system

Abstract

The system according to this embodiment aims to efficiently collect and manage information on application procedures related to childcare. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a listing unit, a provision unit, and a notification unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The listing unit lists application procedures based on the information analyzed by the analysis unit. The provision unit provides detailed information regarding the application procedures listed by the listing unit. The notification unit sets reminders for procedures and notifies when the deadline approaches.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that the information collection and management of application procedures related to child-rearing were complicated and difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently perform information collection and management of application procedures related to child-rearing. <00​​​​​The system according to the embodiment comprises a collection unit, an analysis unit, a listing unit, a provision unit, and a notification unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The listing unit lists application procedures based on the information analyzed by the analysis unit. The provision unit provides detailed information regarding the application procedures listed by the listing unit. The notification unit sets reminders for procedures and notifies when deadlines approach. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and manage information on application procedures related to childcare. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The childcare assistance agent according to an embodiment of the present invention is a system that provides comprehensive support to people involved in childcare. Based on the information collected, this system lists the relevant application procedures and provides detailed information about each application procedure (required documents, application methods, etc.). It also sets reminders for important procedures and notifies users when the deadline approaches. Furthermore, it provides the latest information on social security and benefits and updates it regularly. It responds to user questions via FAQ or chatbot. For example, as part of vaccination schedule management, if a vaccination cannot be given due to the child's health condition, etc., it can be difficult to set the next appointment and consider vaccine compatibility, but the generating AI automatically tracks and adjusts the vaccination schedule and proposes a new schedule. Next, as childcare support, it uses the generating AI to provide real-time expert advice on childcare questions. Furthermore, as community building, the generating AI matches mothers who gave birth around the same time, forming a community where they can exchange information and consult with each other. As child health management, the generating AI analyzes health data in real time and alerts parents to changes in their child's health. Finally, as a growth record, the generating AI automatically collects and organizes growth data such as weight and height. This allows parents to centrally manage all childcare-related information and receive expert advice in real time. It also facilitates communication with other mothers with similar experiences and enables them to quickly identify changes in their child's health. This makes the parenting period more fulfilling and promotes the child's best growth and development. As a result, childcare assistance agents can fully support those involved in childcare, centrally manage childcare-related information, and provide expert advice in real time.

[0029] The childcare assistance agent according to this embodiment comprises a collection unit, an analysis unit, a listing unit, a provision unit, and a notification unit. The collection unit collects information. The collection unit can collect information such as text data, image data, and audio data. The collection unit can collect information from the internet, for example. The collection unit can also collect information directly from users. Furthermore, the collection unit can also collect environmental information using sensors. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can analyze collected text data using natural language processing technology, for example. The analysis unit can also analyze collected image data using image recognition technology. Furthermore, the analysis unit can also analyze collected audio data using speech recognition technology. The listing unit lists application procedures based on the information analyzed by the analysis unit. The listing unit can list application procedures such as patent applications, trademark registrations, and visa applications. The listing unit extracts and lists necessary application procedures from the analyzed information. The listing unit can also determine the priority of the application procedures. The provision unit provides detailed information about the application procedures listed by the listing unit. The provision unit can provide details such as the steps of the procedure, required documents, and submission deadlines. The provision unit can notify the user of the progress of the procedure. The provision unit can also provide FAQs about the procedure. Furthermore, the provision unit can respond to user questions using a chatbot. The notification unit sets reminders for important procedures and notifies the user when the deadline approaches. The notification unit can notify reminders by methods such as email notifications, app notifications, and SMS notifications. The notification unit notifies the user when the deadline for a procedure is approaching. The notification unit can also periodically notify the user of the progress of the procedure. Furthermore, the notification unit can also notify the user when the procedure is completed. As a result, the childcare assistance agent according to the embodiment can efficiently collect, analyze, list, provide, and notify information.

[0030] The data collection unit collects information. For example, it can collect text data, image data, and audio data. Specifically, it collects articles and posts related to childcare from websites and social media on the internet and stores this text data in a database. For image data, it collects photos and illustrations related to childcare and performs preprocessing for analysis. For audio data, it collects podcasts and audio messages related to childcare and converts them into text using speech recognition technology. Furthermore, the data collection unit can also collect information directly from users. For example, it collects questions and comments about childcare entered by users through applications and stores this information for analysis. The data collection unit can also collect environmental information using sensors. For example, it collects data from temperature and humidity sensors installed in homes to provide information for maintaining a comfortable environment for children. This allows the data collection unit to collect a wide range of data from diverse sources and build a foundation for providing comprehensive information on childcare.

[0031] The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze information using methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, it analyzes collected text data using natural language processing techniques to extract trends and user concerns related to childcare. For example, it extracts frequently occurring keywords from text data and identifies important childcare themes based on these keywords. It can also analyze collected image data using image recognition technology to identify items and situations related to childcare. For example, it can recognize childcare items such as cribs and toys from image data and provide information about these items. Furthermore, it analyzes collected audio data using speech recognition technology to convert user questions and comments into text data. This allows the analysis unit to comprehensively analyze diverse collected data and extract information useful to users. Additionally, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns related to childcare. For example, based on past childcare data, it can predict childcare challenges and needs during specific periods or seasons and provide appropriate advice to users. This enables the analysis unit to provide comprehensive support to users, not only in real-time information analysis but also in long-term childcare support.

[0032] The listing unit lists application procedures based on the information analyzed by the analysis unit. The listing unit can list application procedures such as patent applications, trademark registrations, and visa applications. Specifically, it extracts the application procedures required by the user from the analyzed information and lists them. For example, if a user develops a new baby product, the listing unit lists the patent application procedures and provides the necessary documents and procedural steps. Similarly, if a user starts a baby-related business, the listing unit lists trademark registration procedures and guides the user through trademark selection and registration. Furthermore, the listing unit can determine the priority of application procedures. For example, if a patent application is urgent, it will be listed with higher priority than other procedures to encourage the user to act quickly. This allows the listing unit to efficiently list the application procedures the user needs and provide them at the appropriate time. Additionally, the listing unit can provide customized lists according to the user's situation and needs. For example, if a user lives in a specific region, the listing unit lists application procedures specific to that region and provides information that takes into account regional requirements and procedural differences. This allows the listing unit to provide users with the most suitable application procedure, supporting efficient and smooth processing.

[0033] The Service Provider provides detailed information about the application procedures listed by the Listing Service Provider. For example, the Service Provider can provide details such as the steps of the procedure, required documents, and submission deadlines. Specifically, it clearly explains each step of the application procedure to the user, and clearly indicates the list of required documents and submission deadlines. The Service Provider also notifies the user of the progress of the procedure. For example, when the application documents are accepted or the review is in progress, it notifies the user of the status and provides instructions on how to proceed to the next step. Furthermore, the Service Provider can also provide FAQs regarding the procedure. For example, it provides information on frequently asked questions and troubleshooting to resolve any problems the user may encounter during the procedure. The Service Provider can also respond to user questions using a chatbot. The chatbot uses natural language processing technology to understand the user's questions and provide appropriate answers. This allows the Service Provider to provide users with quick and accurate information and support the smooth progress of the application procedure. Furthermore, the Service Provider can collect user feedback and continuously improve the quality of the information it provides. For example, based on user opinions and requests, it can review the content and format of the information provided to make it more user-friendly. This allows the service provider to offer users high-quality information and support the successful completion of the application process.

[0034] The notification unit sets reminders for important procedures and notifies users when deadlines are approaching. The notification unit can send reminders via methods such as email, app notifications, and SMS notifications. Specifically, it sends reminders for procedure submission deadlines and important steps based on user-defined deadlines. For example, when the deadline for filing a patent application approaches, it sends a reminder to the user via email or app notification, urging them to complete the procedure within the deadline. The notification unit can also periodically notify users of the progress of the procedure. For example, if an application is in progress, it periodically notifies the user of its progress, encouraging them to prepare for the next step. Furthermore, the notification unit can notify users of the completion of the procedure. For example, when an application is completed, it sends a completion notification to the user, guiding them through the next steps. This allows the notification unit to provide users with timely reminders and updates on important procedures, supporting the smooth progress of the process. Additionally, the notification unit can customize user notification settings. For example, users can set their preferred notification methods and frequency, providing notifications tailored to their individual needs. This allows the notification unit to provide users with important information at the optimal time, supporting the streamlining and success of procedures.

[0035] The data collection unit includes a schedule adjustment unit that automatically tracks and adjusts the vaccination schedule and proposes a new schedule. The data collection unit can adjust the vaccination schedule considering, for example, the child's health condition and vaccine compatibility. The data collection unit tracks information such as the date of vaccination, the injection site, and the interval between vaccinations. The data collection unit can also automatically adjust the vaccination schedule and propose a new schedule. For example, if the child is unwell, the data collection unit will reschedule the next vaccination date. Furthermore, the data collection unit can propose an optimal schedule considering vaccine compatibility. This automates and efficiently manages the vaccination schedule. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the data collection unit can have a generating AI perform the adjustment of the vaccination schedule.

[0036] The service provider includes an advice unit that provides real-time expert advice to questions about childcare. The service provider can provide expert advice on questions about childcare, such as breastfeeding methods, sleep patterns, and developmental milestones. The service provider can, for example, respond to user questions in real time. The service provider can also provide expert information on childcare. Furthermore, the service provider can provide FAQs on childcare questions. For example, the service provider can provide expert advice on questions about breastfeeding methods. The service provider can also provide expert advice on questions about sleep patterns. Furthermore, the service provider can provide expert advice on questions about developmental milestones. This allows for the provision of expert advice on childcare in real time. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can have a generative AI provide advice on childcare questions.

