system

Abstract

Provide a system. 【Solution means】 Means having a function of inputting basic information of a family, Mathematical means for calculating the admission permission points of a childcare institution based on the input information, Means for estimating the probability of admission based on the calculated points, Media generation means for visually presenting the estimated admission probability, Means for automatically generating necessary application records, Means for giving advice for proposing an optimal childcare institution selection policy, Means for immediately updating regional information and admission competition rate information of childcare institutions, Means for cooperating with a device capable of recognizing voices and having a conversation within a family, and providing the advice and information conversationally, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Admission to a daycare facility is a major source of anxiety for many parents due to complex admission point calculations and opaque admission probabilities. In particular, lack of knowledge about the preparation of necessary documents and the selection of effective application strategies has become an obstacle to admission. Therefore, there is a need to simplify the process of daycare facility admission, reduce the mental burden on parents, and solve the problem of waiting children.

Means for Solving the Problems

[0005] This invention provides a means for inputting basic household information and an algorithm that uses AI to calculate admission points for childcare facilities based on that information. Furthermore, it includes means for predicting the probability of admission based on the calculated points and visually displaying the results to the user. In addition, it enables users to easily complete the application process by using means for automatically generating necessary documents. Moreover, an advice means that proposes an optimal childcare facility selection strategy provides a customized strategy based on the household's employment status and income. Finally, means for updating regional and childcare facility admission competition rate data in real time provide users with the latest and most accurate information at all times. In this way, the process of entering childcare facilities is simplified, and admission with a higher probability is achieved.

[0006] A "means for inputting basic household information" refers to an interface that provides users with the ability to input information such as their household situation, employment status, and place of residence into the system.

[0007] The "algorithm for calculating admission points" includes a calculation method for determining admission points for childcare facilities based on the basic information of the family that has been entered.

[0008] "Methods for predicting the probability of admission" include methods for calculating the likelihood of admission to a childcare facility using calculated admission points and regional competition rate data.

[0009] "Means of displaying results" include interfaces that visually present calculated scores and predicted admission probabilities in a way that is easy for users to understand.

[0010] "Means for automatically generating necessary documents" include functions that create and provide templates for documents required for an application based on the user's input information.

[0011] "An advisory tool for proposing childcare facility selection strategies" includes analytical and suggestion functions to recommend the most suitable childcare facility options and application strategies based on the user's family circumstances.

[0012] "Means for updating admission competition rate data in real time" include methods for keeping competition rate information related to regions and childcare facilities up-to-date and providing users with accurate information. [Brief explanation of the drawing]

[0013] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The 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.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system to simplify the process of enrolling children in childcare facilities and reduce the mental burden on parents. This system integrates multiple technologies, which are described in detail below.

[0035] Users input basic household information into the system via a terminal. This information includes elements necessary for calculating admission points to childcare facilities, such as household composition, employment status, and residential location. The terminal formats the input data and sends it to the server.

[0036] The server uses an AI algorithm to calculate admission scores based on the received data. The calculated scores are based on admission criteria for childcare facilities in each region, ensuring an accurate and fair evaluation. Next, based on these scores and competition rate data, the server predicts the probability of admission to each childcare facility and generates data to present to the user.

[0037] The terminal receives result data sent from the server and displays the results visually in a user-friendly format. This includes a graph showing the probability of admission and a comparison list of different childcare facilities. Required application documents are also automatically listed and provided to the user for online download.

[0038] Furthermore, the server has an advisory function to suggest the optimal childcare facility selection strategy. It generates and proposes a customized strategy based on each user's family and employment situation. This allows users to choose a facility that suits their individual circumstances and develop an effective enrollment strategy.

[0039] The real-time information update function ensures that the latest competition rate data and local information are constantly collected, and the entire system is updated accordingly. This function guarantees that users can quickly respond to the rapidly changing admission situation.

[0040] For example, if a user wishes to enroll their child in a childcare facility in a certain city, they can input detailed information about a specific area within that city and compare multiple facilities based on the presented probability of enrollment. The server then suggests the childcare facility with the highest probability of enrollment, supports the optimal enrollment plan, and facilitates a quick and effective enrollment process.

[0041] By using this system, users can efficiently proceed with selecting and applying for childcare facilities, and it is expected to contribute to resolving the problem of children on waiting lists for childcare.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users access a dedicated input form on their device and enter basic information such as their family information, employment status, and residential address. Once they have finished entering the information, they press the submit button to send the data to the server.

[0045] Step 2:

[0046] The terminal formats the data entered by the user and converts it into an appropriate format for transmission to the server. After verifying that the format is correct, it sends the data to the server.

[0047] Step 3:

[0048] The server receives data from the terminal. It checks the data integrity of the received data and verifies that there are no errors. If there are errors, it returns them to the terminal and prompts the user to re-enter the data.

[0049] Step 4:

[0050] The server uses an AI algorithm to calculate the admission score for childcare facilities based on the received user data. This calculation takes into account the user's family information and the standards of local childcare facilities.

[0051] Step 5:

[0052] The server predicts the probability of admission to each childcare facility based on calculated admission points and historical regional data. Statistical information such as admission rates and competition rates are used in the prediction process.

[0053] Step 6:

[0054] The server organizes the calculated data of admission points and admission probability, and converts it into a format that users can view. Visual data representations (graphs, lists, etc.) are also generated during this process.

[0055] Step 7:

[0056] The terminal receives data sent from the server and visually displays the results on the user screen. It also generates a list of documents required for the application and provides a link for the user to download them.

[0057] Step 8:

[0058] The server generates AI-based advice to help users select the most suitable childcare facility. The suggested strategies are customized to each family's individual circumstances.

[0059] Step 9:

[0060] The terminal receives advice sent from the server and presents it to the user. Based on the suggestions, the user can proceed with selecting a childcare facility and completing the application process.

[0061] Step 10:

[0062] The server constantly monitors the latest information on local areas and childcare facilities, updating the database as needed. It notifies users of any changes and provides revised suggestions based on the new information.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] In modern society, the process of enrolling children in childcare facilities is complex and places a significant burden on parents. In particular, providing appropriate information and developing strategies tailored to each family's situation and local area is difficult, resulting in a time-consuming and laborious process. Furthermore, the difficulty in updating information in real time makes it challenging to respond quickly to the rapidly changing enrollment situation.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes means for inputting household attribute information, means for formatting the input attribute information and transmitting the data, and means for a machine learning algorithm for calculating admission points to childcare facilities based on the formatted data. This enables efficient and accurate information provision in complex admission procedures, helping parents quickly select the most suitable childcare facility. Furthermore, by providing strategic advice that takes into account regional competition rates and individual family circumstances, it reduces the burden on parents and contributes to solving the problem of children on waiting lists for childcare.

[0068] "Family attribute information" refers to basic information necessary for enrolling a child in a childcare facility, such as family structure, occupation, and place of residence.

[0069] "Formatting" refers to the process of processing user-entered data into a consistent format and preparing the data according to the required format.

[0070] A "machine learning algorithm" is a computational method that analyzes large amounts of data and learns patterns to perform specific tasks or make predictions.

[0071] "Admission points" are numerical values ​​used to evaluate the criteria for admission applications to childcare facilities, and are calculated based on the family's circumstances and the local admission standards.

[0072] "Admission probability" is a numerical representation of the likelihood of being accepted into a particular childcare facility, and it depends on points and competition rate information.

[0073] "Graph and list format" refers to methods for visually representing data, either by illustrating it as a graph or by listing it as a list.

[0074] "Procedural documents" are official documents required for admission to a childcare facility and are documents that must be submitted at the time of application.

[0075] An "advice generation mechanism" is a mechanism designed to present the optimal action or choice based on the user's input information.

[0076] "Means of collecting and reflecting data in real time" refers to the process of continuously acquiring the latest data and immediately applying it to the system.

[0077] This invention relates to an information processing system for efficiently and effectively facilitating the selection and enrollment procedures for childcare facilities. The embodiments thereof will be described in detail below.

[0078] The user first enters basic household information into the system via a terminal. This information includes household composition, occupation, and place of residence. The terminal formats the entered data and sends it to the server using a secure communication protocol.

[0079] The server uses AI algorithms (including generative AI models) to calculate admission points for childcare facilities based on the received data. The AI ​​algorithms analyze diverse data and calculate points tailored to each family's situation. The calculations take into account regional admission criteria and competition rates, ensuring accurate and fair evaluation.

[0080] Next, the server uses the calculated scores and real-time updated competition rate data to predict the probability of admission to each childcare facility. This generates the information the user needs to make a comparative decision. This result is sent from the server to the terminal and presented to the user visually.

[0081] The terminal displays data in formats such as graphs and lists, making it easy for users to understand. Furthermore, necessary application documents are automatically listed, allowing users to easily access and download them online.

[0082] Furthermore, the server generates customized advice tailored to the user's family and work situation, presenting an optimal childcare facility selection plan. This makes it easier for users to formulate concrete strategies and efficiently proceed with the enrollment process.

[0083] The system is designed to collect up-to-date regional and childcare facility data in real time, ensuring that the entire system always operates based on the latest information. This implementation allows users to respond quickly and flexibly to dynamically changing enrollment situations.

[0084] A concrete example is a user who wishes to enroll their child in a childcare facility in a specific area of ​​Tokyo. The user enters their home information into the terminal, and the system calculates and presents the probability of enrollment based on that information. An example of a prompt message might be, "Calculate the probability of enrollment in a childcare facility within Tokyo's 23 wards and suggest the best option." In this way, the user can make an information-based choice.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The user enters basic household information via a terminal. This information includes household composition, occupation, and place of residence. This information serves as the basis for calculating admission points to childcare facilities. The terminal formats this information into the appropriate format and prepares it for transmission to the server.

[0088] Step 2:

[0089] The terminal sends the formatted data to the server. Data security is ensured through the communication protocol. The input is formatted personal information, and the output is the secure transmission of data to the server.

[0090] Step 3:

[0091] The server uses an AI algorithm to calculate admission points based on the received data. This process employs a generative AI model, which calculates points considering regional admission criteria and family information. The input is formatted family information, and the output is the calculated admission points.

[0092] Step 4:

[0093] The server uses the calculated admission score and real-time updated competition rate data to predict the probability of admission. In this step, a statistical model is used to evaluate the likelihood of admission to multiple childcare facilities. The inputs are admission score and competition rate data, and the output is the probability of admission to each childcare facility.

[0094] Step 5:

[0095] The terminal receives park entry probability data transmitted from the server and presents it to the user visually. Specific display formats include graphs and comparison lists. The input is park entry probability data from the server, and the output is visualized information.

[0096] Step 6:

[0097] The server generates an optimal childcare facility selection plan based on the user's family and occupational circumstances. The generated advice is presented to the user and becomes available as individualized options. The input is family and occupational information, and the output is customized advice.

[0098] Step 7:

[0099] The server collects the latest regional and childcare facility data and updates the system in real time. This functionality makes it possible to always assess enrollment status based on the most up-to-date information. The input is a real-world data feed, and the output is the updated system status.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] There is a need to reduce the complexity and time burden on parents in efficiently navigating the childcare enrollment process. Furthermore, there is a lack of support to effectively determine which childcare facility is most suitable based on individual family circumstances. Additionally, there is a need for methods that allow families to easily access information and proceed with the application process within the home.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes means for inputting basic household information, mathematical means for calculating admission points for childcare facilities based on the input information, and means for estimating the probability of admission based on the calculated points. This allows parents to easily understand childcare facility options through voice-based dialogue within the home and receive customized support to make the best choice.

[0105] The "function for entering basic family information" is a system that allows users to receive information necessary for the enrollment process, such as family composition and housing information, through a terminal.

[0106] "Mathematical methods for calculating admission points to childcare institutions" refers to mathematical methods that use data entered by families to derive points for admission to each childcare institution.

[0107] "Methods for estimating the probability of admission" refers to methods for predicting the probability of successful admission to a specific childcare institution based on the calculated score.

[0108] A "visual presentation medium generation means" is a digital interface for displaying calculation results and recommendations in an intuitively understandable format.

[0109] "Methods for automatically generating application records" refers to procedures for automatically creating the necessary application documents based on the user's input information.

[0110] "Means of providing advice to propose the optimal childcare institution selection policy" refers to an advisory function that presents the best options from multiple childcare institutions to suit the needs of each family.

[0111] "Means for instantly updating local information and admission competition rates for childcare facilities" refers to a system that acquires the latest local conditions and competition rates in real time, keeping the system constantly up-to-date.

[0112] A "speech-recognizing, in-home interactive device" is an interactive device that interprets the user's voice and provides appropriate information and advice in response to their instructions.

[0113] This invention is a system that supports the effective matching of parents and childcare facilities within the home. Users transmit basic information, such as family structure and housing information, to a server via voice or touch operation through an interface. The hardware used could be a home robot or digital device equipped with a microphone for voice recognition. Furthermore, software such as speech recognition is used to convert the voice data into text.

[0114] The server uses a generative AI model to calculate admission points to childcare facilities based on information provided by the user. This calculation is customized based on regional admission competition rates and the user's family circumstances, and is processed using mathematical methods. Next, the server predicts the probability of admission based on the calculated points and presents it to the user in an easily understandable format using a visual media generation system.

[0115] The terminal displays results in visually easy-to-understand graphs and lists, and automatically generates and provides downloadable application records. This allows users to easily obtain information on suitable childcare facilities from the comfort of their homes and quickly proceed with the necessary application procedures.

[0116] For example, if a user asks the robot, "Tell me the most convenient daycare center in Tokyo," the robot will present a list of childcare facilities that best fit that condition. In this case, an example of a prompt to input into the generating AI model would be, "Based on the user's request, 'Tell me the most convenient daycare center in Tokyo,' please recommend the most suitable childcare facility."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The user enters basic household information. Using a voice interface or touch controls, they send household composition, housing information, and other details to a voice recognition device or terminal. This input data is then ready to be sent to the server.

