System

Abstract

An object of a system according to an embodiment is to efficiently manage a ToDo associated with growth of a child and to remind the child of the ToDo at an appropriate timing.SOLUTION: A system includes a birth date input unit, a ToDo list generation unit, and a reminder unit. The birth date input unit inputs a birth date of a child of the user. The ToDo list generation unit generates a ToDo list based on the birth date input by the birth date input unit. The reminder unit may provide a reminder at an appropriate timing based on the ToDo list generated by the ToDo list generator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently manage the wide range of to-do items that come with growing children and to remind them at the appropriate time.

[0005] The system according to the embodiment aims to efficiently manage ToDos that accompany a child's growth and to provide reminders at appropriate times. [Means for solving the problem]

[0006] The system according to the embodiment includes a date of birth input unit, a to-do list generation unit, and a reminder unit. The date of birth input unit inputs the date of birth of the user's child. The to-do list generation unit generates a to-do list based on the date of birth input by the date of birth input unit. The reminder unit issues reminders at appropriate times based on the to-do list generated by the to-do list generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage ToDos that accompany a child's growth and can remind the child at an appropriate time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The ToDo management app according to an embodiment of the present invention is a system in which a user simply inputs their child's date of birth, and the AI ​​automatically generates a ToDo list and sends reminders at the appropriate time. This allows the ToDo management app to thoroughly manage tasks necessary for a child's growth, reducing the burden on the user.

[0029] A to-do management app according to an embodiment includes a birthdate input unit, a to-do list generation unit, and a reminder unit. The birthdate input unit inputs a user's child's birthdate. For example, the birthdate can be input in YYYY / MM / DD format. The birthdate input unit can also select the birthdate using a calendar widget. The birthdate input unit can also input the birthdate using voice input. For example, the user can input the birthdate by voice and convert it into text data using voice recognition technology. The to-do list generation unit generates a to-do list based on the birthdate input by the birthdate input unit. For example, the generation AI automatically adds tasks to the list according to the child's developmental stage. The generation AI can also present predicted development patterns based on past data and provide the user with options. The generation AI can also optimize the schedule for the entire family by taking into account the birthdates of siblings. For example, the generation AI can adjust schedules for vaccinations and hospital visits to ensure that all family members can efficiently complete their schedules. The reminder unit provides reminders at appropriate times based on the to-do list generated by the to-do list generation unit. For example, the generation AI learns the user's past behavioral patterns and suggests optimal reminder timing. The generation AI can also customize reminder content to suit the user's preferences for more effective notifications. The generation AI can also use an emotion estimation function to generate reminder messages based on the user's emotional state to reduce stress. For example, the generation AI analyzes the user's emotional state and generates reminder messages that elicit positive emotions. This allows the to-do management app according to the embodiment to thoroughly manage tasks necessary for a child's development and reduce the user's burden. For example, a user can receive reminders through the app and respond to important tasks at the appropriate time without forgetting them. Furthermore, the user can optimize the entire family's schedule and efficiently manage tasks through the app.

[0030] The to-do list generation unit can present predicted growth patterns based on past data and offer options to the user. For example, when a user inputs a date of birth, the generation AI presents a predicted growth pattern based on past data. For example, it displays growth curves for height and weight, allowing the user to select the pattern that most closely matches. Furthermore, when a user inputs a date of birth, the generation AI presents predicted growth patterns based on past data and offers options to the user. For example, it predicts eating habits and sleep patterns, allowing the user to select the optimal plan. Furthermore, when a user inputs a date of birth, the generation AI presents predicted growth patterns based on past data and offers options to the user. For example, it predicts vaccination schedules and frequency of hospital visits, allowing the user to select the optimal schedule. This allows for the generation of a more personalized to-do list by providing the user with options for growth patterns.

[0031] The to-do list generator can optimize the schedule for the entire family, taking into account the birth dates of siblings. For example, when a user inputs their date of birth, the AI ​​will optimize the schedule for the entire family by taking into account their birth dates. For example, it will adjust vaccination and medical visit schedules to ensure that all family members are able to complete their schedules efficiently. In addition, when a user inputs their date of birth, the AI ​​will optimize the schedule for the entire family by taking into account their birth dates. For example, it will adjust meal times and family event schedules to increase the amount of time the whole family can spend together. In addition, when a user inputs their date of birth, the AI ​​will optimize the schedule for the entire family by taking into account their birth dates. For example, it will adjust schedules for school events and extracurricular activities to ensure that all family members are able to complete their schedules efficiently. This optimizes the schedule for the entire family, enabling efficient task management.

[0032] The to-do list generation unit can also add regional events and activities to the to-do list based on the user's regional information. For example, when the to-do list generation unit inputs the user's date of birth, the generation AI adds regional events and activities to the to-do list based on the user's regional information. For example, local festivals and school events can be added to the list, allowing the user to participate in local events. In addition, when the to-do list generation unit inputs the user's date of birth, the generation AI adds regional events and activities to the to-do list based on the user's regional information. For example, local health checkups and vaccination campaigns can be added to the list, allowing the user to participate at the appropriate time. In addition, when the to-do list generation unit inputs the user's date of birth, the generation AI adds regional events and activities to the to-do list based on the user's regional information. For example, local child-rearing support events and workshops can be added to the list, allowing the user to receive local support. In this way, adding regional events and activities to the to-do list makes it easier for users to participate in local events.

[0033] The to-do list generation unit can generate a to-do list for cases where special care is needed, taking into account the user's health information. For example, when a user inputs their date of birth, the generation AI considers the user's health information and generates a to-do list for cases where special care is needed. For example, meal plans and medical schedules that address allergies and chronic illnesses are added to the list. In addition, when a user inputs their date of birth, the generation AI considers the user's health information and generates a to-do list for cases where special care is needed. For example, regular health checks and special exercise plans are added to the list, making it easier for the user to manage their health. In addition, when a user inputs their date of birth, the generation AI considers the user's health information and generates a to-do list for cases where special care is needed. For example, a schedule for visiting a specific medical institution and necessary tests are added to the list. In this way, generating a to-do list for cases where special care is needed makes it easier for the user to provide appropriate care.