[0037] The data collection unit includes a community formation unit that matches mothers who gave birth around the same time, forming a community where they can exchange information and seek advice. The data collection unit can match mothers based on information such as birth month, birth year, and region. The data collection unit can identify mothers who gave birth around the same time and form a community. The data collection unit can also provide a platform for mothers to exchange information and seek advice. Furthermore, the data collection unit can provide functions for mothers to interact within the community. For example, the data collection unit can match mothers who gave birth in the same month and form a community. The data collection unit can also match mothers who gave birth in the same year and form a community. Furthermore, the data collection unit can match mothers who live in the same region and form a community. This allows for the formation of a community of mothers who gave birth around the same time, where they can exchange information and seek advice. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can have a generative AI perform the matching of mothers.

[0038] The analysis unit includes an alert unit that analyzes health data in real time and alerts parents of changes in their child's health. The analysis unit can analyze health data such as body temperature, heart rate, and blood pressure in real time. The analysis unit can collect health data using sensors and analyze it in real time. The analysis unit can also detect changes in health data and send alerts to parents. Furthermore, the analysis unit can detect abnormalities in health data and notify parents. For example, the analysis unit can analyze changes in body temperature in real time and send an alert to parents if an abnormality is detected. The analysis unit can also analyze changes in heart rate in real time and send an alert to parents if an abnormality is detected. Furthermore, the analysis unit can analyze changes in blood pressure in real time and send an alert to parents if an abnormality is detected. This allows for real-time analysis of health data and rapid alerting of changes in health. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI perform the analysis of health data.

[0039] The data collection unit includes a growth recording unit that automatically collects and organizes weight and height growth data. The data collection unit can automatically collect growth data such as weight, height, and head circumference. The data collection unit can collect growth data using sensors, for example, and automatically organize it. The data collection unit can also organize growth data in chronological order and analyze growth trends. Furthermore, the data collection unit can display growth data as graphs or charts. For example, the data collection unit can organize weight changes in chronological order and display them as a graph. The data collection unit can also organize height changes in chronological order and display them as a chart. Furthermore, the data collection unit can organize head circumference changes in chronological order and display them as a graph. This enables the automatic collection and organization of growth data. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can have a generative AI perform the collection and organization of growth data.

[0040] The data collection unit analyzes the user's past information collection history and selects the optimal collection method. For example, the data collection unit can prioritize collecting similar information based on information the user has frequently collected in the past. For example, the data collection unit can prioritize suggesting data collection methods the user has used in the past (e.g., voice, text). The data collection unit can also predict the information to be collected at a specific time period based on the user's past collection history and suggest the optimal collection method. For example, the data collection unit analyzes information the user has frequently collected in the past and prioritizes collecting similar information. The data collection unit can also analyze data collection methods the user has used in the past and suggest the optimal collection method. Furthermore, the data collection unit can analyze the user's past collection history, predict the information to be collected at a specific time period, and suggest the optimal collection method. This allows the optimal collection method to be selected based on past information collection history. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can have generative AI perform the analysis of the information collection history.

[0041] The data collection unit filters information based on the user's current lifestyle and areas of interest. For example, if the user is interested in childcare, the data collection unit can prioritize collecting childcare-related information. For example, if the user is busy with work, the data collection unit can prioritize collecting work-related information. Furthermore, if the user is interested in health, the data collection unit can prioritize collecting health-related information. For example, the data collection unit analyzes the user's lifestyle and prioritizes collecting childcare-related information. The data collection unit can also analyze the user's areas of interest and prioritize collecting work-related information. In addition, the data collection unit can analyze the user's lifestyle and areas of interest and prioritize collecting health-related information. This allows information to be filtered based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can have a generative AI perform the filtering of information.

[0042] The data collection unit prioritizes collecting highly relevant information based on the user's geographical location. For example, if the user lives in a specific region, the data collection unit can prioritize collecting information related to that region. For example, if the user is traveling, the data collection unit can prioritize collecting information related to the travel destination. Furthermore, if the user is planning to move, the data collection unit can prioritize collecting information related to the new place of residence. For example, the data collection unit prioritizes collecting information related to the user's region based on their geographical location. Furthermore, the data collection unit can prioritize collecting information related to the travel destination based on the user's geographical location. In addition, the data collection unit can prioritize collecting information related to the new place of residence based on the user's geographical location. This allows for the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can have a generative AI perform the analysis of geographical location information.

[0043] The data collection unit analyzes the user's social media activity and collects relevant information when gathering information. For example, if the user posts about childcare on social media, the data collection unit can prioritize collecting childcare-related information. For example, if the user posts about health on social media, the data collection unit can prioritize collecting health-related information. Furthermore, if the user posts about travel on social media, the data collection unit can prioritize collecting travel-related information. For example, the data collection unit analyzes the user's social media activity and prioritizes collecting childcare-related information. Furthermore, the data collection unit can analyze the user's social media activity and prioritize collecting health-related information. In addition, the data collection unit can analyze the user's social media activity and prioritize collecting travel-related information. This allows the data collection unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can have generative AI perform the analysis of social media activity.

[0044] The analysis unit adjusts the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis for important information. For example, the analysis unit can perform a simplified analysis for general information. Furthermore, the analysis unit can perform a rapid analysis for urgent information. For example, the analysis unit can evaluate the importance of the information and perform a detailed analysis for important information. Furthermore, the analysis unit can evaluate the importance of the information and perform a simplified analysis for general information. In addition, the analysis unit can evaluate the urgency of the information and perform a rapid analysis for urgent information. This allows the level of detail of the analysis to be adjusted based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI perform the evaluation of the importance of the information.

[0045] The analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, in the case of childcare-related information, the analysis unit can apply a childcare-specific algorithm. For example, in the case of health-related information, the analysis unit can apply a health-specific algorithm. Furthermore, in the case of education-related information, the analysis unit can apply an education-specific algorithm. For example, the analysis unit can apply a childcare-specific algorithm to childcare-related information. Furthermore, the analysis unit can apply a health-specific algorithm to health-related information. Furthermore, the analysis unit can apply an education-specific algorithm to education-related information. This allows the appropriate analysis algorithm to be applied according to the category of information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI perform the application of the analysis algorithm.

[0046] The analysis unit determines the priority of analysis based on the information submission date during the analysis. For example, the analysis unit can prioritize the analysis of information that is urgent. For example, the analysis unit can postpone the analysis of information that is old. Furthermore, the analysis unit can also quickly analyze information that is new. For example, the analysis unit evaluates the information submission date and prioritizes the analysis of information that is urgent. The analysis unit can also evaluate the information submission date and postpone the analysis of information that is old. Furthermore, the analysis unit can evaluate the information submission date and quickly analyze information that is new. This allows the analysis priority to be determined based on the information submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can have a generative AI perform the evaluation of the information submission date.

[0047] The analysis unit adjusts the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. Furthermore, the analysis unit can perform the analysis of information with a moderate degree of relevance in an appropriate order. For example, the analysis unit evaluates the relevance of the information and prioritizes the analysis of highly relevant information. The analysis unit can also evaluate the relevance of the information and postpone the analysis of less relevant information. Furthermore, the analysis unit can evaluate the relevance of the information and perform the analysis of information with a moderate degree of relevance in an appropriate order. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI perform the evaluation of the relevance of the information.

[0048] The listing unit improves the accuracy of listing by considering the interrelationships of the information during the listing process. For example, the listing unit can prioritize listing information that is highly relevant. For example, the listing unit can analyze the interrelationships of the information and perform optimal listing. The listing unit can also adjust the order of listing based on the relevance of the information. For example, the listing unit evaluates the interrelationships of the information and prioritizes listing information that is highly relevant. The listing unit can also evaluate the interrelationships of the information and perform optimal listing. Furthermore, the listing unit can evaluate the relevance of the information and adjust the order of listing. This improves the accuracy of listing by considering the interrelationships of the information. Some or all of the above processing in the listing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the listing unit can have a generative AI perform the evaluation of the interrelationships of the information.

[0049] The listing unit considers the attribute information of the information submitter when creating a list. For example, if the submitter is an expert, the listing unit can prioritize listing that information. For example, if the submitter is a general user, the listing unit will appropriately list that information. The listing unit can also adjust the order of listing based on the submitter's attribute information. For example, the listing unit evaluates the submitter's attribute information and prioritizes listing information of experts. The listing unit also evaluates the submitter's attribute information and appropriately lists information of general users. Furthermore, the listing unit can evaluate the submitter's attribute information and adjust the order of listing. This improves the accuracy of the listing by considering the attribute information of the information submitter. Some or all of the above processing in the listing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the listing unit can have a generative AI perform the evaluation of the submitter's attribute information.

[0050] The listing unit considers the geographical distribution of information when listing it. For example, the listing unit can prioritize listing information that is geographically close. For example, the listing unit can analyze the geographical distribution and perform optimal listing. The listing unit can also adjust the order of listing based on geographical relevance. For example, the listing unit evaluates the geographical distribution of information and prioritizes listing information that is geographically close. The listing unit can also evaluate the geographical distribution of information and perform optimal listing. Furthermore, the listing unit can evaluate the geographical relevance of information and adjust the order of listing. This improves the accuracy of listing by considering the geographical distribution of information. Some or all of the above processing in the listing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the listing unit can have a generative AI perform the evaluation of geographical distribution.

[0051] The listing unit improves the accuracy of the listing by referring to related literature during the listing process. For example, the listing unit can improve the accuracy of the listing by referring to related literature. For example, the listing unit can perform optimal listing based on the information in the related literature. The listing unit can also adjust the order of listing based on the relevance of the related literature. For example, the listing unit can evaluate related literature to improve the accuracy of the listing. The listing unit can also evaluate the information in related literature to perform optimal listing. Furthermore, the listing unit can evaluate the relevance of related literature and adjust the order of listing. As a result, the accuracy of the listing is improved by referring to related literature. Some or all of the above processing in the listing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the listing unit can have a generative AI perform the evaluation of related literature.

[0052] The information provider adjusts the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide detailed information if the information is important. For example, the provider can provide simplified information if the information is general. The provider can also provide information quickly if it is urgent. For example, the provider can assess the importance of the information and provide detailed information if it is important. The provider can also assess the importance of the information and provide simplified information if it is general. Furthermore, the provider can assess the urgency of the information and provide information quickly if it is urgent. This allows the level of detail provided to be adjusted based on the importance of the information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the provider can have a generative AI perform the assessment of the importance of the information.