[0120] Step 2:

[0121] The server uses speech recognition software to convert received audio data into text data. Using libraries such as the SpeechRecognition library, it accurately identifies speech and processes it as digital text. The output from this process becomes basic family information text.

[0122] Step 3:

[0123] The server uses a generative AI model to calculate admission points for childcare facilities based on the input family information text. It extracts necessary attributes from the input data and applies a point calculation algorithm. This generates an output of admission points based on the circumstances of each family.

[0124] Step 4:

[0125] The server uses the calculated scores and regional competition data to predict the probability of admission to a childcare facility. A data analysis algorithm is used to derive the predicted value. Based on this, an output of admission probabilities is obtained, and the server evaluates which childcare facility has the highest probability.

[0126] Step 5:

[0127] The server visually formats the results and sends them to the terminal. Using visual media generation means, probability data and choices are output as graphs or lists in a format that is easy for the user to understand. Information is included to facilitate the user's selection.

[0128] Step 6:

[0129] Users receive visualized information from their devices and select the most suitable childcare facility. The displayed information also includes links to the necessary application procedures, guiding users as they proceed with the online application. This enables efficient selection of childcare facilities.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention provides a system that takes user emotions into account by combining an emotion engine with a system that supports the process of enrolling in childcare facilities. This system aims to reduce the anxiety and stress that users experience when applying for enrollment in childcare facilities.

[0132] Users access the system via a terminal and input basic information such as household composition, employment status, and residential area. This information is sent to the server via the terminal, which receives it, performs data checks, and then stores it.

[0133] The server uses an AI algorithm to calculate admission points for childcare facilities based on the received basic information. The point calculation takes into account factors such as regional competition and public standards. Based on these points, the server also predicts the probability of admission to each childcare facility and generates data to provide to the user.

[0134] This system incorporates an emotion engine that analyzes the emotions (text-based comments and feedback) expressed by the user during input. The emotion engine evaluates the user's emotional state, and if it determines that the user is experiencing high stress, it provides optimized feedback aimed at input assistance and stress reduction.

[0135] The terminal displays admission points received from the server, admission probability, and feedback generated by the emotion engine. This allows users to consider their own emotional state and develop the optimal childcare facility selection and application strategy.

[0136] Furthermore, the server customizes the childcare facility selection strategy based on the user's emotions and generates personalized advice. This advice is provided based on the family's employment status, income, and specific emotional state, helping users proceed with the application process as smoothly as possible.

[0137] For example, if a user uses the system while experiencing strong emotional anxiety, the emotion engine will detect this, and the server will provide feedback to alleviate this anxiety by suggesting several suitable childcare facilities and offering a detailed guide explaining the application process.

[0138] In this way, this system recognizes the user's emotional state and responds accordingly, providing a user-centered experience and enabling parents to confidently proceed with enrolling their children in childcare facilities.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] Users access the terminal's interface and enter basic household information, employment status, children's ages and names, and residential area. The completed data is then sent from the terminal to the server.

[0142] Step 2:

[0143] The terminal formats the input data and converts it into a format suitable for transmission to the server. During this process, it performs pre-processing such as verifying the input format and required information.

[0144] Step 3:

[0145] The server processes the data received from the terminal and registers it in the database. It checks the received information for defects and inconsistencies, and if there are any problems, it immediately returns an error message to the terminal.

[0146] Step 4:

[0147] The server uses an AI algorithm to calculate admission points for childcare facilities based on accurate data. This calculation takes into account regional characteristics and the admission criteria of the childcare facilities.

[0148] Step 5:

[0149] The server uses the calculated score and past data to estimate the probability of admission. The prediction also includes statistics on regional competition and the number of applications.

[0150] Step 6:

[0151] The terminal receives scores and admission probabilities sent from the server and displays them visualized on the user screen. This allows users to quickly assess their own situation.

[0152] Step 7:

[0153] The emotion engine analyzes the user's emotions from their input. If it detects stress or anxiety from the user's text or selections, it sends the analysis results to the server.

[0154] Step 8:

[0155] The server receives the results from the emotion engine and generates optimal feedback and selection strategies that correspond to the user's emotional state. This feedback is designed to make the user feel secure.

[0156] Step 9:

[0157] The device receives feedback and advice from the server and presents it to the user in an easy-to-understand manner. This allows the user to receive support tailored to their emotional state.

[0158] Step 10:

[0159] The server further updates real-time data on the competition rate of local childcare facilities, obtaining the latest information. Based on the information obtained, it makes further suggestions to the user as needed.

[0160] (Example 2)

[0161] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0162] A system is needed to alleviate the anxiety and stress applicants experience during the childcare facility enrollment process while supporting them in selecting the appropriate facility. Traditional systems only provide enrollment points and probabilities, lacking support that considers the applicant's psychological state. This creates a challenge in that users struggle to make effective decisions after receiving information.

[0163] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0164] In this invention, the server includes means for inputting general household information, an algorithm for calculating admission evaluation scores for educational facilities, and means for analyzing the user's emotional state using an emotion engine and generating stress-reducing feedback. This allows users to select the most suitable educational facility while considering their own psychological state and proceed with the application process with peace of mind.

[0165] "A means of inputting general household information" refers to a method or device for users to input basic information such as household composition, employment status, and residential area.

[0166] An "algorithmic method" refers to a procedure or process that uses a specific calculation method based on input information to calculate an admission evaluation score for an educational facility.

[0167] The "admission evaluation score" is a numerical representation of the likelihood of admission to a childcare facility, calculated based on the submitted information.

[0168] "Possibility of admission" refers to the probability that an applicant's application will be accepted at a particular childcare facility.

[0169] "Advisory tools" refer to methods or devices for proposing optimal educational facility selection strategies to users.

[0170] An "emotion engine" is a technology that analyzes user input and feedback to evaluate their emotional state and generate feedback that reduces stress.

[0171] "Means of generating feedback" refers to a method or device that provides users with information to give them a sense of security or reduce anxiety, based on the results analyzed by an emotion engine.

[0172] The system according to this invention aims to reduce the anxiety and stress that users experience during the application process for admission to childcare facilities, while supporting them in selecting an appropriate facility. The system mainly consists of a server, terminals, and users.

[0173] The user first uses a terminal to input general information such as household composition, employment status, and residential area. The terminal provides an interface for formatting this information appropriately and sending it to the server. In this process, a regular computer or smartphone is used as the terminal, and access is made through a browser or a dedicated application.

[0174] The server executes an AI algorithm to calculate admission evaluation scores for educational facilities based on the received information. This algorithm takes into account multiple factors, including regional competition rates and public standards, to quantitatively evaluate the user's situation. The server is equipped with a dedicated software environment and database to efficiently perform complex calculations.

[0175] Furthermore, the system incorporates an emotion engine that can analyze user emotions from input data. The emotion engine extracts the emotions the user expresses, and if stress is detected, the server generates customized feedback. This allows the server to provide suggestions and guidance for stress relief, in addition to presenting the probability of admission.

[0176] On the device, admission evaluation scores and probabilities sent from the server, along with sentiment-based feedback, are displayed, allowing users to make the best choice. This display is presented in an easy-to-understand manner through the user interface.

[0177] To give a specific example, if a user inputs their emotion as "anxiety," the emotion engine will analyze this as stress, and the server will provide information to alleviate their anxiety, such as listing several suitable childcare facilities and offering a guide that explains the application process in detail.

[0178] An example of a prompt for the generating AI model is, "Generate feedback to alleviate anxiety when applying for admission to a childcare facility. The user's current emotion is anxiety." In this way, the system achieves user-centered service delivery.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] Users input general information such as household composition, employment status, and residential area via a terminal. The entered data is converted into a digital format by the terminal, and the consistency of the input data and the required fields are verified. Once verified, the information is ready to be sent to the server.

[0182] Step 2:

[0183] The terminal sends the entered information to the server. The server analyzes the received data and checks its integrity. If there are inconsistencies or errors in the received data, the server sends an error message to the terminal, prompting the user to re-enter the information. Only accurate data is saved in the database so that it can proceed to the next process.

[0184] Step 3:

[0185] The server uses stored basic information to execute an AI algorithm and calculate an admission evaluation score for educational facilities. Specifically, it scores the applicant based on factors such as regional competition rates and family circumstances extracted from the input data. This evaluation score will later be used as an indicator to predict the likelihood of admission. The evaluation score is generated by a calculation engine within the server and recorded in a database.

[0186] Step 4:

[0187] The server uses the calculated admission evaluation scores to predict the likelihood of admission to each educational facility. This prediction is performed using a statistical model, generating numerical probabilities. The generated probability data is stored on the server and used in subsequent processes.

[0188] Step 5:

[0189] The terminal displays admission evaluation scores and admission probabilities received from the server to the user. The terminal helps the user make choices based on this information. Specifically, the screen displays a list of admission probabilities for each facility, allowing the user to easily compare them.

[0190] Step 6:

[0191] The server analyzes the text data entered by the user using an emotion engine and evaluates their emotional state. Specifically, it quantifies emotions based on keywords and the tone of the sentences in the text, and if it determines that stress is high, it generates feedback to alleviate the stress. This feedback is tailored to the user's emotions, such as messages that provide reassurance.

[0192] Step 7:

[0193] The device displays sentiment analysis results and feedback sent from the server to the user. This includes specific advice and options regarding the enrollment process. Based on this information, the user can confidently proceed with the next application process.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] The process of enrolling families in childcare facilities presents challenges such as the complexity of information, the lack of transparency in application procedures, and the resulting anxiety and stress. In particular, families often experience stress because they lack sufficient information to select and apply for childcare facilities. Furthermore, the lack of information on regional competition rates and the inability to provide success rates and strategies tailored to individual family circumstances makes rational and efficient choices difficult.

[0197] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0198] In this invention, the server includes a device for acquiring basic household information, an inference device for calculating admission points to childcare facilities based on the acquired basic information, and a prediction device for predicting the probability of admission based on the calculated points. This allows families to achieve transparency and rationalization in the admission process, enabling them to make optimal choices and applications while reducing stress.

[0199] An "information acquisition device" is a device that collects basic information from households and has the function of inputting data through a user interface.

[0200] An "inference device" is a means of performing specific analyses based on acquired information and calculating admission points for childcare facilities as a result.

[0201] A "prediction device" is a system that evaluates the probability of future admission success based on calculated admission points.

[0202] A "display device" is a device that visually presents information to the user, and has the function of showing the probability of admission and proposed strategies in a way that is easy for the user to understand.

[0203] A "generator" is a system that automatically creates the necessary application documents based on specific conditions.

[0204] A "guidance system" is a system that provides guidance on selecting the most suitable childcare facility and offers advice tailored to the family's circumstances.

[0205] The "update device" is equipped with a function to keep data on regional and childcare facility admission competition up to date in real time.

[0206] An "emotion analysis device" is a system that detects a user's emotional state and provides appropriate feedback to reduce stress.

[0207] This invention provides a system that allows families to streamline the application process for childcare facilities. A server receives data transmitted from a family's basic information acquisition device and uses this data to calculate a childcare facility admission score using an inference device. The inference is performed using a calculation engine based on given criteria and regional competition data. Next, a prediction device evaluates the probability of admission based on the calculated score and presents the result to the user via a display device. This display is presented in a way that is easy for the user to understand through visualized data.

[0208] Furthermore, the emotion analysis device uses voice analysis and facial expression analysis technologies (e.g., Microsoft® Azure® Face API) to analyze the user's emotional state during input. If the system determines that the user is stressed, it generates relaxing content and feedback, and the generator automatically creates specific application documents.

[0209] For example, when a user is entering information via a terminal in the morning, the display device shows the message, "There are three optimal daycare options for your child. Would you like to see more details?" The generated AI prompt might use text such as, "Please tell me how to design an assistant that provides optimal feedback and specific application support to reduce stress for users who are anxious about the daycare enrollment process."

[0210] This system allows users to reduce anxiety about choosing a childcare facility, obtain appropriate information, and complete a stress-free and smooth application process.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] Users input basic household information (e.g., household composition, employment status, place of residence, etc.) via a terminal. The entered information is acquired as digital data and sent to the server. This allows the system to understand the user's basic circumstances.

[0214] Step 2:

[0215] The server receives the transmitted basic information and uses an inference device to calculate the admission score for childcare facilities. The inputs used are the user's basic information and regional competition rate data. A specified algorithm is used for data calculation, and the final calculated score is output.

[0216] Step 3:

[0217] Based on the calculated score, the server uses a prediction device to calculate the probability of admission. It takes the calculated score as input and performs calculations using probability logic. The calculated admission probability is obtained as output.

[0218] Step 4:

[0219] The calculated admission probability and related information are transmitted from the server to the terminal and displayed on the user's terminal. The display device presents the visualized data to the user, allowing them to understand their current admission probability.

[0220] Step 5:

[0221] The emotion analysis device analyzes the user's voice and facial expression data as input. The user's real-time emotional expressions are used as input data and processed by an emotion analysis algorithm. The analysis results in the user's stress level being output.

[0222] Step 6:

[0223] If a user's stress level is high, the server prepares to provide relaxing feedback. For example, it might use a generative AI model to select specific relaxation content and present it to the user via a display device.

[0224] Step 7:

[0225] The necessary application documents are automatically generated and provided to the user by the generation device. The user's specific conditions are considered as input, the necessary documents are specified by an automated generation algorithm, and the application documents are generated as output. This allows the user to proceed with the application process quickly.

[0226] 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.