[0034] The reminder unit learns the user's past behavioral patterns and can suggest the optimal reminder timing. For example, the generation AI in the reminder unit learns the user's past behavioral patterns and suggests the optimal reminder timing. For example, a reminder is sent during a time when the user is most likely to respond. The reminder unit also learns the user's past behavioral patterns and suggests the optimal reminder timing. For example, a reminder is sent during a time when the user is not busy or relaxed. The reminder unit also learns the user's past behavioral patterns and suggests the optimal reminder timing. For example, a reminder is sent during a time when the user is most likely to complete tasks. In this way, by learning the user's past behavioral patterns, the optimal reminder timing can be suggested.

[0035] The reminder unit can customize the reminder content to suit the user's preferences and provide more effective notifications. For example, the generation AI in the reminder unit customizes the reminder content to suit the user's preferences and provides more effective notifications. For example, the reminder message is created using the user's preferred language and tone. The reminder unit also customizes the reminder content to suit the user's preferences and provides more effective notifications. For example, the reminder is provided in the user's preferred notification format (pop-up, email, SMS, etc.). The reminder unit also customizes the reminder content to suit the user's preferences and provides more effective notifications. For example, the reminder message is displayed in the user's preferred design and color scheme. This improves the effectiveness of notifications by providing reminder content that suits the user's preferences.

[0036] The reminder unit can work in conjunction with other devices to provide reminders across multiple devices. For example, the generation AI in the reminder unit works in conjunction with other devices to provide reminders across multiple devices. For example, it sends reminder messages through a smart watch or smart speaker. The generation AI in the reminder unit can also work in conjunction with other devices to provide reminders across multiple devices. For example, it can provide reminders through smart home devices in addition to smartphones and tablets. The generation AI in the reminder unit can also work in conjunction with other devices to provide reminders across multiple devices. For example, it can send reminder messages through in-car devices or wearable devices. This makes it easier for users to receive reminders by providing reminders across multiple devices.

[0037] The reminder unit can work in conjunction with the user's calendar app to provide reminders that fit the schedule. For example, the generation AI of the reminder unit works in conjunction with the user's calendar app to provide reminders that fit the schedule. For example, it sends reminder messages based on appointments registered in the calendar. The generation AI of the reminder unit also works in conjunction with the user's calendar app to provide reminders that fit the schedule. For example, it finds available time on the calendar and provides reminders at the optimal time. The generation AI of the reminder unit also works in conjunction with the user's calendar app to provide reminders that fit the schedule. For example, it automatically updates reminder messages in response to changes in calendar appointments. This makes it possible to provide reminders that fit the schedule by working in conjunction with the user's calendar app.

[0038] The to-do list generation unit can learn past user data and generate a to-do list optimized for each individual user. In the to-do list generation unit, for example, the generation AI learns past user data and generates a to-do list optimized for each individual user. For example, it adds tasks that are optimal for the user to the list based on past behavioral patterns and preferences. In addition, the to-do list generation unit learns past user data and generates a to-do list optimized for each individual user. For example, it takes into account the user's lifestyle rhythm and health condition to suggest tasks at the optimal time. In addition, the to-do list generation unit learns past user data and generates a to-do list optimized for each individual user. For example, it supports efficient task management based on the user's past task completion rate and priority. In this way, by learning past user data, it is possible to generate a to-do list optimized for each individual user.

[0039] The ToDo list generation unit analyzes the user's lifestyle rhythm and can suggest ToDos at the optimal timing. In the ToDo list generation unit, for example, the generation AI analyzes the user's lifestyle rhythm and suggests ToDos at the optimal timing. For example, it takes into account the user's sleep patterns and activity times to support efficient task management. In addition, the ToDo list generation unit analyzes the user's lifestyle rhythm and suggests ToDos at the optimal timing. For example, it adds tasks to the list at the appropriate time based on the user's meal times and exercise times. In addition, the ToDo list generation unit analyzes the user's lifestyle rhythm and suggests ToDos at the optimal timing. For example, it takes into account the user's work and housework schedules to support efficient task management. In this way, by analyzing the user's lifestyle rhythm, it can suggest ToDos at the optimal timing.

[0040] The to-do list generation unit can work with other childcare apps to share data and generate more accurate to-do lists. For example, the generation AI of the to-do list generation unit works with other childcare apps to share data and generate more accurate to-do lists. For example, it adds tasks that are most suitable for the user to the list based on data from other apps. The to-do list generation unit also works with other childcare apps to share data and generate more accurate to-do lists. For example, it suggests appropriate tasks based on health data and growth records from other apps. The to-do list generation unit also works with other childcare apps to share data and generate more accurate to-do lists. For example, it supports efficient task management based on schedule data from other apps. In this way, by working with other childcare apps, more accurate to-do lists can be generated.

[0041] The to-do list generation unit can link with the user's social media account and reflect advice from friends and family in the to-do list. For example, the generation AI in the to-do list generation unit links with the user's social media account and reflects advice from friends and family in the to-do list. For example, it adds appropriate tasks to the list based on child-rearing advice shared on social media. The to-do list generation unit also links with the user's social media account and reflects advice from friends and family in the to-do list. For example, it suggests the most suitable tasks for the user based on comments and messages on social media. The to-do list generation unit also links with the user's social media account and reflects advice from friends and family in the to-do list. For example, it supports efficient task management based on child-rearing experiences shared on social media. By linking with the social media account, advice from friends and family can be reflected in the to-do list.

