system

Abstract

The system according to this embodiment aims to enhance children's self-esteem while fostering their awareness of the SDGs goals. [Solution] The system according to the embodiment comprises a reception unit, a conversion unit, a savings unit, a detection unit, and a suggestion unit. The reception unit inputs daily activities. The conversion unit automatically converts the content input by the reception unit into SDGs goals. The savings unit stores the content converted by the conversion unit in a self-esteem savings account. The detection unit stores daily entries and detects changes in the child. The suggestion unit proposes follow-up actions in response to the changes detected by the detection unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully achieved to enhance children's self-affirmation and at the same time cultivate awareness of the SDGs goals, and there is room for improvement.

[0005] The system according to the embodiment aims to enhance children's self-affirmation while cultivating awareness of the SDGs goals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a conversion unit, a savings unit, a detection unit, and a suggestion unit. The reception unit receives input for daily activities. The conversion unit automatically converts the input from the reception unit into SDGs goals. The savings unit stores the converted content in a self-esteem savings account. The detection unit stores daily entries and detects changes in the child. The suggestion unit proposes follow-up actions in response to the changes detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can enhance children's self-esteem while fostering their awareness of the SDGs goals. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The application for enhancing SDGs and self-esteem according to an embodiment of the present invention is a system that converts a user's daily actions into SDG goals and enhances their self-esteem. This system allows users to input what they have done each day, automatically detects whether the input corresponds to an SDG goal, and if so, automatically converts it to an SDG goal. The converted content is accumulated as "self-esteem savings," allowing users to feel a sense of accomplishment and self-worth. For example, a user inputs what they have done and accomplished in a day, and the generating AI analyzes the content. If it corresponds to an SDG goal, it is automatically converted, and the converted content is accumulated as coins in the self-esteem savings. This allows users to feel that their actions contribute to the SDGs and enhance their self-esteem. Furthermore, because daily entries are stored, there is a mechanism for AGI (Artificial General Intelligence) to automatically detect if a child's entries decrease or if there are unusual entries. This allows for the detection of changes in the child and appropriate follow-up. For example, the system visualizes and analyzes the child's "I did it!" from the self-esteem savings and suggests follow-up actions based on the changes. By allowing families and teachers to select the most suitable method from the suggestions, children's growth can be supported. Thus, this invention is an educational support tool for promoting SDGs and self-esteem, supporting children's growth and building a better future. As a result, the application for promoting SDGs and self-esteem can translate the user's daily actions into SDG goals and enhance self-esteem.

[0029] The application for enhancing SDGs and self-esteem according to this embodiment comprises a reception unit, a conversion unit, a savings unit, a detection unit, and a suggestion unit. The reception unit receives input from the user about their daily activities. For example, the user can input learning activities, household chores, exercise, etc. The reception unit allows the user to input activities using, for example, a smartphone or personal computer. The reception unit can also support multiple input methods, such as voice input and handwriting input. The conversion unit automatically converts the content entered by the reception unit into SDGs goals. The conversion unit analyzes the input content using, for example, natural language processing technology to determine whether it corresponds to an SDG goal. For example, if the input content is "household chores," the conversion unit can determine that it corresponds to SDG Goal 11 (Make cities and human settlements inclusive, safe, resilient and sustainable). The savings unit stores the content converted by the conversion unit in a self-esteem savings account. The savings unit can store the converted content digitally as coins. The savings unit allows users to input their daily activities, for example, and the converted content is accumulated as coins, allowing them to visually confirm their self-esteem. The detection unit stores daily entries and detects changes in the child. The detection unit can analyze changes in the frequency and content of entries and detect anomalies. For example, the detection unit can detect anomalies if the child's entries decrease or if unusual content is written. The suggestion unit proposes follow-up actions in response to the changes detected by the detection unit. For example, the suggestion unit can suggest encouraging messages or additional tasks based on the detected changes. For example, the suggestion unit can provide information to help family members or teachers select the most suitable method from the suggestions. Thus, the application for enhancing SDGs and self-esteem according to this embodiment can convert the user's daily activities into SDG goals and enhance their self-esteem.

[0030] The reception system allows users to input their daily activities. For example, users can input learning activities, household chores, exercise, etc. The reception system allows users to input their activities using a smartphone or personal computer. It can also support multiple input methods, such as voice input and handwriting input. Specifically, users can open a smartphone application and input their daily activities as text into a dedicated input form. With voice input, users simply speak their activities into the microphone, and voice recognition technology converts it into text. With handwriting input, users input their activities by handwriting on a touchscreen, and handwriting recognition technology converts it into digital text. Furthermore, the reception system has a function to automatically categorize and organize user input. For example, learning activities are categorized as "Education," household chores as "Home," and exercise as "Health." This allows users to easily review their activities and visually see which categories they spend the most time on. The reception system also saves user input data to a cloud server, enabling data backup and access from other devices. This allows users to input and review their activities anytime, anywhere.

[0031] The conversion unit automatically converts the content entered by the reception unit into SDGs goals. For example, the conversion unit analyzes the input content using natural language processing technology to determine whether it corresponds to an SDG goal. Specifically, it analyzes the input text using natural language processing technology and extracts keywords and context. For example, if the input is "helping around the house," the conversion unit extracts keywords such as "household" and "helping around," and determines that this corresponds to SDG Goal 11 (Sustainable Cities and Communities). Furthermore, the conversion unit can also associate user actions with multiple SDGs goals. For example, "learning activities" corresponds to SDG Goal 4 (Quality Education), but may also be related to Goal 8 (Decent Work and Economic Growth). In this way, the conversion unit evaluates user actions from multiple perspectives and associates them with SDGs goals. The conversion unit can continuously learn using AI to improve its conversion accuracy. For example, it can receive user feedback, evaluate whether the conversion result was accurate, and improve the conversion algorithm based on the results. This allows the conversion unit to more accurately associate user behavior with the SDGs goals.

[0032] The Savings Department stores the converted content from the Conversion Department into a self-esteem savings account. For example, the Savings Department can store converted content digitally as coins. Specifically, users input their daily activities, and the converted content is stored as coins, allowing them to visually track their self-esteem. For instance, if a user inputs "helping with household chores" and this is converted to align with SDG Goal 11, the Savings Department will award a certain number of coins for this activity. The coins are displayed digitally, and users can check their savings status within the application. The Savings Department offers flexible coin awarding criteria. For example, different coins can be awarded based on the type, frequency, and importance of the activity. This allows users to concretely understand how much their actions contribute to the SDGs. The Savings Department also features a function to visually display the user's savings status using graphs and charts. This allows users to see their growth and contributions at a glance, boosting their self-esteem. Furthermore, the Savings Department provides a function to share the coins users have accumulated with other users. For example, users can share their coin savings progress with family and friends and encourage each other. This allows the savings department to boost user motivation and encourage sustained savings.

[0033] The detection unit stores daily entries and detects changes in the child's behavior. For example, the detection unit can analyze changes in the frequency and content of entries to detect anomalies. Specifically, it analyzes user-input data in chronological order and detects deviations from normal patterns. For example, if a child's entries suddenly decrease or the content changes to negative, the detection unit will detect this as an anomaly. The detection unit implements an anomaly detection algorithm using AI to perform highly accurate detection. For example, it learns normal patterns based on past data and detects anomalies based on this. In addition, the detection unit can detect not only user behavior data but also changes in emotions and mood. For example, it performs sentiment analysis of text entered by the user and detects anomalies if positive emotions decrease or negative emotions increase. This allows the detection unit to understand the user's overall state and detect anomalies early. Furthermore, the detection unit also has a function to issue alerts when an anomaly is detected. For example, it can send notifications to parents or teachers to encourage early action. This allows the detection unit to quickly detect changes in the user and support appropriate responses.

