system

Abstract

The system according to this embodiment aims to accurately predict the likelihood of admission to a nursery school and provide parents with peace of mind. [Solution] The system according to the embodiment comprises a reception unit, an acquisition unit, an input unit, a scoring unit, and a notification unit. The reception unit accepts user registration. The acquisition unit automatically acquires information from the local government's database based on the information received by the reception unit. The input unit verifies the information acquired by the acquisition unit and inputs supplementary information. The scoring unit performs scoring based on the information entered by the input unit. The notification unit notifies the user of the score and advice obtained by the scoring unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

[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, the method including: 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 as a 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, it is difficult to accurately predict the possibility of a child entering a nursery school, and there is a problem that guardians still feel anxious.

[0005] The system according to the embodiment aims to accurately predict the possibility of a child entering a nursery school and provide a sense of security to guardians.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an acquisition unit, an input unit, a scoring unit, and a notification unit. The reception unit accepts user registrations. The acquisition unit automatically retrieves information from the local government's database based on the information received by the reception unit. The input unit verifies the information obtained by the acquisition unit and inputs supplementary information. The scoring unit performs scoring based on the information entered by the input unit. The notification unit notifies the user of the score and advice obtained by the scoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can accurately predict the likelihood of admission to a nursery school and provide parents with peace of mind. [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 manages communication among a plurality of computers. Examples of communication standards applied 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The nursery school admission possibility determination system according to an embodiment of the present invention is a system that supports parents of children living in areas with high competition for nursery school admission, helping them to increase their chances of getting their child into a nursery school. Generally, admission to nursery schools is based on a point system, with more popular schools requiring higher points. The method for calculating the points required for admission varies by municipality, and even in cases of ties, various methods are used to determine priority. Based on one's own score, priority, and past data, the probability of admission to a desired nursery school can be easily predicted. Specifically, the system consists of the following steps: First, the user registers with the system. Next, the system automatically retrieves necessary information from the municipality's database, the user confirms the automatically retrieved information, and inputs supplementary information. The AI ​​performs scoring based on the input information and notifies the user of their admission possibility score and advice. Specifically, the user specifies their family structure and the nursery school they wish to enroll their child in, and inputs information not held by the municipality according to the questions. The AI ​​performs scoring considering past admission data and the location of the nursery school, predicting the possibility of admission. For example, if the admission probability is 5%, the system might notify the user that nursery school A is close to a train station and is expected to receive many applications, thus indicating a low probability of admission. If the probability of admission is 20%, it is likely that the minimum score requirement will be met, but admission will be difficult because the household income, which is the priority in the event of a tie, is high. If the probability of admission is 80%, it will be notified that it is a newly established nursery school with a large capacity and a location away from train stations and residential areas, so there are few applicants. This system allows parents to narrow down their consideration to nurseries with a high probability of admission, saving them unnecessary effort. In addition, changing the order of preferences may increase the chances of admission, making it a very beneficial system for parents. In this way, the nursery school admission probability assessment system allows parents to select nurseries with a high probability of admission and efficiently aim for admission to a nursery school.

[0029] The nursery school admission possibility determination system according to the embodiment comprises a reception unit, an acquisition unit, an input unit, a scoring unit, and a notification unit. The reception unit accepts user registration. User registration includes, but is not limited to, online forms or email registration. For example, the reception unit allows users to complete registration by entering the necessary information into an online form. The reception unit can also receive user information and register through email registration. Furthermore, the reception unit can also accept registration by telephone or in person. For example, the reception unit allows users to provide registration information by telephone, and an operator enters that information into the system. The acquisition unit automatically acquires necessary information from the local government's database. For example, the acquisition unit acquires information from the local government's database using an API. The acquisition unit can also collect information from websites using scraping technology. Furthermore, the acquisition unit can automatically read data files provided by local governments. For example, the acquisition unit acquires address information and contact information through the local government's API. It collects necessary information from the local government's website using scraping technology. It automatically reads data files and imports them into the system. The input unit verifies the information automatically acquired by the user and inputs supplementary information. For example, the input unit provides an interface for the user to verify the automatically acquired information and input necessary supplementary information. The input unit can also save the supplementary information entered by the user to the system. Furthermore, the input unit can collect data to improve the accuracy of scoring based on the information entered by the user. For example, the input unit verifies the address information automatically acquired by the user and inputs additional address information. The user verifies contact information and inputs necessary supplementary information. The user inputs supplementary information such as family structure and income status. The scoring unit performs scoring based on the information entered by the input unit. The scoring unit performs scoring considering, for example, past enrollment data and the location of the nursery. The scoring unit can use AI to perform scoring based on user information. For example, the scoring unit predicts the user's likelihood of enrollment based on past enrollment data. It performs scoring considering the location of the nursery and the surrounding environment.The system uses AI to analyze user information and perform scoring. The notification unit notifies the user of the score and advice obtained by the scoring unit. The notification unit provides the score and advice to the user, for example, through text messages or email notifications. The notification unit can also display the score and advice to the user through web applications or mobile applications. Furthermore, the notification unit can print the results using a printer for users who prefer paper feedback. For example, the notification unit sends the score and advice to the user via text message. It provides the user with a detailed score and advice via email notification. It displays the score and advice to the user through web applications or mobile applications. As a result, the nursery school admission possibility determination system according to this embodiment automates the entire process from user registration to scoring and notification, enabling efficient determination of nursery school admission possibility.

[0030] The reception desk accepts user registrations. User registration includes, but is not limited to, online forms and email registrations. For example, the reception desk can accept registrations when users enter the necessary information into an online form. The reception desk can also receive user information and register users via email registration. Furthermore, the reception desk can accept registrations by phone or in person. For example, the reception desk can accept registrations when users provide information by phone, and an operator enters that information into the system. With online forms, users can enter their name, address, contact information, the child's age, and information about the desired daycare center. In the case of email registration, users send the necessary information to a designated email address, and the system automatically parses and registers the information. With phone registration, an operator receives information from the user and manually enters it into the system. In the case of in-person registration, users come to the counter in person, submit the necessary documents, and an operator enters that information into the system. This allows the reception desk to accept user registrations in a variety of ways, increasing user convenience. Furthermore, the reception desk also provides an interface for confirming and correcting registration information. For example, if a user wants to change their information after registering, the system can accept the changes via online forms, email, or telephone. This ensures that users always provide the system with the most up-to-date information, improving the accuracy of the scoring system.

[0031] The data acquisition unit automatically retrieves necessary information from local government databases. For example, it can use APIs to obtain information from local government databases. It can also collect information from websites using scraping techniques. Furthermore, the data acquisition unit can automatically read data files provided by local governments. For example, it can obtain address and contact information through local government APIs. It can also collect necessary information from local government websites using scraping techniques. It automatically reads data files and incorporates them into the system. When using APIs, the data acquisition unit can periodically access local government databases to obtain the latest information. When using scraping techniques, the data acquisition unit implements algorithms to analyze the website structure and extract necessary information. For reading data files, it automatically analyzes CSV and Excel files provided by local governments and incorporates the necessary information into the system. This allows the data acquisition unit to collect local government information in diverse ways and reflect it in the system. Furthermore, the data acquisition unit also has verification functions to check the integrity of the acquired information and detect missing or incorrect information. For example, it checks whether the acquired address information is in the correct format and issues an alert if it contains inaccurate information. This allows the data acquisition unit to provide high-quality data to the system, improving the accuracy of the scoring.

[0032] The input unit verifies the information automatically acquired by the user and inputs supplementary information. For example, the input unit provides an interface for the user to verify automatically acquired information and input necessary supplementary information. The input unit can also save the supplementary information entered by the user to the system. Furthermore, the input unit can collect data to improve the accuracy of scoring based on the information entered by the user. For example, the input unit verifies the automatically acquired address information and inputs additional address information. The user verifies contact information and inputs necessary supplementary information. The user inputs supplementary information such as household composition and income status. The input unit can reflect the information entered by the user in the system in real time. For example, if the user corrects address information, that information is immediately saved to the system and becomes accessible to the scoring unit. Furthermore, the input unit also has a function to verify the consistency of the information entered by the user and issue an alert if inaccurate information is included. This allows the input unit to provide high-quality data to the system and improve the accuracy of scoring. The input unit can also collect data to improve the accuracy of scoring based on the information entered by the user. For example, the input unit verifies the automatically acquired address information and inputs additional address information. The user verifies contact information and inputs necessary supplementary information. Users input supplementary information such as their household structure and income status. This allows the input unit to collect data based on the information provided by the user to improve the accuracy of the scoring.

