, the goal setting unit can also propose learning-related goals if the user is interested in learning. This allows the goal setting unit to filter goals based on the user's living situation and areas of interest and set more appropriate goals. Part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to execute filtering of goals.

[0040] The goal setting unit can prioritize a highly relevant goal in consideration of the geographical location information of the user when setting a goal. The goal setting unit, for example, prioritizes a highly relevant goal in consideration of the geographical location information of the user when setting a goal. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the goal setting unit proposes a goal that can be achieved in that area. In addition, when the user is traveling, the goal setting unit can also propose a goal that can be achieved at the travel destination. Furthermore, when the user is at home, the goal setting unit can also propose a goal that can be achieved at home. This allows the goal setting unit to set a more realistic goal in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute the processing, which prioritizes setting a highly relevant goal.

[0041] The goal setting unit can analyze the social media activity of the user at the time of goal setting and suggest related goals. For example, the goal setting unit can analyze the social media activity of the user at the time of goal setting and suggest related goals. Social media activities include, for example, the content of posts and the number of likes, but are not limited to such examples. For example, the goal setting unit can suggest goals based on interests and concerns shared by the user on social media. In addition, the goal setting unit can also suggest goals based on activities of accounts followed by the user. Furthermore, the goal setting unit can also suggest goals based on trends of online communities in which the user participates. This allows the goal setting unit to analyze the social media activity of the user and set more appropriate goals. Part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the social media data of the user to the generation AI and cause the generation AI to execute the processing, which proposes related goals.

[0042] The message generating unit can adjust the level of detail of the message based on the importance of the goal when generating the message. The message generating unit adjusts the level of detail of the message based on the importance of the goal when generating the message, for example. The level of detail includes, for example, the amount of information and specificity, but is not limited to such examples. For example, the message generating unit generates a detailed message for a goal with a high level of importance. In addition, the message generating unit can also generate a concise message for a goal with a low level of importance. Furthermore, the message generating unit can adjust the level of detail according to the progress of the goal. Thereby, the message generating unit can adjust the level of detail of the message based on the importance of the goal and provide a more appropriate message. A part or all of the above-mentioned processing in the message generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the message generating unit inputs the importance data of the goal to the generating AI, and causes the generating AI to execute the process of adjusting the level of detail of the message.

[0043] The message generating unit can apply different message generation algorithms depending on the category of the goal when generating a message. The message generating unit applies different message generation algorithms depending on the category of the goal when generating a message, for example. The message generation algorithm includes, for example, rule-based and machine learning-based, but is not limited to such examples. For example, the message generating unit generates quotes and encouraging words related to health for health-related goals. In addition, the message generating unit can also generate quotes and encouraging words related to learning for learning-related goals. Furthermore, the message generating unit can generate quotes and encouraging words related to work for work-related goals. Thereby, the message generating unit can apply different message generation algorithms depending on the category of the goal and provide a more appropriate message. Part or all of the above-mentioned processing in the message generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the message generating unit can input goal category data to the generating AI and cause the generating AI to apply different message generation algorithms.

[0044] The message generating unit can determine the priority of the message based on the time when the goal was set when generating the message. The message generating unit, for example, determines the priority of the message based on the time when the goal was set when generating the message. The setting time includes, for example, the beginning of the month and the weekend, but is not limited to such examples. For example, the message generating unit generates a reminder message preferentially when time has passed since the goal was set. In addition, the message generating unit can also generate an encouraging message preferentially immediately after the goal was set. Furthermore, the message generating unit can also generate a message that will be the final push preferentially when the goal is close to being achieved. Thereby, the message generating unit can determine the priority of the message based on the time when the goal was set, and provide a more appropriate message. Part or all of the above-mentioned processing in the message generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the message generating unit can input the goal setting time data to the generating AI and have the generating AI execute the message priority.

[0045] The message generating unit can adjust the order of messages based on the relevance of the goal when generating messages. The message generating unit, for example, adjusts the order of messages based on the relevance of the goal when generating messages. The relevance includes, for example, a matching theme and a common purpose, but is not limited to such examples. For example, the message generating unit displays an important message related to the goal first. The message generating unit can also display a supplementary message related to the goal later. Furthermore, the message generating unit can adjust the order of messages according to the progress of the goal. Thereby, the message generating unit can adjust the order of messages based on the relevance of the goal and provide a more appropriate message. A part or all of the above-mentioned processing in the message generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the message generating unit can input the relevance data of the goal to the generating AI and cause the generating AI to execute the adjusting of the order of the messages.

[0046] The notification unit can select an appropriate notification method by referring to the user's past notification history at the time of notification. The notification unit, for example, selects the optimal notification method by referring to the user's past notification history at the time of notification. The notification method includes, for example, push notification and email notification, but is not limited to such examples. For example, the notification unit preferentially selects a notification method that the user has previously preferred. In addition, the notification unit can also avoid a notification method that the user has previously ignored. Furthermore, the notification unit can also select an optimal notification timing from the user's past notification history. Thereby, the notification unit can select a more appropriate notification method by referring to the user's past notification history. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's past notification data to the generation AI and have the generation AI select the optimal notification method.

[0047] The notification unit can customize the content of the notification based on the user's current activity status at the time of notification. The notification unit, for example, customizes the content of the notification based on the user's current activity status at the time of notification. The activity status includes, for example, at work and at rest, but is not limited to such examples. For example, when the user is exercising, the notification unit notifies the user of a message related to exercise. In addition, when the user is at work, the notification unit can also notify the user of a message related to work. Furthermore, when the user is at rest, the notification unit can also notify the user of a message that allows the user to relax. Thereby, the notification unit can customize the content of the notification based on the user's current activity status and provide a more appropriate notification. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's activity status data to the generation AI and cause the generation AI to execute the processing to customize the content of the notification.

[0048] The notification unit can select an appropriate notification method in consideration of the geographical location information of the user at the time of notification. The notification unit, for example, selects an optimal notification method in consideration of the geographical location information of the user at the time of notification. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the notification unit performs a notification related to the area. In addition, when the user is traveling, the notification unit can also perform a notification related to the travel destination. Furthermore, when the user is at home, the notification unit can also perform a notification related to the home. This allows the notification unit to select a more appropriate notification method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the geographical location data of the user to the generation AI and cause the generation AI to select the optimal notification method.

[0049] The notification unit can adjust the content of the notification by analyzing the social media activity of the user at the time of notification. For example, the notification unit can adjust the content of the notification by analyzing the social media activity of the user at the time of notification. The social media activity includes, for example, the content of the post and the number of likes, but is not limited to such examples. For example, the notification unit provides notification based on the interests and concerns shared by the user on social media. The notification unit can also provide notification based on the activities of the accounts the user follows. Furthermore, the notification unit can provide notification based on the trends of the online community in which the user participates. In this way, the notification unit can analyze the social media activity of the user and provide more appropriate notification content. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit inputs the social media data of the user into the generation AI and causes the generation AI to execute the processing, which adjusts the content of the notification.

[0050] The progress management unit can select an optimal management method by referring to the user's past progress data during progress management. The progress management unit, for example, selects an optimal management method by referring to the user's past progress data during progress management. The past progress data includes, for example, past task completion status and progress degree, but is not limited to such examples. For example, the progress management unit preferentially selects a progress management method that the user has been successful in the past. In addition, the progress management unit can avoid a progress management method that the user has failed in the past. Furthermore, the progress management unit can select an optimal management timing from the user's past progress data. Thereby, the progress management unit can select a more appropriate progress management method by referring to the user's past progress data. A part or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the user's past progress data to the generation AI and cause the generation AI to execute the process of selecting the optimal management method.