[0034] The suggestion unit proposes follow-up actions in response to changes detected by the detection unit. For example, based on the detected changes, the suggestion unit can suggest encouraging messages or additional tasks. Specifically, it provides appropriate follow-up in response to changes in the user's behavior and emotions. For example, if a user shows negative emotions, the suggestion unit sends an encouraging message and suggests activities to improve the user's mood. Also, if the user's behavior decreases, the suggestion unit sets new tasks or goals to increase the user's motivation. The suggestion unit uses AI to analyze the user's state and automatically generates the optimal follow-up. For example, it learns the user's preferences and tendencies based on past data and makes personalized suggestions based on that. Furthermore, the suggestion unit provides information to help family members and teachers select the best method from the suggestions. For example, it presents multiple follow-up options and explains the effects and application conditions of each. This allows family members and teachers to select and implement the most suitable follow-up for the user. The suggestion unit can also receive user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the suggestion unit to respond quickly and appropriately to changes in the user, supporting sustainable growth and improved self-esteem.

[0035] The conversion unit can automatically convert input content based on the SDGs goals. For example, the conversion unit analyzes the input content using natural language processing technology to determine whether it corresponds to an SDG goal. For example, if the input content is "helping around the house," the conversion unit can determine that it corresponds to SDG Goal 11 (Sustainable Cities and Communities). For example, if the input content is "recycling activities," the conversion unit can determine that it corresponds to SDG Goal 12 (Responsible Consumption and Production). For example, if the input content is "energy conservation," the conversion unit can determine that it corresponds to SDG Goal 7 (Affordable and Clean Energy). This allows users to feel that their actions are contributing to the SDGs by automatically converting the content based on the SDGs goals. Some or all of the above processing in the conversion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the conversion unit can input the input content into a generation AI, and the generation AI can determine whether it corresponds to an SDG goal.

[0036] The savings unit can store the converted content as coins in a self-esteem savings account. The savings unit can store the converted content as digital coins, for example. The savings unit can visually confirm self-esteem by having the user input their daily activities and seeing the converted content stored as coins. The savings unit can also store the converted content as physical coins, for example. The savings unit can allow the user to experience self-esteem by having the user input their activities and seeing the converted content stored as physical coins. The savings unit can also store the converted content as a point system, for example. The savings unit can enhance self-esteem by having the user input their activities and seeing the converted content stored as points. This allows for visual confirmation of self-esteem by having the converted content stored as coins. Some or all of the above processing in the savings unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the savings unit can propose a method in which the converted content is input into a generation AI, and the generation AI stores it as coins.

[0037] The detection unit can automatically detect when daily postings decrease or when there are unusual postings. The detection unit can, for example, analyze changes in the frequency and content of postings to detect anomalies. The detection unit can, for example, detect anomalies when a child's postings decrease or when unusual content is posted. The detection unit can, for example, analyze the content of postings to detect changes in emotions. The detection unit can, for example, analyze the emotional expressions contained in a child's postings to detect anomalies. The detection unit can, for example, analyze posting patterns to detect anomalies. The detection unit can, for example, detect anomalies when a child's posting pattern changes. This enables appropriate follow-up by automatically detecting changes in the child. Some or all of the above processing in the detection unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the detection unit can input the content of the postings into a generation AI, and the generation AI can detect anomalies.

[0038] The suggestion unit can propose follow-up actions in response to detected changes. For example, based on detected changes, the suggestion unit can suggest encouraging messages or additional tasks. For example, if a child's posting activity decreases, the suggestion unit can suggest encouraging messages. For example, if the content of a child's postings changes, the suggestion unit can suggest additional tasks. For example, based on detected changes, the suggestion unit can provide information to help families and teachers choose the best approach. For example, based on detected changes, the suggestion unit can propose multiple follow-up methods that families and teachers can choose from. For example, based on detected changes, the suggestion unit can present the advantages and disadvantages of the follow-up methods that families and teachers can choose from. This allows for support of a child's growth by proposing follow-up actions in response to detected changes. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input detected changes into a generative AI, which can then propose the best follow-up method.

[0039] The suggestion unit can provide information to help families and teachers select the most suitable method from among the suggestions. For example, the suggestion unit can suggest multiple follow-up methods that families and teachers can choose from based on the detected changes. For example, the suggestion unit can present the advantages and disadvantages of the follow-up methods that families and teachers can choose from based on the detected changes. For example, the suggestion unit can predict the effectiveness of the follow-up methods that families and teachers can choose from based on the detected changes. For example, the suggestion unit can provide the implementation procedures for the follow-up methods that families and teachers can choose from based on the detected changes. For example, the suggestion unit can present examples of the follow-up methods that families and teachers can choose from based on the detected changes. This allows for more effective support of a child's growth by providing information to help families and teachers choose the most suitable method. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the detected changes into a generative AI, which can then suggest the most suitable follow-up method.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions content that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past input history into a generative AI, which can then suggest the optimal input method.

[0041] The reception unit can filter input content based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the reception unit can prioritize displaying health-related input content. For example, if the user is interested in environmental issues, the reception unit can prioritize displaying environment-related input content. For example, if the user is interested in education, the reception unit can prioritize displaying education-related input content. By filtering input content based on the user's lifestyle and areas of interest, more relevant content can be provided. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's lifestyle and areas of interest into a generative AI, which can then filter the input content.

[0042] The reception unit can prioritize inputting highly relevant content by considering the user's geographical location information during input. For example, if the user is in a specific region, the reception unit can prioritize displaying input content related to that region. For example, if the user is traveling, the reception unit can prioritize displaying input content related to their travel destination. For example, if the user is at home, the reception unit can prioritize displaying input content related to their home. This allows for the provision of more relevant content by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize inputting highly relevant content.

[0043] The reception unit can analyze the user's social media activity during input and input relevant content. For example, the reception unit can suggest relevant input content based on what the user has shared on social media. For example, the reception unit can suggest relevant input content based on the content of accounts the user follows on social media. For example, the reception unit can suggest relevant input content based on what the user has "liked" on social media. By analyzing social media activity, it is possible to provide more relevant content. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's social media activity into a generative AI, and the generative AI can input relevant content.

[0044] The conversion unit can adjust the level of detail in the conversion based on the importance of the input content. For example, the conversion unit can convert important content in detail and provide it to the user. For example, the conversion unit can convert general content concisely and provide it to the user. For example, the conversion unit can omit low-priority content during the conversion and provide it to the user. In this way, by adjusting the level of detail in the conversion based on the importance of the input content, a more appropriate conversion result can be provided. Some or all of the above processing in the conversion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the conversion unit can input the importance of the input content to the generation AI, and the generation AI can adjust the level of detail in the conversion.

[0045] The conversion unit can apply different conversion algorithms depending on the category of the input content during conversion. For example, the conversion unit can apply a conversion algorithm specialized in environmental protection to content related to the environment. For example, the conversion unit can apply a conversion algorithm specialized in educational support to content related to education. For example, the conversion unit can apply a conversion algorithm specialized in health promotion to content related to health. By applying different conversion algorithms depending on the category of the input content, a more appropriate conversion result can be provided. Some or all of the above processing in the conversion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversion unit can input the category of the input content into a generative AI, and the generative AI can apply different conversion algorithms.

[0046] The conversion unit can determine the conversion priority based on the submission timing of the input content during the conversion process. For example, the conversion unit can prioritize the conversion of urgent content and provide it to the user. For example, the conversion unit can convert periodic content with normal priority and provide it to the user. For example, the conversion unit can postpone the conversion of long-term content and provide it to the user. This allows for the provision of more appropriate conversion results by determining the conversion priority based on the submission timing of the input content. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit can input the submission timing of the input content into the generating AI, and the generating AI can determine the conversion priority.