[0033] The scoring unit performs scoring based on the information entered by the input unit. The scoring unit considers factors such as past enrollment data and the location of the nursery school when performing scoring. The scoring unit can use AI to perform scoring based on user information. For example, the scoring unit predicts the user's chances of enrollment based on past enrollment data. It performs scoring considering the nursery school's location and surrounding environment. It uses AI to analyze user information and perform scoring. Specifically, the scoring unit analyzes past enrollment data and calculates the success rate of enrollment under specific conditions. For example, it predicts the chances of enrollment considering conditions such as a specific region, family structure, and income situation. Furthermore, the scoring unit performs scoring considering the nursery school's location and surrounding environment. For example, it calculates the score considering factors such as whether there is public transportation near the nursery school and whether the surrounding area is safe. By using AI, the scoring unit can quickly analyze large amounts of data and perform highly accurate scoring. The AI ​​uses machine learning algorithms to learn patterns from past data and can make highly accurate predictions even for new data. This allows the scoring unit to predict a user's likelihood of admission with high accuracy and provide appropriate advice. Furthermore, the scoring unit has established a feedback loop to continuously improve the scoring results. For example, it adjusts the scoring algorithm based on actual admission results to improve prediction accuracy. As a result, the scoring unit can always provide highly accurate scoring based on the latest information and offer optimal advice to users.

[0034] The notification unit notifies the user of the score and advice obtained by the scoring unit. The notification unit provides the score and advice to the user, for example, through text messages or email notifications. The notification unit can also display the score and advice to the user through web applications or mobile applications. Furthermore, the notification unit can print the results using a printer for users who prefer paper feedback. For example, the notification unit sends the score and advice to the user via text message. It provides the user with detailed scores and advice via email notifications. It displays the score and advice to the user through web applications or mobile applications. As a result, the nursery school admission possibility determination system according to the embodiment automates the entire process from user registration to scoring and notification, enabling efficient determination of nursery school admission possibility. The notification unit provides multiple notification means to enhance user convenience. For example, it can quickly convey important information to the user not only through text messages and email notifications, but also through push notifications and voice calls. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, it can provide an interface for users to provide feedback on received notifications and adjust the notification content based on that feedback. This allows the notification unit to provide users with timely and accurate information, improving the reliability and effectiveness of the nursery school admission possibility assessment system.

[0035] The acquisition unit can automatically retrieve necessary information from local government databases. For example, the acquisition unit can retrieve information from local government databases using APIs. The acquisition unit can also collect information from websites using scraping techniques. The acquisition unit can also automatically read data files provided by local governments. For example, the acquisition unit can retrieve address information and contact information via APIs. It can collect necessary information from local government websites using scraping techniques. It automatically reads data files and incorporates them into the system. This allows for the automatic retrieval of necessary information from local government databases, saving users time and effort. Necessary information includes, but is not limited to, address information and contact information. Some or all of the above-described processes in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input information obtained via API into an AI and have the AI ​​analyze the information.

[0036] The input unit allows the user to review automatically acquired information and input supplementary information. For example, the input unit provides an interface for the user to review automatically acquired information and input necessary supplementary information. The input unit can also save the supplementary information entered by the user to the system. The input unit can also collect data to improve the accuracy of scoring based on the information entered by the user. For example, the input unit can review automatically acquired address information and input additional address information. The user can review contact information and input necessary supplementary information. The user can input supplementary information such as household composition and income status. This allows the accuracy of scoring to be improved by the user reviewing automatically acquired information and inputting supplementary information. Supplementary information includes, but is not limited to, additional address information and contact information. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input the supplementary information entered by the user into AI and have the AI ​​perform the analysis of the information.

[0037] The scoring unit can perform scoring by considering past enrollment data and the location of the nursery. For example, the scoring unit predicts the user's likelihood of enrollment based on past enrollment data. The scoring unit performs scoring by considering the nursery's location and surrounding environment. The scoring unit can perform scoring based on user information using AI. For example, the scoring unit predicts the user's likelihood of enrollment based on past enrollment data. The scoring unit performs scoring by considering the nursery's location and surrounding environment. The scoring unit uses AI to analyze user information and perform scoring. This makes it possible to perform more accurate scoring by considering past enrollment data and the nursery's location. Past enrollment data includes, but is not limited to, past enrollment numbers and enrollment conditions. The nursery's location includes, but is not limited to, transportation access and surrounding environment. Some or all of the above processing in the scoring unit may be performed using generative AI, or it may be performed without using generative AI. For example, the scoring unit can input past admission data into a generating AI and have the AI ​​perform scoring predictions.

[0038] The notification unit can notify the user of their admission likelihood score and advice. The notification unit provides the user with the score and advice, for example, through text messages or email notifications. The notification unit can also display the score and advice to the user through web applications or mobile applications. The notification unit can also print the results using a printer for users who prefer paper feedback. For example, the notification unit sends the score and advice to the user via text message. The notification unit provides the user with detailed scores and advice via email notifications. The notification unit displays the score and advice to the user through web applications or mobile applications. This allows the notification unit to provide the user with information to make appropriate decisions by notifying them of their admission likelihood score and advice. Advice includes, but is not limited to, text messages and email notifications. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the score and advice into AI and have the AI ​​generate the notification content.

[0039] The reception desk analyzes the user's past registration history and proposes the optimal registration method. The reception desk can analyze the user's past registration history and propose the optimal registration method. For example, the reception desk may prioritize suggesting registration methods that the user has frequently used in the past (voice, text, etc.). The reception desk may predict and suggest registration methods to be used at specific times based on the user's past registration history. The reception desk automatically inputs the necessary information based on the information the user has previously registered. This allows the reception desk to propose the optimal registration method by analyzing the user's past registration history, thereby improving user convenience. Past registration history includes, but is not limited to, past registration dates and times, and registration content. Optimal registration methods include, but are not limited to, online registration, telephone registration, etc. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past registration history data into AI and have the AI ​​propose the optimal registration method.

[0040] The reception desk customizes the registration details based on the user's family circumstances and the characteristics of the desired nursery school during registration. The reception desk can customize the registration details based on the user's family circumstances and the characteristics of the desired nursery school during registration. For example, the reception desk automatically inputs the necessary information based on the user's family structure. The reception desk customizes the registration details based on the characteristics of the nursery school the user desires. The reception desk proposes the optimal registration method according to the user's family circumstances. This allows for the provision of more appropriate information by customizing the registration details based on the user's family circumstances and the characteristics of the desired nursery school. Family circumstances include, for example, family structure and income status, but are not limited to such examples. Characteristics of the nursery school include, for example, educational policies and facility equipment, but are not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's family circumstances data into AI and have the AI ​​perform the customization of the registration details.

[0041] The reception desk, upon registration, proposes the most suitable daycare center, taking into account the user's geographical location. The reception desk can propose the most suitable daycare center, taking into account the user's geographical location. For example, it can propose the daycare center closest to the user's current location, the daycare center along the user's commute route, or the daycare center midway between the user's residence and workplace. This allows the reception desk to propose the most suitable daycare center for the user by considering their geographical location. Geographical location information includes, but is not limited to, GPS data and address information. The optimal daycare center includes, but is not limited to, distance and facility ratings. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into AI and have the AI ​​propose the most suitable daycare center.

[0042] The reception desk analyzes the user's social media activity during registration and provides relevant information. The reception desk can analyze the user's social media activity during registration and provide relevant information. For example, the reception desk can analyze the user's social media posts and provide information about daycare centers. The reception desk can analyze the user's social media friendships and suggest daycare centers used by their friends. The reception desk can analyze the user's social media activity time and suggest the optimal registration time. By analyzing the user's social media activity, relevant information can be provided, improving user convenience. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, relevant news and event information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into AI and have the AI ​​provide relevant information.

[0043] The data acquisition unit customizes the types of information it acquires from the local government database according to the user's family circumstances. The data acquisition unit can customize the types of information it acquires from the local government database according to the user's family circumstances. For example, the data acquisition unit automatically acquires necessary information based on the user's family structure. The data acquisition unit customizes the types of information it acquires according to the user's family circumstances. The data acquisition unit adjusts the information it acquires based on the characteristics of the nursery school the user desires. By customizing the types of information acquired according to the user's family circumstances, more appropriate information can be provided. Family circumstances include, for example, family structure and income status, but are not limited to such examples. Types of information include, for example, address information and contact information, but are not limited to such examples. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's family circumstances data into AI and have AI perform the customization of the types of information to acquire.

[0044] The acquisition unit selects the optimal acquisition method by referring to past data acquisition history when acquiring information. The acquisition unit can select the optimal acquisition method by referring to past data acquisition history when acquiring information. For example, the acquisition unit automatically acquires necessary information based on information acquired by the user in the past. The acquisition unit selects the optimal acquisition method from the user's past data acquisition history. The acquisition unit analyzes the types of information acquired by the user in the past and acquires the necessary information. By referring to past data acquisition history, it is possible to select the optimal acquisition method and support efficient information acquisition. Past data acquisition history includes, but is not limited to, past acquisition dates and times, and acquisition content. Optimal acquisition methods include, but are not limited to, API acquisition and scraping. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past data acquisition history into AI and have the AI ​​select the optimal acquisition method.

[0045] The acquisition unit prioritizes acquiring highly relevant information by considering the user's geographical location information when acquiring information. The acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location information when acquiring information. For example, the acquisition unit prioritizes acquiring information on the nearest nursery school to the user's current location. The acquisition unit prioritizes acquiring information on nursery schools along the user's commute route. The acquisition unit prioritizes acquiring information on nursery schools located midway between the user's residence and workplace. By considering the user's geographical location information, the acquisition unit can prioritize acquiring highly relevant information and improve user convenience. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant information includes, but is not limited to, information on nearby nursery schools and traffic information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's geographical location information into AI and have the AI ​​perform the acquisition of highly relevant information.