[0227] Data generation model 58 is a type 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0228] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0233] 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.

[0234] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0235] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0236] 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.

[0237] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0238] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0239] The 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.

[0240] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0241] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0242] This invention provides a system to simplify the process of enrolling children in childcare facilities and reduce the mental burden on parents. This system integrates multiple technologies, which are described in detail below.

[0243] Users input basic household information into the system via a terminal. This information includes elements necessary for calculating admission points to childcare facilities, such as household composition, employment status, and residential location. The terminal formats the input data and sends it to the server.

[0244] The server uses an AI algorithm to calculate admission scores based on the received data. The calculated scores are based on admission criteria for childcare facilities in each region, ensuring an accurate and fair evaluation. Next, based on these scores and competition rate data, the server predicts the probability of admission to each childcare facility and generates data to present to the user.

[0245] The terminal receives result data sent from the server and displays the results visually in a user-friendly format. This includes a graph showing the probability of admission and a comparison list of different childcare facilities. Required application documents are also automatically listed and provided to the user for online download.

[0246] Furthermore, the server has an advisory function to suggest the optimal childcare facility selection strategy. It generates and proposes a customized strategy based on each user's family and employment situation. This allows users to choose a facility that suits their individual circumstances and develop an effective enrollment strategy.

[0247] The real-time information update function ensures that the latest competition rate data and local information are constantly collected, and the entire system is updated accordingly. This function guarantees that users can quickly respond to the rapidly changing admission situation.

[0248] For example, if a user wishes to enroll their child in a childcare facility in a certain city, they can input detailed information about a specific area within that city and compare multiple facilities based on the presented probability of enrollment. The server then suggests the childcare facility with the highest probability of enrollment, supports the optimal enrollment plan, and facilitates a quick and effective enrollment process.

[0249] By using this system, users can efficiently proceed with selecting and applying for childcare facilities, and it is expected to contribute to resolving the problem of children on waiting lists for childcare.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] Users access a dedicated input form on their device and enter basic information such as their family information, employment status, and residential address. Once they have finished entering the information, they press the submit button to send the data to the server.

[0253] Step 2:

[0254] The terminal formats the data entered by the user and converts it into an appropriate format for transmission to the server. After verifying that the format is correct, it sends the data to the server.

[0255] Step 3:

[0256] The server receives data from the terminal. It checks the data integrity of the received data and verifies that there are no errors. If there are errors, it returns them to the terminal and prompts the user to re-enter the data.

[0257] Step 4:

[0258] The server uses an AI algorithm to calculate the admission score for childcare facilities based on the received user data. This calculation takes into account the user's family information and the standards of local childcare facilities.

[0259] Step 5:

[0260] The server predicts the probability of admission to each childcare facility based on calculated admission points and historical regional data. Statistical information such as admission rates and competition rates are used in the prediction process.

[0261] Step 6:

[0262] The server organizes the calculated data of admission points and admission probability, and converts it into a format that users can view. Visual data representations (graphs, lists, etc.) are also generated during this process.

[0263] Step 7:

[0264] The terminal receives data sent from the server and visually displays the results on the user screen. It also generates a list of documents required for the application and provides a link for the user to download them.

[0265] Step 8:

[0266] The server generates AI-based advice to help users select the most suitable childcare facility. The suggested strategies are customized to each family's individual circumstances.

[0267] Step 9:

[0268] The terminal receives advice sent from the server and presents it to the user. Based on the suggestions, the user can proceed with selecting a childcare facility and completing the application process.

[0269] Step 10:

[0270] The server constantly monitors the latest information on local areas and childcare facilities, updating the database as needed. It notifies users of any changes and provides revised suggestions based on the new information.

[0271] (Example 1)

[0272] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] In modern society, the process of enrolling children in childcare facilities is complex and places a significant burden on parents. In particular, providing appropriate information and developing strategies tailored to each family's situation and local area is difficult, resulting in a time-consuming and laborious process. Furthermore, the difficulty in updating information in real time makes it challenging to respond quickly to the rapidly changing enrollment situation.

[0274] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0275] In this invention, the server includes means for inputting household attribute information, means for formatting the input attribute information and transmitting the data, and means for a machine learning algorithm for calculating admission points to childcare facilities based on the formatted data. This enables efficient and accurate information provision in complex admission procedures, helping parents quickly select the most suitable childcare facility. Furthermore, by providing strategic advice that takes into account regional competition rates and individual family circumstances, it reduces the burden on parents and contributes to solving the problem of children on waiting lists for childcare.

[0276] "Family attribute information" refers to basic information necessary for enrolling a child in a childcare facility, such as family structure, occupation, and place of residence.

[0277] "Formatting" refers to the process of processing user-entered data into a consistent format and preparing the data according to the required format.

[0278] A "machine learning algorithm" is a computational method that analyzes large amounts of data and learns patterns to perform specific tasks or make predictions.

[0279] "Admission points" are numerical values ​​used to evaluate the criteria for admission applications to childcare facilities, and are calculated based on the family's circumstances and the local admission standards.

[0280] "Admission probability" is a numerical representation of the likelihood of being accepted into a particular childcare facility, and it depends on points and competition rate information.

[0281] "Graph and list format" refers to methods for visually representing data, either by illustrating it as a graph or by listing it as a list.

[0282] "Procedure documents" refer to official documents required for enrollment in a childcare facility and are documents that need to be submitted during the application process.

[0283] "Advice generation means" refers to a mechanism designed to present optimal actions and options based on the input information of the user.

[0284] "Means for collecting and reflecting in real time" refers to a process of continuously obtaining the latest data and immediately applying it to the system.

[0285] This invention relates to an information processing system for efficiently and effectively proceeding with the selection of a childcare facility and the enrollment procedure. Hereinafter, its embodiments will be described in detail.

[0286] The user first inputs the basic information of the family into the system via the terminal. This information includes family composition, employment status, place of residence, etc. The terminal formats the input data and transmits it to the server using a secure communication protocol.

[0287] Based on the received data, the server uses an AI algorithm (including a generative AI model) to calculate the enrollment points of childcare facilities. The AI algorithm analyzes various data and calculates points according to the situation of each family. In the calculation, in order to consider the enrollment criteria and competition rate for each region, accurate and fair evaluation is possible.

[0288] Next, the server uses the calculated points and the real-time updated competition rate data to predict the enrollment probability of each childcare facility. As a result, information necessary for the user to make a comparison and judgment is generated. This result is transmitted from the server to the terminal and visually presented to the user.

[0289] The terminal displays the data in the form of graphs, lists, etc., making it easy for the user to understand. Also, the necessary application documents are automatically listed, and the user can easily access and download them online.

[0290] Furthermore, the server generates customized advice tailored to the user's family and work situation, presenting an optimal childcare facility selection plan. This makes it easier for users to formulate concrete strategies and efficiently proceed with the enrollment process.

[0291] The system is designed to collect up-to-date regional and childcare facility data in real time, ensuring that the entire system always operates based on the latest information. This implementation allows users to respond quickly and flexibly to dynamically changing enrollment situations.

[0292] A concrete example is a user who wishes to enroll their child in a childcare facility in a specific area of ​​Tokyo. The user enters their home information into the terminal, and the system calculates and presents the probability of enrollment based on that information. An example of a prompt message might be, "Calculate the probability of enrollment in a childcare facility within Tokyo's 23 wards and suggest the best option." In this way, the user can make an information-based choice.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] The user enters basic household information via a terminal. This information includes household composition, occupation, and place of residence. This information serves as the basis for calculating admission points to childcare facilities. The terminal formats this information into the appropriate format and prepares it for transmission to the server.

[0296] Step 2:

[0297] The terminal sends the formatted data to the server. Data security is ensured through the communication protocol. The input is formatted personal information, and the output is the secure transmission of data to the server.

[0298] Step 3:

[0299] Based on the received data, the server calculates the admission points using an AI algorithm. In this process, a generative AI model is used to calculate the points considering the admission criteria for each region and the household information. The input is the formatted household information, and the output is the calculated admission points.

[0300] Step 4:

[0301] The server uses the calculated admission points and the real-time updated competition rate data to predict the admission probability. In this step, a statistical model is used to evaluate the admission possibilities for multiple childcare facilities. The input is the admission points and the competition rate data, and the output is the admission probability for each childcare facility.

[0302] Step 5:

[0303] The terminal receives the admission probability data sent from the server and visually presents it to the user. As a specific display format, graphs or comparison lists are used. The input is the admission probability data from the server, and the output is the visualized information.

[0304] Step 6:

[0305] Based on the user's family situation and employment situation, the server generates an optimal childcare facility selection plan. The generated advice is presented to the user and is available as individual options. The input is the family and employment information, and the output is the customized advice.

[0306] Step 7:

[0307] The server collects the latest data on the region and childcare facilities and updates the system in real time. With this function, it becomes possible to always evaluate the admission situation based on the latest information. The input is the real-world data feed, and the output is the updated system state.

[0308] (Application Example 1)

[0309] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0310] There is a need to reduce the complexity and time burden on parents in efficiently navigating the childcare enrollment process. Furthermore, there is a lack of support to effectively determine which childcare facility is most suitable based on individual family circumstances. Additionally, there is a need for methods that allow families to easily access information and proceed with the application process within the home.

[0311] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0312] In this invention, the server includes means for inputting basic household information, mathematical means for calculating admission points for childcare facilities based on the input information, and means for estimating the probability of admission based on the calculated points. This allows parents to easily understand childcare facility options through voice-based dialogue within the home and receive customized support to make the best choice.

[0313] The "function for entering basic family information" is a system that allows users to receive information necessary for the enrollment process, such as family composition and housing information, through a terminal.

[0314] "Mathematical methods for calculating admission points to childcare institutions" refers to mathematical methods that use data entered by families to derive points for admission to each childcare institution.

[0315] "Methods for estimating the probability of admission" refers to methods for predicting the probability of successful admission to a specific childcare institution based on the calculated score.

[0316] A "visual presentation medium generation means" is a digital interface for displaying calculation results and recommendations in an intuitively understandable format.

[0317] "Methods for automatically generating application records" refers to procedures for automatically creating the necessary application documents based on the user's input information.

[0318] "Means of providing advice to propose the optimal childcare institution selection policy" refers to an advisory function that presents the best options from multiple childcare institutions to suit the needs of each family.

[0319] "Means for instantly updating local information and admission competition rates for childcare facilities" refers to a system that acquires the latest local conditions and competition rates in real time, keeping the system constantly up-to-date.

[0320] A "speech-recognizing, in-home interactive device" is an interactive device that interprets the user's voice and provides appropriate information and advice in response to their instructions.

[0321] This invention is a system that supports the effective matching of parents and childcare facilities within the home. Users transmit basic information, such as family structure and housing information, to a server via voice or touch operation through an interface. The hardware used could be a home robot or digital device equipped with a microphone for voice recognition. Furthermore, software such as speech recognition is used to convert the voice data into text.

[0322] The server uses a generative AI model to calculate admission points to childcare facilities based on information provided by the user. This calculation is customized based on regional admission competition rates and the user's family circumstances, and is processed using mathematical methods. Next, the server predicts the probability of admission based on the calculated points and presents it to the user in an easily understandable format using a visual media generation system.

[0323] The terminal displays results in visually easy-to-understand graphs and lists, and automatically generates and provides downloadable application records. This allows users to easily obtain information on suitable childcare facilities from the comfort of their homes and quickly proceed with the necessary application procedures.

[0324] For example, if a user asks the robot, "Tell me the most convenient daycare center in Tokyo," the robot will present a list of childcare facilities that best fit that condition. In this case, an example of a prompt to input into the generating AI model would be, "Based on the user's request, 'Tell me the most convenient daycare center in Tokyo,' please recommend the most suitable childcare facility."

[0325] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0326] Step 1:

[0327] The user enters basic household information. Using a voice interface or touch controls, they send household composition, housing information, and other details to a voice recognition device or terminal. This input data is then ready to be sent to the server.

[0328] Step 2:

[0329] The server uses speech recognition software to convert received audio data into text data. Using libraries such as the SpeechRecognition library, it accurately identifies speech and processes it as digital text. The output from this process becomes basic family information text.

[0330] Step 3:

[0331] The server uses a generative AI model to calculate admission points for childcare facilities based on the input family information text. It extracts necessary attributes from the input data and applies a point calculation algorithm. This generates an output of admission points based on the circumstances of each family.

[0332] Step 4:

[0333] The server uses the calculated scores and regional competition data to predict the probability of admission to a childcare facility. A data analysis algorithm is used to derive the predicted value. Based on this, an output of admission probabilities is obtained, and the server evaluates which childcare facility has the highest probability.

[0334] Step 5:

[0335] The server visually formats the results and sends them to the terminal. Using visual media generation means, probability data and choices are output as graphs or lists in a format that is easy for the user to understand. Information is included to facilitate the user's selection.

[0336] Step 6:

[0337] Users receive visualized information from their devices and select the most suitable childcare facility. The displayed information also includes links to the necessary application procedures, guiding users as they proceed with the online application. This enables efficient selection of childcare facilities.

[0338] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0339] This invention provides a system that takes user emotions into account by combining an emotion engine with a system that supports the process of enrolling in childcare facilities. This system aims to reduce the anxiety and stress that users experience when applying for enrollment in childcare facilities.

[0340] Users access the system via a terminal and input basic information such as household composition, employment status, and residential area. This information is sent to the server via the terminal, which receives it, performs data checks, and then stores it.

[0341] The server uses an AI algorithm to calculate admission points for childcare facilities based on the received basic information. The point calculation takes into account factors such as regional competition and public standards. Based on these points, the server also predicts the probability of admission to each childcare facility and generates data to provide to the user.