[0047] The conversion unit can adjust the order of conversion based on the relevance of the input content during conversion. For example, the conversion unit can prioritize the conversion of highly relevant content and provide it to the user. For example, the conversion unit can postpone the conversion of less relevant content and provide it to the user. For example, the conversion unit can omit irrelevant content during conversion and provide it to the user. In this way, by adjusting the order of conversion based on the relevance of the input content, a more appropriate conversion result can be provided. Some or all of the above processing in the conversion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the conversion unit can input the relevance of the input content to the generation AI, and the generation AI can adjust the order of conversion.

[0048] The savings function can analyze the user's past behavior to select the optimal savings method when saving money. For example, the savings function can suggest the optimal savings method based on actions the user has frequently performed in the past. For example, the savings function can suggest an efficient savings method based on the user's past behavioral history. For example, the savings function can analyze the user's past behavioral patterns to suggest the most effective savings method. In this way, by analyzing past behavior, the savings function can provide the user with the optimal savings method. Some or all of the above processing in the savings function may be performed using, for example, a generative AI, or without a generative AI. For example, the savings function can input the user's past behavioral data into a generative AI, which can then select the optimal savings method.

[0049] The savings function can customize the savings method based on the user's current living situation when they start saving. For example, if the user is busy, the savings function can suggest a simple savings method. For example, if the user is relaxed, the savings function can suggest a detailed savings method. For example, if the user has a specific goal, the savings function can suggest a savings method tailored to that goal. This allows for the provision of a more appropriate savings method by customizing the savings method based on the user's current living situation. Some or all of the above processing in the savings function may be performed using, for example, a generative AI, or without a generative AI. For example, the savings function can input the user's living situation data into a generative AI, which can then customize the savings method.

[0050] The savings unit can select the optimal savings method when a user is saving money, taking into account the user's geographical location. For example, if the user is in a specific region, the savings unit can suggest a savings method related to that region. For example, if the user is traveling, the savings unit can suggest a savings method related to the travel destination. For example, if the user is at home, the savings unit can suggest a savings method related to home. By considering geographical location, the system can provide a more appropriate savings method. Some or all of the above processing in the savings unit may be performed using, for example, a generative AI, or without a generative AI. For example, the savings unit can input the user's geographical location information into a generative AI, which can then select the optimal savings method.

[0051] The savings department can analyze a user's social media activity and suggest savings methods when they make a savings. For example, the savings department can suggest relevant savings methods based on what the user has shared on social media. For example, the savings department can suggest relevant savings methods based on the content of accounts the user follows on social media. For example, the savings department can suggest relevant savings methods based on what the user has "liked" on social media. By analyzing social media activity, it is possible to provide more appropriate savings methods. Some or all of the above processing in the savings department may be performed using, for example, a generative AI, or without a generative AI. For example, the savings department can input the user's social media activity into a generative AI, which can then suggest savings methods.

[0052] The detection unit can predict current changes by referring to past data when detection occurs. For example, the detection unit can predict current changes by analyzing the user's past written data. For example, the detection unit can predict current changes based on the user's past behavior patterns. For example, the detection unit can predict current changes by referring to the user's past emotional data. This allows for more accurate prediction of current changes by referring to past data. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input past data into a generative AI, which can then predict current changes.

[0053] The detection unit can apply different detection algorithms to each category of input content upon detection. For example, the detection unit can apply a detection algorithm specialized for educational support to content related to education. For example, the detection unit can apply a detection algorithm specialized for health promotion to content related to health. For example, the detection unit can apply a detection algorithm specialized for environmental protection to content related to the environment. By applying different detection algorithms to each category, more appropriate detection becomes possible. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the category of input content into a generative AI, and the generative AI can apply different detection algorithms.

[0054] The detection unit can perform detection while considering the geographical distribution of the input content. For example, if the user is in a specific region, the detection unit can prioritize displaying detection results related to that region. For example, if the user is traveling, the detection unit can prioritize displaying detection results related to the travel destination. For example, if the user is at home, the detection unit can prioritize displaying detection results related to home. This makes it possible to perform more appropriate detection by considering geographical distribution. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the geographical distribution of the input content into a generative AI, and the generative AI can perform detection.

[0055] The detection unit can improve the accuracy of detection by referring to relevant literature for the input content during detection. For example, the detection unit can improve the accuracy of detection by referring to relevant academic papers. For example, the detection unit can improve the accuracy of detection by referring to relevant news articles. For example, the detection unit can improve the accuracy of detection by referring to relevant books. In this way, the accuracy of detection can be improved by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input relevant literature for the input content into a generating AI, and the generating AI can improve the accuracy of detection.

[0056] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the detected changes. For example, it can suggest important changes in detail and provide them to the user. For example, it can suggest general changes concisely and provide them to the user. For example, it can suggest low-priority changes in a simplified manner and provide them to the user. This allows for the provision of more appropriate suggestions by adjusting the level of detail in suggestions based on the importance of the changes. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the importance of the detected changes into the generative AI, which can then adjust the level of detail in its suggestions.

[0057] The proposal unit can apply different proposal algorithms depending on the category of the detected change when making a proposal. For example, for educational changes, the proposal unit can apply a proposal algorithm specialized in educational support. For example, for health changes, the proposal unit can apply a proposal algorithm specialized in health promotion. For example, for environmental changes, the proposal unit can apply a proposal algorithm specialized in environmental protection. By applying different proposal algorithms depending on the category, more appropriate proposals can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category of the detected change into a generative AI, and the generative AI can apply a different proposal algorithm.

[0058] The proposal unit can determine the priority of proposals based on the timing of the detected changes when making a proposal. For example, the proposal unit can prioritize and provide urgent changes to the user. For example, the proposal unit can propose and provide regular changes to the user with normal priority. For example, the proposal unit can postpone proposing and provide long-term changes to the user. This allows for the provision of more appropriate proposals by prioritizing proposals based on the submission timing. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the submission timing of detected changes into a generative AI, and the generative AI can determine the priority of proposals.

[0059] The suggestion unit can adjust the order of suggestions based on the relevance of the detected changes when making suggestions. For example, the suggestion unit can prioritize suggesting and providing highly relevant changes to the user. For example, the suggestion unit can postpone suggesting and providing to the user less relevant changes. For example, the suggestion unit can omit suggesting and providing to the user irrelevant changes. By adjusting the order of suggestions based on relevance, more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the relevance of the detected changes into a generative AI, which can then adjust the order of suggestions.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can automatically complete user input by referencing the user's past behavior history. For example, it can automatically display suggestions for actions the user has frequently entered in the past. It can also learn patterns of past user actions and automatically complete similar actions when the user enters them. Furthermore, it can prompt the user for input at the appropriate time, taking into account the time of day and frequency of past user actions. This streamlines the user's input process and enables smoother operation. Some or all of the above processing in the reception desk may be performed using generative AI, or it may be performed without using generative AI.

[0062] The detection unit stores daily entries and, when detecting changes in a child, can predict current changes by referring to past data. For example, it can analyze the user's past entry data to predict current changes. It can predict current changes based on the user's past behavior patterns. It can predict current changes by referring to the user's past emotional data. In this way, by referring to past data, current changes can be predicted more accurately. Some or all of the above processing in the detection unit may be performed using generative AI, or it may be performed without using generative AI.

[0063] The reception desk can prioritize inputting highly relevant content by considering the user's geographical location when inputting user activity. For example, if the user is in a specific region, input content related to that region can be displayed preferentially. If the user is traveling, input content related to their travel destination can be displayed preferentially. If the user is at home, input content related to their home can be displayed preferentially. This allows for the provision of more relevant content by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using generative AI, or it may be performed without using generative AI.