[0046] The acquisition unit analyzes the user's social media activity and acquires relevant information when acquiring information. The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring information. For example, the acquisition unit analyzes the content of the user's social media posts and acquires information about daycare centers. The acquisition unit analyzes the user's social media friendships and acquires information about daycare centers used by their friends. The acquisition unit analyzes the user's social media activity time and suggests the optimal time for acquiring information. By analyzing the user's social media activity, relevant information can be acquired, thereby improving user convenience. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, relevant news and event information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's social media data into AI and have AI acquire relevant information.

[0047] The input unit suggests the optimal input method by referring to the user's past input history during input. The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, the input unit prioritizes suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The input unit predicts and suggests input methods to be used during specific time periods based on the user's past input history. The input unit automatically inputs the necessary information based on information previously entered by the user. This allows the system to suggest the optimal input method by referring to the user's past input history, thereby improving user convenience. Past input history includes, but is not limited to, past input dates and times, and input content. Optimal input methods include, but are not limited to, online input and voice input. Some or all of the above-described processes in the input unit may be performed using AI or not. For example, the input unit can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0048] The input unit customizes the input content based on the user's family situation and the characteristics of the desired nursery school during input. The input unit can customize the input content based on the user's family situation and the characteristics of the desired nursery school during input. For example, the input unit automatically inputs the necessary information based on the user's family structure. The input unit customizes the input content based on the characteristics of the nursery school the user desires. The input unit suggests the optimal input method according to the user's family situation. This allows for the provision of more appropriate information by customizing the input content based on the user's family situation and the characteristics of the desired nursery school. Family situation includes, but is not limited to, family structure and income. Characteristics of the nursery school include, but is not limited to, educational policies and facility equipment. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input the user's family situation data into AI and have AI perform the customization of the input content.

[0049] The input unit prioritizes inputting highly relevant information while considering the user's geographical location. For example, the input unit prioritizes inputting information about the nearest daycare center to the user's current location. It also prioritizes inputting information about daycare centers along the user's commute route. Furthermore, it prioritizes inputting information about daycare centers located midway between the user's residence and workplace. This allows for the prioritization of highly relevant information by considering the user's geographical location, thereby improving user convenience. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant information includes, but is not limited to, information about nearby daycare centers and transportation information. Some or all of the above processing in the input unit may be performed using AI or without AI. For example, the input unit can input the user's geographical location information into AI and have the AI ​​input highly relevant information.

[0050] The input unit analyzes the user's social media activity and inputs relevant information during input. For example, the input unit analyzes the user's social media posts and inputs information about daycare centers. It also analyzes the user's social media friendships and inputs information about daycare centers used by their friends. Furthermore, it analyzes the user's social media activity time and suggests the optimal input time. This allows for the input of relevant information and improved user convenience by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, relevant news and event information. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input the user's social media data into AI and have AI input the relevant information.

[0051] The scoring unit optimizes the scoring algorithm by referring to past admission data during scoring. The scoring unit can optimize the scoring algorithm by referring to past admission data during scoring. For example, the scoring unit adjusts the scoring algorithm based on past admission data. The scoring unit optimizes the scoring criteria under specific conditions from past admission data. The scoring unit analyzes past admission data and improves the scoring algorithm. This allows the scoring algorithm to be optimized by referring to past admission data, supporting more accurate scoring. Past admission data includes, but is not limited to, past admission numbers and admission conditions. The scoring algorithm includes, but is not limited to, the type of algorithm and optimization methods. Some or all of the above processing in the scoring unit may be performed using AI or not. For example, the scoring unit can input past admission data into AI and have the AI ​​perform the optimization of the scoring algorithm.

[0052] The scoring unit customizes the scoring content based on the user's family circumstances and the characteristics of the desired nursery school during the scoring process. The scoring unit can customize the scoring content based on the user's family circumstances and the characteristics of the desired nursery school during the scoring process. For example, the scoring unit customizes the scoring content based on the user's family structure. The scoring unit adjusts the scoring content based on the characteristics of the nursery school the user desires. The scoring unit sets scoring criteria according to the user's family circumstances. This allows for more appropriate scoring by customizing the scoring content based on the user's family circumstances and the characteristics of the desired nursery school. Family circumstances include, for example, family structure and income status, but are not limited to such examples. Characteristics of the nursery school include, for example, educational policies and facility equipment, but are not limited to such examples. Some or all of the above processing in the scoring unit may be performed using AI or not using AI. For example, the scoring unit can input the user's family circumstances data into AI and have AI perform the customization of the scoring content.

[0053] The scoring unit performs scoring while considering the user's geographical location information. The scoring unit can perform scoring while considering the user's geographical location information. For example, the scoring unit may perform scoring based on information about the nearest daycare center to the user's current location. The scoring unit may perform scoring based on information about daycare centers along the user's commute route. The scoring unit may perform scoring based on information about daycare centers located midway between the user's residence and workplace. By considering the user's geographical location information, a more accurate scoring can be provided. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the scoring unit may be performed using AI or not. For example, the scoring unit may input the user's geographical location information into AI and have the AI ​​perform the scoring.

[0054] The scoring unit analyzes the user's social media activity during scoring and reflects relevant information in the score. The scoring unit can analyze the user's social media activity and reflect relevant information in the score. For example, the scoring unit can analyze the content of the user's social media posts and reflect it in the score. The scoring unit can analyze the user's social media friendships and reflect them in the score. The scoring unit can analyze the user's social media activity time and reflect it in the score. This allows for the analysis of the user's social media activity, reflecting relevant information in the score and providing a more accurate score. Social media activity includes, but is not limited to, posts and follower counts. Some or all of the above processing in the scoring unit may be performed using AI or not. For example, the scoring unit can input the user's social media data into AI and reflect it in the score.

[0055] The notification unit, when issuing a notification, refers to the user's past notification history to suggest the most suitable notification method. The notification unit can, when issuing a notification, refer to the user's past notification history to suggest the most suitable notification method. For example, the notification unit prioritizes suggesting notification methods that the user has frequently used in the past (email, push notifications, etc.). The notification unit predicts and suggests notification methods to be used during specific time periods based on the user's past notification history. The notification unit automatically notifies the user of necessary information based on the content of notifications the user has received in the past. This allows the notification unit to suggest the most suitable notification method by referring to the user's past notification history, thereby improving user convenience. Past notification history includes, but is not limited to, past notification dates and times, notification content, etc. The optimal notification method includes, but is not limited to, email notifications, push notifications, etc. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past notification history data into AI and have the AI ​​suggest the most suitable notification method.

[0056] The notification unit customizes notification content based on the user's family circumstances and the characteristics of the desired nursery school. The notification unit can customize notification content based on the user's family circumstances and the characteristics of the desired nursery school. For example, the notification unit automatically notifies the user of necessary information based on the user's family structure. The notification unit customizes notification content based on the characteristics of the nursery school the user desires. The notification unit proposes the optimal notification method according to the user's family circumstances. This allows for the provision of more appropriate information by customizing notification content based on the user's family circumstances and the characteristics of the desired nursery school. Family circumstances include, but are not limited to, family structure and income. Characteristics of the nursery school include, but are not limited to, educational policies and facility equipment. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's family circumstances data into AI and have the AI ​​perform the customization of notification content.

[0057] The notification unit prioritizes notifying users of highly relevant information, taking into account the user's geographical location. For example, the notification unit might prioritize notifying users of information about the nearest daycare center to their current location, or about daycare centers along their commute route, or about daycare centers located midway between their residence and workplace. This allows the notification unit to prioritize notifying users of highly relevant information, thereby improving user convenience. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant information includes, but is not limited to, information about nearby daycare centers and traffic information. Some or all of the above processing in the notification unit may be performed using AI or without AI. For example, the notification unit can input the user's geographical location information into AI and have the AI ​​perform the notification of highly relevant information.

[0058] The notification unit analyzes the user's social media activity and notifies the user of relevant information when a notification is sent. The notification unit can analyze the user's social media activity and notify the user of relevant information when a notification is sent. For example, the notification unit can analyze the content of the user's social media posts and notify the user of information about daycare centers. The notification unit can analyze the user's social media friendships and notify the user of information about daycare centers used by their friends. The notification unit can analyze the user's social media activity time and suggest the optimal notification time. In this way, by analyzing the user's social media activity, relevant information can be notified and user convenience can be improved. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, relevant news and event information. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input the user's social media data into AI and have AI execute notifications of relevant information.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can analyze a user's past registration history and suggest the most suitable registration method. For example, it can prioritize suggesting registration methods that the user has frequently used in the past (such as voice or text). It can also predict and suggest registration methods that the user will use at specific times of day based on their past registration history. Furthermore, it can automatically input necessary information based on information the user has previously registered. In this way, by analyzing a user's past registration history, the system can suggest the most suitable registration method and improve user convenience. Past registration history includes, but is not limited to, past registration dates and times, and registration content. Optimal registration methods include, but are not limited to, online registration, telephone registration, etc.