[0042] The reminder unit can learn the user's past hospital visit history and suggest an optimal hospital visit schedule. For example, the generation AI in the reminder unit learns the user's past hospital visit history and suggests an optimal hospital visit schedule. For example, it adds the optimal hospital visit schedule to a list based on the frequency of past hospital visits and the details of examinations. The reminder unit also learns the user's past hospital visit history and suggests an optimal hospital visit schedule. For example, it takes into account the user's health condition and doctor's instructions to suggest an appropriate hospital visit schedule. The reminder unit also learns the user's past hospital visit history and suggests an optimal hospital visit schedule. For example, it takes into account the user's lifestyle and work schedule to add an efficient hospital visit schedule to a list. In this way, it is possible to suggest an optimal hospital visit schedule by learning from the user's past hospital visit history.

[0043] The reminder unit can work in conjunction with other medical apps to support comprehensive health management. For example, the generation AI in the reminder unit works in conjunction with other medical apps to support comprehensive health management. For example, based on data from other apps, it adds the optimal hospital visit schedule for the user to a list. The generation AI in the reminder unit also works in conjunction with other medical apps to support comprehensive health management. For example, it suggests an appropriate hospital visit schedule based on health data and medical examination records from other apps. The generation AI in the reminder unit also works in conjunction with other medical apps to support comprehensive health management. For example, it supports efficient hospital visit management based on schedule data from other apps. In this way, by working in conjunction with other medical apps, comprehensive health management can be supported.

[0044] The reminder unit can suggest the most suitable medical institution based on information about medical institutions in the user's area. In the reminder unit, for example, the generation AI suggests the most suitable medical institution based on information about medical institutions in the user's area. For example, the most suitable medical institution is added to a list based on the ratings and medical services of medical institutions in the user's area. In addition, the reminder unit suggests the most suitable medical institution based on information about medical institutions in the user's area. For example, it suggests an appropriate medical institution based on the user's health condition and medical specialty. In addition, the reminder unit suggests the most suitable medical institution based on information about medical institutions in the user's area. For example, it adds the most suitable medical institution to a list taking into account the convenience of the user's visits and the medical institution's access information. In this way, suggesting the most suitable medical institution based on information about medical institutions in the area makes it easier for the user to receive appropriate medical care.

[0045] The reminder unit learns the user's past application history and can suggest the optimal application timing. For example, the generation AI in the reminder unit learns the user's past application history and suggests the optimal application timing. For example, it adds the optimal application timing for the user to a list based on past application times and procedure details. The reminder unit also learns the user's past application history and suggests the optimal application timing. For example, it takes into account the user's daily rhythm and work schedule to suggest an efficient application timing. The reminder unit also learns the user's past application history and suggests the optimal application timing. For example, it takes into account application deadlines and the preparation status of necessary documents to remind the user to apply at the appropriate time. In this way, by learning past application history, it can suggest the optimal application timing.

[0046] The reminding unit can automatically generate application documents for users, simplifying their submission. In the reminding unit, for example, a generation AI automatically generates application documents for users, simplifying their submission. For example, the required application documents are automatically created based on information entered by the user, supporting their submission. In addition, the reminding unit automatically generates application documents for users, simplifying their submission. For example, application documents are efficiently created based on past application history and templates of required documents. In addition, the reminding unit automatically generates application documents for users, simplifying their submission. For example, application document formats and sample entries are provided, allowing users to easily create documents. In this way, the submission process is simplified by automatically generating application documents.

[0047] The reminder unit can work in conjunction with other government service apps to provide comprehensive application support. For example, the generation AI in the reminder unit works in conjunction with other government service apps to provide comprehensive application support. For example, based on data from other apps, it adds the application procedures that are most suitable for the user to a list. The generation AI in the reminder unit also works in conjunction with other government service apps to provide comprehensive application support. For example, it suggests efficient application procedures based on application history and required document information from other apps. The generation AI in the reminder unit also works in conjunction with other government service apps to provide comprehensive application support. For example, it supports efficient application management based on schedule data from other apps. This allows for comprehensive application support by working in conjunction with other government service apps.

[0048] The reminder unit can suggest the optimal application method based on administrative information for the user's area. For example, the generation AI in the reminder unit suggests the optimal application method based on administrative information for the user's area. For example, the optimal application method is added to a list based on information on administrative procedures in the area where the user lives. The reminder unit also suggests the optimal application method based on administrative information for the user's area. For example, it considers the user's lifestyle and work schedule to suggest an efficient application method. The reminder unit also suggests the optimal application method based on administrative information for the user's area. For example, it considers application deadlines and the preparation status of required documents to remind the user to apply at the appropriate time. This allows the user to apply efficiently by suggesting the optimal application method based on administrative information for the area.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The to-do list generator can also take into account the user's hobbies and interests and add related tasks to the to-do list. For example, if the user is interested in gardening, seasonal gardening tasks can be added to the list. If the user's hobby is cooking, new recipe suggestions and ingredient shopping lists can be added. Furthermore, if the user is interested in fitness, regular exercise schedules and health management tasks can be added to the list. This makes using the to-do list more enjoyable by providing tasks based on the user's hobbies and interests.

[0051] The reminder unit can also analyze the user's sleep patterns and suggest optimal reminder timing. For example, if the user has a lifestyle rhythm of going to bed early and getting up early, a reminder can be sent in the morning. On the other hand, for a user with a nocturnal lifestyle, a reminder can be sent in the evening. Furthermore, taking into account the user's sleep quality, it is possible to adjust the reminder so that it avoids reminders during deep sleep. This allows for more effective task management by providing reminders that match the user's sleep pattern.