[0342] This system incorporates an emotion engine that analyzes the emotions (text-based comments and feedback) expressed by the user during input. The emotion engine evaluates the user's emotional state, and if it determines that the user is experiencing high stress, it provides optimized feedback aimed at input assistance and stress reduction.

[0343] The terminal displays admission points received from the server, admission probability, and feedback generated by the emotion engine. This allows users to consider their own emotional state and develop the optimal childcare facility selection and application strategy.

[0344] Furthermore, the server customizes the childcare facility selection strategy based on the user's emotions and generates personalized advice. This advice is provided based on the family's employment status, income, and specific emotional state, helping users proceed with the application process as smoothly as possible.

[0345] For example, if a user uses the system while experiencing strong emotional anxiety, the emotion engine will detect this, and the server will provide feedback to alleviate this anxiety by suggesting several suitable childcare facilities and offering a detailed guide explaining the application process.

[0346] In this way, this system recognizes the user's emotional state and responds accordingly, providing a user-centered experience and enabling parents to confidently proceed with enrolling their children in childcare facilities.

[0347] The following describes the processing flow.

[0348] Step 1:

[0349] Users access the terminal's interface and enter basic household information, employment status, children's ages and names, and residential area. The completed data is then sent from the terminal to the server.

[0350] Step 2:

[0351] The terminal formats the input data and converts it into a format suitable for transmission to the server. During this process, it performs pre-processing such as verifying the input format and required information.

[0352] Step 3:

[0353] The server processes the data received from the terminal and registers it in the database. It checks the received information for defects and inconsistencies, and if there are any problems, it immediately returns an error message to the terminal.

[0354] Step 4:

[0355] The server uses an AI algorithm to calculate admission points for childcare facilities based on accurate data. This calculation takes into account regional characteristics and the admission criteria of the childcare facilities.

[0356] Step 5:

[0357] The server uses the calculated score and past data to estimate the probability of admission. The prediction also includes statistics on regional competition and the number of applications.

[0358] Step 6:

[0359] The terminal receives scores and admission probabilities sent from the server and displays them visualized on the user screen. This allows users to quickly assess their own situation.

[0360] Step 7:

[0361] The emotion engine analyzes the user's emotions from their input. If it detects stress or anxiety from the user's text or selections, it sends the analysis results to the server.

[0362] Step 8:

[0363] The server receives the results from the emotion engine and generates optimal feedback and selection strategies that correspond to the user's emotional state. This feedback is designed to make the user feel secure.

[0364] Step 9:

[0365] The device receives feedback and advice from the server and presents it to the user in an easy-to-understand manner. This allows the user to receive support tailored to their emotional state.

[0366] Step 10:

[0367] The server further updates real-time data on the competition rate of local childcare facilities, obtaining the latest information. Based on the information obtained, it makes further suggestions to the user as needed.

[0368] (Example 2)

[0369] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0370] A system is needed to alleviate the anxiety and stress applicants experience during the childcare facility enrollment process while supporting them in selecting the appropriate facility. Traditional systems only provide enrollment points and probabilities, lacking support that considers the applicant's psychological state. This creates a challenge in that users struggle to make effective decisions after receiving information.

[0371] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0372] In this invention, the server includes means for inputting general household information, an algorithm for calculating admission evaluation scores for educational facilities, and means for analyzing the user's emotional state using an emotion engine and generating stress-reducing feedback. This allows users to select the most suitable educational facility while considering their own psychological state and proceed with the application process with peace of mind.

[0373] "A means of inputting general household information" refers to a method or device for users to input basic information such as household composition, employment status, and residential area.

[0374] An "algorithmic method" refers to a procedure or process that uses a specific calculation method based on input information to calculate an admission evaluation score for an educational facility.

[0375] The "admission evaluation score" is a numerical representation of the likelihood of admission to a childcare facility, calculated based on the submitted information.

[0376] "Possibility of admission" refers to the probability that an applicant's application will be accepted at a particular childcare facility.

[0377] "Advisory tools" refer to methods or devices for proposing optimal educational facility selection strategies to users.

[0378] An "emotion engine" is a technology that analyzes user input and feedback to evaluate their emotional state and generate feedback that reduces stress.

[0379] "Means of generating feedback" refers to a method or device that provides users with information to give them a sense of security or reduce anxiety, based on the results analyzed by an emotion engine.

[0380] The system according to this invention aims to reduce the anxiety and stress that users experience during the application process for admission to childcare facilities, while supporting them in selecting an appropriate facility. The system mainly consists of a server, terminals, and users.

[0381] The user first uses a terminal to input general information such as household composition, employment status, and residential area. The terminal provides an interface for formatting this information appropriately and sending it to the server. In this process, a regular computer or smartphone is used as the terminal, and access is made through a browser or a dedicated application.

[0382] The server executes an AI algorithm to calculate admission evaluation scores for educational facilities based on the received information. This algorithm takes into account multiple factors, including regional competition rates and public standards, to quantitatively evaluate the user's situation. The server is equipped with a dedicated software environment and database to efficiently perform complex calculations.

[0383] Furthermore, the system incorporates an emotion engine that can analyze user emotions from input data. The emotion engine extracts the emotions the user expresses, and if stress is detected, the server generates customized feedback. This allows the server to provide suggestions and guidance for stress relief, in addition to presenting the probability of admission.

[0384] On the device, admission evaluation scores and probabilities sent from the server, along with sentiment-based feedback, are displayed, allowing users to make the best choice. This display is presented in an easy-to-understand manner through the user interface.

[0385] To give a specific example, if a user inputs their emotion as "anxiety," the emotion engine will analyze this as stress, and the server will provide information to alleviate their anxiety, such as listing several suitable childcare facilities and offering a guide that explains the application process in detail.

[0386] An example of a prompt for the generating AI model is, "Generate feedback to alleviate anxiety when applying for admission to a childcare facility. The user's current emotion is anxiety." In this way, the system achieves user-centered service delivery.

[0387] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0388] Step 1:

[0389] Users input general information such as household composition, employment status, and residential area via a terminal. The entered data is converted into a digital format by the terminal, and the consistency of the input data and the required fields are verified. Once verified, the information is ready to be sent to the server.

[0390] Step 2:

[0391] The terminal sends the entered information to the server. The server analyzes the received data and checks its integrity. If there are inconsistencies or errors in the received data, the server sends an error message to the terminal, prompting the user to re-enter the information. Only accurate data is saved in the database so that it can proceed to the next process.

[0392] Step 3:

[0393] The server uses stored basic information to execute an AI algorithm and calculate an admission evaluation score for educational facilities. Specifically, it scores the applicant based on factors such as regional competition rates and family circumstances extracted from the input data. This evaluation score will later be used as an indicator to predict the likelihood of admission. The evaluation score is generated by a calculation engine within the server and recorded in a database.

[0394] Step 4:

[0395] The server uses the calculated admission evaluation scores to predict the likelihood of admission to each educational facility. This prediction is performed using a statistical model, generating numerical probabilities. The generated probability data is stored on the server and used in subsequent processes.

[0396] Step 5:

[0397] The terminal displays admission evaluation scores and admission probabilities received from the server to the user. The terminal helps the user make choices based on this information. Specifically, the screen displays a list of admission probabilities for each facility, allowing the user to easily compare them.

[0398] Step 6:

[0399] The server analyzes the text data entered by the user using an emotion engine and evaluates their emotional state. Specifically, it quantifies emotions based on keywords and the tone of the sentences in the text, and if it determines that stress is high, it generates feedback to alleviate the stress. This feedback is tailored to the user's emotions, such as messages that provide reassurance.

[0400] Step 7:

[0401] The device displays sentiment analysis results and feedback sent from the server to the user. This includes specific advice and options regarding the enrollment process. Based on this information, the user can confidently proceed with the next application process.

[0402] (Application Example 2)

[0403] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0404] The process of enrolling families in childcare facilities presents challenges such as the complexity of information, the lack of transparency in application procedures, and the resulting anxiety and stress. In particular, families often experience stress because they lack sufficient information to select and apply for childcare facilities. Furthermore, the lack of information on regional competition rates and the inability to provide success rates and strategies tailored to individual family circumstances makes rational and efficient choices difficult.

[0405] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0406] In this invention, the server includes a device for acquiring basic household information, an inference device for calculating admission points to childcare facilities based on the acquired basic information, and a prediction device for predicting the probability of admission based on the calculated points. This allows families to achieve transparency and rationalization in the admission process, enabling them to make optimal choices and applications while reducing stress.

[0407] An "information acquisition device" is a device that collects basic information from households and has the function of inputting data through a user interface.

[0408] An "inference device" is a means of performing specific analyses based on acquired information and calculating admission points for childcare facilities as a result.

[0409] A "prediction device" is a system that evaluates the probability of future admission success based on calculated admission points.

[0410] A "display device" is a device that visually presents information to the user, and has the function of showing the probability of admission and proposed strategies in a way that is easy for the user to understand.

[0411] A "generator" is a system that automatically creates the necessary application documents based on specific conditions.

[0412] A "guidance system" is a system that provides guidance on selecting the most suitable childcare facility and offers advice tailored to the family's circumstances.

[0413] The "update device" is equipped with a function to keep data on regional and childcare facility admission competition up to date in real time.

[0414] An "emotion analysis device" is a system that detects a user's emotional state and provides appropriate feedback to reduce stress.

[0415] This invention provides a system that allows families to streamline the application process for childcare facilities. A server receives data transmitted from a family's basic information acquisition device and uses this data to calculate a childcare facility admission score using an inference device. The inference is performed using a calculation engine based on given criteria and regional competition data. Next, a prediction device evaluates the probability of admission based on the calculated score and presents the result to the user via a display device. This display is presented in a way that is easy for the user to understand through visualized data.

[0416] Furthermore, the emotion analysis device uses voice analysis and facial expression analysis technologies (e.g., Microsoft Azure's Face API) to analyze the user's emotional state during input. If the system determines that the user is stressed, it generates relaxing content and feedback, and the generator automatically creates specific application documents.

[0417] For example, when a user is entering information via a terminal in the morning, the display device shows the message, "There are three optimal daycare options for your child. Would you like to see more details?" The generated AI prompt might use text such as, "Please tell me how to design an assistant that provides optimal feedback and specific application support to reduce stress for users who are anxious about the daycare enrollment process."

[0418] This system allows users to reduce anxiety about choosing a childcare facility, obtain appropriate information, and complete a stress-free and smooth application process.

[0419] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0420] Step 1:

[0421] Users input basic household information (e.g., household composition, employment status, place of residence, etc.) via a terminal. The entered information is acquired as digital data and sent to the server. This allows the system to understand the user's basic circumstances.

[0422] Step 2:

[0423] The server receives the transmitted basic information and uses an inference device to calculate the admission score for childcare facilities. The inputs used are the user's basic information and regional competition rate data. A specified algorithm is used for data calculation, and the final calculated score is output.

[0424] Step 3:

[0425] Based on the calculated score, the server uses a prediction device to calculate the probability of admission. It takes the calculated score as input and performs calculations using probability logic. The calculated admission probability is obtained as output.

[0426] Step 4:

[0427] The calculated admission probability and related information are transmitted from the server to the terminal and displayed on the user's terminal. The display device presents the visualized data to the user, allowing them to understand their current admission probability.

[0428] Step 5:

[0429] The emotion analysis device analyzes the user's voice and facial expression data as input. The user's real-time emotional expressions are used as input data and processed by an emotion analysis algorithm. The analysis results in the user's stress level being output.

[0430] Step 6:

[0431] If a user's stress level is high, the server prepares to provide relaxing feedback. For example, it might use a generative AI model to select specific relaxation content and present it to the user via a display device.

[0432] Step 7:

[0433] The necessary application documents are automatically generated and provided to the user by the generation device. The user's specific conditions are considered as input, the necessary documents are specified by an automated generation algorithm, and the application documents are generated as output. This allows the user to proceed with the application process quickly.

[0434] 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.

[0435] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0436] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] 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.

[0440] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0441] 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.

[0442] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0443] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0444] 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.

[0445] 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.

[0446] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0447] The 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.

[0448] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0449] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0450] This invention provides a system to simplify the process of enrolling children in childcare facilities and reduce the mental burden on parents. This system integrates multiple technologies, which are described in detail below.

[0451] Users input basic household information into the system via a terminal. This information includes elements necessary for calculating admission points to childcare facilities, such as household composition, employment status, and residential location. The terminal formats the input data and sends it to the server.

[0452] The server uses an AI algorithm to calculate admission scores based on the received data. The calculated scores are based on admission criteria for childcare facilities in each region, ensuring an accurate and fair evaluation. Next, based on these scores and competition rate data, the server predicts the probability of admission to each childcare facility and generates data to present to the user.

[0453] The terminal receives result data sent from the server and displays the results visually in a user-friendly format. This includes a graph showing the probability of admission and a comparison list of different childcare facilities. Required application documents are also automatically listed and provided to the user for online download.

[0454] Furthermore, the server has an advisory function to suggest the optimal childcare facility selection strategy. It generates and proposes a customized strategy based on each user's family and employment situation. This allows users to choose a facility that suits their individual circumstances and develop an effective enrollment strategy.

[0455] The real-time information update function ensures that the latest competition rate data and local information are constantly collected, and the entire system is updated accordingly. This function guarantees that users can quickly respond to the rapidly changing admission situation.

[0456] For example, if a user wishes to enroll their child in a childcare facility in a certain city, they can input detailed information about a specific area within that city and compare multiple facilities based on the presented probability of enrollment. The server then suggests the childcare facility with the highest probability of enrollment, supports the optimal enrollment plan, and facilitates a quick and effective enrollment process.

[0457] By using this system, users can efficiently proceed with selecting and applying for childcare facilities, and it is expected to contribute to resolving the problem of children on waiting lists for childcare.