[0061] The information acquisition unit can customize the types of information acquired from the local government database according to the user's family circumstances. For example, it can automatically acquire necessary information based on the user's family structure. It can also customize the types of information acquired according to the user's family circumstances. Furthermore, it can adjust the information acquired based on the characteristics of the daycare center the user desires. By customizing the types of information acquired according to the user's family circumstances, more appropriate information can be provided. Family circumstances include, for example, family structure and income status, but are not limited to such examples. Types of information include, for example, address information and contact information, but are not limited to such examples.

[0062] The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). It can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, it can automatically input necessary information based on information the user has previously entered. In this way, by referring to the user's past input history, the optimal input method can be suggested, improving user convenience. Past input history includes, but is not limited to, past input dates and times, and input content. Optimal input methods include, but are not limited to, online input and voice input.

[0063] The scoring unit can optimize the scoring algorithm by referring to past admission data during the scoring process. For example, it can adjust the scoring algorithm based on past admission data. It can also optimize scoring criteria under specific conditions based on past admission data. Furthermore, it can analyze past admission data to improve the scoring algorithm. This allows for the optimization of the scoring algorithm by referring to past admission data, supporting more accurate scoring. Past admission data includes, but is not limited to, past admission numbers and admission conditions. The scoring algorithm includes, but is not limited to, the type of algorithm and optimization methods.

[0064] The notification unit can suggest the most suitable notification method by referring to the user's past notification history when sending a notification. For example, it can prioritize suggesting notification methods that the user has frequently used in the past (email, push notifications, etc.). It can also predict and suggest notification methods to be used during specific time periods based on the user's past notification history. Furthermore, it can automatically notify users of necessary information based on the content of notifications they have received in the past. This allows the system to suggest the most suitable notification method by referring to the user's past notification history, thereby improving user convenience. Past notification history includes, but is not limited to, past notification dates and times, and notification content. The most suitable notification method includes, but is not limited to, email notifications and push notifications.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk accepts user registrations. User registration can be done via online forms, email registration, or telephone or in-person registration. For example, a user can complete registration by entering the required information in an online form. Alternatively, information can be received and registration can be done via email registration. Furthermore, registration can also be accepted by telephone or in person. Step 2: The acquisition unit automatically retrieves the necessary information from the local government's database. The acquisition unit can retrieve information from the local government's database using APIs, or it can collect information from websites using scraping techniques. Furthermore, it can automatically read data files provided by the local government. For example, it can retrieve address information and contact information. Step 3: The input section allows the user to review the automatically acquired information and enter supplementary information. The input section provides an interface for the user to review the automatically acquired information and enter any necessary supplementary information. It can also save the supplementary information entered by the user to the system. Furthermore, it can collect data to improve the accuracy of scoring. For example, supplementary information such as address information, contact information, household structure, and income status can be entered. Step 4: The scoring unit performs scoring based on the information entered by the input unit. The scoring unit performs scoring considering past enrollment data and the location of the nursery. Using AI, scoring can be performed based on user information. For example, it can predict the user's likelihood of enrollment based on past enrollment data and perform scoring considering the nursery's location and surrounding environment. Step 5: The notification unit notifies the user of the score and advice obtained by the scoring unit. The notification unit provides the score and advice to the user through text messages or email notifications. It can also display the score and advice to the user through web applications or mobile applications. Furthermore, for users who prefer paper feedback, the results can be printed using a printer.

[0067] (Example of form 2) The nursery school admission possibility determination system according to an embodiment of the present invention is a system that supports parents of children living in areas with high competition for nursery school admission, helping them to increase their chances of getting their child into a nursery school. Generally, admission to nursery schools is based on a point system, with more popular schools requiring higher points. The method for calculating the points required for admission varies by municipality, and even in cases of ties, various methods are used to determine priority. Based on one's own score, priority, and past data, the probability of admission to a desired nursery school can be easily predicted. Specifically, the system consists of the following steps: First, the user registers with the system. Next, the system automatically retrieves necessary information from the municipality's database, the user confirms the automatically retrieved information, and inputs supplementary information. The AI ​​performs scoring based on the input information and notifies the user of their admission possibility score and advice. Specifically, the user specifies their family structure and the nursery school they wish to enroll their child in, and inputs information not held by the municipality according to the questions. The AI ​​performs scoring considering past admission data and the location of the nursery school, predicting the possibility of admission. For example, if the admission probability is 5%, the system might notify the user that nursery school A is close to a train station and is expected to receive many applications, thus indicating a low probability of admission. If the probability of admission is 20%, it is likely that the minimum score requirement will be met, but admission will be difficult because the household income, which is the priority in the event of a tie, is high. If the probability of admission is 80%, it will be notified that it is a newly established nursery school with a large capacity and a location away from train stations and residential areas, so there are few applicants. This system allows parents to narrow down their consideration to nurseries with a high probability of admission, saving them unnecessary effort. In addition, changing the order of preferences may increase the chances of admission, making it a very beneficial system for parents. In this way, the nursery school admission probability assessment system allows parents to select nurseries with a high probability of admission and efficiently aim for admission to a nursery school.

[0068] The nursery school admission possibility determination system according to the embodiment comprises a reception unit, an acquisition unit, an input unit, a scoring unit, and a notification unit. The reception unit accepts user registration. User registration includes, but is not limited to, online forms or email registration. For example, the reception unit allows users to complete registration by entering the necessary information into an online form. The reception unit can also receive user information and register through email registration. Furthermore, the reception unit can also accept registration by telephone or in person. For example, the reception unit allows users to provide registration information by telephone, and an operator enters that information into the system. The acquisition unit automatically acquires necessary information from the local government's database. For example, the acquisition unit acquires information from the local government's database using an API. The acquisition unit can also collect information from websites using scraping technology. Furthermore, the acquisition unit can automatically read data files provided by local governments. For example, the acquisition unit acquires address information and contact information through the local government's API. It collects necessary information from the local government's website using scraping technology. It automatically reads data files and imports them into the system. The input unit verifies the information automatically acquired by the user and inputs supplementary information. For example, the input unit provides an interface for the user to verify the automatically acquired information and input necessary supplementary information. The input unit can also save the supplementary information entered by the user to the system. Furthermore, the input unit can collect data to improve the accuracy of scoring based on the information entered by the user. For example, the input unit verifies the address information automatically acquired by the user and inputs additional address information. The user verifies contact information and inputs necessary supplementary information. The user inputs supplementary information such as family structure and income status. The scoring unit performs scoring based on the information entered by the input unit. The scoring unit performs scoring considering, for example, past enrollment data and the location of the nursery. The scoring unit can use AI to perform scoring based on user information. For example, the scoring unit predicts the user's likelihood of enrollment based on past enrollment data. It performs scoring considering the location of the nursery and the surrounding environment.The system uses AI to analyze user information and perform scoring. The notification unit notifies the user of the score and advice obtained by the scoring unit. The notification unit provides the score and advice to the user, for example, through text messages or email notifications. The notification unit can also display the score and advice to the user through web applications or mobile applications. Furthermore, the notification unit can print the results using a printer for users who prefer paper feedback. For example, the notification unit sends the score and advice to the user via text message. It provides the user with a detailed score and advice via email notification. It displays the score and advice to the user through web applications or mobile applications. As a result, the nursery school admission possibility determination system according to this embodiment automates the entire process from user registration to scoring and notification, enabling efficient determination of nursery school admission possibility.

[0069] The reception desk accepts user registrations. User registration includes, but is not limited to, online forms and email registrations. For example, the reception desk can accept registrations when users enter the necessary information into an online form. The reception desk can also receive user information and register users via email registration. Furthermore, the reception desk can accept registrations by phone or in person. For example, the reception desk can accept registrations when users provide information by phone, and an operator enters that information into the system. With online forms, users can enter their name, address, contact information, the child's age, and information about the desired daycare center. In the case of email registration, users send the necessary information to a designated email address, and the system automatically parses and registers the information. With phone registration, an operator receives information from the user and manually enters it into the system. In the case of in-person registration, users come to the counter in person, submit the necessary documents, and an operator enters that information into the system. This allows the reception desk to accept user registrations in a variety of ways, increasing user convenience. Furthermore, the reception desk also provides an interface for confirming and correcting registration information. For example, if a user wants to change their information after registering, the system can accept the changes via online forms, email, or telephone. This ensures that users always provide the system with the most up-to-date information, improving the accuracy of the scoring system.

[0070] The data acquisition unit automatically retrieves necessary information from local government databases. For example, it can use APIs to obtain information from local government databases. It can also collect information from websites using scraping techniques. Furthermore, the data acquisition unit can automatically read data files provided by local governments. For example, it can obtain address and contact information through local government APIs. It can also collect necessary information from local government websites using scraping techniques. It automatically reads data files and incorporates them into the system. When using APIs, the data acquisition unit can periodically access local government databases to obtain the latest information. When using scraping techniques, the data acquisition unit implements algorithms to analyze the website structure and extract necessary information. For reading data files, it automatically analyzes CSV and Excel files provided by local governments and incorporates the necessary information into the system. This allows the data acquisition unit to collect local government information in diverse ways and reflect it in the system. Furthermore, the data acquisition unit also has verification functions to check the integrity of the acquired information and detect missing or incorrect information. For example, it checks whether the acquired address information is in the correct format and issues an alert if it contains inaccurate information. This allows the data acquisition unit to provide high-quality data to the system, improving the accuracy of the scoring.