[0052] The reminder unit can also learn the user's past task completion rate and suggest the optimal reminder frequency. For example, if the user frequently completes tasks, the frequency of reminders can be reduced to reduce stress. Also, if the task completion rate is low, the frequency of reminders can be increased to encourage task completion. Furthermore, it is possible to analyze the user's task completion patterns and concentrate reminders on specific time periods or days of the week. This allows for efficient task management by providing reminders based on the user's task completion rate.

[0053] The ToDo list generator can also suggest health management-related tasks based on the user's health data. For example, a daily walking task can be added to the list based on the user's step count data. A balanced meal plan can also be suggested based on the user's dietary data. Furthermore, a task for ensuring adequate sleep can be added to the list based on the user's sleep data. This makes it possible to support health management by providing tasks based on the user's health data.

[0054] The to-do list generator can also learn the user's past task history to support efficient task management. For example, it can analyze patterns of previously completed tasks and suggest similar tasks. It can also predict the appropriate time required for a task based on the time it took to complete a past task. It can also take into account the priority of past tasks and add important tasks to the list first. This enables efficient task management based on the user's past task history.

[0055] The ToDo list generator can also analyze the user's lifestyle and suggest ToDos at the optimal time. For example, it can support efficient task management by taking into account the user's sleep patterns and activity times. It can also add tasks to the list at the appropriate time based on the user's meal times and exercise times. It can also support efficient task management by taking into account the user's work and housework schedules. This allows it to analyze the user's lifestyle and suggest ToDos at the optimal time.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The birthdate input unit inputs the birthdate of the user's child. For example, the birthdate can be input in YYYY / MM / DD format. The birthdate input unit can also select the birthdate using a calendar widget. The birthdate input unit can also input the birthdate using voice input. For example, the user inputs the birthdate by voice, and the voice recognition technology converts it into text data. Step 2: The to-do list generator generates a to-do list based on the date of birth entered by the date of birth input unit. For example, the generator AI automatically adds tasks to the list according to a child's developmental stage. The generator AI can also present predicted development patterns based on past data and provide options to the user. Furthermore, the generator AI can also optimize the schedule for the entire family, taking into account the birth dates of siblings. For example, the generator AI can adjust schedules for vaccinations and hospital visits to ensure that everyone in the family can efficiently complete their schedules. Step 3: The reminder unit provides reminders at appropriate times based on the to-do list generated by the to-do list generator. For example, the generation AI learns the user's past behavioral patterns and suggests the optimal reminder timing. The generation AI can also customize the reminder content to suit the user's preferences for more effective notifications. Furthermore, the generation AI can use its emotion estimation function to generate reminder messages that correspond to the user's emotional state, thereby reducing stress. For example, the generation AI can analyze the user's emotional state and generate reminder messages that elicit positive emotions.

[0058] (Example 2) The ToDo management app according to an embodiment of the present invention is a system in which a user simply inputs their child's date of birth, and the AI ​​automatically generates a ToDo list and sends reminders at the appropriate time. This allows the ToDo management app to thoroughly manage tasks necessary for a child's growth, reducing the burden on the user.

[0059] A to-do management app according to an embodiment includes a birthdate input unit, a to-do list generation unit, and a reminder unit. The birthdate input unit inputs a user's child's birthdate. For example, the birthdate can be input in YYYY / MM / DD format. The birthdate input unit can also select the birthdate using a calendar widget. The birthdate input unit can also input the birthdate using voice input. For example, the user can input the birthdate by voice and convert it into text data using voice recognition technology. The to-do list generation unit generates a to-do list based on the birthdate input by the birthdate input unit. For example, the generation AI automatically adds tasks to the list according to the child's developmental stage. The generation AI can also present predicted development patterns based on past data and provide the user with options. The generation AI can also optimize the schedule for the entire family by taking into account the birthdates of siblings. For example, the generation AI can adjust schedules for vaccinations and hospital visits to ensure that all family members can efficiently complete their schedules. The reminder unit provides reminders at appropriate times based on the to-do list generated by the to-do list generation unit. For example, the generation AI learns the user's past behavioral patterns and suggests optimal reminder timing. The generation AI can also customize reminder content to suit the user's preferences for more effective notifications. The generation AI can also use an emotion estimation function to generate reminder messages based on the user's emotional state to reduce stress. For example, the generation AI analyzes the user's emotional state and generates reminder messages that elicit positive emotions. This allows the to-do management app according to the embodiment to thoroughly manage tasks necessary for a child's development and reduce the user's burden. For example, a user can receive reminders through the app and respond to important tasks at the appropriate time without forgetting them. Furthermore, the user can optimize the entire family's schedule and efficiently manage tasks through the app.

[0060] The to-do list generation unit can present predicted growth patterns based on past data and offer options to the user. For example, when a user inputs a date of birth, the generation AI presents a predicted growth pattern based on past data. For example, it displays growth curves for height and weight, allowing the user to select the pattern that most closely matches. Furthermore, when a user inputs a date of birth, the generation AI presents predicted growth patterns based on past data and offers options to the user. For example, it predicts eating habits and sleep patterns, allowing the user to select the optimal plan. Furthermore, when a user inputs a date of birth, the generation AI presents predicted growth patterns based on past data and offers options to the user. For example, it predicts vaccination schedules and frequency of hospital visits, allowing the user to select the optimal schedule. This allows for the generation of a more personalized to-do list by providing the user with options for growth patterns.

[0061] The to-do list generator can optimize the schedule for the entire family, taking into account the birth dates of siblings. For example, when a user inputs their date of birth, the AI ​​will optimize the schedule for the entire family by taking into account their birth dates. For example, it will adjust vaccination and medical visit schedules to ensure that all family members are able to complete their schedules efficiently. In addition, when a user inputs their date of birth, the AI ​​will optimize the schedule for the entire family by taking into account their birth dates. For example, it will adjust meal times and family event schedules to increase the amount of time the whole family can spend together. In addition, when a user inputs their date of birth, the AI ​​will optimize the schedule for the entire family by taking into account their birth dates. For example, it will adjust schedules for school events and extracurricular activities to ensure that all family members are able to complete their schedules efficiently. This optimizes the schedule for the entire family, enabling efficient task management.