[0051] The progress management unit can customize the progress management means based on the user's current living situation during progress management. The progress management unit, for example, customizes the progress management means based on the user's current living situation during progress management. The living situation includes, for example, a work situation and a home situation, but is not limited to such examples. For example, the progress management unit provides a simple progress management means when the user is busy. In addition, the progress management unit can also provide a detailed progress management means when the user is relaxed. Furthermore, the progress management unit can also provide a progress management means that allows the user to relax when the user is stressed. This allows the progress management unit to customize the progress management means based on the user's current living situation and perform more appropriate progress management. Part or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the user's living situation data into the generation AI and have the generation AI execute the process to customize the progress management means.

[0052] The progress management unit can select an appropriate management method in consideration of the geographical location information of the user during progress management. The progress management unit, for example, selects an optimal management method in consideration of the geographical location information of the user during progress management. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the progress management unit performs progress management related to the area. In addition, when the user is traveling, the progress management unit can also perform progress management related to the travel destination. Furthermore, when the user is at home, the progress management unit can also perform progress management related to the home. This allows the progress management unit to select a more appropriate progress management method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the geographical location data of the user to the generation AI and cause the generation AI to select the optimal management method.

[0053] The progress management unit can analyze the social media activity of the user and propose a means of progress management during progress management. The progress management unit, for example, analyzes the social media activity of the user and proposes a means of progress management during progress management. The social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. For example, the progress management unit proposes a management means based on the progress shared by the user on social media. The progress management unit can also propose a management means based on the activity of an account followed by the user. Furthermore, the progress management unit can also propose a management means based on the trend of an online community in which the user participates. In this way, the progress management unit can analyze the social media activity of the user and provide a more appropriate progress management means. A part or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit inputs the social media data of the user into a generation AI and causes the generation AI to execute the proposed means of progress management.

[0054] The sharing unit can select the optimal sharing method by referring to the user's past sharing history at the time of sharing. For example, the sharing unit selects the optimal sharing method by referring to the user's past sharing history at the time of sharing. The sharing method includes, for example, sharing on an SNS or sharing within a specific group, but is not limited to such examples. For example, the sharing unit preferentially selects a sharing method that the user has preferred in the past. In addition, the sharing unit can avoid a sharing method that the user has ignored in the past. Furthermore, the sharing unit can select the optimal sharing timing from the user's past sharing history. Thereby, the sharing unit can select a more appropriate sharing method by referring to the user's past sharing history. A part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input the user's past sharing data to the generation AI and cause the generation AI to select the optimal sharing method.

[0055] The sharing unit can select an appropriate sharing method in consideration of the geographical location information of the user at the time of sharing. For example, the sharing unit selects an optimal sharing method in consideration of the geographical location information of the user at the time of sharing. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the sharing unit performs sharing related to the area. In addition, when the user is traveling, the sharing unit can also perform sharing related to the travel destination. Furthermore, when the user is at home, the sharing unit can also perform sharing related to the home. This allows the sharing unit to select a more appropriate sharing method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input the geographical location data of the user to the generation AI and cause the generation AI to select the optimal sharing method.

[0056] The feedback unit can select the optimal feedback method by referring to the user's past feedback history at the time of feedback. The feedback unit, for example, selects the optimal feedback method by referring to the user's past feedback history at the time of feedback. The feedback method includes, for example, a text message and an evaluation score, but is not limited to such examples. For example, the feedback unit preferentially selects a feedback method that the user has preferred in the past. The feedback unit can also avoid a feedback method that the user has ignored in the past. Furthermore, the feedback unit can select the optimal feedback timing from the user's past feedback history. Thereby, the feedback unit can select a more appropriate feedback method by referring to the user's past feedback history. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data to the generation AI and cause the generation AI to select the optimal feedback method.

[0057] The feedback unit can select an appropriate feedback method in consideration of the geographical location information of the user at the time of feedback. The feedback unit, for example, selects an optimal feedback method in consideration of the geographical location information of the user at the time of feedback. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the feedback unit performs feedback related to the area. In addition, when the user is traveling, the feedback unit can also perform feedback related to the travel destination. Furthermore, when the user is at home, the feedback unit can also perform feedback related to the home. Thereby, the feedback unit can select a more appropriate feedback method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the geographical location data of the user to the generation AI and cause the generation AI to select the optimal feedback method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The motivation maintenance system may also include a visual feedback unit for visually feeding back the progress of the user toward achieving the goal. The visual feedback unit may, for example, display the user's progress as a graph or chart, and provide the progress in a visually easy-to-understand format. Furthermore, the visual feedback unit may emphasize the feedback using colors or animations according to the user's progress. For example, the color of the graph may change to green as the user approaches achieving the goal, or a red warning may be displayed if the progress is delayed. This allows the user to intuitively grasp his or her own progress and maintain motivation.

[0060] The motivation maintenance system may also include a competition section for allowing a user to compete with other users over progress toward achieving a goal. The competition section may, for example, provide a function for matching users with the same goal and comparing their progress. Furthermore, the competition section may provide a ranking function for increasing motivation by allowing a user to compete with other users. For example, each time a user achieves a goal, points are acquired and reflected in the ranking. This allows a user to make an effort toward achieving a goal while competing with other users.

[0061] The motivation maintenance system may also include a journal section for recording the user's progress toward achieving a goal. The journal section provides, for example, a function for the user to record daily progress and impressions. Furthermore, the journal section may provide a reminder function for the user to maintain motivation by looking back on past records. For example, by being reminded of the contents recorded a week ago, the user can reconfirm past efforts and increase motivation. This allows the user to maintain motivation toward achieving a goal by recording and looking back on their own progress.

[0062] The motivation maintenance system may also include a resource providing unit for supporting the progress of the user toward achieving the goal. The resource providing unit may, for example, provide a function for providing information and tools related to the user's goal. Furthermore, the resource providing unit may provide learning content for the user to acquire skills and knowledge necessary to achieve the goal. For example, if the user sets a goal of "losing 5 kg in one month," the resource providing unit may provide information about dieting and exercise videos. This allows the user to utilize resources toward achieving the goal and effectively achieve the goal.

[0063] The motivation maintenance system may also include an alert unit for supporting the user's progress toward achieving the goal. For example, the alert unit provides a function of issuing a warning when the user's progress toward achieving the goal is delayed. Furthermore, the alert unit may provide a reminder function for the user to maintain the progress toward achieving the goal. For example, if the user sets a goal of "continuing to exercise for one hour every day," the alert unit may send a reminder if the user forgets to exercise. This makes it easier for the user to maintain the progress toward achieving the goal.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The goal setting unit accepts goal input from the user. The goal input from the user includes short-term goals, long-term goals, numerical goals, etc. For example, the unit accepts the user input of goals such as "continue to exercise for one hour every day" or "lose 5 kg in one month." Step 2: The message generation unit uses the generation AI to generate encouraging and supportive messages and quotes based on the goals accepted by the goal setting unit. Generated messages include "Let's exercise hard today!" and "Success is a one-step-at-a-time process." The generation AI uses a text generation AI (e.g., LLM) to generate messages tailored to the user's goals. It can also generate messages taking into account the user's progress and past data. Step 3: The notification unit notifies the user of the message generated by the message generation unit at the set time. The timing of the notification includes a time specified by the user and a time recommended by the system. For example, the notification unit can be set to notify the user of the message "Let's do our best today!" at 7:00 a.m. every morning. The notification unit can also estimate the user's emotions and adjust the timing of the notification based on the estimated user's emotions. For example, the notification can be made immediately if the user is relaxed, and can be postponed if the user is busy.