[0071] The input unit verifies the information automatically acquired by the user and inputs supplementary information. For example, the input unit provides an interface for the user to verify automatically acquired information and input necessary supplementary information. The input unit can also save the supplementary information entered by the user to the system. Furthermore, the input unit can collect data to improve the accuracy of scoring based on the information entered by the user. For example, the input unit verifies the automatically acquired address information and inputs additional address information. The user verifies contact information and inputs necessary supplementary information. The user inputs supplementary information such as household composition and income status. The input unit can reflect the information entered by the user in the system in real time. For example, if the user corrects address information, that information is immediately saved to the system and becomes accessible to the scoring unit. Furthermore, the input unit also has a function to verify the consistency of the information entered by the user and issue an alert if inaccurate information is included. This allows the input unit to provide high-quality data to the system and improve the accuracy of scoring. The input unit can also collect data to improve the accuracy of scoring based on the information entered by the user. For example, the input unit verifies the automatically acquired address information and inputs additional address information. The user verifies contact information and inputs necessary supplementary information. Users input supplementary information such as their household structure and income status. This allows the input unit to collect data based on the information provided by the user to improve the accuracy of the scoring.

[0072] The scoring unit performs scoring based on the information entered by the input unit. The scoring unit considers factors such as past enrollment data and the location of the nursery school when performing scoring. The scoring unit can use AI to perform scoring based on user information. For example, the scoring unit predicts the user's chances of enrollment based on past enrollment data. It performs scoring considering the nursery school's location and surrounding environment. It uses AI to analyze user information and perform scoring. Specifically, the scoring unit analyzes past enrollment data and calculates the success rate of enrollment under specific conditions. For example, it predicts the chances of enrollment considering conditions such as a specific region, family structure, and income situation. Furthermore, the scoring unit performs scoring considering the nursery school's location and surrounding environment. For example, it calculates the score considering factors such as whether there is public transportation near the nursery school and whether the surrounding area is safe. By using AI, the scoring unit can quickly analyze large amounts of data and perform highly accurate scoring. The AI ​​uses machine learning algorithms to learn patterns from past data and can make highly accurate predictions even for new data. This allows the scoring unit to predict a user's likelihood of admission with high accuracy and provide appropriate advice. Furthermore, the scoring unit has established a feedback loop to continuously improve the scoring results. For example, it adjusts the scoring algorithm based on actual admission results to improve prediction accuracy. As a result, the scoring unit can always provide highly accurate scoring based on the latest information and offer optimal advice to users.

[0073] The notification unit notifies the user of the score and advice obtained by the scoring unit. The notification unit provides the score and advice to the user, for example, through text messages or email notifications. The notification unit can also display the score and advice to the user through web applications or mobile applications. Furthermore, the notification unit can print the results using a printer for users who prefer paper feedback. For example, the notification unit sends the score and advice to the user via text message. It provides the user with detailed scores and advice via email notifications. It displays the score and advice to the user through web applications or mobile applications. As a result, the nursery school admission possibility determination system according to the embodiment automates the entire process from user registration to scoring and notification, enabling efficient determination of nursery school admission possibility. The notification unit provides multiple notification means to enhance user convenience. For example, it can quickly convey important information to the user not only through text messages and email notifications, but also through push notifications and voice calls. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, it can provide an interface for users to provide feedback on received notifications and adjust the notification content based on that feedback. This allows the notification unit to provide users with timely and accurate information, improving the reliability and effectiveness of the nursery school admission possibility assessment system.

[0074] The acquisition unit can automatically retrieve necessary information from local government databases. For example, the acquisition unit can retrieve information from local government databases using APIs. The acquisition unit can also collect information from websites using scraping techniques. The acquisition unit can also automatically read data files provided by local governments. For example, the acquisition unit can retrieve address information and contact information via APIs. It can collect necessary information from local government websites using scraping techniques. It automatically reads data files and incorporates them into the system. This allows for the automatic retrieval of necessary information from local government databases, saving users time and effort. Necessary information includes, but is not limited to, address information and contact information. Some or all of the above-described processes in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input information obtained via API into an AI and have the AI ​​analyze the information.

[0075] The input unit allows the user to review automatically acquired information and input supplementary information. For example, the input unit provides an interface for the user to review automatically acquired information and input necessary supplementary information. The input unit can also save the supplementary information entered by the user to the system. The input unit can also collect data to improve the accuracy of scoring based on the information entered by the user. For example, the input unit can review automatically acquired address information and input additional address information. The user can review contact information and input necessary supplementary information. The user can input supplementary information such as household composition and income status. This allows the accuracy of scoring to be improved by the user reviewing automatically acquired information and inputting supplementary information. Supplementary information includes, but is not limited to, additional address information and contact information. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input the supplementary information entered by the user into AI and have the AI ​​perform the analysis of the information.

[0076] The scoring unit can perform scoring by considering past enrollment data and the location of the nursery. For example, the scoring unit predicts the user's likelihood of enrollment based on past enrollment data. The scoring unit performs scoring by considering the nursery's location and surrounding environment. The scoring unit can perform scoring based on user information using AI. For example, the scoring unit predicts the user's likelihood of enrollment based on past enrollment data. The scoring unit performs scoring by considering the nursery's location and surrounding environment. The scoring unit uses AI to analyze user information and perform scoring. This makes it possible to perform more accurate scoring by considering past enrollment data and the nursery's location. Past enrollment data includes, but is not limited to, past enrollment numbers and enrollment conditions. The nursery's location includes, but is not limited to, transportation access and surrounding environment. Some or all of the above processing in the scoring unit may be performed using generative AI, or it may be performed without using generative AI. For example, the scoring unit can input past admission data into a generating AI and have the AI ​​perform scoring predictions.

[0077] The notification unit can notify the user of their admission likelihood score and advice. The notification unit provides the user with the score and advice, for example, through text messages or email notifications. The notification unit can also display the score and advice to the user through web applications or mobile applications. The notification unit can also print the results using a printer for users who prefer paper feedback. For example, the notification unit sends the score and advice to the user via text message. The notification unit provides the user with detailed scores and advice via email notifications. The notification unit displays the score and advice to the user through web applications or mobile applications. This allows the notification unit to provide the user with information to make appropriate decisions by notifying them of their admission likelihood score and advice. Advice includes, but is not limited to, text messages and email notifications. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the score and advice into AI and have the AI ​​generate the notification content.

[0078] The reception desk estimates the user's emotions and adjusts the registration process based on those emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the registration steps. If the user is relaxed, the reception desk offers detailed registration options and suggests a customizable registration method. If the user is in a hurry, the reception desk prioritizes voice input to allow for quick completion of the registration process. This reduces user stress and facilitates smooth registration by adjusting the registration process according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Adjustments to the guidance method include, but are not limited to, the tone of the guidance and the frequency of guidance. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0079] The reception desk analyzes the user's past registration history and proposes the optimal registration method. The reception desk can analyze the user's past registration history and propose the optimal registration method. For example, the reception desk may prioritize suggesting registration methods that the user has frequently used in the past (voice, text, etc.). The reception desk may predict and suggest registration methods to be used at specific times based on the user's past registration history. The reception desk automatically inputs the necessary information based on the information the user has previously registered. This allows the reception desk to propose the optimal registration method by analyzing the user's past registration history, thereby improving user convenience. Past registration history includes, but is not limited to, past registration dates and times, and registration content. Optimal registration methods include, but are not limited to, online registration, telephone registration, etc. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past registration history data into AI and have the AI ​​propose the optimal registration method.

[0080] The reception desk customizes the registration details based on the user's family circumstances and the characteristics of the desired nursery school during registration. The reception desk can customize the registration details based on the user's family circumstances and the characteristics of the desired nursery school during registration. For example, the reception desk automatically inputs the necessary information based on the user's family structure. The reception desk customizes the registration details based on the characteristics of the nursery school the user desires. The reception desk proposes the optimal registration method according to the user's family circumstances. This allows for the provision of more appropriate information by customizing the registration details based on the user's family circumstances and the characteristics of the desired nursery school. Family circumstances include, for example, family structure and income status, but are not limited to such examples. Characteristics of the nursery school include, for example, educational policies and facility equipment, but are not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's family circumstances data into AI and have the AI ​​perform the customization of the registration details.

[0081] The reception desk estimates the user's emotions and determines the priority of the registration process based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize important procedures. If the user is relaxed, the reception desk will postpone detailed procedures. If the user is in a hurry, the reception desk will guide them through the most important procedures first. This allows for flexible responses tailored to user needs by prioritizing registration procedures according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Prioritization includes, but is not limited to, urgency and importance. 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) and multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0082] The reception desk, upon registration, proposes the most suitable daycare center, taking into account the user's geographical location. The reception desk can propose the most suitable daycare center, taking into account the user's geographical location. For example, it can propose the daycare center closest to the user's current location, the daycare center along the user's commute route, or the daycare center midway between the user's residence and workplace. This allows the reception desk to propose the most suitable daycare center for the user by considering their geographical location. Geographical location information includes, but is not limited to, GPS data and address information. The optimal daycare center includes, but is not limited to, distance and facility ratings. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into AI and have the AI ​​propose the most suitable daycare center.