[0062] The to-do list generation unit can also add regional events and activities to the to-do list based on the user's regional information. For example, when the to-do list generation unit inputs the user's date of birth, the generation AI adds regional events and activities to the to-do list based on the user's regional information. For example, local festivals and school events can be added to the list, allowing the user to participate in local events. In addition, when the to-do list generation unit inputs the user's date of birth, the generation AI adds regional events and activities to the to-do list based on the user's regional information. For example, local health checkups and vaccination campaigns can be added to the list, allowing the user to participate at the appropriate time. In addition, when the to-do list generation unit inputs the user's date of birth, the generation AI adds regional events and activities to the to-do list based on the user's regional information. For example, local child-rearing support events and workshops can be added to the list, allowing the user to receive local support. In this way, adding regional events and activities to the to-do list makes it easier for users to participate in local events.

[0063] The to-do list generation unit can generate a to-do list for cases where special care is needed, taking into account the user's health information. For example, when a user inputs their date of birth, the generation AI considers the user's health information and generates a to-do list for cases where special care is needed. For example, meal plans and medical schedules that address allergies and chronic illnesses are added to the list. In addition, when a user inputs their date of birth, the generation AI considers the user's health information and generates a to-do list for cases where special care is needed. For example, regular health checks and special exercise plans are added to the list, making it easier for the user to manage their health. In addition, when a user inputs their date of birth, the generation AI considers the user's health information and generates a to-do list for cases where special care is needed. For example, a schedule for visiting a specific medical institution and necessary tests are added to the list. In this way, generating a to-do list for cases where special care is needed makes it easier for the user to provide appropriate care.

[0064] The to-do list generation unit analyzes the user's emotional state and provides positive feedback, making the input task more enjoyable. For example, when entering a date of birth, the generation AI analyzes the user's emotional state and provides positive feedback. For example, it displays a message such as, "Great! Now you can fully support your child's growth." Also, when entering a date of birth, the generation AI analyzes the user's emotional state and provides positive feedback. For example, it displays a message such as, "This is a great step in watching your child grow up!" Also, when entering a date of birth, the generation AI analyzes the user's emotional state and provides positive feedback. For example, it displays a message such as, "We're excited about your child's future!" In this way, providing positive feedback makes the input task more enjoyable for the user.

[0065] The reminder unit learns the user's past behavioral patterns and can suggest the optimal reminder timing. For example, the generation AI in the reminder unit learns the user's past behavioral patterns and suggests the optimal reminder timing. For example, a reminder is sent during a time when the user is most likely to respond. The reminder unit also learns the user's past behavioral patterns and suggests the optimal reminder timing. For example, a reminder is sent during a time when the user is not busy or relaxed. The reminder unit also learns the user's past behavioral patterns and suggests the optimal reminder timing. For example, a reminder is sent during a time when the user is most likely to complete tasks. In this way, by learning the user's past behavioral patterns, the optimal reminder timing can be suggested.

[0066] The reminder unit can customize the reminder content to suit the user's preferences and provide more effective notifications. For example, the generation AI in the reminder unit customizes the reminder content to suit the user's preferences and provides more effective notifications. For example, the reminder message is created using the user's preferred language and tone. The reminder unit also customizes the reminder content to suit the user's preferences and provides more effective notifications. For example, the reminder is provided in the user's preferred notification format (pop-up, email, SMS, etc.). The reminder unit also customizes the reminder content to suit the user's preferences and provides more effective notifications. For example, the reminder message is displayed in the user's preferred design and color scheme. This improves the effectiveness of notifications by providing reminder content that suits the user's preferences.

[0067] The reminder unit can work in conjunction with other devices to provide reminders across multiple devices. For example, the generation AI in the reminder unit works in conjunction with other devices to provide reminders across multiple devices. For example, it sends reminder messages through a smart watch or smart speaker. The generation AI in the reminder unit can also work in conjunction with other devices to provide reminders across multiple devices. For example, it can provide reminders through smart home devices in addition to smartphones and tablets. The generation AI in the reminder unit can also work in conjunction with other devices to provide reminders across multiple devices. For example, it can send reminder messages through in-car devices or wearable devices. This makes it easier for users to receive reminders by providing reminders across multiple devices.

[0068] The reminder unit can work in conjunction with the user's calendar app to provide reminders that fit the schedule. For example, the generation AI of the reminder unit works in conjunction with the user's calendar app to provide reminders that fit the schedule. For example, it sends reminder messages based on appointments registered in the calendar. The generation AI of the reminder unit also works in conjunction with the user's calendar app to provide reminders that fit the schedule. For example, it finds available time on the calendar and provides reminders at the optimal time. The generation AI of the reminder unit also works in conjunction with the user's calendar app to provide reminders that fit the schedule. For example, it automatically updates reminder messages in response to changes in calendar appointments. This makes it possible to provide reminders that fit the schedule by working in conjunction with the user's calendar app.

[0069] The reminder unit can analyze the user's emotional state and generate a reminder message that elicits positive emotions. For example, the reminder unit uses a generation AI to analyze the user's emotional state and generate a reminder message that elicits positive emotions. For example, it displays a message that makes the user smile. The reminder unit also uses a generation AI to analyze the user's emotional state and generate a reminder message that elicits positive emotions. For example, it displays a message that makes the user feel a sense of accomplishment. The reminder unit also uses a generation AI to analyze the user's emotional state and generate a reminder message that elicits positive emotions. For example, it displays a message that makes the user feel relaxed. In this way, by providing a reminder message that elicits positive emotions, the user's motivation is increased.