[0066] (Example 2) In the motivation maintenance system according to the embodiment of the present invention, an AI generates encouraging and supportive messages and quotes according to the goals set by the user, and notifies the user of the generated messages at the set time like a daily calendar. This motivation maintenance system generates messages based on the goals set by the user and notifies the user at the set time, so that the user can frequently touch the goals, and can maintain motivation and manage tasks. In addition, the user can share messages that he likes with other users, and can encourage users who have similar goals. For example, the user sets his own goals. For example, he inputs specific goals such as "continue to exercise for one hour every day" or "lose 5 kg in one month". This information is input to the AI. Next, the AI ​​generates encouraging and supportive messages and quotes according to the user's goals. For example, messages such as "Let's exercise hard today!" and "Success is a step-by-step accumulation" are generated. The AI ​​generates appropriate messages according to the user's goals and progress. The generated messages are notified at the time set by the user. For example, it is possible to set it so that the message "Let's do our best today!" is notified at 7:00 a.m. every morning. This allows the user to touch the goals every day and maintain motivation. Furthermore, users can share messages they like with other users. For example, users with similar goals can encourage each other through social media or messaging apps. This allows users to support each other and encourage each other to achieve their goals. This service is intended to support users in the process of working toward their goals and help them maintain their motivation and manage their tasks. The messages generated by the AI ​​are customized according to the user's goals and progress, so that optimal support can be provided to each individual user. This allows the motivation maintenance system to generate messages based on the user's goals and notify them at the set time, thereby maintaining the user's motivation and supporting task management.

[0067] The motivation maintenance system according to the embodiment includes a goal setting unit, a message generating unit, and a notification unit. The goal setting unit accepts a goal input from the user. The goal input from the user includes, but is not limited to, a short-term goal, a long-term goal, and a numerical goal. For example, the goal setting unit accepts a user's input of a goal such as "continue to exercise for one hour every day." The goal setting unit also accepts a user's input of a goal such as "lose 5 kg in one month." The message generating unit uses a generation AI to generate an encouraging / supportive message or a quote based on the goal accepted by the goal setting unit. The generated messages include, but are not limited to, messages such as "Let's exercise hard today too!" and "Success is a step-by-step accumulation." For example, the generation AI uses a text generation AI (for example, LLM) to generate a message that matches the user's goal. The message generating unit can also generate a message taking into account the user's progress and past data. For example, the generation AI generates an appropriate message based on the user's past goal achievement status and progress data. The notification unit notifies the user of the message generated by the message generation unit at a set time. The timing of the notification includes, for example, a time specified by the user or a time recommended by the system, but is not limited to such examples. For example, the notification unit can be set to notify the message "Let's do our best today too!" at 7:00 a.m. every morning. The notification unit can also estimate the user's emotions and adjust the timing of the notification based on the estimated user's emotions. For example, the notification unit can notify the user immediately when the user is relaxed, and can postpone the notification when the user is busy. Thereby, the motivation maintenance system according to the embodiment can maintain the user's motivation and support task management by generating a message based on the user's goal and notifying the user at a set time. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the timing of the notification using an AI model that inputs the generated message into an AI model and outputs the optimal notification timing.

[0068] The goal setting unit accepts goal input from the user. The goal input from the user includes, but is not limited to, short-term goals, long-term goals, and numerical goals, for example. For example, the goal setting unit accepts the user's input of a goal such as "continue to exercise for one hour every day." The goal setting unit also accepts the user's input of a goal such as "lose 5 kg in one month." The goal setting unit provides an interface for the user to input a goal, allowing the user to easily set a goal. For example, the goal setting unit provides a form or template for the user to input a goal through a smartphone app or a web application. This allows the user to intuitively set a goal. Furthermore, the goal setting unit saves the goal set by the user and allows the user to refer to it later. For example, the goal setting unit displays a list of goals set by the user in the past, allowing the user to check the progress. The goal setting unit also has a function of recording whether the user has achieved the goal and providing feedback on the achievement status. This allows the user to grasp his / her progress and maintain motivation. Furthermore, the goal setting unit can provide specific advice and guidelines when the user sets a goal. For example, when a user sets a goal of "losing 5 kg in one month," the system can provide appropriate diet and exercise advice and show specific steps to achieve the goal, allowing the user to create a specific action plan to achieve the goal.

[0069] The message generation unit uses the generation AI to generate encouraging and supportive messages and quotes based on the goals accepted by the goal setting unit. Examples of messages that are generated include, but are not limited to, "Let's exercise hard today!" and "Success is a step-by-step process." For example, the generation AI uses a text generation AI (e.g., LLM) to generate a message that matches the user's goal. The message generation unit can also generate a message taking into account the user's progress and past data. For example, the generation AI generates an appropriate message based on the user's past goal achievement status and progress data. The generation AI selects encouraging words and quotes according to the user's goal and progress status, and provides the most effective message for the user. Furthermore, the message generation unit can generate a message taking into account the user's emotions and motivation state. For example, the generation AI generates a particularly encouraging message when the user is working hard to achieve a goal but is feeling less motivated. On the other hand, when the user is smoothly achieving his or her goal, a message that encourages further challenges can be generated. This allows the message generation unit to provide the optimal message according to the user's situation and maintain motivation. Furthermore, the message generator can customize messages based on the user's preferences and interests. For example, if a user likes quotes from a particular celebrity, a message can be generated that includes many quotes from that celebrity. This allows the user to receive a message that is tailored to them and to maintain their motivation more effectively.

[0070] The notification unit notifies the user of the message generated by the message generation unit at a set time. The timing of the notification includes, for example, a time specified by the user or a time recommended by the system, but is not limited to such examples. For example, the notification unit can be set to notify the message "Let's do our best today too!" at 7:00 a.m. every morning. The notification unit can also estimate the user's emotions and adjust the timing of the notification based on the estimated user's emotions. For example, the notification unit can notify the user immediately when the user is relaxed, and can postpone the notification when the user is busy. The notification unit notifies the user of the message through the user's smartphone or wearable device. For example, the message can be delivered to the user by using a push notification of the smartphone or a vibration notification of the wearable device. This allows the user to receive important messages without missing them. Furthermore, the notification unit can learn the user's schedule and daily activity patterns and automatically adjust the optimal notification timing. For example, if the user has a habit of exercising at a specific time every day, the user can be motivated by notifying the user of a message at that time. The notification unit also has a function of collecting user feedback and evaluating the effectiveness of the notification. For example, the system can record changes in a user's behavior and emotions after receiving a notification and use the data to improve the timing and content of notifications. This allows the notification unit to find the most effective notification method for the user and maximize the effectiveness of the motivation maintenance system.

[0071] The motivation maintenance system includes a progress management unit that manages the progress of the user. The progress management unit manages the progress of the user. The progress includes, for example, the completion status and progress of a task, but is not limited to such examples. For example, the progress management unit records and manages the progress for a goal set by the user. The progress management unit can also visualize the progress of the user so that the user can check his or her own progress. For example, the progress management unit displays the progress of the user as a graph or chart, and provides it in a visually easy-to-understand format. In this way, the progress management unit can manage the progress of the user and support the achievement of the goal. A part or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the progress data of the user into an AI model and perform progress management using the AI ​​model that manages the progress.