[0053] The information provider applies different information provision algorithms depending on the category of the information at the time of provision. For example, in the case of childcare-related information, the information provider can apply a childcare-specific algorithm. For example, in the case of health-related information, the information provider can apply a health-specific algorithm. Furthermore, in the case of education-related information, the information provider can also apply an education-specific algorithm. For example, the information provider can apply a childcare-specific algorithm to childcare-related information. Furthermore, the information provider can apply a health-specific algorithm to health-related information. Furthermore, the information provider can apply an education-specific algorithm to education-related information. This allows for the application of an appropriate information provision algorithm depending on the category of the information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can have a generative AI perform the application of the information provision algorithm.

[0054] The information provisioning department determines the priority of information provision based on the timing of its submission. For example, the information provisioning department can prioritize providing information that is urgent. For example, the information provisioning department can postpone providing information that is old. Furthermore, the information provisioning department can also provide information quickly if it is new. For example, the information provisioning department evaluates the timing of information submission and prioritizes providing information that is urgent. The information provisioning department can also evaluate the timing of information submission and postpone providing information that is old. Furthermore, the information provisioning department can evaluate the timing of information submission and provide information quickly if it is new. This allows the provisioning department to determine the priority of information provision based on the timing of its submission. Some or all of the above processing in the information provisioning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provisioning department can have a generative AI perform the evaluation of the timing of information submission.

[0055] The information provider adjusts the order of information delivery based on the relevance of the information. For example, the provider can prioritize providing highly relevant information. For example, it can postpone providing less relevant information. It can also provide information of moderate relevance in an appropriate order. For example, the provider evaluates the relevance of the information and prioritizes providing highly relevant information. It can also evaluate the relevance of the information and postpone providing less relevant information. Furthermore, it can evaluate the relevance of the information and provide information of moderate relevance in an appropriate order. This allows the order of information delivery to be adjusted based on the relevance of the information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can have a generative AI perform the evaluation of the relevance of the information.

[0056] The notification unit adjusts the level of detail of the notification based on the importance of the information. For example, the notification unit can provide a detailed notification for important information. For example, it can provide a simplified notification for general information. The notification unit can also provide a rapid notification for urgent information. For example, the notification unit can assess the importance of the information and provide a detailed notification for important information. The notification unit can also assess the importance of the information and provide a simplified notification for general information. Furthermore, the notification unit can assess the urgency of the information and provide a rapid notification for urgent information. This allows the level of detail of the notification to be adjusted based on the importance of the information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can have a generative AI perform the assessment of the importance of the information.

[0057] The notification unit applies different notification algorithms depending on the category of information when it sends a notification. For example, the notification unit can apply a childcare-specific algorithm to childcare-related information. For example, the notification unit can apply a health-specific algorithm to health-related information. Furthermore, the notification unit can apply an education-specific algorithm to education-related information. This allows the notification unit to apply an appropriate notification algorithm depending on the category of information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can have a generative AI perform the application of the notification algorithm.

[0058] The notification unit adjusts the order of notifications based on the timing of information submission. For example, the notification unit can prioritize notifications for highly urgent information. For example, the notification unit can postpone notifications for older information. The notification unit can also quickly notify for newer information. For example, the notification unit evaluates the timing of information submission and prioritizes notifications for highly urgent information. The notification unit can also evaluate the timing of information submission and postpone notifications for older information. Furthermore, the notification unit can evaluate the timing of information submission and quickly notify for newer information. This allows the order of notifications to be adjusted based on the timing of information submission. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can have a generative AI perform the evaluation of the timing of information submission.

[0059] The notification unit adjusts the content of notifications based on the relevance of the information. For example, the notification unit can prioritize notifications for highly relevant information. For example, it can postpone notifications for less relevant information. It can also send notifications in an appropriate order for information of moderate relevance. For example, the notification unit evaluates the relevance of the information and prioritizes notifications for highly relevant information. It can also evaluate the relevance of the information and postpone notifications for less relevant information. Furthermore, it can evaluate the relevance of the information and send notifications in an appropriate order for information of moderate relevance. This allows the content of notifications to be adjusted based on the relevance of the information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can have a generative AI perform the evaluation of the relevance of the information.

[0060] The schedule adjustment unit proposes the optimal schedule by referring to past schedule data during schedule adjustment. For example, the schedule adjustment unit can propose the optimal schedule based on the user's past schedule data. For example, the schedule adjustment unit can propose a schedule that avoids congestion based on the user's past schedule data. The schedule adjustment unit can also analyze the user's past schedule data and propose the most efficient schedule. For example, the schedule adjustment unit evaluates the user's past schedule data and proposes the optimal schedule. The schedule adjustment unit can also evaluate the user's past schedule data and propose a schedule that avoids congestion. Furthermore, the schedule adjustment unit can evaluate the user's past schedule data and propose the most efficient schedule. This allows the system to propose the optimal schedule based on past schedule data. Some or all of the above-described processes in the schedule adjustment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the schedule adjustment unit can have a generative AI perform the evaluation of past schedule data.

[0061] The schedule adjustment unit proposes the optimal schedule by considering the user's geographical location information during schedule adjustment. For example, if the user lives in a specific region, the schedule adjustment unit can prioritize suggesting schedules related to that region. For example, if the user is traveling, the schedule adjustment unit can prioritize suggesting schedules related to the travel destination. Furthermore, if the user is planning to move, the schedule adjustment unit can prioritize suggesting schedules related to the new place of residence. For example, the schedule adjustment unit can prioritize suggesting schedules related to the region based on the user's geographical location information. Furthermore, the schedule adjustment unit can prioritize suggesting schedules related to the travel destination based on the user's geographical location information. In addition, the schedule adjustment unit can prioritize suggesting schedules related to the new place of residence based on the user's geographical location information. This allows the system to propose the optimal schedule based on the user's geographical location information. Some or all of the above processing in the schedule adjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the schedule adjustment unit can have a generative AI perform the evaluation of geographical location information.

[0062] The community formation unit proposes the most suitable community when forming a community, taking into account the user's geographical location information. For example, if the user lives in a specific area, the community formation unit can prioritize suggesting communities related to that area. For example, if the user is traveling, the community formation unit can prioritize suggesting communities related to the travel destination. Furthermore, if the user is planning to move, the community formation unit can prioritize suggesting communities related to the new place of residence. For example, the community formation unit prioritizes suggesting communities related to the user's geographical location information. Furthermore, the community formation unit can prioritize suggesting communities related to the travel destination based on the user's geographical location information. In addition, the community formation unit can prioritize suggesting communities related to the new place of residence based on the user's geographical location information. This allows the community formation unit to propose the most suitable community based on the user's geographical location information. Some or all of the above processing in the community formation unit may be performed using, for example, a generative AI, or not. For example, the community formation unit can have a generative AI perform the evaluation of geographical location information.

[0063] The alert unit adjusts the level of detail of an alert based on the importance of the information when an alert occurs. For example, the alert unit can provide a detailed alert for important information. For example, the alert unit can provide a simplified alert for general information. The alert unit can also provide a rapid alert for urgent information. For example, the alert unit can assess the importance of the information and provide a detailed alert for important information. The alert unit can also assess the importance of the information and provide a simplified alert for general information. Furthermore, the alert unit can assess the urgency of the information and provide a rapid alert for urgent information. This allows the level of detail of the alert to be adjusted based on the importance of the information. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the alert unit can have a generative AI perform the assessment of the importance of the information.

[0064] The alert unit applies different alert algorithms depending on the category of information when an alert occurs. For example, the alert unit can apply a childcare-specific algorithm to childcare-related information. For example, the alert unit can apply a health-specific algorithm to health-related information. Furthermore, the alert unit can apply an education-specific algorithm to education-related information. This allows the appropriate alert algorithm to be applied according to the category of information. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alert unit can have a generative AI perform the application of the alert algorithm.

[0065] The alert unit adjusts the order of alerts based on the timing of information submission when an alert occurs. For example, the alert unit can prioritize alerts for highly urgent information. For example, the alert unit can postpone alerts for older information. The alert unit can also quickly alert for newer information. For example, the alert unit evaluates the timing of information submission and prioritizes alerts for highly urgent information. The alert unit can also evaluate the timing of information submission and postpone alerts for older information. Furthermore, the alert unit can evaluate the timing of information submission and quickly alert for newer information. This allows the order of alerts to be adjusted based on the timing of information submission. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or not. For example, the alert unit can have a generative AI perform the evaluation of the timing of information submission.

[0066] The growth recording unit, when recording growth, refers to past growth data to propose the optimal recording method. For example, the growth recording unit can propose the optimal recording method based on the user's past growth data. For example, the growth recording unit can analyze growth trends from the user's past growth data and propose the optimal recording method. The growth recording unit can also analyze the user's past growth data and propose the most efficient recording method. For example, the growth recording unit evaluates the user's past growth data and proposes the optimal recording method. The growth recording unit can also evaluate the user's past growth data, analyze growth trends, and propose the optimal recording method. Furthermore, the growth recording unit can evaluate the user's past growth data and propose the optimal recording method. For example, the growth recording unit evaluates the user's past growth data and proposes the optimal recording method. Furthermore, the growth recording unit can evaluate the user's past growth data, analyze growth trends, and propose the optimal recording method. Furthermore, the growth recording unit can evaluate the user's past growth data and propose the most efficient recording method. This allows the optimal recording method to be proposed based on past growth data. Some or all of the above-described processes in the growth recording unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the growth recording unit can have a generative AI perform an evaluation of past growth data.