[0070] The to-do list generation unit can learn past user data and generate a to-do list optimized for each individual user. In the to-do list generation unit, for example, the generation AI learns past user data and generates a to-do list optimized for each individual user. For example, it adds tasks that are optimal for the user to the list based on past behavioral patterns and preferences. In addition, the to-do list generation unit learns past user data and generates a to-do list optimized for each individual user. For example, it takes into account the user's lifestyle rhythm and health condition to suggest tasks at the optimal time. In addition, the to-do list generation unit learns past user data and generates a to-do list optimized for each individual user. For example, it supports efficient task management based on the user's past task completion rate and priority. In this way, by learning past user data, it is possible to generate a to-do list optimized for each individual user.

[0071] The ToDo list generation unit analyzes the user's lifestyle rhythm and can suggest ToDos at the optimal timing. In the ToDo list generation unit, for example, the generation AI analyzes the user's lifestyle rhythm and suggests ToDos at the optimal timing. For example, it takes into account the user's sleep patterns and activity times to support efficient task management. In addition, the ToDo list generation unit analyzes the user's lifestyle rhythm and suggests ToDos at the optimal timing. For example, it adds tasks to the list at the appropriate time based on the user's meal times and exercise times. In addition, the ToDo list generation unit analyzes the user's lifestyle rhythm and suggests ToDos at the optimal timing. For example, it takes into account the user's work and housework schedules to support efficient task management. In this way, by analyzing the user's lifestyle rhythm, it can suggest ToDos at the optimal timing.

[0072] The to-do list generation unit uses the emotion estimation function to generate a to-do list according to the user's emotional state, thereby reducing stress. The to-do list generation unit, for example, uses the emotion estimation function to generate a to-do list according to the user's emotional state. For example, when stress is high, relaxing tasks are suggested to reduce stress. The to-do list generation unit also uses the emotion estimation function to generate a to-do list according to the user's emotional state. For example, when positive emotions are strong, challenging tasks are suggested to increase a sense of accomplishment. The to-do list generation unit also uses the emotion estimation function to generate a to-do list according to the user's emotional state. For example, when negative emotions are strong, easy tasks are suggested to increase successful experiences. In this way, stress can be reduced by providing a to-do list according to the user's emotional state.

[0073] The to-do list generation unit can work with other childcare apps to share data and generate more accurate to-do lists. For example, the generation AI of the to-do list generation unit works with other childcare apps to share data and generate more accurate to-do lists. For example, it adds tasks that are most suitable for the user to the list based on data from other apps. The to-do list generation unit also works with other childcare apps to share data and generate more accurate to-do lists. For example, it suggests appropriate tasks based on health data and growth records from other apps. The to-do list generation unit also works with other childcare apps to share data and generate more accurate to-do lists. For example, it supports efficient task management based on schedule data from other apps. In this way, by working with other childcare apps, more accurate to-do lists can be generated.

[0074] The to-do list generation unit can link with the user's social media account and reflect advice from friends and family in the to-do list. For example, the generation AI in the to-do list generation unit links with the user's social media account and reflects advice from friends and family in the to-do list. For example, it adds appropriate tasks to the list based on child-rearing advice shared on social media. The to-do list generation unit also links with the user's social media account and reflects advice from friends and family in the to-do list. For example, it suggests the most suitable tasks for the user based on comments and messages on social media. The to-do list generation unit also links with the user's social media account and reflects advice from friends and family in the to-do list. For example, it supports efficient task management based on child-rearing experiences shared on social media. By linking with the social media account, advice from friends and family can be reflected in the to-do list.

[0075] The to-do list generation unit can analyze the user's emotional state and generate a to-do list that elicits positive emotions. In the to-do list generation unit, for example, a generation AI analyzes the user's emotional state and generates a to-do list that elicits positive emotions. For example, tasks that the user can enjoy or that help them relax are added to the list. In addition, the to-do list generation unit analyzes the user's emotional state and generates a to-do list that elicits positive emotions. For example, tasks that the user can enjoy or that help them relax are suggested. In addition, the to-do list generation unit analyzes the user's emotional state and generates a to-do list that elicits positive emotions. For example, tasks that make the user smile or that help them feel refreshed are added to the list. In this way, a to-do list that elicits positive emotions is provided, thereby increasing the user's motivation.

[0076] The reminder unit can learn the user's past hospital visit history and suggest an optimal hospital visit schedule. For example, the generation AI in the reminder unit learns the user's past hospital visit history and suggests an optimal hospital visit schedule. For example, it adds the optimal hospital visit schedule to a list based on the frequency of past hospital visits and the details of examinations. The reminder unit also learns the user's past hospital visit history and suggests an optimal hospital visit schedule. For example, it takes into account the user's health condition and doctor's instructions to suggest an appropriate hospital visit schedule. The reminder unit also learns the user's past hospital visit history and suggests an optimal hospital visit schedule. For example, it takes into account the user's lifestyle and work schedule to add an efficient hospital visit schedule to a list. In this way, it is possible to suggest an optimal hospital visit schedule by learning from the user's past hospital visit history.

[0077] The reminding unit uses the emotion estimation function to provide hospital visit reminders in accordance with the user's emotional state, thereby reducing stress. The reminding unit, for example, uses the emotion estimation function to provide hospital visit reminders in accordance with the user's emotional state. For example, when stress is high, a relaxing message is displayed to reduce the stress of hospital visits. The reminding unit also uses the emotion estimation function to provide hospital visit reminders in accordance with the user's emotional state. For example, when positive emotions are strong, an encouraging message is displayed to increase motivation to visit the hospital. The reminding unit also uses the emotion estimation function to provide hospital visit reminders in accordance with the user's emotional state. For example, when negative emotions are strong, a gentle reminder is displayed to provide a sense of security. In this way, by providing hospital visit reminders in accordance with the user's emotional state, stress can be reduced.