[0072] The motivation maintenance system includes a sharing unit that shares the generated message with other users. The sharing unit shares the generated message with other users. The sharing method includes, for example, sharing on an SNS or sharing within a specific group, but is not limited to such examples. For example, the sharing unit can share the generated message through an SNS. The sharing unit can also share the message through a message app. For example, the sharing unit can share the generated message within a specific group through a message app. In this way, the sharing unit can share the generated message with other users and encourage each other. A part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input the generated message into an AI model and select a sharing method using an AI model that outputs an optimal sharing method.

[0073] The motivation maintenance system includes a feedback unit that provides feedback when a goal is achieved. The feedback unit provides feedback when the goal is achieved. The content of the feedback includes, for example, a text message and an evaluation score, but is not limited to such examples. For example, the feedback unit provides a message saying "Congratulations!" when the user achieves the goal. The feedback unit can also provide an evaluation score according to the degree of goal achievement of the user. For example, when the user achieves a goal set by the user, the feedback unit gives a score according to the degree of achievement. In this way, the feedback unit can provide feedback when the goal is achieved and maintain the motivation of the user. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's goal achievement data into an AI model and adjust the content of the feedback using an AI model that outputs optimal feedback.

[0074] The message generating unit can generate a message based on the user's progress and past data. The message generating unit generates a message based on, for example, the user's progress and past data. The past data includes, for example, past goal achievement status and past message content, but is not limited to such examples. For example, the message generating unit analyzes the user's past goal achievement status and generates an appropriate message. The message generating unit can also generate a new message taking into account the user's past message content. For example, the message generating unit generates a new message based on a pattern of messages that the user liked in the past. This allows the message generating unit to provide a more appropriate message based on the user's progress and past data.

[0075] The sharing unit can share the message through an SNS or a messaging app. The sharing unit, for example, shares the message through an SNS or a messaging app. Specific SNSs and messaging apps include, but are not limited to, Facebook, X (formerly Twitter), LINE, and the like. For example, the sharing unit can share the generated message through Facebook. The sharing unit can also share the generated message through X (formerly Twitter). Furthermore, the sharing unit can share the generated message through LINE. In this way, the sharing unit can share the message through an SNS or a messaging app, and users can encourage each other.

[0076] The goal setting unit can estimate the user's emotion and adjust the timing of goal setting based on the estimated user's emotion. The goal setting unit can estimate the user's emotion and adjust the timing of goal setting based on the estimated user's emotion, for example. The emotion estimation includes, but is not limited to, facial expression recognition and text analysis. For example, when the user is stressed, the goal setting unit can prompt the user to set a goal during a time period when the user can relax. In addition, when the user is highly motivated, the goal setting unit can prompt the user to set a goal immediately. Furthermore, when the user is tired, the goal setting unit can prompt the user to set a goal after resting. Thereby, the goal setting unit can adjust the timing of goal setting based on the user's emotion and perform more effective goal setting. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the goal setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the goal setting unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the timing of goal setting.

[0077] The goal setting unit can analyze the user's past goal setting history and propose an appropriate goal setting method. The goal setting unit can, for example, analyze the user's past goal setting history and propose an optimal goal setting method. The past goal setting history includes, for example, the type of past goals and achievement status, but is not limited to such examples. For example, the goal setting unit preferentially proposes a goal setting method that the user has been successful in the past. In addition, the goal setting unit can also suggest that the user avoid a goal setting method that has failed in the past. Furthermore, the goal setting unit can also propose a realistic goal setting based on the user's past goal achievement rate. Thereby, the goal setting unit can analyze the user's past goal setting history and set a more realistic goal. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the user's past goal setting data into the generation AI and have the generation AI execute the proposed optimal goal setting method.

[0078] The goal setting unit can filter goals based on the user's current living situation and areas of interest when setting goals. The goal setting unit, for example, filters goals based on the user's current living situation and areas of interest when setting goals. Living situations include, for example, work situations and home situations, but are not limited to such examples. Areas of interest include, for example, hobbies and topics of interest, but are not limited to such examples. For example, the goal setting unit proposes goals that can be achieved in a short period of time if the user is busy. In addition, the goal setting unit can also propose health-related goals if the user is interested in health. Furthermore, the goal setting unit can also propose learning-related goals if the user is interested in learning. This allows the goal setting unit to filter goals based on the user's living situation and areas of interest and set more appropriate goals. Part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to execute filtering of goals.

[0079] The goal setting unit can estimate the user's emotion and determine the priority order of goals based on the estimated user's emotion. The goal setting unit can estimate the user's emotion and determine the priority order of goals based on the estimated user's emotion, for example. The emotion estimation includes, but is not limited to, facial expression recognition and text analysis, for example. For example, the goal setting unit prioritizes a goal with a high level of difficulty when the user is highly motivated. In addition, the goal setting unit can also prioritize an easy goal when the user is tired. Furthermore, the goal setting unit can also prioritize a goal that allows the user to relax when the user is stressed. This allows the goal setting unit to determine the priority order of goals based on the user's emotion and achieve the goal more effectively. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the goal setting unit can input user emotion data to the generation AI and have the generation AI determine the priority of goals.

[0080] The goal setting unit can prioritize a highly relevant goal in consideration of the geographical location information of the user when setting a goal. The goal setting unit, for example, prioritizes a highly relevant goal in consideration of the geographical location information of the user when setting a goal. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the goal setting unit proposes a goal that can be achieved in that area. In addition, when the user is traveling, the goal setting unit can also propose a goal that can be achieved at the travel destination. Furthermore, when the user is at home, the goal setting unit can also propose a goal that can be achieved at home. This allows the goal setting unit to set a more realistic goal in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute the processing, which prioritizes setting a highly relevant goal.

[0081] The goal setting unit can analyze the social media activity of the user at the time of goal setting and suggest related goals. For example, the goal setting unit can analyze the social media activity of the user at the time of goal setting and suggest related goals. Social media activities include, for example, the content of posts and the number of likes, but are not limited to such examples. For example, the goal setting unit can suggest goals based on interests and concerns shared by the user on social media. In addition, the goal setting unit can also suggest goals based on activities of accounts followed by the user. Furthermore, the goal setting unit can also suggest goals based on trends of online communities in which the user participates. This allows the goal setting unit to analyze the social media activity of the user and set more appropriate goals. Part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the social media data of the user to the generation AI and cause the generation AI to execute the processing, which proposes related goals.

[0082] The message generation unit can estimate the user's emotion and adjust the expression method of the message based on the estimated user's emotion. The message generation unit, for example, estimates the user's emotion and adjusts the expression method of the message based on the estimated user's emotion. The emotion estimation includes, for example, facial expression recognition and text analysis, but is not limited to such examples. For example, the message generation unit generates a message containing many encouraging words when the user is depressed. In addition, the message generation unit can also generate a challenging message when the user is highly motivated. Furthermore, the message generation unit can also generate a gentle message when the user is relaxed. Thereby, the message generation unit can adjust the expression method of the message based on the user's emotion and provide a more effective message. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the message generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the message generation unit can input a user's emotional data into a generation AI and have the generation AI adjust the way the message is expressed.