[0067] The growth recording unit proposes the optimal recording method when recording growth, taking into account the user's geographical location information. For example, if the user lives in a specific region, the growth recording unit can prioritize suggesting a recording method related to that region. For example, if the user is traveling, the growth recording unit can prioritize suggesting a recording method related to the travel destination. Furthermore, if the user is planning to move, the growth recording unit can prioritize suggesting a recording method related to the new place of residence. For example, the growth recording unit can prioritize suggesting a recording method related to the region based on the user's geographical location information. Furthermore, the growth recording unit can prioritize suggesting a recording method related to the travel destination based on the user's geographical location information. In addition, the growth recording unit can prioritize suggesting a recording method related to the new place of residence based on the user's geographical location information. This allows the optimal recording method to be proposed based on the user's geographical location information. Some or all of the above processing in the growth recording unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the growth recording unit can have a generative AI perform the evaluation of geographical location information.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The data collection unit can analyze the user's past behavior patterns and suggest the optimal timing for information collection. For example, if a user has collected information during a specific time period in the past, the unit can collect information during that time period. Similarly, if a user has collected information on a specific day of the week, the unit can collect information on that day. Furthermore, by analyzing the user's behavior patterns and suggesting the optimal timing for information collection, the unit can efficiently collect information based on the user's behavior patterns.

[0070] The data collection unit can also prioritize the collection of region-specific information based on the user's geographical location. For example, if a user lives in a specific region, it can prioritize the collection of information related to that region. Similarly, if a user is traveling, it can prioritize the collection of information related to their travel destination. Furthermore, if a user is planning to move, it can prioritize the collection of information related to their new residence. This allows for the collection of highly relevant information based on the user's geographical location.

[0071] The data collection unit can also analyze users' social media activity and collect relevant information. For example, if a user posts about childcare on social media, it can prioritize collecting childcare-related information. Similarly, if a user posts about health on social media, it can prioritize collecting health-related information. Furthermore, if a user posts about travel on social media, it can prioritize collecting travel-related information. This allows for the collection of relevant information based on the user's social media activity.

[0072] The data collection unit can analyze the user's past information collection history and select the optimal collection method. For example, it can prioritize collecting similar information based on information the user has frequently collected in the past. It can also prioritize suggesting collection methods the user has used in the past (e.g., voice, text). Furthermore, it can predict the information to be collected at a specific time period based on the user's past collection history and suggest the optimal collection method. This allows for the selection of the optimal collection method based on past information collection history.

[0073] The data collection unit can also filter information based on the user's current lifestyle and areas of interest. For example, if the user is interested in childcare, it can prioritize collecting childcare-related information. Similarly, if the user is busy with work, it can prioritize collecting work-related information. Furthermore, if the user is interested in health, it can prioritize collecting health-related information. This allows for information filtering based on the user's lifestyle and areas of interest.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The collection unit collects information. The collection unit can collect information such as text data, image data, and audio data. The collection unit can collect information from the internet, for example. It can also collect information directly from users. Furthermore, the collection unit can collect environmental information using sensors. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can analyze collected text data using natural language processing techniques. The analysis unit can also analyze collected image data using image recognition techniques. Furthermore, the analysis unit can analyze collected audio data using speech recognition techniques. Step 3: The listing unit lists the application procedures based on the information analyzed by the analysis unit. The listing unit can list application procedures such as patent applications, trademark registrations, and visa applications. For example, the listing unit extracts and lists the necessary application procedures from the analyzed information. The listing unit can also determine the priority of the application procedures. Step 4: The provisioning department provides detailed information about the application procedures listed by the listing department. The provisioning department can provide details such as the steps of the procedure, required documents, and submission deadlines. The provisioning department can also notify users of the progress of the procedure. In addition, the provisioning department can provide FAQs about the procedure. Furthermore, the provisioning department can respond to user questions using a chatbot. Step 5: The notification unit sets reminders for important procedures and notifies users when deadlines are approaching. The notification unit can send reminders via methods such as email, app notifications, and SMS notifications. For example, the notification unit notifies the user when a procedure deadline is approaching. The notification unit can also periodically notify users of the progress of the procedure. Furthermore, the notification unit can also notify users when a procedure is completed.

[0076] (Example of form 2) The childcare assistance agent according to an embodiment of the present invention is a system that provides comprehensive support to people involved in childcare. Based on the information collected, this system lists the relevant application procedures and provides detailed information about each application procedure (required documents, application methods, etc.). It also sets reminders for important procedures and notifies users when the deadline approaches. Furthermore, it provides the latest information on social security and benefits and updates it regularly. It responds to user questions via FAQ or chatbot. For example, as part of vaccination schedule management, if a vaccination cannot be given due to the child's health condition, etc., it can be difficult to set the next appointment and consider vaccine compatibility, but the generating AI automatically tracks and adjusts the vaccination schedule and proposes a new schedule. Next, as childcare support, it uses the generating AI to provide real-time expert advice on childcare questions. Furthermore, as community building, the generating AI matches mothers who gave birth around the same time, forming a community where they can exchange information and consult with each other. As child health management, the generating AI analyzes health data in real time and alerts parents to changes in their child's health. Finally, as a growth record, the generating AI automatically collects and organizes growth data such as weight and height. This allows parents to centrally manage all childcare-related information and receive expert advice in real time. It also facilitates communication with other mothers with similar experiences and enables them to quickly identify changes in their child's health. This makes the parenting period more fulfilling and promotes the child's best growth and development. As a result, childcare assistance agents can fully support those involved in childcare, centrally manage childcare-related information, and provide expert advice in real time.

[0077] The childcare assistance agent according to this embodiment comprises a collection unit, an analysis unit, a listing unit, a provision unit, and a notification unit. The collection unit collects information. The collection unit can collect information such as text data, image data, and audio data. The collection unit can collect information from the internet, for example. The collection unit can also collect information directly from users. Furthermore, the collection unit can also collect environmental information using sensors. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can analyze collected text data using natural language processing technology, for example. The analysis unit can also analyze collected image data using image recognition technology. Furthermore, the analysis unit can also analyze collected audio data using speech recognition technology. The listing unit lists application procedures based on the information analyzed by the analysis unit. The listing unit can list application procedures such as patent applications, trademark registrations, and visa applications. The listing unit extracts and lists necessary application procedures from the analyzed information. The listing unit can also determine the priority of the application procedures. The provision unit provides detailed information about the application procedures listed by the listing unit. The provision unit can provide details such as the steps of the procedure, required documents, and submission deadlines. The provision unit can notify the user of the progress of the procedure. The provision unit can also provide FAQs about the procedure. Furthermore, the provision unit can respond to user questions using a chatbot. The notification unit sets reminders for important procedures and notifies the user when the deadline approaches. The notification unit can notify reminders by methods such as email notifications, app notifications, and SMS notifications. The notification unit notifies the user when the deadline for a procedure is approaching. The notification unit can also periodically notify the user of the progress of the procedure. Furthermore, the notification unit can also notify the user when the procedure is completed. As a result, the childcare assistance agent according to the embodiment can efficiently collect, analyze, list, provide, and notify information.

[0078] The data collection unit collects information. For example, it can collect text data, image data, and audio data. Specifically, it collects articles and posts related to childcare from websites and social media on the internet and stores this text data in a database. For image data, it collects photos and illustrations related to childcare and performs preprocessing for analysis. For audio data, it collects podcasts and audio messages related to childcare and converts them into text using speech recognition technology. Furthermore, the data collection unit can also collect information directly from users. For example, it collects questions and comments about childcare entered by users through applications and stores this information for analysis. The data collection unit can also collect environmental information using sensors. For example, it collects data from temperature and humidity sensors installed in homes to provide information for maintaining a comfortable environment for children. This allows the data collection unit to collect a wide range of data from diverse sources and build a foundation for providing comprehensive information on childcare.

[0079] The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze information using methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, it analyzes collected text data using natural language processing techniques to extract trends and user concerns related to childcare. For example, it extracts frequently occurring keywords from text data and identifies important childcare themes based on these keywords. It can also analyze collected image data using image recognition technology to identify items and situations related to childcare. For example, it can recognize childcare items such as cribs and toys from image data and provide information about these items. Furthermore, it analyzes collected audio data using speech recognition technology to convert user questions and comments into text data. This allows the analysis unit to comprehensively analyze diverse collected data and extract information useful to users. Additionally, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns related to childcare. For example, based on past childcare data, it can predict childcare challenges and needs during specific periods or seasons and provide appropriate advice to users. This enables the analysis unit to provide comprehensive support to users, not only in real-time information analysis but also in long-term childcare support.

[0080] The listing unit lists application procedures based on the information analyzed by the analysis unit. The listing unit can list application procedures such as patent applications, trademark registrations, and visa applications. Specifically, it extracts the application procedures required by the user from the analyzed information and lists them. For example, if a user develops a new baby product, the listing unit lists the patent application procedures and provides the necessary documents and procedural steps. Similarly, if a user starts a baby-related business, the listing unit lists trademark registration procedures and guides the user through trademark selection and registration. Furthermore, the listing unit can determine the priority of application procedures. For example, if a patent application is urgent, it will be listed with higher priority than other procedures to encourage the user to act quickly. This allows the listing unit to efficiently list the application procedures the user needs and provide them at the appropriate time. Additionally, the listing unit can provide customized lists according to the user's situation and needs. For example, if a user lives in a specific region, the listing unit lists application procedures specific to that region and provides information that takes into account regional requirements and procedural differences. This allows the listing unit to provide users with the most suitable application procedure, supporting efficient and smooth processing.

[0081] The Service Provider provides detailed information about the application procedures listed by the Listing Service Provider. For example, the Service Provider can provide details such as the steps of the procedure, required documents, and submission deadlines. Specifically, it clearly explains each step of the application procedure to the user, and clearly indicates the list of required documents and submission deadlines. The Service Provider also notifies the user of the progress of the procedure. For example, when the application documents are accepted or the review is in progress, it notifies the user of the status and provides instructions on how to proceed to the next step. Furthermore, the Service Provider can also provide FAQs regarding the procedure. For example, it provides information on frequently asked questions and troubleshooting to resolve any problems the user may encounter during the procedure. The Service Provider can also respond to user questions using a chatbot. The chatbot uses natural language processing technology to understand the user's questions and provide appropriate answers. This allows the Service Provider to provide users with quick and accurate information and support the smooth progress of the application procedure. Furthermore, the Service Provider can collect user feedback and continuously improve the quality of the information it provides. For example, based on user opinions and requests, it can review the content and format of the information provided to make it more user-friendly. This allows the service provider to offer users high-quality information and support the successful completion of the application process.