[0083] The reception desk analyzes the user's social media activity during registration and provides relevant information. The reception desk can analyze the user's social media activity during registration and provide relevant information. For example, the reception desk can analyze the user's social media posts and provide information about daycare centers. The reception desk can analyze the user's social media friendships and suggest daycare centers used by their friends. The reception desk can analyze the user's social media activity time and suggest the optimal registration time. By analyzing the user's social media activity, relevant information can be provided, improving user convenience. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, relevant news and event information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into AI and have the AI ​​provide relevant information.

[0084] The acquisition unit estimates the user's emotions and adjusts the timing of information acquisition based on the estimated emotions. The acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit reduces the frequency of information acquisition. If the user is relaxed, the acquisition unit increases the frequency of information acquisition. If the user is in a hurry, the acquisition unit quickly acquires the necessary information. In this way, by adjusting the timing of information acquisition according to the user's emotions, it is possible to reduce user stress and support smooth information acquisition. The user's emotions are estimated using technologies such as facial recognition and voice analysis. The adjustment of the timing of information acquisition includes, but is not limited to, the user's activity time and stress level. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using AI or not using AI. For example, the acquisition unit can input the user's facial expression data into the generating AI, allowing the generating AI to perform emotion estimation.

[0085] The data acquisition unit customizes the types of information it acquires from the local government database according to the user's family circumstances. The data acquisition unit can customize the types of information it acquires from the local government database according to the user's family circumstances. For example, the data acquisition unit automatically acquires necessary information based on the user's family structure. The data acquisition unit customizes the types of information it acquires according to the user's family circumstances. The data acquisition unit adjusts the information it acquires based on the characteristics of the nursery school the user desires. By customizing the types of information acquired according to the user's family circumstances, more appropriate information can be provided. Family circumstances include, for example, family structure and income status, but are not limited to such examples. Types of information include, for example, address information and contact information, but are not limited to such examples. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's family circumstances data into AI and have AI perform the customization of the types of information to acquire.

[0086] The acquisition unit selects the optimal acquisition method by referring to past data acquisition history when acquiring information. The acquisition unit can select the optimal acquisition method by referring to past data acquisition history when acquiring information. For example, the acquisition unit automatically acquires necessary information based on information acquired by the user in the past. The acquisition unit selects the optimal acquisition method from the user's past data acquisition history. The acquisition unit analyzes the types of information acquired by the user in the past and acquires the necessary information. By referring to past data acquisition history, it is possible to select the optimal acquisition method and support efficient information acquisition. Past data acquisition history includes, but is not limited to, past acquisition dates and times, and acquisition content. Optimal acquisition methods include, but are not limited to, API acquisition and scraping. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past data acquisition history into AI and have the AI ​​select the optimal acquisition method.

[0087] The acquisition unit estimates the user's emotions and determines the priority of information to acquire based on the estimated emotions. The acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is stressed, the acquisition unit will prioritize acquiring important information. If the user is relaxed, the acquisition unit will postpone acquiring detailed information. If the user is in a hurry, the acquisition unit will acquire the most important information first. This allows for flexible responses to user needs by determining the priority of information to acquire according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. The determination of information priority includes, but is not limited to, urgency and importance. Emotion estimation is implemented 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) and multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's facial expression data into the generating AI, allowing the generating AI to perform emotion estimation.

[0088] The acquisition unit prioritizes acquiring highly relevant information by considering the user's geographical location information when acquiring information. The acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location information when acquiring information. For example, the acquisition unit prioritizes acquiring information on the nearest nursery school to the user's current location. The acquisition unit prioritizes acquiring information on nursery schools along the user's commute route. The acquisition unit prioritizes acquiring information on nursery schools located midway between the user's residence and workplace. By considering the user's geographical location information, the acquisition unit can prioritize acquiring highly relevant information and improve user convenience. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant information includes, but is not limited to, information on nearby nursery schools and traffic information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's geographical location information into AI and have the AI ​​perform the acquisition of highly relevant information.

[0089] The acquisition unit analyzes the user's social media activity and acquires relevant information when acquiring information. The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring information. For example, the acquisition unit analyzes the content of the user's social media posts and acquires information about daycare centers. The acquisition unit analyzes the user's social media friendships and acquires information about daycare centers used by their friends. The acquisition unit analyzes the user's social media activity time and suggests the optimal time for acquiring information. By analyzing the user's social media activity, relevant information can be acquired, thereby improving user convenience. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, relevant news and event information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's social media data into AI and have AI acquire relevant information.

[0090] The input unit estimates the user's emotions and adjusts the input guidance method based on the estimated emotions. For example, if the user is stressed, the input unit provides a simple interface and minimizes the input steps. If the user is relaxed, the input unit provides detailed input options and suggests a customizable input method. If the user is in a hurry, the input unit prioritizes voice input to allow for quick completion of the input process. This reduces user stress and facilitates smooth input by adjusting the input guidance method according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Adjustments to the input guidance method include, but are not limited to, the tone of guidance and the frequency of guidance. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processing described above in the input unit may be performed using AI, or it may be performed without using AI. For example, the input unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0091] The input unit suggests the optimal input method by referring to the user's past input history during input. The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, the input unit prioritizes suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The input unit predicts and suggests input methods to be used during specific time periods based on the user's past input history. The input unit automatically inputs the necessary information based on information previously entered by the user. This allows the system to suggest the optimal input method by referring to the user's past input history, thereby improving user convenience. Past input history includes, but is not limited to, past input dates and times, and input content. Optimal input methods include, but are not limited to, online input and voice input. Some or all of the above-described processes in the input unit may be performed using AI or not. For example, the input unit can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0092] The input unit customizes the input content based on the user's family situation and the characteristics of the desired nursery school during input. The input unit can customize the input content based on the user's family situation and the characteristics of the desired nursery school during input. For example, the input unit automatically inputs the necessary information based on the user's family structure. The input unit customizes the input content based on the characteristics of the nursery school the user desires. The input unit suggests the optimal input method according to the user's family situation. This allows for the provision of more appropriate information by customizing the input content based on the user's family situation and the characteristics of the desired nursery school. Family situation includes, but is not limited to, family structure and income. Characteristics of the nursery school include, but is not limited to, educational policies and facility equipment. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input the user's family situation data into AI and have AI perform the customization of the input content.

[0093] The input unit estimates the user's emotions and determines the priority of input content based on the estimated emotions. For example, if the user is stressed, the input unit prioritizes important information. If the user is relaxed, the input unit postpones detailed information. If the user is in a hurry, the input unit inputs the most important information first. This allows for flexible responses tailored to the user's needs by prioritizing input content according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and speech analysis. Prioritization of input content includes, but is not limited to, urgency and importance. 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 processing in the input unit may be performed using AI or not. For example, the input unit can input the user's facial expression data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0094] The input unit prioritizes inputting highly relevant information while considering the user's geographical location. For example, the input unit prioritizes inputting information about the nearest daycare center to the user's current location. It also prioritizes inputting information about daycare centers along the user's commute route. Furthermore, it prioritizes inputting information about daycare centers located midway between the user's residence and workplace. This allows for the prioritization of highly relevant information by considering the user's geographical location, thereby improving user convenience. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant information includes, but is not limited to, information about nearby daycare centers and transportation information. Some or all of the above processing in the input unit may be performed using AI or without AI. For example, the input unit can input the user's geographical location information into AI and have the AI ​​input highly relevant information.

[0095] The input unit analyzes the user's social media activity and inputs relevant information during input. For example, the input unit analyzes the user's social media posts and inputs information about daycare centers. It also analyzes the user's social media friendships and inputs information about daycare centers used by their friends. Furthermore, it analyzes the user's social media activity time and suggests the optimal input time. This allows for the input of relevant information and improved user convenience by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, relevant news and event information. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input the user's social media data into AI and have AI input the relevant information.

[0096] The scoring unit estimates the user's emotions and adjusts the scoring criteria based on the estimated emotions. The scoring unit can estimate the user's emotions and adjust the scoring criteria based on the estimated emotions. For example, if the user is stressed, the scoring unit will relax the scoring criteria. If the user is relaxed, the scoring unit will tighten the scoring criteria. If the user is in a hurry, the scoring unit will set criteria for quick scoring. This allows for reduced user stress and smoother scoring by adjusting the scoring criteria according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Adjustments to the scoring criteria include, but are not limited to, evaluation items and evaluation methods. 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) and multimodal generation AI. Some or all of the above-described processes in the scoring unit may be performed using AI or not. For example, the scoring unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0097] The scoring unit optimizes the scoring algorithm by referring to past admission data during scoring. The scoring unit can optimize the scoring algorithm by referring to past admission data during scoring. For example, the scoring unit adjusts the scoring algorithm based on past admission data. The scoring unit optimizes the scoring criteria under specific conditions from past admission data. The scoring unit analyzes past admission data and improves the scoring algorithm. This allows the scoring algorithm to be optimized by referring to past admission data, supporting more accurate scoring. Past admission data includes, but is not limited to, past admission numbers and admission conditions. The scoring algorithm includes, but is not limited to, the type of algorithm and optimization methods. Some or all of the above processing in the scoring unit may be performed using AI or not. For example, the scoring unit can input past admission data into AI and have the AI ​​perform the optimization of the scoring algorithm.

