system

Abstract

The system according to this embodiment aims to help parents find the most suitable facility when choosing a kindergarten or nursery school. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an update unit. The collection unit collects information on childcare facilities. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes appropriate childcare facilities based on the analysis results obtained by the analysis unit. The update unit immediately reflects legal revisions and local rules.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for guardians to collect complex information in the selection of kindergartens or nurseries and find the optimal facilities.

[0005] The system according to the embodiment aims to enable guardians to find the optimal facilities in the selection of kindergartens or nurseries.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an update unit. The collection unit collects information on childcare facilities. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes appropriate childcare facilities based on the analysis results obtained by the analysis unit. The update unit immediately reflects legal revisions and local rules. [Effects of the Invention]

[0007] The system according to this embodiment allows parents to find the most suitable facility when choosing a kindergarten or nursery school. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Kids School Selector, according to an embodiment of the present invention, is a service that utilizes an AI agent to solve the complex problems that parents face when choosing a kindergarten or nursery school. Specifically, it consists of the following steps. First, the AI ​​agent automatically collects information on each childcare facility and proposes the most suitable facility that matches the desired conditions entered by the parents. Next, it also considers dynamic conditions such as income, commuting distance, city rules, and legal revisions in real time, and provides support to increase the chances of admission. Furthermore, by inputting the child's interests and characteristics, as well as the parents' educational philosophy, the AI ​​agent predicts future learning milestones and career aptitudes, and proposes the most suitable childcare facility and educational plan based on this. This allows parents to understand their child's future aptitudes and make the best choice of path, aiming for a society where children can enjoy education and growth that is better suited to them. By providing the optimal educational environment, future generations are expected to grow up healthier, resulting in an improvement in the overall well-being and productivity of society. For example, the AI ​​agent collects information on childcare facilities based on the desired conditions entered by the parents. The collected information includes facility location, services offered, fees, and reputation. Next, the AI ​​agent analyzes the collected information to identify the optimal childcare facility that matches the parents' preferences. For example, it considers dynamic factors such as parents' income, commuting distance, city rules, and legal changes to suggest the best facility. Furthermore, the AI ​​agent proposes an optimal educational plan considering the child's interests and characteristics, as well as the parents' educational philosophy. For instance, it considers the child's learning style, hobbies, personality, parents' educational policies, values, and goals to predict future learning milestones and career aptitudes, and proposes the most suitable childcare facility and educational plan. This allows parents to understand their child's future aptitudes and make optimal career choices, aiming to create a society where children can enjoy a more personalized education and growth. Providing an optimal educational environment is expected to lead to healthier development for future generations, resulting in increased overall societal well-being and productivity.This allows Kids School Selector to provide a system that solves the complex problems parents face when choosing a kindergarten or daycare center.

[0029] The Kids School Selector according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and an update unit. The collection unit collects information on childcare facilities. For example, the collection unit collects information on childcare facilities based on the desired conditions entered by parents. The collected information includes the location of the facility, the services offered, fees, and reputation. For example, the collection unit collects publicly available information from the internet. The collection unit can also collect information from the official websites of childcare facilities and review sites. Furthermore, the collection unit can also collect information from brochures and informational materials of childcare facilities. For example, the collection unit collects information on childcare facilities using an internet search engine. It collects information such as the services offered, fees, and reputation from the official websites of childcare facilities. It collects parent evaluations and opinions from review sites. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit identifies the optimal childcare facility that matches the parents' desired conditions based on the collected information. For example, the analysis unit suggests the optimal childcare facility by considering dynamic conditions such as income, commuting distance, city rules, and legal changes. For example, the analysis unit identifies an economically suitable childcare facility based on income status. The system identifies convenient childcare facilities based on commuting distance. It identifies appropriate childcare facilities based on city rules and legal revisions. The proposal department proposes the optimal childcare facilities based on the analysis results obtained by the analysis department. The proposal department proposes the optimal childcare facilities that match the parents' desired conditions, for example. The proposal department proposes the optimal childcare facilities considering dynamic conditions such as income, commuting distance, city rules, and legal revisions, for example. The proposal department proposes economically suitable childcare facilities based on income, for example. It proposes convenient childcare facilities based on commuting distance. It proposes appropriate childcare facilities based on city rules and legal revisions. The update department reflects legal revisions and local rules in real time. The update department reflects legal revisions and local rules immediately, for example. The update department collects information such as the content, effective date, and scope of impact of legal revisions and reflects it in the system, for example. It updates childcare facility information based on local rules. As a result, the Kids School Selector according to this embodiment can provide a system to solve the complex problems that parents face when choosing a kindergarten or nursery school.