[0458] The following describes the processing flow.

[0459] Step 1:

[0460] Users access a dedicated input form on their device and enter basic information such as their family information, employment status, and residential address. Once they have finished entering the information, they press the submit button to send the data to the server.

[0461] Step 2:

[0462] The terminal formats the data entered by the user and converts it into an appropriate format for transmission to the server. After verifying that the format is correct, it sends the data to the server.

[0463] Step 3:

[0464] The server receives data from the terminal. It checks the data integrity of the received data and verifies that there are no errors. If there are errors, it returns them to the terminal and prompts the user to re-enter the data.

[0465] Step 4:

[0466] The server uses an AI algorithm to calculate the admission score for childcare facilities based on the received user data. This calculation takes into account the user's family information and the standards of local childcare facilities.

[0467] Step 5:

[0468] The server predicts the probability of admission to each childcare facility based on calculated admission points and historical regional data. Statistical information such as admission rates and competition rates are used in the prediction process.

[0469] Step 6:

[0470] The server organizes the calculated data of admission points and admission probability, and converts it into a format that users can view. Visual data representations (graphs, lists, etc.) are also generated during this process.

[0471] Step 7:

[0472] The terminal receives data sent from the server and visually displays the results on the user screen. It also generates a list of documents required for the application and provides a link for the user to download them.

[0473] Step 8:

[0474] The server generates AI-based advice to help users select the most suitable childcare facility. The suggested strategies are customized to each family's individual circumstances.

[0475] Step 9:

[0476] The terminal receives advice sent from the server and presents it to the user. Based on the suggestions, the user can proceed with selecting a childcare facility and completing the application process.

[0477] Step 10:

[0478] The server constantly monitors the latest information on local areas and childcare facilities, updating the database as needed. It notifies users of any changes and provides revised suggestions based on the new information.

[0479] (Example 1)

[0480] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0481] In modern society, the process of enrolling children in childcare facilities is complex and places a significant burden on parents. In particular, providing appropriate information and developing strategies tailored to each family's situation and local area is difficult, resulting in a time-consuming and laborious process. Furthermore, the difficulty in updating information in real time makes it challenging to respond quickly to the rapidly changing enrollment situation.

[0482] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0483] In this invention, the server includes means for inputting household attribute information, means for formatting the input attribute information and transmitting the data, and means for a machine learning algorithm for calculating admission points to childcare facilities based on the formatted data. This enables efficient and accurate information provision in complex admission procedures, helping parents quickly select the most suitable childcare facility. Furthermore, by providing strategic advice that takes into account regional competition rates and individual family circumstances, it reduces the burden on parents and contributes to solving the problem of children on waiting lists for childcare.

[0484] "Family attribute information" refers to basic information necessary for enrolling a child in a childcare facility, such as family structure, occupation, and place of residence.

[0485] "Formatting" refers to the process of processing user-entered data into a consistent format and preparing the data according to the required format.

[0486] A "machine learning algorithm" is a computational method that analyzes large amounts of data and learns patterns to perform specific tasks or make predictions.

[0487] "Admission points" are numerical values ​​used to evaluate the criteria for admission applications to childcare facilities, and are calculated based on the family's circumstances and the local admission standards.

[0488] "Admission probability" is a numerical representation of the likelihood of being accepted into a particular childcare facility, and it depends on points and competition rate information.

[0489] "Graph and list format" refers to methods for visually representing data, either by illustrating it as a graph or by listing it as a list.

[0490] "Procedural documents" are official documents required for admission to a childcare facility and are documents that must be submitted at the time of application.

[0491] An "advice generation mechanism" is a mechanism designed to present the optimal action or choice based on the user's input information.

[0492] "Means of collecting and reflecting data in real time" refers to the process of continuously acquiring the latest data and immediately applying it to the system.

[0493] This invention relates to an information processing system for efficiently and effectively facilitating the selection and enrollment procedures for childcare facilities. The embodiments thereof will be described in detail below.

[0494] The user first enters basic household information into the system via a terminal. This information includes household composition, occupation, and place of residence. The terminal formats the entered data and sends it to the server using a secure communication protocol.

[0495] The server uses AI algorithms (including generative AI models) to calculate admission points for childcare facilities based on the received data. The AI ​​algorithms analyze diverse data and calculate points tailored to each family's situation. The calculations take into account regional admission criteria and competition rates, ensuring accurate and fair evaluation.

[0496] Next, the server uses the calculated scores and real-time updated competition rate data to predict the probability of admission to each childcare facility. This generates the information the user needs to make a comparative decision. This result is sent from the server to the terminal and presented to the user visually.

[0497] The terminal displays data in formats such as graphs and lists, making it easy for users to understand. Furthermore, necessary application documents are automatically listed, allowing users to easily access and download them online.

[0498] Furthermore, the server generates customized advice tailored to the user's family and work situation, presenting an optimal childcare facility selection plan. This makes it easier for users to formulate concrete strategies and efficiently proceed with the enrollment process.

[0499] The system is designed to collect up-to-date regional and childcare facility data in real time, ensuring that the entire system always operates based on the latest information. This implementation allows users to respond quickly and flexibly to dynamically changing enrollment situations.

[0500] A concrete example is a user who wishes to enroll their child in a childcare facility in a specific area of ​​Tokyo. The user enters their home information into the terminal, and the system calculates and presents the probability of enrollment based on that information. An example of a prompt message might be, "Calculate the probability of enrollment in a childcare facility within Tokyo's 23 wards and suggest the best option." In this way, the user can make an information-based choice.

[0501] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0502] Step 1:

[0503] The user enters basic household information via a terminal. This information includes household composition, occupation, and place of residence. This information serves as the basis for calculating admission points to childcare facilities. The terminal formats this information into the appropriate format and prepares it for transmission to the server.

[0504] Step 2:

[0505] The terminal sends the formatted data to the server. Data security is ensured through the communication protocol. The input is formatted personal information, and the output is the secure transmission of data to the server.

[0506] Step 3:

[0507] The server uses an AI algorithm to calculate admission points based on the received data. This process employs a generative AI model, which calculates points considering regional admission criteria and family information. The input is formatted family information, and the output is the calculated admission points.

[0508] Step 4:

[0509] The server uses the calculated admission score and real-time updated competition rate data to predict the probability of admission. In this step, a statistical model is used to evaluate the likelihood of admission to multiple childcare facilities. The inputs are admission score and competition rate data, and the output is the probability of admission to each childcare facility.

[0510] Step 5:

[0511] The terminal receives park entry probability data transmitted from the server and presents it to the user visually. Specific display formats include graphs and comparison lists. The input is park entry probability data from the server, and the output is visualized information.

[0512] Step 6:

[0513] The server generates an optimal childcare facility selection plan based on the user's family and occupational circumstances. The generated advice is presented to the user and becomes available as individualized options. The input is family and occupational information, and the output is customized advice.

[0514] Step 7:

[0515] The server collects the latest regional and childcare facility data and updates the system in real time. This functionality makes it possible to always assess enrollment status based on the most up-to-date information. The input is a real-world data feed, and the output is the updated system status.

[0516] (Application Example 1)

[0517] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0518] There is a need to reduce the complexity and time burden on parents in efficiently navigating the childcare enrollment process. Furthermore, there is a lack of support to effectively determine which childcare facility is most suitable based on individual family circumstances. Additionally, there is a need for methods that allow families to easily access information and proceed with the application process within the home.

[0519] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0520] In this invention, the server includes means for inputting basic household information, mathematical means for calculating admission points for childcare facilities based on the input information, and means for estimating the probability of admission based on the calculated points. This allows parents to easily understand childcare facility options through voice-based dialogue within the home and receive customized support to make the best choice.

[0521] The "function for entering basic family information" is a system that allows users to receive information necessary for the enrollment process, such as family composition and housing information, through a terminal.

[0522] "Mathematical methods for calculating admission points to childcare institutions" refers to mathematical methods that use data entered by families to derive points for admission to each childcare institution.

[0523] "Methods for estimating the probability of admission" refers to methods for predicting the probability of successful admission to a specific childcare institution based on the calculated score.

[0524] A "visual presentation medium generation means" is a digital interface for displaying calculation results and recommendations in an intuitively understandable format.

[0525] "Methods for automatically generating application records" refers to procedures for automatically creating the necessary application documents based on the user's input information.

[0526] "Means of providing advice to propose the optimal childcare institution selection policy" refers to an advisory function that presents the best options from multiple childcare institutions to suit the needs of each family.

[0527] "Means for instantly updating local information and admission competition rates for childcare facilities" refers to a system that acquires the latest local conditions and competition rates in real time, keeping the system constantly up-to-date.

[0528] A "speech-recognizing, in-home interactive device" is an interactive device that interprets the user's voice and provides appropriate information and advice in response to their instructions.

[0529] This invention is a system that supports the effective matching of parents and childcare facilities within the home. Users transmit basic information, such as family structure and housing information, to a server via voice or touch operation through an interface. The hardware used could be a home robot or digital device equipped with a microphone for voice recognition. Furthermore, software such as speech recognition is used to convert the voice data into text.

[0530] The server uses a generative AI model to calculate admission points to childcare facilities based on information provided by the user. This calculation is customized based on regional admission competition rates and the user's family circumstances, and is processed using mathematical methods. Next, the server predicts the probability of admission based on the calculated points and presents it to the user in an easily understandable format using a visual media generation system.

[0531] The terminal displays results in visually easy-to-understand graphs and lists, and automatically generates and provides downloadable application records. This allows users to easily obtain information on suitable childcare facilities from the comfort of their homes and quickly proceed with the necessary application procedures.

[0532] For example, if a user asks the robot, "Tell me the most convenient daycare center in Tokyo," the robot will present a list of childcare facilities that best fit that condition. In this case, an example of a prompt to input into the generating AI model would be, "Based on the user's request, 'Tell me the most convenient daycare center in Tokyo,' please recommend the most suitable childcare facility."

[0533] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0534] Step 1:

[0535] The user enters basic household information. Using a voice interface or touch controls, they send household composition, housing information, and other details to a voice recognition device or terminal. This input data is then ready to be sent to the server.

[0536] Step 2:

[0537] The server uses speech recognition software to convert received audio data into text data. Using libraries such as the SpeechRecognition library, it accurately identifies speech and processes it as digital text. The output from this process becomes basic family information text.

[0538] Step 3:

[0539] The server uses a generative AI model to calculate admission points for childcare facilities based on the input family information text. It extracts necessary attributes from the input data and applies a point calculation algorithm. This generates an output of admission points based on the circumstances of each family.

[0540] Step 4:

[0541] The server uses the calculated scores and regional competition data to predict the probability of admission to a childcare facility. A data analysis algorithm is used to derive the predicted value. Based on this, an output of admission probabilities is obtained, and the server evaluates which childcare facility has the highest probability.

[0542] Step 5:

[0543] The server visually formats the results and sends them to the terminal. Using visual media generation means, probability data and choices are output as graphs or lists in a format that is easy for the user to understand. Information is included to facilitate the user's selection.

[0544] Step 6:

[0545] Users receive visualized information from their devices and select the most suitable childcare facility. The displayed information also includes links to the necessary application procedures, guiding users as they proceed with the online application. This enables efficient selection of childcare facilities.

[0546] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0547] This invention provides a system that takes user emotions into account by combining an emotion engine with a system that supports the process of enrolling in childcare facilities. This system aims to reduce the anxiety and stress that users experience when applying for enrollment in childcare facilities.

[0548] Users access the system via a terminal and input basic information such as household composition, employment status, and residential area. This information is sent to the server via the terminal, which receives it, performs data checks, and then stores it.

[0549] The server uses an AI algorithm to calculate admission points for childcare facilities based on the received basic information. The point calculation takes into account factors such as regional competition and public standards. Based on these points, the server also predicts the probability of admission to each childcare facility and generates data to provide to the user.

[0550] This system incorporates an emotion engine that analyzes the emotions (text-based comments and feedback) expressed by the user during input. The emotion engine evaluates the user's emotional state, and if it determines that the user is experiencing high stress, it provides optimized feedback aimed at input assistance and stress reduction.

[0551] The terminal displays admission points received from the server, admission probability, and feedback generated by the emotion engine. This allows users to consider their own emotional state and develop the optimal childcare facility selection and application strategy.

[0552] Furthermore, the server customizes the childcare facility selection strategy based on the user's emotions and generates personalized advice. This advice is provided based on the family's employment status, income, and specific emotional state, helping users proceed with the application process as smoothly as possible.

[0553] For example, if a user uses the system while experiencing strong emotional anxiety, the emotion engine will detect this, and the server will provide feedback to alleviate this anxiety by suggesting several suitable childcare facilities and offering a detailed guide explaining the application process.

[0554] In this way, this system recognizes the user's emotional state and responds accordingly, providing a user-centered experience and enabling parents to confidently proceed with enrolling their children in childcare facilities.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] Users access the terminal's interface and enter basic household information, employment status, children's ages and names, and residential area. The completed data is then sent from the terminal to the server.

[0558] Step 2:

[0559] The terminal formats the input data and converts it into a format suitable for transmission to the server. During this process, it performs pre-processing such as verifying the input format and required information.

[0560] Step 3:

[0561] The server processes the data received from the terminal and registers it in the database. It checks the received information for defects and inconsistencies, and if there are any problems, it immediately returns an error message to the terminal.

[0562] Step 4:

[0563] The server uses an AI algorithm to calculate admission points for childcare facilities based on accurate data. This calculation takes into account regional characteristics and the admission criteria of the childcare facilities.

[0564] Step 5:

[0565] The server uses the calculated score and past data to estimate the probability of admission. The prediction also includes statistics on regional competition and the number of applications.