[0083] The message generating unit can adjust the level of detail of the message based on the importance of the goal when generating the message. The message generating unit adjusts the level of detail of the message based on the importance of the goal when generating the message, for example. The level of detail includes, for example, the amount of information and specificity, but is not limited to such examples. For example, the message generating unit generates a detailed message for a goal with a high level of importance. In addition, the message generating unit can also generate a concise message for a goal with a low level of importance. Furthermore, the message generating unit can adjust the level of detail according to the progress of the goal. Thereby, the message generating unit can adjust the level of detail of the message based on the importance of the goal and provide a more appropriate message. A part or all of the above-mentioned processing in the message generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the message generating unit inputs the importance data of the goal to the generating AI, and causes the generating AI to execute the process of adjusting the level of detail of the message.

[0084] The message generating unit can apply different message generation algorithms depending on the category of the goal when generating a message. The message generating unit applies different message generation algorithms depending on the category of the goal when generating a message, for example. The message generation algorithm includes, for example, rule-based and machine learning-based, but is not limited to such examples. For example, the message generating unit generates quotes and encouraging words related to health for health-related goals. In addition, the message generating unit can also generate quotes and encouraging words related to learning for learning-related goals. Furthermore, the message generating unit can generate quotes and encouraging words related to work for work-related goals. Thereby, the message generating unit can apply different message generation algorithms depending on the category of the goal and provide a more appropriate message. Part or all of the above-mentioned processing in the message generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the message generating unit can input goal category data to the generating AI and cause the generating AI to apply different message generation algorithms.

[0085] The message generating unit can estimate the user's emotion and adjust the length of the message based on the estimated user's emotion. The message generating unit, for example, estimates the user's emotion and adjusts the length of the message based on the estimated user's emotion. The emotion estimation includes, for example, facial expression recognition and text analysis, but is not limited to such examples. For example, the message generating unit generates a short and to the point message when the user is in a hurry. In addition, the message generating unit can also generate a longer message including a detailed explanation when the user is relaxed. Furthermore, the message generating unit can also generate a message with a visually stimulating effect when the user is excited. Thereby, the message generating unit can adjust the length of the message based on the user's emotion and provide a more appropriate message. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the message generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the message generation unit can input user emotion data to a generation AI and have the generation AI adjust the length of the message.

[0086] The message generating unit can determine the priority of the message based on the time when the goal was set when generating the message. The message generating unit, for example, determines the priority of the message based on the time when the goal was set when generating the message. The setting time includes, for example, the beginning of the month and the weekend, but is not limited to such examples. For example, the message generating unit generates a reminder message preferentially when time has passed since the goal was set. In addition, the message generating unit can also generate an encouraging message preferentially immediately after the goal was set. Furthermore, the message generating unit can also generate a message that will be the final push preferentially when the goal is close to being achieved. Thereby, the message generating unit can determine the priority of the message based on the time when the goal was set, and provide a more appropriate message. Part or all of the above-mentioned processing in the message generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the message generating unit can input the goal setting time data to the generating AI and have the generating AI execute the message priority.

[0087] The message generating unit can adjust the order of messages based on the relevance of the goal when generating messages. The message generating unit, for example, adjusts the order of messages based on the relevance of the goal when generating messages. The relevance includes, for example, a matching theme and a common purpose, but is not limited to such examples. For example, the message generating unit displays an important message related to the goal first. The message generating unit can also display a supplementary message related to the goal later. Furthermore, the message generating unit can adjust the order of messages according to the progress of the goal. Thereby, the message generating unit can adjust the order of messages based on the relevance of the goal and provide a more appropriate message. A part or all of the above-mentioned processing in the message generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the message generating unit can input the relevance data of the goal to the generating AI and cause the generating AI to execute the adjusting of the order of the messages.

[0088] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. The notification unit can estimate the user's emotion, for example, and adjust the timing of the notification based on the estimated user's emotion. The estimation of the emotion includes, for example, facial expression recognition and text analysis, but is not limited to such examples. For example, the notification unit immediately notifies the user when the user is relaxed. Also, the notification unit can postpone the notification when the user is busy. Furthermore, the notification unit can refrain from notifying the user when the user is stressed. Thereby, the notification unit can adjust the timing of the notification based on the user's emotion and notify at a more appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the timing of the notification.

[0089] The notification unit can select an appropriate notification method by referring to the user's past notification history at the time of notification. The notification unit, for example, selects the optimal notification method by referring to the user's past notification history at the time of notification. The notification method includes, for example, push notification and email notification, but is not limited to such examples. For example, the notification unit preferentially selects a notification method that the user has previously preferred. In addition, the notification unit can also avoid a notification method that the user has previously ignored. Furthermore, the notification unit can also select an optimal notification timing from the user's past notification history. Thereby, the notification unit can select a more appropriate notification method by referring to the user's past notification history. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's past notification data to the generation AI and have the generation AI select the optimal notification method.

[0090] The notification unit can customize the content of the notification based on the user's current activity status at the time of notification. The notification unit, for example, customizes the content of the notification based on the user's current activity status at the time of notification. The activity status includes, for example, at work and at rest, but is not limited to such examples. For example, when the user is exercising, the notification unit notifies the user of a message related to exercise. In addition, when the user is at work, the notification unit can also notify the user of a message related to work. Furthermore, when the user is at rest, the notification unit can also notify the user of a message that allows the user to relax. Thereby, the notification unit can customize the content of the notification based on the user's current activity status and provide a more appropriate notification. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's activity status data to the generation AI and cause the generation AI to execute the processing to customize the content of the notification.

[0091] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and text analysis, but is not limited to such examples. For example, the notification unit prioritizes important notifications when the user is highly motivated. The notification unit can also prioritize simple notifications when the user is tired. Furthermore, the notification unit can also prioritize notifications that can relax the user when the user is stressed. This allows the notification unit to determine the priority of notifications based on the user's emotion and provide more appropriate notifications. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and have the generation AI execute the notification priority.

[0092] The notification unit can select an appropriate notification method in consideration of the geographical location information of the user at the time of notification. The notification unit, for example, selects an optimal notification method in consideration of the geographical location information of the user at the time of notification. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the notification unit performs a notification related to the area. In addition, when the user is traveling, the notification unit can also perform a notification related to the travel destination. Furthermore, when the user is at home, the notification unit can also perform a notification related to the home. This allows the notification unit to select a more appropriate notification method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the geographical location data of the user to the generation AI and cause the generation AI to select the optimal notification method.

[0093] The notification unit can adjust the content of the notification by analyzing the social media activity of the user at the time of notification. For example, the notification unit can adjust the content of the notification by analyzing the social media activity of the user at the time of notification. The social media activity includes, for example, the content of the post and the number of likes, but is not limited to such examples. For example, the notification unit provides notification based on the interests and concerns shared by the user on social media. The notification unit can also provide notification based on the activities of the accounts the user follows. Furthermore, the notification unit can provide notification based on the trends of the online community in which the user participates. In this way, the notification unit can analyze the social media activity of the user and provide more appropriate notification content. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit inputs the social media data of the user into the generation AI and causes the generation AI to execute the processing, which adjusts the content of the notification.