[0078] The reminder unit can work in conjunction with other medical apps to support comprehensive health management. For example, the generation AI in the reminder unit works in conjunction with other medical apps to support comprehensive health management. For example, based on data from other apps, it adds the optimal hospital visit schedule for the user to a list. The generation AI in the reminder unit also works in conjunction with other medical apps to support comprehensive health management. For example, it suggests an appropriate hospital visit schedule based on health data and medical examination records from other apps. The generation AI in the reminder unit also works in conjunction with other medical apps to support comprehensive health management. For example, it supports efficient hospital visit management based on schedule data from other apps. In this way, by working in conjunction with other medical apps, comprehensive health management can be supported.

[0079] The reminder unit can suggest the most suitable medical institution based on information about medical institutions in the user's area. In the reminder unit, for example, the generation AI suggests the most suitable medical institution based on information about medical institutions in the user's area. For example, the most suitable medical institution is added to a list based on the ratings and medical services of medical institutions in the user's area. In addition, the reminder unit suggests the most suitable medical institution based on information about medical institutions in the user's area. For example, it suggests an appropriate medical institution based on the user's health condition and medical specialty. In addition, the reminder unit suggests the most suitable medical institution based on information about medical institutions in the user's area. For example, it adds the most suitable medical institution to a list taking into account the convenience of the user's visits and the medical institution's access information. In this way, suggesting the most suitable medical institution based on information about medical institutions in the area makes it easier for the user to receive appropriate medical care.

[0080] The reminder unit can analyze the user's emotional state and provide hospital visit reminders that elicit positive emotions. For example, the reminder unit uses a generation AI to analyze the user's emotional state and provide hospital visit reminders that elicit positive emotions. For example, the reminder unit displays a message that puts the user at ease, thereby reducing the stress of visiting the hospital. The reminder unit also uses a generation AI to analyze the user's emotional state and provide hospital visit reminders that elicit positive emotions. For example, the reminder unit displays a message that makes the user feel positive, thereby increasing motivation to visit the hospital. The reminder unit also uses a generation AI to analyze the user's emotional state and provide hospital visit reminders that elicit positive emotions. For example, the reminder unit displays a message that makes the user feel relaxed, thereby reducing anxiety about visiting the hospital. In this way, the provision of hospital visit reminders that elicit positive emotions increases the user's motivation.

[0081] The reminder unit learns the user's past application history and can suggest the optimal application timing. For example, the generation AI in the reminder unit learns the user's past application history and suggests the optimal application timing. For example, it adds the optimal application timing for the user to a list based on past application times and procedure details. The reminder unit also learns the user's past application history and suggests the optimal application timing. For example, it takes into account the user's daily rhythm and work schedule to suggest an efficient application timing. The reminder unit also learns the user's past application history and suggests the optimal application timing. For example, it takes into account application deadlines and the preparation status of necessary documents to remind the user to apply at the appropriate time. In this way, by learning past application history, it can suggest the optimal application timing.

[0082] The reminding unit can automatically generate application documents for users, simplifying their submission. In the reminding unit, for example, a generation AI automatically generates application documents for users, simplifying their submission. For example, the required application documents are automatically created based on information entered by the user, supporting their submission. In addition, the reminding unit automatically generates application documents for users, simplifying their submission. For example, application documents are efficiently created based on past application history and templates of required documents. In addition, the reminding unit automatically generates application documents for users, simplifying their submission. For example, application document formats and sample entries are provided, allowing users to easily create documents. In this way, the submission process is simplified by automatically generating application documents.

[0083] The reminding unit uses the emotion estimation function to generate an application support message according to the user's emotional state, thereby reducing stress. The reminding unit, for example, uses the emotion estimation function to generate an application support message according to the user's emotional state. For example, when stress is high, a relaxing message is displayed to reduce the stress of applying. The reminding unit also uses the emotion estimation function to generate an application support message according to the user's emotional state. For example, when positive emotions are strong, an encouraging message is displayed to increase motivation for applying. The reminding unit also uses the emotion estimation function to generate an application support message according to the user's emotional state. For example, when negative emotions are strong, support is provided in kind words, giving a sense of security. In this way, stress can be reduced by providing an application support message according to the user's emotional state.

[0084] The reminder unit can work in conjunction with other government service apps to provide comprehensive application support. For example, the generation AI in the reminder unit works in conjunction with other government service apps to provide comprehensive application support. For example, based on data from other apps, it adds the application procedures that are most suitable for the user to a list. The generation AI in the reminder unit also works in conjunction with other government service apps to provide comprehensive application support. For example, it suggests efficient application procedures based on application history and required document information from other apps. The generation AI in the reminder unit also works in conjunction with other government service apps to provide comprehensive application support. For example, it supports efficient application management based on schedule data from other apps. This allows for comprehensive application support by working in conjunction with other government service apps.

[0085] The reminder unit can suggest the optimal application method based on administrative information for the user's area. For example, the generation AI in the reminder unit suggests the optimal application method based on administrative information for the user's area. For example, the optimal application method is added to a list based on information on administrative procedures in the area where the user lives. The reminder unit also suggests the optimal application method based on administrative information for the user's area. For example, it considers the user's lifestyle and work schedule to suggest an efficient application method. The reminder unit also suggests the optimal application method based on administrative information for the user's area. For example, it considers application deadlines and the preparation status of required documents to remind the user to apply at the appropriate time. This allows the user to apply efficiently by suggesting the optimal application method based on administrative information for the area.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The to-do list generator can also take into account the user's hobbies and interests and add related tasks to the to-do list. For example, if the user is interested in gardening, seasonal gardening tasks can be added to the list. If the user's hobby is cooking, new recipe suggestions and ingredient shopping lists can be added. Furthermore, if the user is interested in fitness, regular exercise schedules and health management tasks can be added to the list. This makes using the to-do list more enjoyable by providing tasks based on the user's hobbies and interests.