[0064] The conversion unit can apply different conversion algorithms depending on the category of the input content during conversion. For example, content related to the environment can be given a conversion algorithm specialized in environmental protection. Content related to education can be given a conversion algorithm specialized in educational support. Content related to health can be given a conversion algorithm specialized in health promotion. By applying different conversion algorithms depending on the category of the input content, more appropriate conversion results can be provided. Some or all of the above processing in the conversion unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0065] The savings function can analyze the user's past behavior to select the optimal savings method when saving money. For example, it can suggest the optimal savings method based on actions the user has frequently performed in the past. It can suggest an efficient savings method from the user's past behavioral history. It can analyze the user's past behavioral patterns and suggest the most effective savings method. In this way, by analyzing past behavior, it can provide the user with the optimal savings method. Some or all of the above processing in the savings function may be performed using generative AI, or it may be performed without using generative AI.

[0066] The detection unit can apply different detection algorithms to each category of input content upon detection. For example, content related to education can be detected using a detection algorithm specialized in educational support. Content related to health can be detected using a detection algorithm specialized in health promotion. Content related to the environment can be detected using a detection algorithm specialized in environmental protection. By applying different detection algorithms to each category, more appropriate detection becomes possible. Some or all of the above processing in the detection unit may be performed using generative AI, or it may be performed without using generative AI.

[0067] The proposal department can determine the priority of proposals based on the timing of the detected changes when submitting them. For example, urgent changes can be proposed and provided to the user with priority. Regular changes can be proposed and provided to the user with normal priority. Long-term changes can be proposed and provided to the user at a later date. This allows for the provision of more appropriate proposals by prioritizing proposals based on the submission timing. Some or all of the above processing in the proposal department may be performed using generative AI, or it may be performed without using generative AI.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk inputs the user's daily activities. For example, users can input learning activities, household chores, exercise, etc. The reception desk can input activities using a smartphone or personal computer and can also support multiple input methods, such as voice input and handwriting input. Step 2: The conversion unit automatically converts the content entered by the reception unit into SDGs goals. The conversion unit uses natural language processing technology to analyze the input content and determine whether it corresponds to an SDG goal. For example, if the input content is "helping around the house," it can be determined to correspond to SDG Goal 11 (Sustainable Cities and Communities). Step 3: The Savings Unit stores the converted content from the Conversion Unit into a Self-Esteem Savings Account. The Savings Unit can store the converted content as coins in digital format. Users can input their daily activities, and the converted content is stored as coins, allowing them to visually confirm their self-esteem. Step 4: The detection unit stores daily entries and detects changes in the child. The detection unit can analyze changes in the frequency and content of entries and detect anomalies. For example, it can detect anomalies if the child's entries decrease or if unusual content is written. Step 5: The suggestion unit proposes follow-up actions based on the changes detected by the detection unit. Based on the detected changes, the suggestion unit can propose encouraging messages or additional tasks. It can also provide information to help family members and teachers choose the best approach from the suggestions.

[0070] (Example of form 2) The application for enhancing SDGs and self-esteem according to an embodiment of the present invention is a system that converts a user's daily actions into SDG goals and enhances their self-esteem. This system allows users to input what they have done each day, automatically detects whether the input corresponds to an SDG goal, and if so, automatically converts it to an SDG goal. The converted content is accumulated as "self-esteem savings," allowing users to feel a sense of accomplishment and self-worth. For example, a user inputs what they have done and accomplished in a day, and the generating AI analyzes the content. If it corresponds to an SDG goal, it is automatically converted, and the converted content is accumulated as coins in the self-esteem savings. This allows users to feel that their actions contribute to the SDGs and enhance their self-esteem. Furthermore, because daily entries are stored, there is a mechanism for AGI (Artificial General Intelligence) to automatically detect if a child's entries decrease or if there are unusual entries. This allows for the detection of changes in the child and appropriate follow-up. For example, the system visualizes and analyzes the child's "I did it!" from the self-esteem savings and suggests follow-up actions based on the changes. By allowing families and teachers to select the most suitable method from the suggestions, children's growth can be supported. Thus, this invention is an educational support tool for promoting SDGs and self-esteem, supporting children's growth and building a better future. As a result, the application for promoting SDGs and self-esteem can translate the user's daily actions into SDG goals and enhance self-esteem.

[0071] The application for enhancing SDGs and self-esteem according to this embodiment comprises a reception unit, a conversion unit, a savings unit, a detection unit, and a suggestion unit. The reception unit receives input from the user about their daily activities. For example, the user can input learning activities, household chores, exercise, etc. The reception unit allows the user to input activities using, for example, a smartphone or personal computer. The reception unit can also support multiple input methods, such as voice input and handwriting input. The conversion unit automatically converts the content entered by the reception unit into SDGs goals. The conversion unit analyzes the input content using, for example, natural language processing technology to determine whether it corresponds to an SDG goal. For example, if the input content is "household chores," the conversion unit can determine that it corresponds to SDG Goal 11 (Make cities and human settlements inclusive, safe, resilient and sustainable). The savings unit stores the content converted by the conversion unit in a self-esteem savings account. The savings unit can store the converted content digitally as coins. The savings unit allows users to input their daily activities, for example, and the converted content is accumulated as coins, allowing them to visually confirm their self-esteem. The detection unit stores daily entries and detects changes in the child. The detection unit can analyze changes in the frequency and content of entries and detect anomalies. For example, the detection unit can detect anomalies if the child's entries decrease or if unusual content is written. The suggestion unit proposes follow-up actions in response to the changes detected by the detection unit. For example, the suggestion unit can suggest encouraging messages or additional tasks based on the detected changes. For example, the suggestion unit can provide information to help family members or teachers select the most suitable method from the suggestions. Thus, the application for enhancing SDGs and self-esteem according to this embodiment can convert the user's daily activities into SDG goals and enhance their self-esteem.

[0072] The reception system allows users to input their daily activities. For example, users can input learning activities, household chores, exercise, etc. The reception system allows users to input their activities using a smartphone or personal computer. It can also support multiple input methods, such as voice input and handwriting input. Specifically, users can open a smartphone application and input their daily activities as text into a dedicated input form. With voice input, users simply speak their activities into the microphone, and voice recognition technology converts it into text. With handwriting input, users input their activities by handwriting on a touchscreen, and handwriting recognition technology converts it into digital text. Furthermore, the reception system has a function to automatically categorize and organize user input. For example, learning activities are categorized as "Education," household chores as "Home," and exercise as "Health." This allows users to easily review their activities and visually see which categories they spend the most time on. The reception system also saves user input data to a cloud server, enabling data backup and access from other devices. This allows users to input and review their activities anytime, anywhere.

[0073] The conversion unit automatically converts the content entered by the reception unit into SDGs goals. For example, the conversion unit analyzes the input content using natural language processing technology to determine whether it corresponds to an SDG goal. Specifically, it analyzes the input text using natural language processing technology and extracts keywords and context. For example, if the input is "helping around the house," the conversion unit extracts keywords such as "household" and "helping around," and determines that this corresponds to SDG Goal 11 (Sustainable Cities and Communities). Furthermore, the conversion unit can also associate user actions with multiple SDGs goals. For example, "learning activities" corresponds to SDG Goal 4 (Quality Education), but may also be related to Goal 8 (Decent Work and Economic Growth). In this way, the conversion unit evaluates user actions from multiple perspectives and associates them with SDGs goals. The conversion unit can continuously learn using AI to improve its conversion accuracy. For example, it can receive user feedback, evaluate whether the conversion result was accurate, and improve the conversion algorithm based on the results. This allows the conversion unit to more accurately associate user behavior with the SDGs goals.