[0094] The progress management unit can estimate the user's emotion and adjust the progress management method based on the estimated user's emotion. The progress management unit, for example, estimates the user's emotion and adjusts the progress management method based on the estimated user's emotion. The emotion estimation includes, for example, facial expression recognition and text analysis, but is not limited to such examples. For example, the progress management unit performs detailed progress management when the user is highly motivated. In addition, the progress management unit can also perform simple progress management when the user is tired. Furthermore, the progress management unit can also perform progress management that allows the user to relax when the user is stressed. Thereby, the progress management unit can adjust the progress management method based on the user's emotion and perform more appropriate progress management. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the progress management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the progress management unit can input user emotion data into the generation AI and have the generation AI execute an adjustment of the progress management method.

[0095] The progress management unit can select an optimal management method by referring to the user's past progress data during progress management. The progress management unit, for example, selects an optimal management method by referring to the user's past progress data during progress management. The past progress data includes, for example, past task completion status and progress degree, but is not limited to such examples. For example, the progress management unit preferentially selects a progress management method that the user has been successful in the past. In addition, the progress management unit can avoid a progress management method that the user has failed in the past. Furthermore, the progress management unit can select an optimal management timing from the user's past progress data. Thereby, the progress management unit can select a more appropriate progress management method by referring to the user's past progress data. A part or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the user's past progress data to the generation AI and cause the generation AI to execute the process of selecting the optimal management method.

[0096] The progress management unit can customize the progress management means based on the user's current living situation during progress management. The progress management unit, for example, customizes the progress management means based on the user's current living situation during progress management. The living situation includes, for example, a work situation and a home situation, but is not limited to such examples. For example, the progress management unit provides a simple progress management means when the user is busy. In addition, the progress management unit can also provide a detailed progress management means when the user is relaxed. Furthermore, the progress management unit can also provide a progress management means that allows the user to relax when the user is stressed. This allows the progress management unit to customize the progress management means based on the user's current living situation and perform more appropriate progress management. Part or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the user's living situation data into the generation AI and have the generation AI execute the process to customize the progress management means.

[0097] The progress management unit can estimate the user's emotion and determine the priority of the progress management based on the estimated user's emotion. The progress management unit, for example, estimates the user's emotion and determines the priority of the progress management based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and text analysis, but is not limited to such examples. For example, when the user is highly motivated, the progress management unit prioritizes important progress management. In addition, when the user is tired, the progress management unit can also prioritize simple progress management. Furthermore, when the user is stressed, the progress management unit can also prioritize relaxing progress management. In this way, the progress management unit can determine the priority of the progress management based on the user's emotion and perform more appropriate progress management. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the progress management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the progress management unit can input user emotion data into a generation AI and have the generation AI execute a process that determines the priority of progress management.

[0098] The progress management unit can select an appropriate management method in consideration of the geographical location information of the user during progress management. The progress management unit, for example, selects an optimal management method in consideration of the geographical location information of the user during progress management. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the progress management unit performs progress management related to the area. In addition, when the user is traveling, the progress management unit can also perform progress management related to the travel destination. Furthermore, when the user is at home, the progress management unit can also perform progress management related to the home. This allows the progress management unit to select a more appropriate progress management method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the geographical location data of the user to the generation AI and cause the generation AI to select the optimal management method.

[0099] The progress management unit can analyze the social media activity of the user and propose a means of progress management during progress management. The progress management unit, for example, analyzes the social media activity of the user and proposes a means of progress management during progress management. The social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. For example, the progress management unit proposes a management means based on the progress shared by the user on social media. The progress management unit can also propose a management means based on the activity of an account followed by the user. Furthermore, the progress management unit can also propose a management means based on the trend of an online community in which the user participates. In this way, the progress management unit can analyze the social media activity of the user and provide a more appropriate progress management means. A part or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit inputs the social media data of the user into a generation AI and causes the generation AI to execute the proposed means of progress management.

[0100] The sharing unit can estimate the user's emotion and select a message to be shared based on the estimated user's emotion. The sharing unit, for example, estimates the user's emotion and selects a message to be shared based on the estimated user's emotion. The emotion estimation includes, for example, facial expression recognition and text analysis, but is not limited to such examples. For example, the sharing unit shares a gentle message when the user is relaxed. In addition, the sharing unit can also share a challenging message when the user is highly motivated. Furthermore, the sharing unit can share an encouraging message when the user is stressed. Thereby, the sharing unit can select a message to be shared based on the user's emotion and share a more appropriate message. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sharing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the sharing unit can input a user's emotional data into a generation AI and have the generation AI select a message to be shared.

[0101] The sharing unit can select the optimal sharing method by referring to the user's past sharing history at the time of sharing. For example, the sharing unit selects the optimal sharing method by referring to the user's past sharing history at the time of sharing. The sharing method includes, for example, sharing on an SNS or sharing within a specific group, but is not limited to such examples. For example, the sharing unit preferentially selects a sharing method that the user has preferred in the past. In addition, the sharing unit can avoid a sharing method that the user has ignored in the past. Furthermore, the sharing unit can select the optimal sharing timing from the user's past sharing history. Thereby, the sharing unit can select a more appropriate sharing method by referring to the user's past sharing history. A part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input the user's past sharing data to the generation AI and cause the generation AI to select the optimal sharing method.

[0102] The sharing unit can estimate the user's emotion and determine the priority of sharing based on the estimated user's emotion. The sharing unit can estimate the user's emotion and determine the priority of sharing based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and text analysis, but is not limited to such examples. For example, the sharing unit prioritizes important sharing when the user is highly motivated. In addition, the sharing unit can also prioritize simple sharing when the user is tired. Furthermore, the sharing unit can also prioritize relaxing sharing when the user is stressed. In this way, the sharing unit can determine the priority of sharing based on the user's emotion and perform more appropriate sharing. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the data, which determines the priority of sharing.

[0103] The sharing unit can select an appropriate sharing method in consideration of the geographical location information of the user at the time of sharing. For example, the sharing unit selects an optimal sharing method in consideration of the geographical location information of the user at the time of sharing. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the sharing unit performs sharing related to the area. In addition, when the user is traveling, the sharing unit can also perform sharing related to the travel destination. Furthermore, when the user is at home, the sharing unit can also perform sharing related to the home. This allows the sharing unit to select a more appropriate sharing method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input the geographical location data of the user to the generation AI and cause the generation AI to select the optimal sharing method.

[0104] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. The feedback unit can, for example, estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and text analysis, but is not limited to such examples. For example, the feedback unit provides challenging feedback when the user is highly motivated. In addition, the feedback unit can also provide encouraging feedback when the user is tired. Furthermore, the feedback unit can also provide relaxing feedback when the user is stressed. Thereby, the feedback unit can adjust the content of the feedback based on the user's emotion and provide more appropriate feedback. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the content of the feedback.

[0105] The feedback unit can select the optimal feedback method by referring to the user's past feedback history at the time of feedback. The feedback unit, for example, selects the optimal feedback method by referring to the user's past feedback history at the time of feedback. The feedback method includes, for example, a text message and an evaluation score, but is not limited to such examples. For example, the feedback unit preferentially selects a feedback method that the user has preferred in the past. The feedback unit can also avoid a feedback method that the user has ignored in the past. Furthermore, the feedback unit can select the optimal feedback timing from the user's past feedback history. Thereby, the feedback unit can select a more appropriate feedback method by referring to the user's past feedback history. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data to the generation AI and cause the generation AI to select the optimal feedback method.