[0098] The scoring unit customizes the scoring content based on the user's family circumstances and the characteristics of the desired nursery school during the scoring process. The scoring unit can customize the scoring content based on the user's family circumstances and the characteristics of the desired nursery school during the scoring process. For example, the scoring unit customizes the scoring content based on the user's family structure. The scoring unit adjusts the scoring content based on the characteristics of the nursery school the user desires. The scoring unit sets scoring criteria according to the user's family circumstances. This allows for more appropriate scoring by customizing the scoring content based on the user's family circumstances and the characteristics of the desired nursery school. Family circumstances include, for example, family structure and income status, but are not limited to such examples. Characteristics of the nursery school include, for example, educational policies and facility equipment, but are not limited to such examples. Some or all of the above processing in the scoring unit may be performed using AI or not using AI. For example, the scoring unit can input the user's family circumstances data into AI and have AI perform the customization of the scoring content.

[0099] The scoring unit estimates the user's emotions and adjusts the display method of the scoring results based on the estimated emotions. The scoring unit can estimate the user's emotions and adjust the display method of the scoring results based on the estimated emotions. For example, if the user is stressed, the scoring unit provides a simple and highly visible display method. If the user is relaxed, the scoring unit provides a display method that includes detailed information. If the user is in a hurry, the scoring unit provides a concise display method. By adjusting the display method of the scoring results according to the user's emotions, it is possible to reduce user stress and support smooth information delivery. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Adjustments to the display method include, but are not limited to, display tone and display frequency. 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) and multimodal generation AI. Some or all of the above-described processes in the scoring unit may be performed using AI or not. For example, the scoring unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0100] The scoring unit performs scoring while considering the user's geographical location information. The scoring unit can perform scoring while considering the user's geographical location information. For example, the scoring unit may perform scoring based on information about the nearest daycare center to the user's current location. The scoring unit may perform scoring based on information about daycare centers along the user's commute route. The scoring unit may perform scoring based on information about daycare centers located midway between the user's residence and workplace. By considering the user's geographical location information, a more accurate scoring can be provided. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the scoring unit may be performed using AI or not. For example, the scoring unit may input the user's geographical location information into AI and have the AI ​​perform the scoring.

[0101] The scoring unit analyzes the user's social media activity during scoring and reflects relevant information in the score. The scoring unit can analyze the user's social media activity and reflect relevant information in the score. For example, the scoring unit can analyze the content of the user's social media posts and reflect it in the score. The scoring unit can analyze the user's social media friendships and reflect them in the score. The scoring unit can analyze the user's social media activity time and reflect it in the score. This allows for the analysis of the user's social media activity, reflecting relevant information in the score and providing a more accurate score. Social media activity includes, but is not limited to, posts and follower counts. Some or all of the above processing in the scoring unit may be performed using AI or not. For example, the scoring unit can input the user's social media data into AI and reflect it in the score.

[0102] The notification unit estimates the user's emotions and adjusts the way the notification content is presented based on the estimated emotions. For example, if the user is stressed, the notification unit provides a simple and easily visible notification. If the user is relaxed, the notification unit provides a notification containing detailed information. If the user is in a hurry, the notification unit provides a concise notification. By adjusting the way the notification content is presented according to the user's emotions, it is possible to reduce user stress and support smooth information delivery. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Adjustments to the way the notification content is presented include, but are not limited to, the tone of the notification and the frequency of notifications. 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) and multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0103] The notification unit, when issuing a notification, refers to the user's past notification history to suggest the most suitable notification method. The notification unit can, when issuing a notification, refer to the user's past notification history to suggest the most suitable notification method. For example, the notification unit prioritizes suggesting notification methods that the user has frequently used in the past (email, push notifications, etc.). The notification unit predicts and suggests notification methods to be used during specific time periods based on the user's past notification history. The notification unit automatically notifies the user of necessary information based on the content of notifications the user has received in the past. This allows the notification unit to suggest the most suitable notification method by referring to the user's past notification history, thereby improving user convenience. Past notification history includes, but is not limited to, past notification dates and times, notification content, etc. The optimal notification method includes, but is not limited to, email notifications, push notifications, etc. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past notification history data into AI and have the AI ​​suggest the most suitable notification method.

[0104] The notification unit customizes notification content based on the user's family circumstances and the characteristics of the desired nursery school. The notification unit can customize notification content based on the user's family circumstances and the characteristics of the desired nursery school. For example, the notification unit automatically notifies the user of necessary information based on the user's family structure. The notification unit customizes notification content based on the characteristics of the nursery school the user desires. The notification unit proposes the optimal notification method according to the user's family circumstances. This allows for the provision of more appropriate information by customizing notification content based on the user's family circumstances and the characteristics of the desired nursery school. Family circumstances include, but are not limited to, family structure and income. Characteristics of the nursery school include, but are not limited to, educational policies and facility equipment. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's family circumstances data into AI and have the AI ​​perform the customization of notification content.

[0105] The notification unit estimates the user's emotions and determines the priority of notification content based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize important information. If the user is relaxed, the notification unit will postpone detailed information. If the user is in a hurry, the notification unit will notify the most important information first. This allows for flexible responses tailored to the user's needs by prioritizing notification content according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Prioritization of notification content includes, but is not limited to, urgency and importance. 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 processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0106] The notification unit prioritizes notifying users of highly relevant information, taking into account the user's geographical location. For example, the notification unit might prioritize notifying users of information about the nearest daycare center to their current location, or about daycare centers along their commute route, or about daycare centers located midway between their residence and workplace. This allows the notification unit to prioritize notifying users of highly relevant information, thereby improving user convenience. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant information includes, but is not limited to, information about nearby daycare centers and traffic information. Some or all of the above processing in the notification unit may be performed using AI or without AI. For example, the notification unit can input the user's geographical location information into AI and have the AI ​​perform the notification of highly relevant information.

[0107] The notification unit analyzes the user's social media activity and notifies the user of relevant information when a notification is sent. The notification unit can analyze the user's social media activity and notify the user of relevant information when a notification is sent. For example, the notification unit can analyze the content of the user's social media posts and notify the user of information about daycare centers. The notification unit can analyze the user's social media friendships and notify the user of information about daycare centers used by their friends. The notification unit can analyze the user's social media activity time and suggest the optimal notification time. In this way, by analyzing the user's social media activity, relevant information can be notified and user convenience can be improved. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, relevant news and event information. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input the user's social media data into AI and have AI execute notifications of relevant information.

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

[0109] The reception desk can estimate the user's emotions and adjust the registration process based on those emotions. For example, if the user is stressed, a simple interface can be provided and the registration process minimized. If the user is relaxed, detailed registration options can be offered and a customizable registration method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick completion of the registration process. In this way, by adjusting the registration process according to the user's emotions, it is possible to reduce user stress and support a smooth registration process. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Adjustments to the guidance method include, but are not limited to, the tone of the guidance and the frequency of guidance.

[0110] The information acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated emotions. For example, if the user is stressed, the frequency of information acquisition can be reduced. Conversely, if the user is relaxed, the frequency of information acquisition can be increased. Furthermore, if the user is in a hurry, necessary information can be acquired quickly. In this way, by adjusting the timing of information acquisition according to the user's emotions, it is possible to reduce user stress and support smooth information acquisition. The user's emotions are estimated using technologies such as facial recognition and voice analysis. The adjustment of the timing of information acquisition includes, but is not limited to, the user's activity time and stress level.

[0111] The input unit can estimate the user's emotions and adjust the input guidance method based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided and the input steps minimized. If the user is relaxed, detailed input options can be provided and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick completion of the input process. In this way, by adjusting the input guidance method according to the user's emotions, it is possible to reduce user stress and support smooth input. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Adjustments to the input guidance method include, but are not limited to, the tone of guidance and the frequency of guidance.

[0112] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on those emotions. For example, if the user is stressed, the scoring criteria can be relaxed. Conversely, if the user is relaxed, the scoring criteria can be made stricter. Furthermore, if the user is in a hurry, criteria can be set to enable quick scoring. By adjusting the scoring criteria according to the user's emotions, it is possible to reduce user stress and support smooth scoring. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Adjusting the scoring criteria includes, but is not limited to, adjusting evaluation items and evaluation methods.

[0113] The notification unit can estimate the user's emotions and adjust the way the notification content is presented based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible notification. If the user is relaxed, it can provide a notification that includes detailed information. Furthermore, if the user is in a hurry, it can provide a notification that gets straight to the point. By adjusting the way the notification content is presented according to the user's emotions, it is possible to reduce user stress and support smooth information delivery. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Adjustments to the way the notification content is presented include, but are not limited to, the tone of the notification and the frequency of notifications.