[0082] The notification unit sets reminders for important procedures and notifies users when deadlines are approaching. The notification unit can send reminders via methods such as email, app notifications, and SMS notifications. Specifically, it sends reminders for procedure submission deadlines and important steps based on user-defined deadlines. For example, when the deadline for filing a patent application approaches, it sends a reminder to the user via email or app notification, urging them to complete the procedure within the deadline. The notification unit can also periodically notify users of the progress of the procedure. For example, if an application is in progress, it periodically notifies the user of its progress, encouraging them to prepare for the next step. Furthermore, the notification unit can notify users of the completion of the procedure. For example, when an application is completed, it sends a completion notification to the user, guiding them through the next steps. This allows the notification unit to provide users with timely reminders and updates on important procedures, supporting the smooth progress of the process. Additionally, the notification unit can customize user notification settings. For example, users can set their preferred notification methods and frequency, providing notifications tailored to their individual needs. This allows the notification unit to provide users with important information at the optimal time, supporting the streamlining and success of procedures.

[0083] The data collection unit includes a schedule adjustment unit that automatically tracks and adjusts the vaccination schedule and proposes a new schedule. The data collection unit can adjust the vaccination schedule considering, for example, the child's health condition and vaccine compatibility. The data collection unit tracks information such as the date of vaccination, the injection site, and the interval between vaccinations. The data collection unit can also automatically adjust the vaccination schedule and propose a new schedule. For example, if the child is unwell, the data collection unit will reschedule the next vaccination date. Furthermore, the data collection unit can propose an optimal schedule considering vaccine compatibility. This automates and efficiently manages the vaccination schedule. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the data collection unit can have a generating AI perform the adjustment of the vaccination schedule.

[0084] The service provider includes an advice unit that provides real-time expert advice to questions about childcare. The service provider can provide expert advice on questions about childcare, such as breastfeeding methods, sleep patterns, and developmental milestones. The service provider can, for example, respond to user questions in real time. The service provider can also provide expert information on childcare. Furthermore, the service provider can provide FAQs on childcare questions. For example, the service provider can provide expert advice on questions about breastfeeding methods. The service provider can also provide expert advice on questions about sleep patterns. Furthermore, the service provider can provide expert advice on questions about developmental milestones. This allows for the provision of expert advice on childcare in real time. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can have a generative AI provide advice on childcare questions.

[0085] The data collection unit includes a community formation unit that matches mothers who gave birth around the same time, forming a community where they can exchange information and seek advice. The data collection unit can match mothers based on information such as birth month, birth year, and region. The data collection unit can identify mothers who gave birth around the same time and form a community. The data collection unit can also provide a platform for mothers to exchange information and seek advice. Furthermore, the data collection unit can provide functions for mothers to interact within the community. For example, the data collection unit can match mothers who gave birth in the same month and form a community. The data collection unit can also match mothers who gave birth in the same year and form a community. Furthermore, the data collection unit can match mothers who live in the same region and form a community. This allows for the formation of a community of mothers who gave birth around the same time, where they can exchange information and seek advice. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can have a generative AI perform the matching of mothers.

[0086] The analysis unit includes an alert unit that analyzes health data in real time and alerts parents of changes in their child's health. The analysis unit can analyze health data such as body temperature, heart rate, and blood pressure in real time. The analysis unit can collect health data using sensors and analyze it in real time. The analysis unit can also detect changes in health data and send alerts to parents. Furthermore, the analysis unit can detect abnormalities in health data and notify parents. For example, the analysis unit can analyze changes in body temperature in real time and send an alert to parents if an abnormality is detected. The analysis unit can also analyze changes in heart rate in real time and send an alert to parents if an abnormality is detected. Furthermore, the analysis unit can analyze changes in blood pressure in real time and send an alert to parents if an abnormality is detected. This allows for real-time analysis of health data and rapid alerting of changes in health. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI perform the analysis of health data.

[0087] The data collection unit includes a growth recording unit that automatically collects and organizes weight and height growth data. The data collection unit can automatically collect growth data such as weight, height, and head circumference. The data collection unit can collect growth data using sensors, for example, and automatically organize it. The data collection unit can also organize growth data in chronological order and analyze growth trends. Furthermore, the data collection unit can display growth data as graphs or charts. For example, the data collection unit can organize weight changes in chronological order and display them as a graph. The data collection unit can also organize height changes in chronological order and display them as a chart. Furthermore, the data collection unit can organize head circumference changes in chronological order and display them as a graph. This enables the automatic collection and organization of growth data. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can have a generative AI perform the collection and organization of growth data.

[0088] The data collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect information when the user is relaxed. For example, if the user is relaxed, the data collection unit can increase the frequency of information collection and collect more detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of important information and provide it quickly. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the timing of information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform emotion estimation.

[0089] The data collection unit analyzes the user's past information collection history and selects the optimal collection method. For example, the data collection unit can prioritize collecting similar information based on information the user has frequently collected in the past. For example, the data collection unit can prioritize suggesting data collection methods the user has used in the past (e.g., voice, text). The data collection unit can also predict the information to be collected at a specific time period based on the user's past collection history and suggest the optimal collection method. For example, the data collection unit analyzes information the user has frequently collected in the past and prioritizes collecting similar information. The data collection unit can also analyze data collection methods the user has used in the past and suggest the optimal collection method. Furthermore, the data collection unit can analyze the user's past collection history, predict the information to be collected at a specific time period, and suggest the optimal collection method. This allows the optimal collection method to be selected based on past information collection history. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can have generative AI perform the analysis of the information collection history.

[0090] The data collection unit filters information based on the user's current lifestyle and areas of interest. For example, if the user is interested in childcare, the data collection unit can prioritize collecting childcare-related information. For example, if the user is busy with work, the data collection unit can prioritize collecting work-related information. Furthermore, if the user is interested in health, the data collection unit can prioritize collecting health-related information. For example, the data collection unit analyzes the user's lifestyle and prioritizes collecting childcare-related information. The data collection unit can also analyze the user's areas of interest and prioritize collecting work-related information. In addition, the data collection unit can analyze the user's lifestyle and areas of interest and prioritize collecting health-related information. This allows information to be filtered based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can have a generative AI perform the filtering of information.

[0091] The data collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information that promotes relaxation. If the user is relaxed, the data collection unit can prioritize collecting detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting important information. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the prioritization of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform emotion estimation.

[0092] The data collection unit prioritizes collecting highly relevant information based on the user's geographical location. For example, if the user lives in a specific region, the data collection unit can prioritize collecting information related to that region. For example, if the user is traveling, the data collection unit can prioritize collecting information related to the travel destination. Furthermore, if the user is planning to move, the data collection unit can prioritize collecting information related to the new place of residence. For example, the data collection unit prioritizes collecting information related to the user's region based on their geographical location. Furthermore, the data collection unit can prioritize collecting information related to the travel destination based on the user's geographical location. In addition, the data collection unit can prioritize collecting information related to the new place of residence based on the user's geographical location. This allows for the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can have a generative AI perform the analysis of geographical location information.

[0093] The data collection unit analyzes the user's social media activity and collects relevant information when gathering information. For example, if the user posts about childcare on social media, the data collection unit can prioritize collecting childcare-related information. For example, if the user posts about health on social media, the data collection unit can prioritize collecting health-related information. Furthermore, if the user posts about travel on social media, the data collection unit can prioritize collecting travel-related information. For example, the data collection unit analyzes the user's social media activity and prioritizes collecting childcare-related information. Furthermore, the data collection unit can analyze the user's social media activity and prioritize collecting health-related information. In addition, the data collection unit can analyze the user's social media activity and prioritize collecting travel-related information. This allows the data collection unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can have generative AI perform the analysis of social media activity.

[0094] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visual presentation. If the user is relaxed, the analysis unit can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise presentation. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generative AI perform emotion estimation.

[0095] The analysis unit adjusts the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis for important information. For example, the analysis unit can perform a simplified analysis for general information. Furthermore, the analysis unit can perform a rapid analysis for urgent information. For example, the analysis unit can evaluate the importance of the information and perform a detailed analysis for important information. Furthermore, the analysis unit can evaluate the importance of the information and perform a simplified analysis for general information. In addition, the analysis unit can evaluate the urgency of the information and perform a rapid analysis for urgent information. This allows the level of detail of the analysis to be adjusted based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI perform the evaluation of the importance of the information.

[0096] The analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, in the case of childcare-related information, the analysis unit can apply a childcare-specific algorithm. For example, in the case of health-related information, the analysis unit can apply a health-specific algorithm. Furthermore, in the case of education-related information, the analysis unit can apply an education-specific algorithm. For example, the analysis unit can apply a childcare-specific algorithm to childcare-related information. Furthermore, the analysis unit can apply a health-specific algorithm to health-related information. Furthermore, the analysis unit can apply an education-specific algorithm to education-related information. This allows the appropriate analysis algorithm to be applied according to the category of information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI perform the application of the analysis algorithm.

[0097] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can also perform a visually stimulating analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have the generative AI perform emotion estimation.

[0098] The analysis unit determines the priority of analysis based on the information submission date during the analysis. For example, the analysis unit can prioritize the analysis of information that is urgent. For example, the analysis unit can postpone the analysis of information that is old. Furthermore, the analysis unit can also quickly analyze information that is new. For example, the analysis unit evaluates the information submission date and prioritizes the analysis of information that is urgent. The analysis unit can also evaluate the information submission date and postpone the analysis of information that is old. Furthermore, the analysis unit can evaluate the information submission date and quickly analyze information that is new. This allows the analysis priority to be determined based on the information submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can have a generative AI perform the evaluation of the information submission date.