[0566] Step 6:

[0567] The terminal receives scores and admission probabilities sent from the server and displays them in a visualized format on the user screen. This allows users to quickly assess their own situation.

[0568] Step 7:

[0569] The emotion engine analyzes the user's emotions from their input. If it detects stress or anxiety from the user's text or selections, it sends the analysis results to the server.

[0570] Step 8:

[0571] The server receives the results from the emotion engine and generates optimal feedback and selection strategies that correspond to the user's emotional state. This feedback is designed to make the user feel secure.

[0572] Step 9:

[0573] The device receives feedback and advice from the server and presents it to the user in an easy-to-understand manner. This allows the user to receive support tailored to their emotional state.

[0574] Step 10:

[0575] The server further updates real-time data on the competition rate of local childcare facilities, obtaining the latest information. Based on the information obtained, it makes further suggestions to the user as needed.

[0576] (Example 2)

[0577] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0578] A system is needed to alleviate the anxiety and stress applicants experience during the childcare facility enrollment process while supporting them in selecting the appropriate facility. Traditional systems only provide enrollment points and probabilities, lacking support that considers the applicant's psychological state. This creates a challenge in that users struggle to make effective decisions after receiving information.

[0579] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0580] In this invention, the server includes means for inputting general household information, an algorithm for calculating admission evaluation scores for educational facilities, and means for analyzing the user's emotional state using an emotion engine and generating stress-reducing feedback. This allows users to select the most suitable educational facility while considering their own psychological state and proceed with the application process with peace of mind.

[0581] "A means of inputting general household information" refers to a method or device for users to input basic information such as household composition, employment status, and residential area.

[0582] An "algorithmic method" refers to a procedure or process that uses a specific calculation method based on input information to calculate an admission evaluation score for an educational facility.

[0583] The "admission evaluation score" is a numerical representation of the likelihood of admission to a childcare facility, calculated based on the submitted information.

[0584] "Possibility of admission" refers to the probability that an applicant's application will be accepted at a particular childcare facility.

[0585] "Advisory tools" refer to methods or devices for proposing optimal educational facility selection strategies to users.

[0586] An "emotion engine" is a technology that analyzes user input and feedback to evaluate their emotional state and generate feedback that reduces stress.

[0587] "Means of generating feedback" refers to methods or devices that provide users with information to give them a sense of security or reduce anxiety, based on the results analyzed by the emotion engine.

[0588] The system according to this invention aims to reduce the anxiety and stress that users experience during the application process for admission to childcare facilities, while supporting them in selecting an appropriate facility. The system mainly consists of a server, terminals, and users.

[0589] The user first uses a terminal to input general information such as household composition, employment status, and residential area. The terminal provides an interface for formatting this information appropriately and sending it to the server. In this process, a regular computer or smartphone is used as the terminal, and access is made through a browser or a dedicated application.

[0590] The server executes an AI algorithm to calculate admission evaluation scores for educational facilities based on the received information. This algorithm takes into account multiple factors, including regional competition rates and public standards, to quantitatively evaluate the user's situation. The server is equipped with a dedicated software environment and database to efficiently perform complex calculations.

[0591] Furthermore, the system incorporates an emotion engine that can analyze user emotions from input data. The emotion engine extracts the emotions the user expresses, and if stress is detected, the server generates customized feedback. This allows the server to provide suggestions and guidance for stress relief, in addition to presenting the probability of admission.

[0592] On the device, admission evaluation scores and probabilities sent from the server, along with sentiment-based feedback, are displayed, allowing users to make the best choice. This display is presented in an easy-to-understand manner through the user interface.

[0593] To give a specific example, if a user inputs their emotion as "anxiety," the emotion engine will analyze this as stress, and the server will provide information to alleviate their anxiety, such as listing several suitable childcare facilities and providing a guide that explains the application process in detail.

[0594] An example of a prompt for the generating AI model is, "Generate feedback to alleviate anxiety when applying for admission to a childcare facility. The user's current emotion is anxiety." In this way, the system achieves user-centered service delivery.

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1:

[0597] Users input general information such as household composition, employment status, and residential area via a terminal. The entered data is converted into a digital format by the terminal, and the consistency of the input data and the required fields are verified. Once verified, the information is ready to be sent to the server.

[0598] Step 2:

[0599] The terminal sends the entered information to the server. The server analyzes the received data and checks its integrity. If there are inconsistencies or errors in the received data, the server sends an error message to the terminal, prompting the user to re-enter the information. Only accurate data is saved in the database so that it can proceed to the next process.

[0600] Step 3:

[0601] The server uses stored basic information to execute an AI algorithm and calculate an admission evaluation score for educational facilities. Specifically, it scores the applicant based on factors such as regional competition rates and family circumstances extracted from the input data. This evaluation score will later be used as an indicator to predict the likelihood of admission. The evaluation score is generated by a calculation engine within the server and recorded in a database.

[0602] Step 4:

[0603] The server uses the calculated admission evaluation score to predict the likelihood of admission to each educational facility. This prediction is performed using a statistical model, generating numerical probabilities. The generated probability data is stored on the server and used in subsequent processes.

[0604] Step 5:

[0605] The terminal displays admission evaluation scores and admission probabilities received from the server to the user. The terminal helps the user make choices based on this information. Specifically, the screen displays a list of admission probabilities for each facility, allowing the user to easily compare them.

[0606] Step 6:

[0607] The server analyzes the text data entered by the user using an emotion engine and evaluates their emotional state. Specifically, it quantifies emotions based on keywords and the tone of the sentences in the text, and if it determines that stress is high, it generates feedback to alleviate the stress. This feedback is tailored to the user's emotions, such as messages that provide reassurance.

[0608] Step 7:

[0609] The device displays sentiment analysis results and feedback sent from the server to the user. This includes specific advice and options regarding the enrollment process. Based on this information, the user can confidently proceed with the next application process.

[0610] (Application Example 2)

[0611] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0612] The process of enrolling families in childcare facilities presents challenges such as the complexity of information, the lack of transparency in application procedures, and the resulting anxiety and stress. In particular, families often experience stress because they lack sufficient information to select and apply for childcare facilities. Furthermore, the lack of information on regional competition rates and the inability to provide success rates and strategies tailored to individual family circumstances makes rational and efficient choices difficult.

[0613] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0614] In this invention, the server includes a device for acquiring basic household information, an inference device for calculating admission points to childcare facilities based on the acquired basic information, and a prediction device for predicting the probability of admission based on the calculated points. This allows families to achieve transparency and rationalization in the admission process, enabling them to make optimal choices and applications while reducing stress.

[0615] An "information acquisition device" is a device that collects basic information from households and has the function of inputting data through a user interface.

[0616] An "inference device" is a means of performing specific analyses based on acquired information and calculating admission points for childcare facilities as a result.

[0617] A "prediction device" is a system that evaluates the probability of future admission success based on calculated admission points.

[0618] A "display device" is a device that visually presents information to the user, and has the function of showing the probability of admission and proposed strategies in a way that is easy for the user to understand.

[0619] A "generator" is a system that automatically creates the necessary application documents based on specific conditions.

[0620] A "guidance system" is a system that provides guidance on selecting the most suitable childcare facility and offers advice tailored to the family's circumstances.

[0621] The "update device" is equipped with a function to keep data on regional and childcare facility admission competition up to date in real time.

[0622] An "emotion analysis device" is a system that detects a user's emotional state and provides appropriate feedback to reduce stress.

[0623] This invention provides a system that allows families to streamline the application process for childcare facilities. A server receives data transmitted from a family's basic information acquisition device and uses this data to calculate a childcare facility admission score using an inference device. The inference is performed using a calculation engine based on given criteria and regional competition data. Next, a prediction device evaluates the probability of admission based on the calculated score and presents the result to the user via a display device. This display is presented in a way that is easy for the user to understand through visualized data.

[0624] Furthermore, the emotion analysis device uses voice analysis and facial expression analysis technologies (e.g., Microsoft Azure's Face API) to analyze the user's emotional state during input. If the system determines that the user is stressed, it generates relaxing content and feedback, and the generator automatically creates specific application documents.

[0625] For example, when a user is entering information via a terminal in the morning, the display device shows the message, "There are three optimal daycare options for your child. Would you like to see more details?" The generated AI prompt might use text such as, "Please tell me how to design an assistant that provides optimal feedback and specific application support to reduce stress for users who are anxious about the daycare enrollment process."

[0626] This system allows users to reduce anxiety about choosing a childcare facility, obtain appropriate information, and complete a stress-free and smooth application process.

[0627] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0628] Step 1:

[0629] Users input basic household information (e.g., household composition, employment status, place of residence, etc.) via a terminal. The entered information is acquired as digital data and sent to the server. This allows the system to understand the user's basic circumstances.

[0630] Step 2:

[0631] The server receives the transmitted basic information and uses an inference device to calculate the admission score for childcare facilities. The inputs used are the user's basic information and regional competition rate data. A specified algorithm is used for data calculation, and the final calculated score is output.

[0632] Step 3:

[0633] Based on the calculated score, the server uses a prediction device to calculate the probability of admission. It takes the calculated score as input and performs calculations using probability logic. The calculated admission probability is obtained as output.

[0634] Step 4:

[0635] The calculated admission probability and related information are transmitted from the server to the terminal and displayed on the user's terminal. The display device presents the visualized data to the user, allowing them to understand their current admission probability.

[0636] Step 5:

[0637] The emotion analysis device analyzes the user's voice and facial expression data as input. The user's real-time emotional expressions are used as input data and processed by an emotion analysis algorithm. The analysis results in the user's stress level being output.

[0638] Step 6:

[0639] If a user's stress level is high, the server prepares to provide relaxing feedback. For example, it might use a generative AI model to select specific relaxation content and present it to the user via a display device.

[0640] Step 7:

[0641] The necessary application documents are automatically generated and provided to the user by the generation device. The user's specific conditions are considered as input, the necessary documents are specified by an automated generation algorithm, and the application documents are generated as output. This allows the user to proceed with the application process quickly.

[0642] 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.

[0643] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0644] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0645] [Fourth Embodiment]

[0646] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0647] 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.

[0648] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0649] 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.

[0650] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0651] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0652] 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.

[0653] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0654] 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.

[0655] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0656] The 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.

[0657] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0658] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] This invention provides a system to simplify the process of enrolling children in childcare facilities and reduce the mental burden on parents. This system integrates multiple technologies, which are described in detail below.

[0660] Users input basic household information into the system via a terminal. This information includes elements necessary for calculating admission points to childcare facilities, such as household composition, employment status, and residential location. The terminal formats the input data and sends it to the server.

[0661] The server uses an AI algorithm to calculate admission scores based on the received data. The calculated scores are based on admission criteria for childcare facilities in each region, ensuring an accurate and fair evaluation. Next, based on these scores and competition rate data, the server predicts the probability of admission to each childcare facility and generates data to present to the user.

[0662] The terminal receives result data sent from the server and displays the results visually in a user-friendly format. This includes a graph showing the probability of admission and a comparison list of different childcare facilities. Required application documents are also automatically listed and provided to the user for online download.

[0663] Furthermore, the server has an advisory function to suggest the optimal childcare facility selection strategy. It generates and proposes a customized strategy based on each user's family and employment situation. This allows users to choose a facility that suits their individual circumstances and develop an effective enrollment strategy.

[0664] The real-time information update function ensures that the latest competition rate data and local information are constantly collected, and the entire system is updated accordingly. This function guarantees that users can quickly respond to the rapidly changing admission situation.

[0665] For example, if a user wishes to enroll their child in a childcare facility in a certain city, they can input detailed information about a specific area within that city and compare multiple facilities based on the presented probability of enrollment. The server then suggests the childcare facility with the highest probability of enrollment, supports the optimal enrollment plan, and facilitates a quick and effective enrollment process.

[0666] By using this system, users can efficiently proceed with selecting and applying for childcare facilities, and it is expected to contribute to resolving the problem of children on waiting lists for childcare.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] Users access a dedicated input form on their device and enter basic information such as their family information, employment status, and residential address. Once they have finished entering the information, they press the submit button to send the data to the server.

[0670] Step 2:

[0671] The terminal formats the data entered by the user and converts it into an appropriate format for transmission to the server. After verifying that the format is correct, it sends the data to the server.

[0672] Step 3:

[0673] The server receives data from the terminal. It checks the data integrity of the received data and verifies that there are no errors. If there are errors, it returns them to the terminal and prompts the user to re-enter the data.

[0674] Step 4:

[0675] The server uses an AI algorithm to calculate the admission score for childcare facilities based on the received user data. This calculation takes into account the user's family information and the standards of local childcare facilities.

[0676] Step 5:

[0677] The server predicts the probability of admission to each childcare facility based on calculated admission points and historical regional data. Statistical information such as admission rates and competition rates are used in the prediction process.

[0678] Step 6:

[0679] The server organizes the calculated data of admission points and admission probability, and converts it into a format that users can view. Visual data representations (graphs, lists, etc.) are also generated during this process.

[0680] Step 7:

[0681] The terminal receives data sent from the server and visually displays the results on the user screen. It also generates a list of documents required for the application and provides a link for the user to download them.

[0682] Step 8:

[0683] The server generates AI-based advice to help users select the most suitable childcare facility. The suggested strategies are customized to each family's individual circumstances.

[0684] Step 9:

[0685] The terminal receives advice sent from the server and presents it to the user. Based on the suggestions, the user can proceed with selecting a childcare facility and completing the application process.

[0686] Step 10:

[0687] The server constantly monitors the latest information on local areas and childcare facilities, updating the database as needed. It notifies users of any changes and provides revised suggestions based on the new information.