[0074] The Savings Department stores the converted content from the Conversion Department into a self-esteem savings account. For example, the Savings Department can store converted content digitally as coins. Specifically, users input their daily activities, and the converted content is stored as coins, allowing them to visually track their self-esteem. For instance, if a user inputs "helping with household chores" and this is converted to align with SDG Goal 11, the Savings Department will award a certain number of coins for this activity. The coins are displayed digitally, and users can check their savings status within the application. The Savings Department offers flexible coin awarding criteria. For example, different coins can be awarded based on the type, frequency, and importance of the activity. This allows users to concretely understand how much their actions contribute to the SDGs. The Savings Department also features a function to visually display the user's savings status using graphs and charts. This allows users to see their growth and contributions at a glance, boosting their self-esteem. Furthermore, the Savings Department provides a function to share the coins users have accumulated with other users. For example, users can share their coin savings progress with family and friends and encourage each other. This allows the savings department to boost user motivation and encourage sustained savings.

[0075] The detection unit stores daily entries and detects changes in the child's behavior. For example, the detection unit can analyze changes in the frequency and content of entries to detect anomalies. Specifically, it analyzes user-input data in chronological order and detects deviations from normal patterns. For example, if a child's entries suddenly decrease or the content changes to negative, the detection unit will detect this as an anomaly. The detection unit implements an anomaly detection algorithm using AI to perform highly accurate detection. For example, it learns normal patterns based on past data and detects anomalies based on this. In addition, the detection unit can detect not only user behavior data but also changes in emotions and mood. For example, it performs sentiment analysis of text entered by the user and detects anomalies if positive emotions decrease or negative emotions increase. This allows the detection unit to understand the user's overall state and detect anomalies early. Furthermore, the detection unit also has a function to issue alerts when an anomaly is detected. For example, it can send notifications to parents or teachers to encourage early action. This allows the detection unit to quickly detect changes in the user and support appropriate responses.

[0076] The suggestion unit proposes follow-up actions in response to changes detected by the detection unit. For example, based on the detected changes, the suggestion unit can suggest encouraging messages or additional tasks. Specifically, it provides appropriate follow-up in response to changes in the user's behavior and emotions. For example, if a user shows negative emotions, the suggestion unit sends an encouraging message and suggests activities to improve the user's mood. Also, if the user's behavior decreases, the suggestion unit sets new tasks or goals to increase the user's motivation. The suggestion unit uses AI to analyze the user's state and automatically generates the optimal follow-up. For example, it learns the user's preferences and tendencies based on past data and makes personalized suggestions based on that. Furthermore, the suggestion unit provides information to help family members and teachers select the best method from the suggestions. For example, it presents multiple follow-up options and explains the effects and application conditions of each. This allows family members and teachers to select and implement the most suitable follow-up for the user. The suggestion unit can also receive user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the suggestion unit to respond quickly and appropriately to changes in the user, supporting sustainable growth and improved self-esteem.

[0077] The conversion unit can automatically convert input content based on the SDGs goals. For example, the conversion unit analyzes the input content using natural language processing technology to determine whether it corresponds to an SDG goal. For example, if the input content is "helping around the house," the conversion unit can determine that it corresponds to SDG Goal 11 (Sustainable Cities and Communities). For example, if the input content is "recycling activities," the conversion unit can determine that it corresponds to SDG Goal 12 (Responsible Consumption and Production). For example, if the input content is "energy conservation," the conversion unit can determine that it corresponds to SDG Goal 7 (Affordable and Clean Energy). This allows users to feel that their actions are contributing to the SDGs by automatically converting the content based on the SDGs goals. Some or all of the above processing in the conversion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the conversion unit can input the input content into a generation AI, and the generation AI can determine whether it corresponds to an SDG goal.

[0078] The savings unit can store the converted content as coins in a self-esteem savings account. The savings unit can store the converted content as digital coins, for example. The savings unit can visually confirm self-esteem by having the user input their daily activities and seeing the converted content stored as coins. The savings unit can also store the converted content as physical coins, for example. The savings unit can allow the user to experience self-esteem by having the user input their activities and seeing the converted content stored as physical coins. The savings unit can also store the converted content as a point system, for example. The savings unit can enhance self-esteem by having the user input their activities and seeing the converted content stored as points. This allows for visual confirmation of self-esteem by having the converted content stored as coins. Some or all of the above processing in the savings unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the savings unit can propose a method in which the converted content is input into a generation AI, and the generation AI stores it as coins.

[0079] The detection unit can automatically detect when daily postings decrease or when there are unusual postings. The detection unit can, for example, analyze changes in the frequency and content of postings to detect anomalies. The detection unit can, for example, detect anomalies when a child's postings decrease or when unusual content is posted. The detection unit can, for example, analyze the content of postings to detect changes in emotions. The detection unit can, for example, analyze the emotional expressions contained in a child's postings to detect anomalies. The detection unit can, for example, analyze posting patterns to detect anomalies. The detection unit can, for example, detect anomalies when a child's posting pattern changes. This enables appropriate follow-up by automatically detecting changes in the child. Some or all of the above processing in the detection unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the detection unit can input the content of the postings into a generation AI, and the generation AI can detect anomalies.

[0080] The suggestion unit can propose follow-up actions in response to detected changes. For example, based on detected changes, the suggestion unit can suggest encouraging messages or additional tasks. For example, if a child's posting activity decreases, the suggestion unit can suggest encouraging messages. For example, if the content of a child's postings changes, the suggestion unit can suggest additional tasks. For example, based on detected changes, the suggestion unit can provide information to help families and teachers choose the best approach. For example, based on detected changes, the suggestion unit can propose multiple follow-up methods that families and teachers can choose from. For example, based on detected changes, the suggestion unit can present the advantages and disadvantages of the follow-up methods that families and teachers can choose from. This allows for support of a child's growth by proposing follow-up actions in response to detected changes. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input detected changes into a generative AI, which can then propose the best follow-up method.

[0081] The suggestion unit can provide information to help families and teachers select the most suitable method from among the suggestions. For example, the suggestion unit can suggest multiple follow-up methods that families and teachers can choose from based on the detected changes. For example, the suggestion unit can present the advantages and disadvantages of the follow-up methods that families and teachers can choose from based on the detected changes. For example, the suggestion unit can predict the effectiveness of the follow-up methods that families and teachers can choose from based on the detected changes. For example, the suggestion unit can provide the implementation procedures for the follow-up methods that families and teachers can choose from based on the detected changes. For example, the suggestion unit can present examples of the follow-up methods that families and teachers can choose from based on the detected changes. This allows for more effective support of a child's growth by providing information to help families and teachers choose the most suitable method. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the detected changes into a generative AI, which can then suggest the most suitable follow-up method.

[0082] The reception unit can estimate the user's emotions and adjust the timing of input based on the estimated emotions. For example, if the user is stressed, the reception unit can reduce the frequency of notifications prompting input. For example, if the user is relaxed, the reception unit can frequently prompt input. For example, if the user is busy, the reception unit can temporarily stop prompting input. This allows for prompting input at a more appropriate time by adjusting the timing of input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using a generative AI, or not using a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then adjust the timing of input.

[0083] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions content that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past input history into a generative AI, which can then suggest the optimal input method.

[0084] The reception unit can filter input content based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the reception unit can prioritize displaying health-related input content. For example, if the user is interested in environmental issues, the reception unit can prioritize displaying environment-related input content. For example, if the user is interested in education, the reception unit can prioritize displaying education-related input content. By filtering input content based on the user's lifestyle and areas of interest, more relevant content can be provided. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's lifestyle and areas of interest into a generative AI, which can then filter the input content.