[0099] The analysis unit adjusts the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. Furthermore, the analysis unit can perform the analysis of information with a moderate degree of relevance in an appropriate order. For example, the analysis unit evaluates the relevance of the information and prioritizes the analysis of highly relevant information. The analysis unit can also evaluate the relevance of the information and postpone the analysis of less relevant information. Furthermore, the analysis unit can evaluate the relevance of the information and perform the analysis of information with a moderate degree of relevance in an appropriate order. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI perform the evaluation of the relevance of the information.

[0100] The listing unit estimates the user's emotions and adjusts the listing criteria based on the estimated emotions. For example, if the user is nervous, the listing unit can provide simple and easily visible criteria. If the user is relaxed, the listing unit can provide detailed criteria. Furthermore, if the user is in a hurry, the listing unit can provide concise criteria. For example, the listing unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the listing unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the listing criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can have a generative AI perform sentiment estimation.

[0101] The listing unit improves the accuracy of listing by considering the interrelationships of the information during the listing process. For example, the listing unit can prioritize listing information that is highly relevant. For example, the listing unit can analyze the interrelationships of the information and perform optimal listing. The listing unit can also adjust the order of listing based on the relevance of the information. For example, the listing unit evaluates the interrelationships of the information and prioritizes listing information that is highly relevant. The listing unit can also evaluate the interrelationships of the information and perform optimal listing. Furthermore, the listing unit can evaluate the relevance of the information and adjust the order of listing. This improves the accuracy of listing by considering the interrelationships of the information. Some or all of the above processing in the listing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the listing unit can have a generative AI perform the evaluation of the interrelationships of the information.

[0102] The listing unit considers the attribute information of the information submitter when creating a list. For example, if the submitter is an expert, the listing unit can prioritize listing that information. For example, if the submitter is a general user, the listing unit will appropriately list that information. The listing unit can also adjust the order of listing based on the submitter's attribute information. For example, the listing unit evaluates the submitter's attribute information and prioritizes listing information of experts. The listing unit also evaluates the submitter's attribute information and appropriately lists information of general users. Furthermore, the listing unit can evaluate the submitter's attribute information and adjust the order of listing. This improves the accuracy of the listing by considering the attribute information of the information submitter. Some or all of the above processing in the listing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the listing unit can have a generative AI perform the evaluation of the submitter's attribute information.

[0103] The listing unit estimates the user's emotions and adjusts the order in which the listing results are displayed based on the estimated emotions. For example, if the user is tense, the listing unit can display the results in a simple and easy-to-read order. If the user is relaxed, the listing unit can display the results in a more detailed order. If the user is in a hurry, the listing unit can also display the results in a concise and to-the-point order. For example, the listing unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the listing unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the order in which the listing results are displayed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can have a generative AI perform sentiment estimation.

[0104] The listing unit considers the geographical distribution of information when listing it. For example, the listing unit can prioritize listing information that is geographically close. For example, the listing unit can analyze the geographical distribution and perform optimal listing. The listing unit can also adjust the order of listing based on geographical relevance. For example, the listing unit evaluates the geographical distribution of information and prioritizes listing information that is geographically close. The listing unit can also evaluate the geographical distribution of information and perform optimal listing. Furthermore, the listing unit can evaluate the geographical relevance of information and adjust the order of listing. This improves the accuracy of listing by considering the geographical distribution of information. Some or all of the above processing in the listing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the listing unit can have a generative AI perform the evaluation of geographical distribution.

[0105] The listing unit improves the accuracy of the listing by referring to related literature during the listing process. For example, the listing unit can improve the accuracy of the listing by referring to related literature. For example, the listing unit can perform optimal listing based on the information in the related literature. The listing unit can also adjust the order of listing based on the relevance of the related literature. For example, the listing unit can evaluate related literature to improve the accuracy of the listing. The listing unit can also evaluate the information in related literature to perform optimal listing. Furthermore, the listing unit can evaluate the relevance of related literature and adjust the order of listing. As a result, the accuracy of the listing is improved by referring to related literature. Some or all of the above processing in the listing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the listing unit can have a generative AI perform the evaluation of related literature.

[0106] The service provider estimates the user's emotions and adjusts the presentation of the information based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible presentation. If the user is relaxed, the service provider can provide a presentation that includes detailed information. If the user is in a hurry, the service provider can also provide a concise presentation. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the service provider to adjust the presentation of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have a generative AI perform emotion estimation.

[0107] The information provider adjusts the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide detailed information if the information is important. For example, the provider can provide simplified information if the information is general. The provider can also provide information quickly if it is urgent. For example, the provider can assess the importance of the information and provide detailed information if it is important. The provider can also assess the importance of the information and provide simplified information if it is general. Furthermore, the provider can assess the urgency of the information and provide information quickly if it is urgent. This allows the level of detail provided to be adjusted based on the importance of the information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the provider can have a generative AI perform the assessment of the importance of the information.

[0108] The information provider applies different information provision algorithms depending on the category of the information at the time of provision. For example, in the case of childcare-related information, the information provider can apply a childcare-specific algorithm. For example, in the case of health-related information, the information provider can apply a health-specific algorithm. Furthermore, in the case of education-related information, the information provider can also apply an education-specific algorithm. For example, the information provider can apply a childcare-specific algorithm to childcare-related information. Furthermore, the information provider can apply a health-specific algorithm to health-related information. Furthermore, the information provider can apply an education-specific algorithm to education-related information. This allows for the application of an appropriate information provision algorithm depending on the category of the information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can have a generative AI perform the application of the information provision algorithm.

[0109] The service provider estimates the user's emotions and adjusts the length of the information provided based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise information. If the user is relaxed, the service provider can provide detailed information. If the user is excited, the service provider can also provide visually stimulating information. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the length of information to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have the generative AI perform emotion estimation.

[0110] The information provisioning department determines the priority of information provision based on the timing of its submission. For example, the information provisioning department can prioritize providing information that is urgent. For example, the information provisioning department can postpone providing information that is old. Furthermore, the information provisioning department can also provide information quickly if it is new. For example, the information provisioning department evaluates the timing of information submission and prioritizes providing information that is urgent. The information provisioning department can also evaluate the timing of information submission and postpone providing information that is old. Furthermore, the information provisioning department can evaluate the timing of information submission and provide information quickly if it is new. This allows the provisioning department to determine the priority of information provision based on the timing of its submission. Some or all of the above processing in the information provisioning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provisioning department can have a generative AI perform the evaluation of the timing of information submission.

[0111] The information provider adjusts the order of information delivery based on the relevance of the information. For example, the provider can prioritize providing highly relevant information. For example, it can postpone providing less relevant information. It can also provide information of moderate relevance in an appropriate order. For example, the provider evaluates the relevance of the information and prioritizes providing highly relevant information. It can also evaluate the relevance of the information and postpone providing less relevant information. Furthermore, it can evaluate the relevance of the information and provide information of moderate relevance in an appropriate order. This allows the order of information delivery to be adjusted based on the relevance of the information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can have a generative AI perform the evaluation of the relevance of the information.

[0112] The notification unit estimates the user's emotions and adjusts the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can reduce the frequency of notifications and send notifications when the user is relaxed. If the user is relaxed, the notification unit can increase the frequency of notifications and provide more detailed information. Furthermore, if the user is in a hurry, the notification unit can prioritize important information and provide it quickly. For example, the notification unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the notification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for adjustment of notification timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may have a generative AI perform emotion estimation.

[0113] The notification unit adjusts the level of detail of the notification based on the importance of the information. For example, the notification unit can provide a detailed notification for important information. For example, it can provide a simplified notification for general information. The notification unit can also provide a rapid notification for urgent information. For example, the notification unit can assess the importance of the information and provide a detailed notification for important information. The notification unit can also assess the importance of the information and provide a simplified notification for general information. Furthermore, the notification unit can assess the urgency of the information and provide a rapid notification for urgent information. This allows the level of detail of the notification to be adjusted based on the importance of the information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can have a generative AI perform the assessment of the importance of the information.

[0114] The notification unit applies different notification algorithms depending on the category of information when it sends a notification. For example, the notification unit can apply a childcare-specific algorithm to childcare-related information. For example, the notification unit can apply a health-specific algorithm to health-related information. Furthermore, the notification unit can apply an education-specific algorithm to education-related information. This allows the notification unit to apply an appropriate notification algorithm depending on the category of information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can have a generative AI perform the application of the notification algorithm.

[0115] The notification unit estimates the user's emotions and determines notification priorities based on the estimated emotions. For example, if the user is stressed, the notification unit can prioritize notifications of relaxing information. For example, if the user is relaxed, the notification unit can prioritize notifications of detailed information. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications of important information. For example, the notification unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The notification unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the notification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the notification unit to determine notification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may have a generative AI perform emotion estimation.

[0116] The notification unit adjusts the order of notifications based on the timing of information submission. For example, the notification unit can prioritize notifications for highly urgent information. For example, the notification unit can postpone notifications for older information. The notification unit can also quickly notify for newer information. For example, the notification unit evaluates the timing of information submission and prioritizes notifications for highly urgent information. The notification unit can also evaluate the timing of information submission and postpone notifications for older information. Furthermore, the notification unit can evaluate the timing of information submission and quickly notify for newer information. This allows the order of notifications to be adjusted based on the timing of information submission. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can have a generative AI perform the evaluation of the timing of information submission.

[0117] The notification unit adjusts the content of notifications based on the relevance of the information. For example, the notification unit can prioritize notifications for highly relevant information. For example, it can postpone notifications for less relevant information. It can also send notifications in an appropriate order for information of moderate relevance. For example, the notification unit evaluates the relevance of the information and prioritizes notifications for highly relevant information. It can also evaluate the relevance of the information and postpone notifications for less relevant information. Furthermore, it can evaluate the relevance of the information and send notifications in an appropriate order for information of moderate relevance. This allows the content of notifications to be adjusted based on the relevance of the information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can have a generative AI perform the evaluation of the relevance of the information.