[0688] (Example 1)

[0689] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0690] In modern society, the process of enrolling children in childcare facilities is complex and places a significant burden on parents. In particular, providing appropriate information and developing strategies tailored to each family's situation and local area is difficult, resulting in a time-consuming and laborious process. Furthermore, the difficulty in updating information in real time makes it challenging to respond quickly to the rapidly changing enrollment situation.

[0691] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0692] In this invention, the server includes means for inputting household attribute information, means for formatting the input attribute information and transmitting the data, and means for a machine learning algorithm for calculating admission points to childcare facilities based on the formatted data. This enables efficient and accurate information provision in complex admission procedures, helping parents quickly select the most suitable childcare facility. Furthermore, by providing strategic advice that takes into account regional competition rates and individual family circumstances, it reduces the burden on parents and contributes to solving the problem of children on waiting lists for childcare.

[0693] "Family attribute information" refers to basic information necessary for enrolling a child in a childcare facility, such as family structure, occupation, and place of residence.

[0694] "Formatting" refers to the process of processing user-entered data into a consistent format and preparing the data according to the required format.

[0695] A "machine learning algorithm" is a computational method that analyzes large amounts of data and learns patterns to perform specific tasks or make predictions.

[0696] "Admission points" are numerical values ​​used to evaluate the criteria for admission applications to childcare facilities, and are calculated based on the family's circumstances and the local admission standards.

[0697] "Admission probability" is a numerical representation of the likelihood of being accepted into a particular childcare facility, and it depends on points and competition rate information.

[0698] "Graph and list format" refers to methods for visually representing data, either by illustrating it as a graph or by listing it as a list.

[0699] "Procedural documents" are official documents required for admission to a childcare facility and are documents that must be submitted at the time of application.

[0700] An "advice generation mechanism" is a mechanism designed to present the optimal action or choice based on the user's input information.

[0701] "Means of collecting and reflecting data in real time" refers to the process of continuously acquiring the latest data and immediately applying it to the system.

[0702] This invention relates to an information processing system for efficiently and effectively facilitating the selection and enrollment procedures for childcare facilities. The embodiments thereof will be described in detail below.

[0703] The user first enters basic household information into the system via a terminal. This information includes household composition, occupation, and place of residence. The terminal formats the entered data and sends it to the server using a secure communication protocol.

[0704] The server uses AI algorithms (including generative AI models) to calculate admission points for childcare facilities based on the received data. The AI ​​algorithms analyze diverse data and calculate points tailored to each family's situation. The calculations take into account regional admission criteria and competition rates, ensuring accurate and fair evaluation.

[0705] Next, the server uses the calculated scores and real-time updated competition rate data to predict the probability of admission to each childcare facility. This generates the information the user needs to make a comparative decision. This result is sent from the server to the terminal and presented to the user visually.

[0706] The terminal displays data in formats such as graphs and lists, making it easy for users to understand. Furthermore, necessary application documents are automatically listed, allowing users to easily access and download them online.

[0707] Furthermore, the server generates customized advice tailored to the user's family and work situation, presenting an optimal childcare facility selection plan. This makes it easier for users to formulate concrete strategies and efficiently proceed with the enrollment process.

[0708] The system is designed to collect up-to-date regional and childcare facility data in real time, ensuring that the entire system always operates based on the latest information. This implementation allows users to respond quickly and flexibly to dynamically changing enrollment situations.

[0709] A concrete example is a user who wishes to enroll their child in a childcare facility in a specific area of ​​Tokyo. The user enters their home information into the terminal, and the system calculates and presents the probability of enrollment based on that information. An example of a prompt message might be, "Calculate the probability of enrollment in a childcare facility within Tokyo's 23 wards and suggest the best option." In this way, the user can make an information-based choice.

[0710] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0711] Step 1:

[0712] The user enters basic household information via a terminal. This information includes household composition, occupation, and place of residence. This information serves as the basis for calculating admission points to childcare facilities. The terminal formats this information into the appropriate format and prepares it for transmission to the server.

[0713] Step 2:

[0714] The terminal sends the formatted data to the server. Data security is ensured through the communication protocol. The input is formatted personal information, and the output is the secure transmission of data to the server.

[0715] Step 3:

[0716] The server uses an AI algorithm to calculate admission points based on the received data. This process employs a generative AI model, which calculates points considering regional admission criteria and family information. The input is formatted family information, and the output is the calculated admission points.

[0717] Step 4:

[0718] The server uses the calculated admission score and real-time updated competition rate data to predict the probability of admission. In this step, a statistical model is used to evaluate the likelihood of admission to multiple childcare facilities. The inputs are admission score and competition rate data, and the output is the probability of admission to each childcare facility.

[0719] Step 5:

[0720] The terminal receives park entry probability data transmitted from the server and presents it to the user visually. Specific display formats include graphs and comparison lists. The input is park entry probability data from the server, and the output is visualized information.

[0721] Step 6:

[0722] The server generates an optimal childcare facility selection plan based on the user's family and occupational circumstances. The generated advice is presented to the user and becomes available as individualized options. The input is family and occupational information, and the output is customized advice.

[0723] Step 7:

[0724] The server collects the latest regional and childcare facility data and updates the system in real time. This functionality makes it possible to always assess enrollment status based on the most up-to-date information. The input is a real-world data feed, and the output is the updated system status.

[0725] (Application Example 1)

[0726] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0727] There is a need to reduce the complexity and time burden on parents in efficiently navigating the childcare enrollment process. Furthermore, there is a lack of support to effectively determine which childcare facility is most suitable based on individual family circumstances. Additionally, there is a need for methods that allow families to easily access information and proceed with the application process within the home.

[0728] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0729] In this invention, the server includes means for inputting basic household information, mathematical means for calculating admission points for childcare facilities based on the input information, and means for estimating the probability of admission based on the calculated points. This allows parents to easily understand childcare facility options through voice-based dialogue within the home and receive customized support to make the best choice.

[0730] The "function for entering basic family information" is a system that allows users to receive information necessary for the enrollment process, such as family composition and housing information, through a terminal.

[0731] "Mathematical methods for calculating admission points to childcare institutions" refers to mathematical methods that use data entered by families to derive points for admission to each childcare institution.

[0732] "Methods for estimating the probability of admission" refers to methods for predicting the probability of successful admission to a specific childcare institution based on the calculated score.

[0733] A "visual presentation medium generation means" is a digital interface for displaying calculation results and recommendations in an intuitively understandable format.

[0734] "Methods for automatically generating application records" refers to procedures for automatically creating the necessary application documents based on the user's input information.

[0735] "Means of providing advice to propose the optimal childcare institution selection policy" refers to an advisory function that presents the best options from multiple childcare institutions to suit the needs of each family.

[0736] "Means for instantly updating local information and admission competition rates for childcare facilities" refers to a system that acquires the latest local conditions and competition rates in real time, keeping the system constantly up-to-date.

[0737] A "speech-recognizing, in-home interactive device" is an interactive device that interprets the user's voice and provides appropriate information and advice in response to their instructions.

[0738] This invention is a system that supports the effective matching of parents and childcare facilities within the home. Users transmit basic information, such as family structure and housing information, to a server via voice or touch operation through an interface. The hardware used could be a home robot or digital device equipped with a microphone for voice recognition. Furthermore, software such as speech recognition is used to convert the voice data into text.

[0739] The server uses a generative AI model to calculate admission points to childcare facilities based on information provided by the user. This calculation is customized based on regional admission competition rates and the user's family circumstances, and is processed using mathematical methods. Next, the server predicts the probability of admission based on the calculated points and presents it to the user in an easily understandable format using a visual media generation system.

[0740] The terminal displays results in visually easy-to-understand graphs and lists, and automatically generates and provides downloadable application records. This allows users to easily obtain information on suitable childcare facilities from the comfort of their homes and quickly proceed with the necessary application procedures.

[0741] For example, if a user asks the robot, "Tell me the most convenient daycare center in Tokyo," the robot will present a list of childcare facilities that best fit that condition. In this case, an example of a prompt to input into the generating AI model would be, "Based on the user's request, 'Tell me the most convenient daycare center in Tokyo,' please recommend the most suitable childcare facility."

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] The user enters basic household information. Using a voice interface or touch controls, they send household composition, housing information, and other details to a voice recognition device or terminal. This input data is then ready to be sent to the server.

[0745] Step 2:

[0746] The server uses speech recognition software to convert received audio data into text data. Using libraries such as the SpeechRecognition library, it accurately identifies speech and processes it as digital text. The output from this process becomes basic family information text.

[0747] Step 3:

[0748] The server uses a generative AI model to calculate admission points for childcare facilities based on the input family information text. It extracts necessary attributes from the input data and applies a point calculation algorithm. This generates an output of admission points based on the circumstances of each family.

[0749] Step 4:

[0750] The server uses the calculated scores and regional competition data to predict the probability of admission to a childcare facility. A data analysis algorithm is used to derive the predicted value. Based on this, an output of admission probabilities is obtained, and the server evaluates which childcare facility has the highest probability.

[0751] Step 5:

[0752] The server visually formats the results and sends them to the terminal. Using visual media generation means, probability data and choices are output as graphs or lists in a format that is easy for the user to understand. Information is included to facilitate the user's selection.

[0753] Step 6:

[0754] Users receive visualized information from their devices and select the most suitable childcare facility. The displayed information also includes links to the necessary application procedures, guiding users as they proceed with the online application. This enables efficient selection of childcare facilities.

[0755] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0756] This invention provides a system that takes user emotions into account by combining an emotion engine with a system that supports the process of enrolling in childcare facilities. This system aims to reduce the anxiety and stress that users experience when applying for enrollment in childcare facilities.

[0757] Users access the system via a terminal and input basic information such as household composition, employment status, and residential area. This information is sent to the server via the terminal, which receives it, performs data checks, and then stores it.

[0758] The server uses an AI algorithm to calculate admission points for childcare facilities based on the received basic information. The point calculation takes into account factors such as regional competition and public standards. Based on these points, the server also predicts the probability of admission to each childcare facility and generates data to provide to the user.

[0759] This system incorporates an emotion engine that analyzes the emotions (text-based comments and feedback) expressed by the user during input. The emotion engine evaluates the user's emotional state, and if it determines that the user is experiencing high stress, it provides optimized feedback aimed at input assistance and stress reduction.

[0760] The terminal displays admission points received from the server, admission probability, and feedback generated by the emotion engine. This allows users to consider their own emotional state and develop the optimal childcare facility selection and application strategy.

[0761] Furthermore, the server customizes the childcare facility selection strategy based on the user's emotions and generates personalized advice. This advice is provided based on the family's employment status, income, and specific emotional state, helping users proceed with the application process as smoothly as possible.

[0762] For example, if a user uses the system while experiencing strong emotional anxiety, the emotion engine will detect this, and the server will provide feedback to alleviate this anxiety by suggesting several suitable childcare facilities and offering a detailed guide explaining the application process.

[0763] In this way, this system recognizes the user's emotional state and responds accordingly, providing a user-centered experience and enabling parents to confidently proceed with enrolling their children in childcare facilities.

[0764] The following describes the processing flow.

[0765] Step 1:

[0766] Users access the terminal's interface and enter basic household information, employment status, children's ages and names, and residential area. The completed data is then sent from the terminal to the server.

[0767] Step 2:

[0768] The terminal formats the input data and converts it into a format suitable for transmission to the server. During this process, it performs pre-processing such as verifying the input format and required information.

[0769] Step 3:

[0770] The server processes the data received from the terminal and registers it in the database. It checks the received information for defects and inconsistencies, and if there are any problems, it immediately returns an error message to the terminal.

[0771] Step 4:

[0772] The server uses an AI algorithm to calculate admission points for childcare facilities based on accurate data. This calculation takes into account regional characteristics and the admission criteria of the childcare facilities.

[0773] Step 5:

[0774] The server uses the calculated score and past data to estimate the probability of admission. The prediction also includes statistics on regional competition and the number of applications.

[0775] Step 6:

[0776] The terminal receives scores and admission probabilities sent from the server and displays them in a visualized format on the user screen. This allows users to quickly assess their own situation.

[0777] Step 7:

[0778] The emotion engine analyzes the user's emotions from their input. If it detects stress or anxiety from the user's text or selections, it sends the analysis results to the server.

[0779] Step 8:

[0780] The server receives the results from the emotion engine and generates optimal feedback and selection strategies that correspond to the user's emotional state. This feedback is designed to make the user feel secure.

[0781] Step 9:

[0782] The device receives feedback and advice from the server and presents it to the user in an easy-to-understand manner. This allows the user to receive support tailored to their emotional state.

[0783] Step 10:

[0784] The server further updates real-time data on the competition rate of local childcare facilities, obtaining the latest information. Based on the information obtained, it makes further suggestions to the user as needed.

[0785] (Example 2)

[0786] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0787] A system is needed to alleviate the anxiety and stress applicants experience during the childcare facility enrollment process while supporting them in selecting the appropriate facility. Traditional systems only provide enrollment points and probabilities, lacking support that considers the applicant's psychological state. This creates a challenge in that users struggle to make effective decisions after receiving information.

[0788] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0789] In this invention, the server includes means for inputting general household information, an algorithm for calculating admission evaluation scores for educational facilities, and means for analyzing the user's emotional state using an emotion engine and generating stress-reducing feedback. This allows users to select the most suitable educational facility while considering their own psychological state and proceed with the application process with peace of mind.

[0790] "A means of inputting general household information" refers to a method or device for users to input basic information such as household composition, employment status, and residential area.

[0791] An "algorithmic method" refers to a procedure or process that uses a specific calculation method based on input information to calculate an admission evaluation score for an educational facility.

[0792] The "admission evaluation score" is a numerical representation of the likelihood of admission to a childcare facility, calculated based on the submitted information.