[0085] The reception unit can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is stressed, the reception unit can prioritize displaying simple input content. For example, if the user is relaxed, the reception unit can prioritize displaying detailed input content. For example, if the user is busy, the reception unit can prioritize displaying important input content. This allows for the provision of more appropriate content by prioritizing input content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input user emotion data into a generative AI, which can then determine the priority of input content.

[0086] The reception unit can prioritize inputting highly relevant content by considering the user's geographical location information during input. For example, if the user is in a specific region, the reception unit can prioritize displaying input content related to that region. For example, if the user is traveling, the reception unit can prioritize displaying input content related to their travel destination. For example, if the user is at home, the reception unit can prioritize displaying input content related to their home. This allows for the provision of more relevant content by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize inputting highly relevant content.

[0087] The reception unit can analyze the user's social media activity during input and input relevant content. For example, the reception unit can suggest relevant input content based on what the user has shared on social media. For example, the reception unit can suggest relevant input content based on the content of accounts the user follows on social media. For example, the reception unit can suggest relevant input content based on what the user has "liked" on social media. By analyzing social media activity, it is possible to provide more relevant content. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's social media activity into a generative AI, and the generative AI can input relevant content.

[0088] The transformation unit can estimate the user's emotions and adjust the way the transformation is expressed based on the estimated emotions. For example, if the user is relaxed, the transformation unit can display the transformation result in a soft expression. For example, if the user is in a hurry, the transformation unit can display the transformation result in a concise expression. For example, if the user is excited, the transformation unit can display the transformation result in a visually stimulating expression. In this way, by adjusting the way the transformation is expressed based on the user's emotions, a more appropriate transformation result can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the transformation unit may be performed using a generative AI, or not using a generative AI. For example, the transformation unit can input user emotion data into a generative AI, and the generative AI can adjust the way the transformation is expressed.

[0089] The conversion unit can adjust the level of detail in the conversion based on the importance of the input content. For example, the conversion unit can convert important content in detail and provide it to the user. For example, the conversion unit can convert general content concisely and provide it to the user. For example, the conversion unit can omit low-priority content during the conversion and provide it to the user. In this way, by adjusting the level of detail in the conversion based on the importance of the input content, a more appropriate conversion result can be provided. Some or all of the above processing in the conversion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the conversion unit can input the importance of the input content to the generation AI, and the generation AI can adjust the level of detail in the conversion.

[0090] The conversion unit can apply different conversion algorithms depending on the category of the input content during conversion. For example, the conversion unit can apply a conversion algorithm specialized in environmental protection to content related to the environment. For example, the conversion unit can apply a conversion algorithm specialized in educational support to content related to education. For example, the conversion unit can apply a conversion algorithm specialized in health promotion to content related to health. By applying different conversion algorithms depending on the category of the input content, a more appropriate conversion result can be provided. Some or all of the above processing in the conversion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversion unit can input the category of the input content into a generative AI, and the generative AI can apply different conversion algorithms.

[0091] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated emotions. For example, if the user is in a hurry, the translation unit can provide a short, concise translation. For example, if the user is relaxed, the translation unit can provide a longer translation that includes detailed explanations. For example, if the user is excited, the translation unit can provide a translation with visually stimulating effects. By adjusting the length of the translation based on the user's emotions, a more appropriate translation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using a generative AI, or not using a generative AI. For example, the translation unit can input user emotion data into a generative AI, which can then adjust the length of the translation.

[0092] The conversion unit can determine the conversion priority based on the submission timing of the input content during the conversion process. For example, the conversion unit can prioritize the conversion of urgent content and provide it to the user. For example, the conversion unit can convert periodic content with normal priority and provide it to the user. For example, the conversion unit can postpone the conversion of long-term content and provide it to the user. This allows for the provision of more appropriate conversion results by determining the conversion priority based on the submission timing of the input content. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit can input the submission timing of the input content into the generating AI, and the generating AI can determine the conversion priority.

[0093] The conversion unit can adjust the order of conversion based on the relevance of the input content during conversion. For example, the conversion unit can prioritize the conversion of highly relevant content and provide it to the user. For example, the conversion unit can postpone the conversion of less relevant content and provide it to the user. For example, the conversion unit can omit irrelevant content during conversion and provide it to the user. In this way, by adjusting the order of conversion based on the relevance of the input content, a more appropriate conversion result can be provided. Some or all of the above processing in the conversion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the conversion unit can input the relevance of the input content to the generation AI, and the generation AI can adjust the order of conversion.

[0094] The savings unit can estimate the user's emotions and adjust the savings method based on the estimated emotions. For example, if the user is relaxed, the savings unit can allow savings at a leisurely pace. If the user is in a hurry, the savings unit can allow savings quickly. If the user is excited, the savings unit can provide a savings method with visually stimulating effects. This allows for the provision of a more appropriate savings method by adjusting the savings method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the savings unit may be performed using a generative AI, or not. For example, the savings unit can input user emotion data into a generative AI, which can then adjust the savings method.

[0095] The savings function can analyze the user's past behavior to select the optimal savings method when saving money. For example, the savings function can suggest the optimal savings method based on actions the user has frequently performed in the past. For example, the savings function can suggest an efficient savings method based on the user's past behavioral history. For example, the savings function can analyze the user's past behavioral patterns to suggest the most effective savings method. In this way, by analyzing past behavior, the savings function can provide the user with the optimal savings method. Some or all of the above processing in the savings function may be performed using, for example, a generative AI, or without a generative AI. For example, the savings function can input the user's past behavioral data into a generative AI, which can then select the optimal savings method.

[0096] The savings function can customize the savings method based on the user's current living situation when they start saving. For example, if the user is busy, the savings function can suggest a simple savings method. For example, if the user is relaxed, the savings function can suggest a detailed savings method. For example, if the user has a specific goal, the savings function can suggest a savings method tailored to that goal. This allows for the provision of a more appropriate savings method by customizing the savings method based on the user's current living situation. Some or all of the above processing in the savings function may be performed using, for example, a generative AI, or without a generative AI. For example, the savings function can input the user's living situation data into a generative AI, which can then customize the savings method.

[0097] The savings unit can estimate the user's emotions and determine savings priorities based on those emotions. For example, if the user is stressed, the savings unit can prioritize suggesting simple savings methods. For example, if the user is relaxed, the savings unit can prioritize suggesting detailed savings methods. For example, if the user is busy, the savings unit can prioritize suggesting important savings methods. This allows for the provision of more appropriate savings methods by prioritizing savings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the savings unit may be performed using a generative AI, or not. For example, the savings unit can input user emotion data into a generative AI, which can then determine savings priorities.

[0098] The savings unit can select the optimal savings method when a user is saving money, taking into account the user's geographical location. For example, if the user is in a specific region, the savings unit can suggest a savings method related to that region. For example, if the user is traveling, the savings unit can suggest a savings method related to the travel destination. For example, if the user is at home, the savings unit can suggest a savings method related to home. By considering geographical location, the system can provide a more appropriate savings method. Some or all of the above processing in the savings unit may be performed using, for example, a generative AI, or without a generative AI. For example, the savings unit can input the user's geographical location information into a generative AI, which can then select the optimal savings method.

[0099] The savings department can analyze a user's social media activity and suggest savings methods when they make a savings. For example, the savings department can suggest relevant savings methods based on what the user has shared on social media. For example, the savings department can suggest relevant savings methods based on the content of accounts the user follows on social media. For example, the savings department can suggest relevant savings methods based on what the user has "liked" on social media. By analyzing social media activity, it is possible to provide more appropriate savings methods. Some or all of the above processing in the savings department may be performed using, for example, a generative AI, or without a generative AI. For example, the savings department can input the user's social media activity into a generative AI, which can then suggest savings methods.