[0118] The scheduling unit estimates the user's emotions and modifies the scheduling method based on the estimated emotions. For example, if the user is stressed, the scheduling unit can suggest ways to reduce the burden of the schedule. For example, if the user is relaxed, the scheduling unit can suggest a detailed schedule. The scheduling unit can also quickly adjust the schedule if the user is in a hurry. For example, the scheduling unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The scheduling unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the scheduling unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the scheduling method to be changed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can have a generating AI perform emotion estimation.

[0119] The schedule adjustment unit proposes the optimal schedule by referring to past schedule data during schedule adjustment. For example, the schedule adjustment unit can propose the optimal schedule based on the user's past schedule data. For example, the schedule adjustment unit can propose a schedule that avoids congestion based on the user's past schedule data. The schedule adjustment unit can also analyze the user's past schedule data and propose the most efficient schedule. For example, the schedule adjustment unit evaluates the user's past schedule data and proposes the optimal schedule. The schedule adjustment unit can also evaluate the user's past schedule data and propose a schedule that avoids congestion. Furthermore, the schedule adjustment unit can evaluate the user's past schedule data and propose the most efficient schedule. This allows the system to propose the optimal schedule based on past schedule data. Some or all of the above-described processes in the schedule adjustment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the schedule adjustment unit can have a generative AI perform the evaluation of past schedule data.

[0120] The scheduling unit estimates the user's emotions and determines schedule priorities based on the estimated emotions. For example, if the user is stressed, the scheduling unit can prioritize suggesting relaxing schedules. For example, if the user is relaxed, the scheduling unit can prioritize suggesting detailed schedules. Furthermore, if the user is in a hurry, the scheduling unit can prioritize suggesting important schedules. For example, the scheduling unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The scheduling unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the scheduling unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the scheduling unit to determine schedule priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can have a generating AI perform emotion estimation.

[0121] The schedule adjustment unit proposes the optimal schedule by considering the user's geographical location information during schedule adjustment. For example, if the user lives in a specific region, the schedule adjustment unit can prioritize suggesting schedules related to that region. For example, if the user is traveling, the schedule adjustment unit can prioritize suggesting schedules related to the travel destination. Furthermore, if the user is planning to move, the schedule adjustment unit can prioritize suggesting schedules related to the new place of residence. For example, the schedule adjustment unit can prioritize suggesting schedules related to the region based on the user's geographical location information. Furthermore, the schedule adjustment unit can prioritize suggesting schedules related to the travel destination based on the user's geographical location information. In addition, the schedule adjustment unit can prioritize suggesting schedules related to the new place of residence based on the user's geographical location information. This allows the system to propose the optimal schedule based on the user's geographical location information. Some or all of the above processing in the schedule adjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the schedule adjustment unit can have a generative AI perform the evaluation of geographical location information.

[0122] The community building unit estimates the user's emotions and prioritizes communities based on those estimated emotions. For example, if the user is stressed, the community building unit can prioritize suggesting relaxing communities. If the user is relaxed, for example, the community building unit can prioritize suggesting detailed communities. Furthermore, if the user is in a hurry, the community building unit can prioritize suggesting important communities. For example, the community building unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the community building unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the prioritization of communities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the community formation unit may be performed using AI, for example, or without AI. For example, the community formation unit can have a generative AI perform emotion estimation.

[0123] The community formation unit proposes the most suitable community when forming a community, taking into account the user's geographical location information. For example, if the user lives in a specific area, the community formation unit can prioritize suggesting communities related to that area. For example, if the user is traveling, the community formation unit can prioritize suggesting communities related to the travel destination. Furthermore, if the user is planning to move, the community formation unit can prioritize suggesting communities related to the new place of residence. For example, the community formation unit prioritizes suggesting communities related to the user's geographical location information. Furthermore, the community formation unit can prioritize suggesting communities related to the travel destination based on the user's geographical location information. In addition, the community formation unit can prioritize suggesting communities related to the new place of residence based on the user's geographical location information. This allows the community formation unit to propose the most suitable community based on the user's geographical location information. Some or all of the above processing in the community formation unit may be performed using, for example, a generative AI, or not. For example, the community formation unit can have a generative AI perform the evaluation of geographical location information.

[0124] The alert unit estimates the user's emotions and adjusts the timing of alerts based on the estimated emotions. For example, if the user is stressed, the alert unit can reduce the frequency of alerts and send alerts when the user is relaxed. If the user is relaxed, the alert unit can increase the frequency of alerts and provide more detailed information. The alert unit can also prioritize alerts and quickly provide important information if the user is in a hurry. For example, the alert unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The alert unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the alert unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the timing of alerts to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can have a generative AI perform emotion estimation.

[0125] The alert unit adjusts the level of detail of an alert based on the importance of the information when an alert occurs. For example, the alert unit can provide a detailed alert for important information. For example, the alert unit can provide a simplified alert for general information. The alert unit can also provide a rapid alert for urgent information. For example, the alert unit can assess the importance of the information and provide a detailed alert for important information. The alert unit can also assess the importance of the information and provide a simplified alert for general information. Furthermore, the alert unit can assess the urgency of the information and provide a rapid alert for urgent information. This allows the level of detail of the alert to be adjusted based on the importance of the information. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the alert unit can have a generative AI perform the assessment of the importance of the information.

[0126] The alert unit applies different alert algorithms depending on the category of information when an alert occurs. For example, the alert unit can apply a childcare-specific algorithm to childcare-related information. For example, the alert unit can apply a health-specific algorithm to health-related information. Furthermore, the alert unit can apply an education-specific algorithm to education-related information. This allows the appropriate alert algorithm to be applied according to the category of information. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alert unit can have a generative AI perform the application of the alert algorithm.

[0127] The alert unit estimates the user's emotions and determines the priority of alerts based on the estimated emotions. For example, if the user is stressed, the alert unit can prioritize alerts on information that helps them relax. For example, if the user is relaxed, the alert unit can prioritize alerts on detailed information. Also, if the user is in a hurry, the alert unit can prioritize alerts on important information. For example, the alert unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The alert unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the alert unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the alert unit to determine the priority of alerts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can have a generative AI perform emotion estimation.

[0128] The alert unit adjusts the order of alerts based on the timing of information submission when an alert occurs. For example, the alert unit can prioritize alerts for highly urgent information. For example, the alert unit can postpone alerts for older information. The alert unit can also quickly alert for newer information. For example, the alert unit evaluates the timing of information submission and prioritizes alerts for highly urgent information. The alert unit can also evaluate the timing of information submission and postpone alerts for older information. Furthermore, the alert unit can evaluate the timing of information submission and quickly alert for newer information. This allows the order of alerts to be adjusted based on the timing of information submission. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or not. For example, the alert unit can have a generative AI perform the evaluation of the timing of information submission.

[0129] The growth recording unit estimates the user's emotions and adjusts the method of collecting growth records based on the estimated emotions. For example, if the user is stressed, the growth recording unit can reduce the collection frequency and collect growth records when the user is relaxed. If the user is relaxed, for example, the growth recording unit can increase the collection frequency and collect more detailed growth records. Also, if the user is in a hurry, the growth recording unit can prioritize the collection of important growth data and provide it quickly. For example, the growth recording unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The growth recording unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the growth recording unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. This allows the method of collecting growth records to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the growth recording unit may be performed using AI, for example, or not using AI. For example, the growth recording unit may have the generative AI perform emotion estimation.

[0130] The growth recording unit, when recording growth, refers to past growth data to propose the optimal recording method. For example, the growth recording unit can propose the optimal recording method based on the user's past growth data. For example, the growth recording unit can analyze growth trends from the user's past growth data and propose the optimal recording method. The growth recording unit can also analyze the user's past growth data and propose the most efficient recording method. For example, the growth recording unit evaluates the user's past growth data and proposes the optimal recording method. The growth recording unit can also evaluate the user's past growth data, analyze growth trends, and propose the optimal recording method. Furthermore, the growth recording unit can evaluate the user's past growth data and propose the optimal recording method. For example, the growth recording unit evaluates the user's past growth data and proposes the optimal recording method. Furthermore, the growth recording unit can evaluate the user's past growth data, analyze growth trends, and propose the optimal recording method. Furthermore, the growth recording unit can evaluate the user's past growth data and propose the most efficient recording method. This allows the optimal recording method to be proposed based on past growth data. Some or all of the above-described processes in the growth recording unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the growth recording unit can have a generative AI perform an evaluation of past growth data.

[0131] The growth record unit estimates the user's emotions and prioritizes growth records based on the estimated emotions. For example, if the user is stressed, the growth record unit can prioritize suggesting relaxing growth records. For example, if the user is relaxed, the growth record unit can prioritize suggesting detailed growth records. Furthermore, if the user is in a hurry, the growth record unit can prioritize suggesting important growth records. For example, the growth record unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The growth record unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the growth record unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the growth record unit to prioritize growth records according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the growth recording unit may be performed using AI, for example, or without AI. For example, the growth recording unit can have a generative AI perform emotion estimation.

[0132] The growth recording unit proposes the optimal recording method when recording growth, taking into account the user's geographical location information. For example, if the user lives in a specific region, the growth recording unit can prioritize suggesting a recording method related to that region. For example, if the user is traveling, the growth recording unit can prioritize suggesting a recording method related to the travel destination. Furthermore, if the user is planning to move, the growth recording unit can prioritize suggesting a recording method related to the new place of residence. For example, the growth recording unit can prioritize suggesting a recording method related to the region based on the user's geographical location information. Furthermore, the growth recording unit can prioritize suggesting a recording method related to the travel destination based on the user's geographical location information. In addition, the growth recording unit can prioritize suggesting a recording method related to the new place of residence based on the user's geographical location information. This allows the optimal recording method to be proposed based on the user's geographical location information. Some or all of the above processing in the growth recording unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the growth recording unit can have a generative AI perform the evaluation of geographical location information.