[0106] The feedback unit can estimate the user's emotion and determine the priority of the feedback based on the estimated user's emotion. The feedback unit can estimate the user's emotion and determine the priority of the feedback based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and text analysis, but is not limited to such examples. For example, the feedback unit prioritizes important feedback when the user is highly motivated. The feedback unit can also prioritize simple feedback when the user is tired. Furthermore, the feedback unit can also prioritize relaxing feedback when the user is stressed. In this way, the feedback unit can determine the priority of the feedback based on the user's emotion and provide more appropriate feedback. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to the generation AI and cause the generation AI to execute the feedback priority.

[0107] The feedback unit can select an appropriate feedback method in consideration of the geographical location information of the user at the time of feedback. The feedback unit, for example, selects an optimal feedback method in consideration of the geographical location information of the user at the time of feedback. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the feedback unit performs feedback related to the area. In addition, when the user is traveling, the feedback unit can also perform feedback related to the travel destination. Furthermore, when the user is at home, the feedback unit can also perform feedback related to the home. Thereby, the feedback unit can select a more appropriate feedback method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the geographical location data of the user to the generation AI and cause the generation AI to select the optimal feedback method.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The motivation maintenance system may also include a visual feedback unit for visually feeding back the progress of the user toward achieving the goal. The visual feedback unit may, for example, display the user's progress as a graph or chart, and provide the progress in a visually easy-to-understand format. Furthermore, the visual feedback unit may emphasize the feedback using colors or animations according to the user's progress. For example, the color of the graph may change to green as the user approaches achieving the goal, or a red warning may be displayed if the progress is delayed. This allows the user to intuitively grasp his or her own progress and maintain motivation.

[0110] The motivation maintenance system may also include a competition section for allowing a user to compete with other users over progress toward achieving a goal. The competition section may, for example, provide a function for matching users with the same goal and comparing their progress. Furthermore, the competition section may provide a ranking function for increasing motivation by allowing a user to compete with other users. For example, each time a user achieves a goal, points are acquired and reflected in the ranking. This allows a user to make an effort toward achieving a goal while competing with other users.

[0111] The motivation maintenance system may also include a journal section for recording the user's progress toward achieving a goal. The journal section provides, for example, a function for the user to record daily progress and impressions. Furthermore, the journal section may provide a reminder function for the user to maintain motivation by looking back on past records. For example, by being reminded of the contents recorded a week ago, the user can reconfirm past efforts and increase motivation. This allows the user to maintain motivation toward achieving a goal by recording and looking back on their own progress.

[0112] The motivation maintenance system may also include a resource providing unit for supporting the progress of the user toward achieving the goal. The resource providing unit may, for example, provide a function for providing information and tools related to the user's goal. Furthermore, the resource providing unit may provide learning content for the user to acquire skills and knowledge necessary to achieve the goal. For example, if the user sets a goal of "losing 5 kg in one month," the resource providing unit may provide information about dieting and exercise videos. This allows the user to utilize resources toward achieving the goal and effectively achieve the goal.

[0113] The motivation maintenance system may also include an alert unit for supporting the user's progress toward achieving the goal. For example, the alert unit provides a function of issuing a warning when the user's progress toward achieving the goal is delayed. Furthermore, the alert unit may provide a reminder function for the user to maintain the progress toward achieving the goal. For example, if the user sets a goal of "continuing to exercise for one hour every day," the alert unit may send a reminder if the user forgets to exercise. This makes it easier for the user to maintain the progress toward achieving the goal.

[0114] The motivation maintenance system can estimate the user's emotions and adjust the tone of the message based on the estimated emotions. For example, if the user is feeling stressed, a calm message that helps the user relax can be generated. If the user is highly motivated, a challenging message can be generated. Furthermore, if the user is feeling down, a message that includes many encouraging words can be generated. In this way, it is possible to provide an appropriate message according to the user's emotions and maintain motivation.

[0115] The motivation maintenance system can estimate the user's emotions and adjust the frequency of notifications based on the estimated emotions. For example, if the user is feeling stressed, the frequency of notifications can be reduced to allow the user more time to relax. If the user is highly motivated, the frequency of notifications can be increased to provide stronger support for goal achievement. Furthermore, if the user is tired, the frequency of notifications can be adjusted to encourage the user to rest. This makes it possible to provide an appropriate notification frequency according to the user's emotions and maintain motivation.

[0116] The motivation maintenance system can estimate the user's emotions and adjust the difficulty of the goal based on the estimated emotions. For example, if the user is feeling stressed, the difficulty of the goal can be lowered to set an easier-to-achieve goal. Also, if the user is highly motivated, the difficulty of the goal can be increased to set a more challenging goal. Furthermore, if the user is tired, the difficulty of the goal can be adjusted to encourage rest. In this way, appropriate goal setting according to the user's emotions can be performed to maintain motivation.

[0117] The motivation maintenance system 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, it can provide the user with relaxing feedback. If the user is highly motivated, it can provide challenging feedback. If the user is feeling down, it can provide feedback that includes many encouraging words. In this way, it is possible to provide appropriate feedback according to the user's emotions and maintain the user's motivation.

[0118] The motivation maintenance system can estimate the user's emotions and adjust the progress management method based on the estimated emotions. For example, if the user is feeling stressed, the progress management method can be simplified to reduce the burden on the user. Also, if the user is highly motivated, detailed progress management can be performed to strengthen support for goal achievement. Furthermore, if the user is tired, the progress management method can be adjusted to encourage rest. In this way, appropriate progress management according to the user's emotions can be performed to maintain motivation.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The goal setting unit accepts goal input from the user. The goal input from the user includes short-term goals, long-term goals, numerical goals, etc. For example, the unit accepts the user input of goals such as "continue to exercise for one hour every day" or "lose 5 kg in one month." Step 2: The message generation unit uses the generation AI to generate encouraging and supportive messages and quotes based on the goals accepted by the goal setting unit. Generated messages include "Let's exercise hard today!" and "Success is a one-step-at-a-time process." The generation AI uses a text generation AI (e.g., LLM) to generate messages tailored to the user's goals. It can also generate messages taking into account the user's progress and past data. Step 3: The notification unit notifies the user of the message generated by the message generation unit at the set time. The timing of the notification includes a time specified by the user and a time recommended by the system. For example, the notification unit can be set to notify the user of the message "Let's do our best today!" at 7:00 a.m. every morning. The notification unit can also estimate the user's emotions and adjust the timing of the notification based on the estimated user's emotions. For example, the notification can be made immediately if the user is relaxed, and can be postponed if the user is busy.

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned goal setting unit, message generation unit, notification unit, progress management unit, sharing unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the goal setting unit accepts a goal input from the user using the reception device 38 of the smart device 14. The message generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a message using a generation AI. The notification unit notifies the user of the message generated using the output device 40 of the smart device 14. The progress management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the progress of the user. The sharing unit shares the message generated with other users using the communication I / F 44 of the smart device 14. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback when the goal is achieved. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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. 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.

[0128] 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.

[0129] 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 the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] 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 performed in a secure state.

[0132] Fig. 4 shows an example of 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.

[0133] 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.

[0134] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the 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 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0138] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] 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 be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned goal setting unit, message generation unit, notification unit, progress management unit, sharing unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the goal setting unit accepts a goal input from the user using the microphone 238 of the smart glasses 214. The message generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a message using a generation AI. The notification unit notifies the user of the generated message using the speaker 240 of the smart glasses 214. The progress management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the progress of the user. The sharing unit shares the generated message with other users using the communication I / F 44 of the smart glasses 214. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback when the goal is achieved. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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. 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.