[0793] "Possibility of admission" refers to the probability that an applicant's application will be accepted at a particular childcare facility.

[0794] "Advisory tools" refer to methods or devices for proposing optimal educational facility selection strategies to users.

[0795] An "emotion engine" is a technology that analyzes user input and feedback to evaluate their emotional state and generate feedback that reduces stress.

[0796] "Means of generating feedback" refers to methods or devices that provide users with information to give them a sense of security or reduce anxiety, based on the results analyzed by the emotion engine.

[0797] The system according to this invention aims to reduce the anxiety and stress that users experience during the application process for admission to childcare facilities, while supporting them in selecting an appropriate facility. The system mainly consists of a server, terminals, and users.

[0798] The user first uses a terminal to input general information such as household composition, employment status, and residential area. The terminal provides an interface for formatting this information appropriately and sending it to the server. In this process, a regular computer or smartphone is used as the terminal, and access is made through a browser or a dedicated application.

[0799] The server executes an AI algorithm to calculate admission evaluation scores for educational facilities based on the received information. This algorithm takes into account multiple factors, including regional competition rates and public standards, to quantitatively evaluate the user's situation. The server is equipped with a dedicated software environment and database to efficiently perform complex calculations.

[0800] Furthermore, the system incorporates an emotion engine that can analyze user emotions from input data. The emotion engine extracts the emotions the user expresses, and if stress is detected, the server generates customized feedback. This allows the server to provide suggestions and guidance for stress relief, in addition to presenting the probability of admission.

[0801] On the device, admission evaluation scores and probabilities sent from the server, along with sentiment-based feedback, are displayed, allowing users to make the best choice. This display is presented in an easy-to-understand manner through the user interface.

[0802] To give a specific example, if a user inputs their emotion as "anxiety," the emotion engine will analyze this as stress, and the server will provide information to alleviate their anxiety, such as listing several suitable childcare facilities and providing a guide that explains the application process in detail.

[0803] An example of a prompt for the generating AI model is, "Generate feedback to alleviate anxiety when applying for admission to a childcare facility. The user's current emotion is anxiety." In this way, the system achieves user-centered service delivery.

[0804] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0805] Step 1:

[0806] Users input general information such as household composition, employment status, and residential area via a terminal. The entered data is converted into a digital format by the terminal, and the consistency of the input data and the required fields are verified. Once verified, the information is ready to be sent to the server.

[0807] Step 2:

[0808] The terminal sends the entered information to the server. The server analyzes the received data and checks its integrity. If there are inconsistencies or errors in the received data, the server sends an error message to the terminal, prompting the user to re-enter the information. Only accurate data is saved in the database so that it can proceed to the next process.

[0809] Step 3:

[0810] The server uses stored basic information to execute an AI algorithm and calculate an admission evaluation score for educational facilities. Specifically, it scores the applicant based on factors such as regional competition rates and family circumstances extracted from the input data. This evaluation score will later be used as an indicator to predict the likelihood of admission. The evaluation score is generated by a calculation engine within the server and recorded in a database.

[0811] Step 4:

[0812] The server uses the calculated admission evaluation score to predict the likelihood of admission to each educational facility. This prediction is performed using a statistical model, generating numerical probabilities. The generated probability data is stored on the server and used in subsequent processes.

[0813] Step 5:

[0814] The terminal displays admission evaluation scores and admission probabilities received from the server to the user. The terminal helps the user make choices based on this information. Specifically, the screen displays a list of admission probabilities for each facility, allowing the user to easily compare them.

[0815] Step 6:

[0816] The server analyzes the text data entered by the user using an emotion engine and evaluates their emotional state. Specifically, it quantifies emotions based on keywords and the tone of the sentences in the text, and if it determines that stress is high, it generates feedback to alleviate the stress. This feedback is tailored to the user's emotions, such as messages that provide reassurance.

[0817] Step 7:

[0818] The device displays sentiment analysis results and feedback sent from the server to the user. This includes specific advice and options regarding the enrollment process. Based on this information, the user can confidently proceed with the next application process.

[0819] (Application Example 2)

[0820] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0821] The process of enrolling families in childcare facilities presents challenges such as the complexity of information, the lack of transparency in application procedures, and the resulting anxiety and stress. In particular, families often experience stress because they lack sufficient information to select and apply for childcare facilities. Furthermore, the lack of information on regional competition rates and the inability to provide success rates and strategies tailored to individual family circumstances makes rational and efficient choices difficult.

[0822] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0823] In this invention, the server includes a device for acquiring basic household information, an inference device for calculating admission points to childcare facilities based on the acquired basic information, and a prediction device for predicting the probability of admission based on the calculated points. This allows families to achieve transparency and rationalization in the admission process, enabling them to make optimal choices and applications while reducing stress.

[0824] An "information acquisition device" is a device that collects basic information from households and has the function of inputting data through a user interface.

[0825] An "inference device" is a means of performing specific analyses based on acquired information and calculating admission points for childcare facilities as a result.

[0826] A "prediction device" is a system that evaluates the probability of future admission success based on calculated admission points.

[0827] A "display device" is a device that visually presents information to the user, and has the function of showing the probability of admission and proposed strategies in a way that is easy for the user to understand.

[0828] A "generator" is a system that automatically creates the necessary application documents based on specific conditions.

[0829] A "guidance system" is a system that provides guidance on selecting the most suitable childcare facility and offers advice tailored to the family's circumstances.

[0830] The "update device" is equipped with a function to keep data on regional and childcare facility admission competition up to date in real time.

[0831] An "emotion analysis device" is a system that detects a user's emotional state and provides appropriate feedback to reduce stress.

[0832] This invention provides a system that allows families to streamline the application process for childcare facilities. A server receives data transmitted from a family's basic information acquisition device and uses this data to calculate a childcare facility admission score using an inference device. The inference is performed using a calculation engine based on given criteria and regional competition data. Next, a prediction device evaluates the probability of admission based on the calculated score and presents the result to the user via a display device. This display is presented in a way that is easy for the user to understand through visualized data.

[0833] Furthermore, the emotion analysis device uses voice analysis and facial expression analysis technologies (e.g., Microsoft Azure's Face API) to analyze the user's emotional state during input. If the system determines that the user is stressed, it generates relaxing content and feedback, and the generator automatically creates specific application documents.

[0834] For example, when a user is entering information via a terminal in the morning, the display device shows the message, "There are three optimal daycare options for your child. Would you like to see more details?" The generated AI prompt might use text such as, "Please tell me how to design an assistant that provides optimal feedback and specific application support to reduce stress for users who are anxious about the daycare enrollment process."

[0835] This system allows users to reduce anxiety about choosing a childcare facility, obtain appropriate information, and complete a stress-free and smooth application process.

[0836] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0837] Step 1:

[0838] Users input basic household information (e.g., household composition, employment status, place of residence, etc.) via a terminal. The entered information is acquired as digital data and sent to the server. This allows the system to understand the user's basic circumstances.

[0839] Step 2:

[0840] The server receives the transmitted basic information and uses an inference device to calculate the admission score for childcare facilities. The inputs used are the user's basic information and regional competition rate data. A specified algorithm is used for data calculation, and the final calculated score is output.

[0841] Step 3:

[0842] Based on the calculated score, the server uses a prediction device to calculate the probability of admission. It takes the calculated score as input and performs calculations using probability logic. The calculated admission probability is obtained as output.

[0843] Step 4:

[0844] The calculated admission probability and related information are transmitted from the server to the terminal and displayed on the user's terminal. The display device presents the visualized data to the user, allowing them to understand their current admission probability.

[0845] Step 5:

[0846] The emotion analysis device analyzes the user's voice and facial expression data as input. The user's real-time emotional expressions are used as input data and processed by an emotion analysis algorithm. The analysis results in the user's stress level being output.

[0847] Step 6:

[0848] If a user's stress level is high, the server prepares to provide relaxing feedback. For example, it might use a generative AI model to select specific relaxation content and present it to the user via a display device.

[0849] Step 7:

[0850] The necessary application documents are automatically generated and provided to the user by the generation device. The user's specific conditions are considered as input, the necessary documents are specified by an automated generation algorithm, and the application documents are generated as output. This allows the user to proceed with the application process quickly.

[0851] 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.

[0852] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0853] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0854] 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.

[0855] Figure 9 shows an 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.

[0856] 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.

[0857] 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.

[0858] 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, motorcycles, etc., 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, for example, based 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.

[0859] 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."

[0860] 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.

[0861] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0862] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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 the like 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.

[0871] 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 as being incorporated by reference.

[0872] The following is further disclosed regarding the embodiments described above.

[0873] (Claim 1)

[0874] A means of inputting basic household information,

[0875] An algorithm means for calculating admission points for childcare facilities based on the input basic information,

[0876] A means for predicting the probability of admission based on the calculated score,

[0877] A means for displaying the predicted probability of admission,

[0878] A means to automatically generate the necessary application documents,

[0879] An advisory tool to propose the optimal childcare facility selection strategy,

[0880] A means of updating regional and childcare facility admission competition rate data in real time,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, which takes into account the competition rate for each region when calculating the admission points for the aforementioned childcare facility.

[0884] (Claim 3)

[0885] The system according to claim 1, which provides customized advice based on the family's employment status and income when proposing the aforementioned optimal childcare facility selection strategy.

[0886] "Example 1"

[0887] (Claim 1)

[0888] A means of inputting household attribute information,

[0889] A means for formatting the input attribute information and transmitting the data,

[0890] A machine learning algorithm means for calculating admission points for childcare facilities based on the formatted data,

[0891] A means for predicting the probability of admission using the calculated score and competition rate information,

[0892] A means for presenting the predicted probability of admission in graph and list format,

[0893] A means of listing the necessary procedural documents and providing them in an accessible format,

[0894] An advice generation tool that presents an optimal childcare facility selection plan tailored to the circumstances of each family,

[0895] A means to collect and reflect the latest regional and childcare facility competition rate information in real time,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, which takes into account regional competition rate information when calculating the admission score for the aforementioned childcare facility.

[0899] (Claim 3)

[0900] The system according to claim 1, which provides personalized advice based on the family's occupational status and income information when presenting the aforementioned optimal childcare facility selection plan.

[0901] "Application Example 1"

[0902] (Claim 1)

[0903] A means of inputting basic household information,

[0904] A mathematical means for calculating admission approval points for childcare facilities based on the input information,

[0905] A means for estimating the probability of admission based on the calculated score,

[0906] A means for generating a medium that visually presents the estimated probability of admission,

[0907] A means to automatically generate the necessary application records,

[0908] A means of providing advice to propose the optimal childcare institution selection strategy,

[0909] A means of instantly updating local information and information on the competition rate for admission to childcare facilities,

[0910] A means for interactively providing the aforementioned advice and information in cooperation with a device that recognizes voice and enables dialogue within the home,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, which reflects a regional competitive situation analysis when calculating the admission points for the aforementioned childcare institution.

[0914] (Claim 3)

[0915] The system according to claim 1, which, when proposing the most suitable childcare institution selection policy, provides individually tailored advice based on the family's work environment and income situation.

[0916] "Example 2 of combining an emotion engine"

[0917] (Claim 1)

[0918] A means of inputting general household information,

[0919] An algorithm means for calculating an admission evaluation value for an educational facility based on the aforementioned input general information,

[0920] A means for predicting the likelihood of admission based on the calculated evaluation value,

[0921] The means for displaying the predicted possibility of admission,

[0922] A means to automatically generate the necessary application documents,

[0923] An advisory tool to propose the optimal educational facility selection strategy,

[0924] A means of updating real-time data on the competition rate for admission to local and educational facilities,

[0925] A means of analyzing the user's emotional state using an emotion engine and generating feedback to reduce stress,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, wherein the competition rate for each region is taken into consideration when calculating the admission evaluation value for the aforementioned educational facility.

[0929] (Claim 3)

[0930] The system according to claim 1, which provides customized advice based on the family's employment status and income when proposing the aforementioned optimal educational facility selection strategy.

[0931] "Application example 2 when combining with an emotional engine"

[0932] (Claim 1)

[0933] A device for acquiring basic household information,

[0934] An inference device that calculates admission points for childcare facilities based on the basic information acquired,

[0935] A prediction device that predicts the probability of admission based on the calculated score,

[0936] A display device that displays the predicted probability of admission,

[0937] A generator that automatically generates the documents required for the application,

[0938] A guidance system that provides optimal childcare facility selection policies,

[0939] A device that dynamically updates data on the competition ratio for admission to local and childcare facilities,

[0940] An emotion analysis device that detects the user's emotional state and provides feedback to reduce stress,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, further comprising a device that takes into account the regional competition ratio when calculating the admission score for the aforementioned childcare facility.

[0944] (Claim 3)

[0945] The system according to claim 1, which includes a device that provides individualized guidance according to the family's employment status and income when proposing the aforementioned optimal childcare facility selection policy. [Explanation of Symbols]

[0946] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting basic household information, A mathematical means for calculating admission approval points for childcare facilities based on the input information, A means for estimating the probability of admission based on the calculated score, A means for generating a medium that visually presents the estimated probability of admission, A means to automatically generate the necessary application records, A means of providing advice to propose the optimal childcare institution selection strategy, A means of instantly updating local information and information on the competition rate for admission to childcare facilities, A means for interactively providing the aforementioned advice and information in cooperation with a device that recognizes voice and enables dialogue within the home, A system that includes this.

2. The system according to claim 1, which reflects a regional competitive situation analysis when calculating the admission points for the aforementioned childcare institution.

3. The system according to claim 1, which, when proposing the most suitable childcare institution selection policy, provides individually tailored advice based on the family's work environment and income situation.

Images