[0133] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0134] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, it will prioritize analyzing information that promotes relaxation. If the user is relaxed, it can prioritize analyzing detailed information. Furthermore, if the user is in a hurry, it can prioritize analyzing important information. This allows the analysis priority to be adjusted according to the user's emotions.

[0135] The data collection unit can analyze the user's past behavior patterns and suggest the optimal timing for information collection. For example, if a user has collected information during a specific time period in the past, the unit can collect information during that time period. Similarly, if a user has collected information on a specific day of the week, the unit can collect information on that day. Furthermore, by analyzing the user's behavior patterns and suggesting the optimal timing for information collection, the unit can efficiently collect information based on the user's behavior patterns.

[0136] The information provider can also estimate the user's emotions and adjust the format of the information provided based on those estimates. For example, if the user is stressed, the information can be provided in a simple, easy-to-read format. If the user is relaxed, the information can be provided in a format that includes more detail. Furthermore, if the user is in a hurry, the information can be provided in a concise, to-the-point format. This allows the information format to be adjusted according to the user's emotions.

[0137] The data collection unit can also prioritize the collection of region-specific information based on the user's geographical location. For example, if a user lives in a specific region, it can prioritize the collection of information related to that region. Similarly, if a user is traveling, it can prioritize the collection of information related to their travel destination. Furthermore, if a user is planning to move, it can prioritize the collection of information related to their new residence. This allows for the collection of highly relevant information based on the user's geographical location.

[0138] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis based on those emotions. For example, if the user is stressed, a simplified analysis can be performed. Conversely, if the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is in a hurry, a concise analysis can be performed. This allows the level of detail in the analysis to be adjusted according to the user's emotions.

[0139] The data collection unit can also analyze users' social media activity and collect relevant information. For example, if a user posts about childcare on social media, it can prioritize collecting childcare-related information. Similarly, if a user posts about health on social media, it can prioritize collecting health-related information. Furthermore, if a user posts about travel on social media, it can prioritize collecting travel-related information. This allows for the collection of relevant information based on the user's social media activity.

[0140] The information delivery system can also estimate the user's emotions and prioritize the information provided based on those emotions. For example, if a user is stressed, it can prioritize providing relaxing information. If a user is relaxed, it can prioritize providing detailed information. Furthermore, if a user is in a hurry, it can prioritize providing important information. This allows for adjusting the priority of information according to the user's emotions.

[0141] The data collection unit can analyze the user's past information collection history and select the optimal collection method. For example, it can prioritize collecting similar information based on information the user has frequently collected in the past. It can also prioritize suggesting collection methods the user has used in the past (e.g., voice, text). Furthermore, it can predict the information to be collected at a specific time period based on the user's past collection history and suggest the optimal collection method. This allows for the selection of the optimal collection method based on past information collection history.

[0142] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide a simple and highly visual presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. This allows the presentation of the analysis to be adjusted according to the user's emotions.

[0143] The data collection unit can also filter information based on the user's current lifestyle and areas of interest. For example, if the user is interested in childcare, it can prioritize collecting childcare-related information. Similarly, if the user is busy with work, it can prioritize collecting work-related information. Furthermore, if the user is interested in health, it can prioritize collecting health-related information. This allows for information filtering based on the user's lifestyle and areas of interest.

[0144] The following briefly describes the processing flow for example form 2.

[0145] Step 1: The collection unit collects information. The collection unit can collect information such as text data, image data, and audio data. The collection unit can collect information from the internet, for example. It can also collect information directly from users. Furthermore, the collection unit can collect environmental information using sensors. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can analyze collected text data using natural language processing techniques. The analysis unit can also analyze collected image data using image recognition techniques. Furthermore, the analysis unit can analyze collected audio data using speech recognition techniques. Step 3: The listing unit lists the application procedures based on the information analyzed by the analysis unit. The listing unit can list application procedures such as patent applications, trademark registrations, and visa applications. For example, the listing unit extracts and lists the necessary application procedures from the analyzed information. The listing unit can also determine the priority of the application procedures. Step 4: The provisioning department provides detailed information about the application procedures listed by the listing department. The provisioning department can provide details such as the steps of the procedure, required documents, and submission deadlines. The provisioning department can also notify users of the progress of the procedure. In addition, the provisioning department can provide FAQs about the procedure. Furthermore, the provisioning department can respond to user questions using a chatbot. Step 5: The notification unit sets reminders for important procedures and notifies users when deadlines are approaching. The notification unit can send reminders via methods such as email, app notifications, and SMS notifications. For example, the notification unit notifies the user when a procedure deadline is approaching. The notification unit can also periodically notify users of the progress of the procedure. Furthermore, the notification unit can also notify users when a procedure is completed.

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0148] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, provision unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The listing unit is implemented in the identification processing unit 290 of the data processing unit 12 and lists the application procedures based on the analyzed information. The provision unit is implemented in the control unit 46A of the smart device 14 and provides detailed information about the listed application procedures. The notification unit is implemented in the control unit 46A of the smart device 14 and sets reminders for important procedures and notifies when the deadline approaches. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0151] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, provision unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The listing unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and lists the application procedures based on the analyzed information. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides detailed information about the listed application procedures. The notification unit is implemented, for example, in the control unit 46A of the smart glasses 214 and sets reminders for important procedures and notifies when the deadline approaches. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0167] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, provision unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The listing unit is implemented in the identification processing unit 290 of the data processing unit 12 and lists the application procedures based on the analyzed information. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides detailed information about the listed application procedures. The notification unit is implemented in the control unit 46A of the headset terminal 314 and sets reminders for important procedures and notifies when the deadline approaches. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0182] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0183] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0184] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0185] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0186] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0187] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0188] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0189] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0190] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0191] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0192] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0193] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0194] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0195] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0197] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0198] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, provision unit, and notification unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The listing unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and lists the application procedures based on the analyzed information. The provision unit is implemented, for example, in the control unit 46A of the robot 414 and provides detailed information about the listed application procedures. The notification unit is implemented, for example, in the control unit 46A of the robot 414 and sets reminders for important procedures and notifies when the deadline approaches. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0199] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0200] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0201] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0202] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0203] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0204] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0205] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0206] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0207] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0208] 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.

[0209] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0210] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0211] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0212] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0213] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0214] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0215] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0216] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0217] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A listing unit that lists application procedures based on the information analyzed by the aforementioned analysis unit, A provision unit that provides detailed information regarding the application procedures listed by the aforementioned listing unit, It includes a notification unit that sets reminders for procedures and notifies when the deadline approaches. A system characterized by the following features. (Note 2) The aforementioned collection unit is It features a scheduling unit that automatically tracks and adjusts vaccination schedules and suggests new schedules. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It features an advice department that provides real-time expert advice on parenting questions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It includes a community building department that matches mothers who gave birth around the same time, creating a community where they can exchange information and seek advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It features an alert unit that analyzes health data in real time and alerts parents of changes in their health. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It features a growth record unit that automatically collects and organizes weight and height growth data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past information gathering history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, we analyze users' social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned listing unit is, It estimates the user's sentiment and adjusts the listing criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned listing unit is, When creating a list, consider the interrelationships between the information to improve the accuracy of the list. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned listing unit is, When creating the list, the attribute information of the information submitter should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned listing unit is, It estimates the user's sentiment and adjusts the order in which the listed results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned listing unit is, When creating a list, consider the geographical distribution of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned listing unit is, When creating a list, refer to relevant literature to improve the accuracy of the list. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, When notifying, the order of notifications will be adjusted based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending a notification, adjust the content of the notification based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned schedule adjustment unit, It estimates the user's emotions and changes how the schedule is adjusted based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned schedule adjustment unit, When adjusting schedules, we refer to past schedule data to suggest the optimal schedule. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned schedule adjustment unit, It estimates the user's emotions and determines schedule priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned schedule adjustment unit, When scheduling, we propose the optimal schedule considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 41) The community formation unit is, It estimates user sentiment and determines community priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 42) The community formation unit is, When forming a community, we propose the optimal community by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The alert unit is, It estimates the user's emotions and adjusts the timing of alerts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The alert unit is, When an alert is issued, adjust the level of detail of the alert based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 45) The alert unit is, When an alert is triggered, different alert algorithms are applied depending on the category of the information. The system described in Appendix 1, characterized by the features described herein. (Note 46) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 47) The alert unit is, When an alert is issued, the order of alerts will be adjusted based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned growth recording unit is We estimate the user's emotions and adjust how growth records are collected based on those estimated emotions. The system according to Appendix 1, characterized in that... (Appendix 49) The growth record part... When recording growth, propose an optimal recording method by referring to past growth data The system according to Appendix 1, characterized in that... (Appendix 50) The growth record part... Estimate the user's emotion and determine the priority of growth records based on the estimated user emotion The system according to Appendix 1, characterized in that... (Appendix 51) The growth record part... When recording growth, propose an optimal recording method considering the user's geographical location information The system according to Appendix 1, characterized in that...

Explanation of reference signs

[0218] 10, 210, 310, 410 Data processing system 12 Data processing device '14' Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A listing unit that lists application procedures based on the information analyzed by the aforementioned analysis unit, A provision unit that provides detailed information regarding the application procedures listed by the aforementioned listing unit, It includes a notification unit that sets reminders for procedures and notifies when the deadline approaches. A system characterized by the following features.

2. The aforementioned collection unit is It features a scheduling unit that automatically tracks and adjusts vaccination schedules and proposes new schedules. The system according to feature 1.

3. The aforementioned supply unit is, It features an advice department that provides real-time expert advice on parenting questions. The system according to feature 1.

4. The aforementioned collection unit is It includes a community building department that matches mothers who gave birth around the same time, creating a community where they can exchange information and seek advice. The system according to feature 1.

5. The aforementioned analysis unit, It features an alert unit that analyzes health data in real time and alerts parents of changes in their health. The system according to feature 1.

6. The aforementioned collection unit is It features a growth record unit that automatically collects and organizes weight and height growth data. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the user's past information gathering history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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