[0114] The reception desk can analyze a user's past registration history and suggest the most suitable registration method. For example, it can prioritize suggesting registration methods that the user has frequently used in the past (such as voice or text). It can also predict and suggest registration methods that the user will use at specific times of day based on their past registration history. Furthermore, it can automatically input necessary information based on information the user has previously registered. In this way, by analyzing a user's past registration history, the system can suggest the most suitable registration method and improve user convenience. Past registration history includes, but is not limited to, past registration dates and times, and registration content. Optimal registration methods include, but are not limited to, online registration, telephone registration, etc.

[0115] The information acquisition unit can customize the types of information acquired from the local government database according to the user's family circumstances. For example, it can automatically acquire necessary information based on the user's family structure. It can also customize the types of information acquired according to the user's family circumstances. Furthermore, it can adjust the information acquired based on the characteristics of the daycare center the user desires. By customizing the types of information acquired according to the user's family circumstances, more appropriate information can be provided. Family circumstances include, for example, family structure and income status, but are not limited to such examples. Types of information include, for example, address information and contact information, but are not limited to such examples.

[0116] The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). It can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, it can automatically input necessary information based on information the user has previously entered. In this way, by referring to the user's past input history, the optimal input method can be suggested, improving user convenience. Past input history includes, but is not limited to, past input dates and times, and input content. Optimal input methods include, but are not limited to, online input and voice input.

[0117] The scoring unit can optimize the scoring algorithm by referring to past admission data during the scoring process. For example, it can adjust the scoring algorithm based on past admission data. It can also optimize scoring criteria under specific conditions based on past admission data. Furthermore, it can analyze past admission data to improve the scoring algorithm. This allows for the optimization of the scoring algorithm by referring to past admission data, supporting more accurate scoring. Past admission data includes, but is not limited to, past admission numbers and admission conditions. The scoring algorithm includes, but is not limited to, the type of algorithm and optimization methods.

[0118] The notification unit can suggest the most suitable notification method by referring to the user's past notification history when sending a notification. For example, it can prioritize suggesting notification methods that the user has frequently used in the past (email, push notifications, etc.). It can also predict and suggest notification methods to be used during specific time periods based on the user's past notification history. Furthermore, it can automatically notify users of necessary information based on the content of notifications they have received in the past. This allows the system to suggest the most suitable notification method by referring to the user's past notification history, thereby improving user convenience. Past notification history includes, but is not limited to, past notification dates and times, and notification content. The most suitable notification method includes, but is not limited to, email notifications and push notifications.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk accepts user registrations. User registration can be done via online forms, email registration, or telephone or in-person registration. For example, a user can complete registration by entering the required information in an online form. Alternatively, information can be received and registration can be done via email registration. Furthermore, registration can also be accepted by telephone or in person. Step 2: The acquisition unit automatically retrieves the necessary information from the local government's database. The acquisition unit can retrieve information from the local government's database using APIs, or it can collect information from websites using scraping techniques. Furthermore, it can automatically read data files provided by the local government. For example, it can retrieve address information and contact information. Step 3: The input section allows the user to review the automatically acquired information and enter supplementary information. The input section provides an interface for the user to review the automatically acquired information and enter any necessary supplementary information. It can also save the supplementary information entered by the user to the system. Furthermore, it can collect data to improve the accuracy of scoring. For example, supplementary information such as address information, contact information, household structure, and income status can be entered. Step 4: The scoring unit performs scoring based on the information entered by the input unit. The scoring unit performs scoring considering past enrollment data and the location of the nursery. Using AI, scoring can be performed based on user information. For example, it can predict the user's likelihood of enrollment based on past enrollment data and perform scoring considering the nursery's location and surrounding environment. Step 5: The notification unit notifies the user of the score and advice obtained by the scoring unit. The notification unit provides the score and advice to the user through text messages or email notifications. It can also display the score and advice to the user through web applications or mobile applications. Furthermore, for users who prefer paper feedback, the results can be printed using a printer.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, acquisition unit, input unit, scoring unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit accepts user registration via the control unit 46A of the smart device 14. The acquisition unit automatically retrieves information from the local government's database via the specific processing unit 290 of the data processing unit 12. The input unit verifies the information automatically acquired by the user via the control unit 46A of the smart device 14 and inputs supplementary information. The scoring unit performs scoring based on the input information via the specific processing unit 290 of the data processing unit 12. The notification unit notifies the user of the score and advice via the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0138] 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.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0140] Each of the multiple elements described above, including the reception unit, acquisition unit, input unit, scoring unit, and notification unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit accepts user registration via the control unit 46A of the smart glasses 214. The acquisition unit automatically retrieves information from the local government's database via the identification processing unit 290 of the data processing unit 12. The input unit verifies the information automatically acquired by the user via the control unit 46A of the smart glasses 214 and inputs supplementary information. The scoring unit performs scoring based on the input information via the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user of the score and advice via the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0156] Each of the multiple elements described above, including the reception unit, acquisition unit, input unit, scoring unit, and notification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit accepts user registration via the control unit 46A of the headset terminal 314. The acquisition unit automatically retrieves information from the local government's database via the specific processing unit 290 of the data processing unit 12. The input unit verifies the information automatically acquired by the user via the control unit 46A of the headset terminal 314 and inputs supplementary information. The scoring unit performs scoring based on the input information via the specific processing unit 290 of the data processing unit 12. The notification unit notifies the user of the score and advice via the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0171] 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.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0173] Each of the multiple elements described above, including the reception unit, acquisition unit, input unit, scoring unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit accepts user registration via the control unit 46A of the robot 414. The acquisition unit automatically retrieves information from the local government's database via the specific processing unit 290 of the data processing unit 12. The input unit verifies the information automatically acquired by the user via the control unit 46A of the robot 414 and inputs supplementary information. The scoring unit performs scoring based on the input information via the specific processing unit 290 of the data processing unit 12. The notification unit notifies the user of the score and advice via the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The reception area for accepting user registrations, An acquisition unit that automatically retrieves information from the local government's database based on the information received by the aforementioned reception unit, An input unit that confirms the information acquired by the acquisition unit and inputs supplementary information, A scoring unit that performs scoring based on the information input by the aforementioned input unit, The system includes a notification unit that notifies the user of the score and advice obtained by the scoring unit. A system characterized by the following features. (Note 2) The acquisition unit is, Automatically retrieve necessary information from local government databases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned input unit is The user reviews the automatically retrieved information and enters supplementary information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The scoring unit is, The scoring system takes into account past enrollment data and the location of the nursery school. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Notify users of their chances of admission and provide advice. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the registration process guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the user's past registration history and suggest the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is During registration, the registration details are customized based on the user's family circumstances and the characteristics of the desired daycare center. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the registration process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is During registration, we will suggest the most suitable daycare center based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During registration, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, Customize the types of information retrieved from local government databases according to the user's household circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, When acquiring information, the system selects the optimal acquisition method by referring to past data acquisition history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, When retrieving information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, When acquiring information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned input unit is The system estimates the user's emotions and adjusts the input guidance method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned input unit is During input, the system refers to the user's past input history to suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned input unit is During input, the input content is customized based on the user's family situation and the characteristics of the desired daycare center. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned input unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned input unit is When inputting data, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned input unit is During input, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The scoring unit is, The system estimates the user's emotions and adjusts the scoring criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The scoring unit is, During the scoring process, the scoring algorithm is optimized by referring to past admission data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The scoring unit is, During the scoring process, the scoring criteria are customized based on the user's family circumstances and the characteristics of the desired daycare center. The system described in Appendix 1, characterized by the features described herein. (Note 27) The scoring unit is, The system estimates the user's emotions and adjusts how the scoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The scoring unit is, When scoring, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The scoring unit is, During the scoring process, the system analyzes the user's social media activity and incorporates relevant information into the scoring. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to suggest the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, the content of the notifications will be customized based on the user's family situation and the characteristics of the desired daycare center. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, It estimates the user's emotions and prioritizes notification content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, the system prioritizes sending highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity and provides relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area for accepting user registrations, An acquisition unit that automatically retrieves information from the local government's database based on the information received by the aforementioned reception unit, An input unit that confirms the information acquired by the acquisition unit and inputs supplementary information, A scoring unit that performs scoring based on the information input by the aforementioned input unit, The system includes a notification unit that notifies the user of the score and advice obtained by the scoring unit. A system characterized by the following features.

2. The acquisition unit is, Automatically retrieve necessary information from local government databases. The system according to feature 1.

3. The aforementioned input unit is The user reviews the automatically retrieved information and enters supplementary information. The system according to feature 1.

4. The scoring unit is, The scoring system takes into account past enrollment data and the location of the nursery school. The system according to feature 1.

5. The aforementioned notification unit, Notify users of their chances of admission and provide advice. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the registration process guidance based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the user's past registration history and suggest the optimal registration method. The system according to feature 1.

8. The aforementioned reception unit is During registration, the registration details are customized based on the user's family circumstances and the characteristics of the desired daycare center. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the registration process based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is During registration, we will suggest the most suitable daycare center based on the user's geographical location. The system according to feature 1.

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