[0100] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated emotions. For example, the detection unit can relax the detection criteria if the user is stressed. For example, the detection unit can tighten the detection criteria if the user is relaxed. For example, the detection unit can temporarily relax the detection criteria if the user is busy. This allows for more accurate detection by adjusting the detection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using a generative AI, or not using a generative AI. For example, the detection unit can input user emotion data into a generative AI, which can then adjust the detection criteria.

[0101] The detection unit can predict current changes by referring to past data when detection occurs. For example, the detection unit can predict current changes by analyzing the user's past written data. For example, the detection unit can predict current changes based on the user's past behavior patterns. For example, the detection unit can predict current changes by referring to the user's past emotional data. This allows for more accurate prediction of current changes by referring to past data. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input past data into a generative AI, which can then predict current changes.

[0102] The detection unit can apply different detection algorithms to each category of input content upon detection. For example, the detection unit can apply a detection algorithm specialized for educational support to content related to education. For example, the detection unit can apply a detection algorithm specialized for health promotion to content related to health. For example, the detection unit can apply a detection algorithm specialized for environmental protection to content related to the environment. By applying different detection algorithms to each category, more appropriate detection becomes possible. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the category of input content into a generative AI, and the generative AI can apply different detection algorithms.

[0103] The detection unit can estimate the user's emotions and adjust the order in which the detection results are displayed based on the estimated emotions. For example, if the user is stressed, the detection unit can prioritize displaying important detection results. For example, if the user is relaxed, the detection unit can prioritize displaying detailed detection results. For example, if the user is busy, the detection unit can prioritize displaying concise detection results. This allows for the provision of more appropriate information by adjusting the order in which detection results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using a generative AI, or not using a generative AI. For example, the detection unit can input user emotion data into a generative AI, and the generative AI can adjust the order in which the detection results are displayed.

[0104] The detection unit can perform detection while considering the geographical distribution of the input content. For example, if the user is in a specific region, the detection unit can prioritize displaying detection results related to that region. For example, if the user is traveling, the detection unit can prioritize displaying detection results related to the travel destination. For example, if the user is at home, the detection unit can prioritize displaying detection results related to home. This makes it possible to perform more appropriate detection by considering geographical distribution. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the geographical distribution of the input content into a generative AI, and the generative AI can perform detection.

[0105] The detection unit can improve the accuracy of detection by referring to relevant literature for the input content during detection. For example, the detection unit can improve the accuracy of detection by referring to relevant academic papers. For example, the detection unit can improve the accuracy of detection by referring to relevant news articles. For example, the detection unit can improve the accuracy of detection by referring to relevant books. In this way, the accuracy of detection can be improved by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input relevant literature for the input content into a generating AI, and the generating AI can improve the accuracy of detection.

[0106] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can present suggestions in a gentle manner. If the user is in a hurry, the suggestion unit can present suggestions in a concise manner. If the user is excited, the suggestion unit can present suggestions in a visually stimulating manner. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the way suggestions are presented.

[0107] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the detected changes. For example, it can suggest important changes in detail and provide them to the user. For example, it can suggest general changes concisely and provide them to the user. For example, it can suggest low-priority changes in a simplified manner and provide them to the user. This allows for the provision of more appropriate suggestions by adjusting the level of detail in suggestions based on the importance of the changes. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the importance of the detected changes into the generative AI, which can then adjust the level of detail in its suggestions.

[0108] The proposal unit can apply different proposal algorithms depending on the category of the detected change when making a proposal. For example, for educational changes, the proposal unit can apply a proposal algorithm specialized in educational support. For example, for health changes, the proposal unit can apply a proposal algorithm specialized in health promotion. For example, for environmental changes, the proposal unit can apply a proposal algorithm specialized in environmental protection. By applying different proposal algorithms depending on the category, more appropriate proposals can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category of the detected change into a generative AI, and the generative AI can apply a different proposal algorithm.

[0109] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the length of the suggestions.

[0110] The proposal unit can determine the priority of proposals based on the timing of the detected changes when making a proposal. For example, the proposal unit can prioritize and provide urgent changes to the user. For example, the proposal unit can propose and provide regular changes to the user with normal priority. For example, the proposal unit can postpone proposing and provide long-term changes to the user. This allows for the provision of more appropriate proposals by prioritizing proposals based on the submission timing. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the submission timing of detected changes into a generative AI, and the generative AI can determine the priority of proposals.

[0111] The suggestion unit can adjust the order of suggestions based on the relevance of the detected changes when making suggestions. For example, the suggestion unit can prioritize suggesting and providing highly relevant changes to the user. For example, the suggestion unit can postpone suggesting and providing to the user less relevant changes. For example, the suggestion unit can omit suggesting and providing to the user irrelevant changes. By adjusting the order of suggestions based on relevance, more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the relevance of the detected changes into a generative AI, which can then adjust the order of suggestions.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception desk can automatically complete user input by referencing the user's past behavior history. For example, it can automatically display suggestions for actions the user has frequently entered in the past. It can also learn patterns of past user actions and automatically complete similar actions when the user enters them. Furthermore, it can prompt the user for input at the appropriate time, taking into account the time of day and frequency of past user actions. This streamlines the user's input process and enables smoother operation. Some or all of the above processing in the reception desk may be performed using generative AI, or it may be performed without using generative AI.

[0114] The conversion unit can estimate the user's emotions when converting user behavior into SDGs goals, and adjust the expression of the conversion based on the estimated emotions. For example, if the user is relaxed, the conversion result can be displayed in a gentle expression. If the user is in a hurry, the conversion result can be displayed in a concise expression. If the user is excited, the conversion result can be displayed in a visually stimulating expression. In this way, by adjusting the expression of the conversion based on the user's emotions, a more appropriate conversion result can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the conversion unit may be performed using generative AI, or it may be performed without using generative AI.

[0115] The savings unit can estimate the user's emotions when depositing the converted content into self-esteem savings and adjust the savings method based on the estimated emotions. For example, if the user is relaxed, savings can be made at a leisurely pace. If the user is in a hurry, savings can be made quickly. If the user is excited, a savings method with visually stimulating effects can be provided. In this way, a more appropriate savings method can be provided by adjusting the savings method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the savings unit may be performed using generative AI or not using generative AI.

[0116] The detection unit stores daily entries and, when detecting changes in a child, can predict current changes by referring to past data. For example, it can analyze the user's past entry data to predict current changes. It can predict current changes based on the user's past behavior patterns. It can predict current changes by referring to the user's past emotional data. In this way, by referring to past data, current changes can be predicted more accurately. Some or all of the above processing in the detection unit may be performed using generative AI, or it may be performed without using generative AI.

[0117] The suggestion unit can estimate the user's emotions when suggesting follow-up actions in response to detected changes, and adjust the expression of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestions can be expressed in a gentle manner. If the user is in a hurry, the suggestions can be expressed in a concise manner. If the user is excited, the suggestions can be expressed in a visually stimulating manner. In this way, by adjusting the expression of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the processing described above in the suggestion unit may be performed using generative AI, or it may be performed without using generative AI.

[0118] The reception desk can prioritize inputting highly relevant content by considering the user's geographical location when inputting user activity. For example, if the user is in a specific region, input content related to that region can be displayed preferentially. If the user is traveling, input content related to their travel destination can be displayed preferentially. If the user is at home, input content related to their home can be displayed preferentially. This allows for the provision of more relevant content by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using generative AI, or it may be performed without using generative AI.