[0144] 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.

[0145] 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 the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] 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 performed in a secure state.

[0148] 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.

[0149] 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.

[0150] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 a voice indicating a user input for 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.

[0154] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned goal setting unit, message generating unit, notification unit, progress management unit, sharing unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the goal setting unit accepts a goal input from the user using the microphone 238 of the headset type terminal 314. The message generating unit is realized by the specific processing unit 290 of the data processing device 12 and generates a message using a generation AI. The notification unit notifies the user of the generated message using the speaker 240 of the headset type terminal 314. The progress management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the progress of the user. The sharing unit shares the generated message with other users using the communication I / F 44 of the headset type terminal 314. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback when the goal is achieved. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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. 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.

[0160] 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.

[0161] 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 the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] 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 performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0165] 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.

[0166] 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.

[0167] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0169] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] 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 a voice indicating a user input for 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.

[0171] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] 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 be executed 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 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.

[0173] Each of the multiple elements including the above-mentioned goal setting unit, message generating unit, notification unit, progress management unit, sharing unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the goal setting unit accepts a goal input from the user using the microphone 238 of the robot 414. The message generating unit is realized by the specific processing unit 290 of the data processing device 12 and generates a message using a generation AI. The notification unit notifies the user of the message generated using the speaker 240 of the robot 414. The progress management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the progress of the user. The sharing unit shares the message generated with other users using the communication I / F 44 of the robot 414. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback when the goal is achieved. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0181] 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.

[0182] 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 Universal Serial Bus (USB) 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.

[0183] In addition, 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 upon request from the data processing device 12.

[0184] 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.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0187] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) a goal setting unit that accepts goal input from a user; a message generating unit that generates an encouraging or supportive message or a quote based on the goal received by the goal setting unit; a notification unit that notifies the message generated by the message generation unit at a set time. A system characterized by: (Appendix 2) Equipped with a progress management section that manages the user's progress 2. The system of claim 1, (Appendix 3) It has a sharing section for sharing the generated message with other users. 2. The system of claim 1, (Appendix 4) Equipped with a feedback section that provides feedback when a goal is achieved 2. The system of claim 1, (Appendix 5) The message generation unit Generate messages based on the user's progress and past data 2. The system of claim 1, (Appendix 6) The common part is Share your message via social media or messaging apps 4. The system of claim 3. (Appendix 7) The goal setting unit, Estimate the user's emotions and adjust the timing of goal setting based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The goal setting unit, Analyze the user's past goal setting history and suggest appropriate goal setting methods 2. The system of claim 1, (Appendix 9) The goal setting unit, During goal setting, filter goals based on the user's current life situation and interests 2. The system of claim 1, (Appendix 10) The goal setting unit, Estimate user emotions and prioritize goals based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The goal setting unit, When setting goals, the app takes into account the user's geographic location to prioritize more relevant goals. 2. The system of claim 1, (Appendix 12) The goal setting unit, When setting goals, analyze users' social media activity to suggest relevant goals 2. The system of claim 1, (Appendix 13) The message generation unit Infer user emotions and adjust message expression based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The message generation unit When generating messages, adjust the level of detail in your messages based on the importance of your goals 2. The system of claim 1, (Appendix 15) The message generation unit During message generation, different message generation algorithms are applied depending on the goal category. 2. The system of claim 1, (Appendix 16) The message generation unit Infer user sentiment and adjust message length based on the estimated user sentiment 2. The system of claim 1, (Appendix 17) The message generation unit Prioritize messages based on goal timing when generating messages 2. The system of claim 1, (Appendix 18) The message generation unit During message generation, adjust the order of messages based on goal relevance 2. The system of claim 1, (Appendix 19) The notification unit is Infer user emotions and adjust notification timing based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The notification unit is When notifying users, select the appropriate notification method by referring to the user's past notification history. 2. The system of claim 1, (Appendix 21) The notification unit is Customize the content of notifications based on the user's current activity at the time of notification 2. The system of claim 1, (Appendix 22) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 2. The system of claim 1, (Appendix 23) The notification unit is When notifying users, select the appropriate notification method based on the user's geographic location. 2. The system of claim 1, (Appendix 24) The notification unit is When notifying users, analyze their social media activity to tailor the content of the notification 2. The system of claim 1, (Appendix 25) The progress management unit To estimate a user's emotion and adjust a progress management method based on the estimated user's emotion. 3. The system of claim 2, (Appendix 26) The progress management unit When managing progress, select the optimal management method by referring to the user's past progress data. 3. The system of claim 2, (Appendix 27) The progress management unit When tracking progress, customize the means of tracking progress based on the user's current life situation 3. The system of claim 2, (Appendix 28) The progress management unit To estimate a user's emotion and determine a priority of progress management based on the estimated user's emotion. 3. The system of claim 2, (Appendix 29) The progress management unit When managing progress, select an appropriate management method taking into account the user's geographic location information. 3. The system of claim 2, (Appendix 30) The progress management unit When managing progress, analyze users' social media activity to suggest ways to manage progress. 3. The system of claim 2, (Appendix 31) The common part is Estimate the user's emotions and select messages to share based on the estimated user emotions. 4. The system of claim 3. (Appendix 32) The common part is When sharing, refer to the user's past sharing history to select the optimal sharing method 4. The system of claim 3. (Appendix 33) The common part is Estimate user sentiment and determine sharing priorities based on the estimated user sentiment 4. The system of claim 3. (Appendix 34) The common part is When sharing, choose the appropriate sharing method based on the user's geographic location 4. The system of claim 3. (Appendix 35) The feedback unit is Inferring user emotions and adjusting feedback based on the estimated user emotions 5. The system of claim 4. (Appendix 36) The feedback unit is When providing feedback, select the most appropriate feedback method by referring to the user's past feedback history. 5. The system of claim 4. (Appendix 37) The feedback unit is Estimate user emotions and prioritize feedback based on the estimated user emotions 5. The system of claim 4. (Appendix 38) The feedback unit is When providing feedback, select the appropriate feedback method taking into account the user's geographic location information. 5. The system of claim 4. [Explanation of symbols]

[0193] 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 goal setting unit that accepts goal input from a user; a message generating unit that generates an encouraging / supporting message or a quote based on the goal received by the goal setting unit; a notification unit that notifies the message generated by the message generation unit at a set time. A system characterized by:

2. Equipped with a progress management section that manages the user's progress 2. The system of claim 1 .

3. It has a sharing section for sharing the generated message with other users.

2. The system of claim 1 .

4. Equipped with a feedback section that provides feedback when a goal is achieved 2. The system of claim 1 .

5. The message generation unit Generate messages based on the user's progress and past data 2. The system of claim 1 .

6. The common part is Share your message via social media or messaging apps 4. The system of claim 3.

7. The goal setting unit, Estimate the user's emotions and adjust the timing of goal setting based on the estimated user's emotions.

2. The system of claim 1 .

8. The goal setting unit, Analyze the user's past goal setting history and suggest appropriate goal setting methods 2. The system of claim 1 .

9. The goal setting unit, During goal setting, filter goals based on the user's current life situation and interests 2. The system of claim 1 .

10. The goal setting unit, Estimating user emotions and prioritizing goals based on the estimated user emotions 2. The system of claim 1 .

11. The goal setting unit, When setting goals, the app takes into account the user's geographic location to prioritize more relevant goals.

2. The system of claim 1 .

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