[0088] The reminder unit can also analyze the user's sleep patterns and suggest optimal reminder timing. For example, if the user has a lifestyle rhythm of going to bed early and getting up early, a reminder can be sent in the morning. On the other hand, for a user with a nocturnal lifestyle, a reminder can be sent in the evening. Furthermore, taking into account the user's sleep quality, it is possible to adjust the reminder so that it avoids reminders during deep sleep. This allows for more effective task management by providing reminders that match the user's sleep pattern.

[0089] The to-do list generator can also analyze the user's emotional state and suggest tasks that elicit positive emotions. For example, if the user is feeling stressed, it can suggest relaxing tasks or tasks related to hobbies. If the user is feeling positive, it can suggest challenging tasks or tasks for learning new skills. It can also suggest tasks that will make the user feel grateful, eliciting positive emotions. This can increase the user's motivation by providing tasks that match the user's emotional state.

[0090] The reminder unit can also learn the user's past task completion rate and suggest the optimal reminder frequency. For example, if the user frequently completes tasks, the frequency of reminders can be reduced to reduce stress. Also, if the task completion rate is low, the frequency of reminders can be increased to encourage task completion. Furthermore, it is possible to analyze the user's task completion patterns and concentrate reminders on specific time periods or days of the week. This allows for efficient task management by providing reminders based on the user's task completion rate.

[0091] The ToDo list generator can also suggest health management-related tasks based on the user's health data. For example, a daily walking task can be added to the list based on the user's step count data. A balanced meal plan can also be suggested based on the user's dietary data. Furthermore, a task for ensuring adequate sleep can be added to the list based on the user's sleep data. This makes it possible to support health management by providing tasks based on the user's health data.

[0092] The reminder unit can also analyze the user's emotional state and generate reminder messages that elicit positive emotions. For example, if the user is feeling stressed, a relaxing message can be displayed. If the user is feeling positive, an encouraging message can be displayed. Furthermore, if the user is feeling negative, a gentle reminder can be sent to provide a sense of security. In this way, by providing reminder messages that correspond to the user's emotional state, stress can be reduced and motivation can be increased.

[0093] The to-do list generator can also learn the user's past task history to support efficient task management. For example, it can analyze patterns of previously completed tasks and suggest similar tasks. It can also predict the appropriate time required for a task based on the time it took to complete a past task. It can also take into account the priority of past tasks and add important tasks to the list first. This enables efficient task management based on the user's past task history.

[0094] The reminder unit can also analyze the user's emotional state and generate reminder messages that elicit positive emotions. For example, if the user is feeling stressed, a relaxing message can be displayed. If the user is feeling positive, an encouraging message can be displayed. Furthermore, if the user is feeling negative, a gentle reminder can be sent to provide a sense of security. In this way, by providing reminder messages that correspond to the user's emotional state, stress can be reduced and motivation can be increased.

[0095] The ToDo list generator can also analyze the user's lifestyle and suggest ToDos at the optimal time. For example, it can support efficient task management by taking into account the user's sleep patterns and activity times. It can also add tasks to the list at the appropriate time based on the user's meal times and exercise times. It can also support efficient task management by taking into account the user's work and housework schedules. This allows it to analyze the user's lifestyle and suggest ToDos at the optimal time.

[0096] The reminder unit can also analyze the user's emotional state and generate reminder messages that elicit positive emotions. For example, if the user is feeling stressed, a relaxing message can be displayed. If the user is feeling positive, an encouraging message can be displayed. Furthermore, if the user is feeling negative, a gentle reminder can be sent to provide a sense of security. In this way, by providing reminder messages that correspond to the user's emotional state, stress can be reduced and motivation can be increased.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The birthdate input unit inputs the birthdate of the user's child. For example, the birthdate can be input in YYYY / MM / DD format. The birthdate input unit can also select the birthdate using a calendar widget. The birthdate input unit can also input the birthdate using voice input. For example, the user inputs the birthdate by voice, and the voice recognition technology converts it into text data. Step 2: The to-do list generator generates a to-do list based on the date of birth entered by the date of birth input unit. For example, the generator AI automatically adds tasks to the list according to a child's developmental stage. The generator AI can also present predicted development patterns based on past data and provide options to the user. Furthermore, the generator AI can also optimize the schedule for the entire family, taking into account the birth dates of siblings. For example, the generator AI can adjust schedules for vaccinations and hospital visits to ensure that everyone in the family can efficiently complete their schedules. Step 3: The reminder unit provides reminders at appropriate times based on the to-do list generated by the to-do list generator. For example, the generation AI learns the user's past behavioral patterns and suggests the optimal reminder timing. The generation AI can also customize the reminder content to suit the user's preferences for more effective notifications. Furthermore, the generation AI can use its emotion estimation function to generate reminder messages that correspond to the user's emotional state, thereby reducing stress. For example, the generation AI can analyze the user's emotional state and generate reminder messages that elicit positive emotions.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a date of birth input section for inputting the date of birth of the user's child; a ToDo list generation unit that generates a ToDo list based on the date of birth input by the date of birth input unit; a reminder unit that reminds the user at an appropriate time based on the ToDo list generated by the ToDo list generation unit; A system characterized by:

2. The ToDo list generation unit Predicted growth patterns based on past data are presented, providing users with options.

2. The system of claim 1.

3. The ToDo list generation unit Optimize schedules for the entire family, taking into account siblings' birth dates 2. The system of claim 1.

4. The ToDo list generation unit Add local events and activities to your to-do list based on your local area information.

2. The system of claim 1.

5. The ToDo list generation unit Considering the user's health information, generate a to-do list for special care needs 2. The system of claim 1.

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