[0119] The conversion unit can apply different conversion algorithms depending on the category of the input content during conversion. For example, content related to the environment can be given a conversion algorithm specialized in environmental protection. Content related to education can be given a conversion algorithm specialized in educational support. Content related to health can be given a conversion algorithm specialized in health promotion. By applying different conversion algorithms depending on the category of the input content, more appropriate conversion results can be provided. Some or all of the above processing in the conversion unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0120] The savings function can analyze the user's past behavior to select the optimal savings method when saving money. For example, it can suggest the optimal savings method based on actions the user has frequently performed in the past. It can suggest an efficient savings method from the user's past behavioral history. It can analyze the user's past behavioral patterns and suggest the most effective savings method. In this way, by analyzing past behavior, it can provide the user with the optimal savings method. Some or all of the above processing in the savings function may be performed using generative AI, or it may be performed without using generative AI.

[0121] The detection unit can apply different detection algorithms to each category of input content upon detection. For example, content related to education can be detected using a detection algorithm specialized in educational support. Content related to health can be detected using a detection algorithm specialized in health promotion. Content related to the environment can be detected using a detection algorithm specialized in environmental protection. By applying different detection algorithms to each category, more appropriate detection becomes possible. Some or all of the above processing in the detection unit may be performed using generative AI, or it may be performed without using generative AI.

[0122] The proposal department can determine the priority of proposals based on the timing of the detected changes when submitting them. For example, urgent changes can be proposed and provided to the user with priority. Regular changes can be proposed and provided to the user with normal priority. Long-term changes can be proposed and provided to the user at a later date. This allows for the provision of more appropriate proposals by prioritizing proposals based on the submission timing. Some or all of the above processing in the proposal department may be performed using generative AI, or it may be performed without using generative AI.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk inputs the user's daily activities. For example, users can input learning activities, household chores, exercise, etc. The reception desk can input activities using a smartphone or personal computer and can also support multiple input methods, such as voice input and handwriting input. Step 2: The conversion unit automatically converts the content entered by the reception unit into SDGs goals. The conversion unit uses natural language processing technology to analyze the input content and determine whether it corresponds to an SDG goal. For example, if the input content is "helping around the house," it can be determined to correspond to SDG Goal 11 (Sustainable Cities and Communities). Step 3: The Savings Unit stores the converted content from the Conversion Unit into a Self-Esteem Savings Account. The Savings Unit can store the converted content as coins in digital format. Users can input their daily activities, and the converted content is stored as coins, allowing them to visually confirm their self-esteem. Step 4: The detection unit stores daily entries and detects changes in the child. The detection unit can analyze changes in the frequency and content of entries and detect anomalies. For example, it can detect anomalies if the child's entries decrease or if unusual content is written. Step 5: The suggestion unit proposes follow-up actions based on the changes detected by the detection unit. Based on the detected changes, the suggestion unit can propose encouraging messages or additional tasks. It can also provide information to help family members and teachers choose the best approach from the suggestions.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, conversion unit, savings unit, detection unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can input the user's daily actions using the reception device 38 of the smart device 14. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the input content using natural language processing technology to determine whether it corresponds to the SDGs goals. The savings unit is implemented in the control unit 46A of the smart device 14, for example, and can store the converted content in digital form as coins. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and stores daily entries to detect changes in the child. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes follow-up based on the detected changes. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, conversion unit, savings unit, detection unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can input the user's daily activities by voice using the microphone 238 of the smart glasses 214. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the input content using natural language processing technology to determine whether it corresponds to the SDGs goals. The savings unit is implemented in the control unit 46A of the smart glasses 214, for example, and can store the converted content in digital form as coins. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and stores daily entries to detect changes in the child. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes follow-up based on the detected changes. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the reception unit, conversion unit, savings unit, detection unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can input the user's daily activities by voice using the microphone 238 of the headset terminal 314. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the input content using natural language processing technology to determine whether it corresponds to the SDGs goals. The savings unit is implemented in the control unit 46A of the headset terminal 314, for example, and can store the converted content in digital form as coins. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and stores daily entries to detect changes in the child. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes follow-up based on the detected changes. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the reception unit, conversion unit, savings unit, detection unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit can use the microphone 238 of the robot 414 to input the user's daily activities by voice. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the input content using natural language processing technology to determine whether it corresponds to the SDGs goals. The savings unit is implemented in the control unit 46A of the robot 414, for example, and can store the converted content in digital form as coins. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and stores daily entries to detect changes in the child. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes follow-up based on the detected changes. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) The reception desk where daily activities are entered, A conversion unit that automatically converts the content entered by the reception unit into SDGs goals, A savings unit stores the content converted by the conversion unit into a self-esteem savings unit, A detection unit that stores daily entries and detects changes in the child, The system includes a suggestion unit that proposes follow-up actions in response to the changes detected by the detection unit. A system characterized by the following features. (Note 2) The conversion unit is The system automatically converts the entered content based on the SDGs goals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned savings section is, The converted content is saved as coins in a self-esteem savings account. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is It automatically detects when the number of daily posts decreases or when there are posts that are different from the usual. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Follow-up suggestions based on detected changes The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Provides information to help families and teachers choose the best method from the proposed options. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input data, the system filters the input based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When inputting data, the system prioritizes input of highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During input, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The conversion unit is It estimates the user's emotions and adjusts the way the transformation is expressed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The conversion unit is During conversion, the level of detail in the conversion is adjusted based on the importance of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The conversion unit is During conversion, different conversion algorithms are applied depending on the category of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The conversion unit is It estimates the user's emotions and adjusts the length of the conversion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The conversion unit is During the conversion process, the conversion priority is determined based on when the input content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is During conversion, the order of conversions is adjusted based on the relevance of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned savings section is, It estimates the user's emotions and adjusts the savings method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned savings section is, When saving money, the system analyzes the user's past behavior to select the optimal saving method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned savings section is, When saving money, the savings method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned savings section is, It estimates the user's emotions and determines savings priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned savings section is, When saving money, the system selects the optimal savings method by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned savings section is, When users are saving money, the system analyzes their social media activity to suggest ways to save. The system described in Appendix 1, characterized by the features described herein. (Note 25) The detection unit is It estimates the user's emotions and adjusts the detection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The detection unit is When detection occurs, past data is referenced to predict current changes. The system described in Appendix 1, characterized by the features described herein. (Note 27) The detection unit is When detection occurs, a different detection algorithm is applied for each category of input content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The detection unit is It estimates the user's emotions and adjusts the order in which detection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The detection unit is During detection, the geographical distribution of the input content is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The detection unit is During detection, the system improves detection accuracy by referring to relevant literature related to the input content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the detected changes. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the detected change. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When submitting a proposal, prioritize it based on when the detected changes were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the detected changes. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where daily activities are entered, A conversion unit that automatically converts the content entered by the reception unit into SDGs goals, A savings unit that stores the content converted by the conversion unit into a self-esteem savings unit, A detection unit that stores daily entries and detects changes in the child, The system includes a suggestion unit that proposes follow-up actions in response to the changes detected by the detection unit. A system characterized by the following features.

2. The conversion unit is The system automatically converts the entered content based on the SDGs goals. The system according to feature 1.

3. The aforementioned savings section is, The converted content is saved as coins in a self-esteem savings account. The system according to feature 1.

4. The detection unit is It automatically detects when the number of daily posts decreases or when there are posts that are different from the usual. The system according to feature 1.

5. The aforementioned proposal section is, Follow-up suggestions based on detected changes The system according to feature 1.

6. The aforementioned proposal section is, Provides information to help families and teachers choose the best method from the proposed options. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of input based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

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