[0030] The data collection department collects information on childcare facilities. For example, it collects information on childcare facilities based on the preferences entered by parents. The collected information includes the facility's location, services offered, fees, and reputation. The data collection department also collects publicly available information from the internet. Specifically, it uses internet search engines to collect information on childcare facilities. It collects information such as services offered, fees, and reputation from the official websites of childcare facilities. It collects parent ratings and opinions from review sites. Furthermore, the data collection department can also collect information from brochures and informational materials of childcare facilities. For example, the official websites of childcare facilities often contain an overview of the facility, the programs offered, fee structures, photos of the facility, and staff introductions. By collecting this information, it is possible to provide parents with the detailed information they seek. In addition, review sites contain ratings and opinions from parents who have actually used the facilities, making them an important source of information for understanding the reputation and reliability of facilities. The data collection department centrally manages this information and can link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis department and the proposal department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The Analysis Department analyzes the information collected by the Data Collection Department. For example, based on the collected information, the Analysis Department identifies the optimal childcare facility that matches the parents' desired conditions. Specifically, it proposes the optimal childcare facility by considering dynamic conditions such as income, commuting distance, city rules, and legal changes. For example, it identifies economically suitable childcare facilities based on income. It identifies convenient childcare facilities based on commuting distance. It identifies appropriate childcare facilities based on city rules and legal changes. The Analysis Department uses AI to analyze the collected data and identify the childcare facility that best suits the parents' desired conditions. The AI ​​performs complex calculations based on the collected data to identify childcare facilities that match the parents' desired conditions. For example, the AI ​​identifies the optimal childcare facility by considering dynamic conditions such as income, commuting distance, city rules, and legal changes. This allows the Analysis Department to quickly and accurately analyze the collected data and identify the childcare facility that best suits the parents' desired conditions. Furthermore, the Analysis Department can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past usage and evaluation data of childcare facilities, the system can predict fluctuations in the demand and supply of childcare facilities in specific areas and time periods, and formulate future countermeasures. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The Proposal Department proposes the most suitable childcare facilities based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes the most suitable childcare facilities that match the parents' desired conditions. Specifically, it proposes the most suitable childcare facilities by considering dynamic conditions such as income, commuting distance, city rules, and legal revisions. For example, it proposes economically suitable childcare facilities based on income. For example, it proposes convenient childcare facilities based on commuting distance. For example, it proposes appropriate childcare facilities based on city rules and legal revisions. The Proposal Department uses AI to propose the childcare facilities that best suit the parents' desired conditions based on the analysis results. The AI ​​performs complex calculations to identify childcare facilities that match the parents' desired conditions based on the collected data. For example, the AI ​​identifies the most suitable childcare facilities by considering dynamic conditions such as income, commuting distance, city rules, and legal revisions. This allows the Proposal Department to quickly and accurately propose the childcare facilities that best suit the parents' desired conditions. Furthermore, the Proposal Department can collect feedback from parents and continuously improve the accuracy and effectiveness of its proposals. For example, it can review and improve its proposals based on feedback from parents who have used the proposed childcare facilities. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For example, it can use email, SMS, and app notifications in combination to ensure that important information is delivered reliably. This allows the proposal department to quickly and reliably propose the most suitable childcare facilities to parents, thereby improving parental satisfaction.

[0033] The update department reflects legal revisions and regional rules in real time. For example, the update department immediately reflects legal revisions and regional rules. Specifically, it collects information such as the content of legal revisions, the effective date, and the scope of impact, and reflects it in the system. It updates information on childcare facilities based on regional rules. For example, if a legal revision is made, the update department quickly collects the content and reflects it in the system. This allows parents to select childcare facilities based on the latest information. Furthermore, by updating information on childcare facilities based on regional rules, it can provide appropriate information tailored to the characteristics and requirements of each region. The update department uses AI to automatically detect legal revisions and changes in regional rules and reflect them in the system. The AI ​​monitors publicly available information on the internet and official announcements to detect legal revisions and rule changes. This allows the update department to quickly and accurately reflect legal revisions and rule changes, improving the reliability and security of the entire system. In addition, the update department can collect feedback from parents and use it to improve the system. For example, it improves the system's functions and information provision methods based on opinions and requests from parents. This allows the update department to always provide the latest information and meet the needs of parents.

[0034] The proposal department can propose appropriate educational plans based on the child's interests and characteristics, and the parents' educational philosophy. For example, the proposal department proposes the optimal educational plan by considering the child's interests and characteristics and the parents' educational philosophy. For example, the proposal department considers the child's learning style, hobbies, personality, parents' educational policies, values, and goals to predict future learning milestones and career aptitudes, and proposes the most suitable childcare facilities and educational plans. For example, the proposal department proposes appropriate educational plans based on the child's interests and characteristics. For example, the proposal department proposes appropriate educational plans based on the parents' educational philosophy. This allows the department to predict the child's future learning milestones and career aptitudes, and propose the optimal educational plan.

[0035] The data collection unit can analyze parents' past selection history and select appropriate information collection methods. For example, the data collection unit can analyze the characteristics of childcare facilities previously chosen by parents and prioritize collecting information on similar facilities. For example, the data collection unit can prioritize using information collection methods (online, telephone, etc.) that parents have used in the past. For example, the data collection unit can collect information at specific time periods based on parents' past selection history. By analyzing past selection history, the data collection unit can provide parents with the most suitable information collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input parents' past selection history data into a generating AI and have the generating AI select the information collection method.

[0036] The data collection unit can filter information on childcare facilities based on the parents' current living situation and areas of interest. For example, the data collection unit can collect information on economically suitable childcare facilities based on the parents' current income. For example, the data collection unit can prioritize the collection of relevant information based on the parents' areas of interest (educational policies, facility facilities, etc.). For example, the data collection unit can collect information on conveniently located childcare facilities based on the parents' living situation (working hours, commuting distance, etc.). By filtering information based on the parents' living situation and areas of interest, more appropriate information can be provided. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input parents' living situation data into a generating AI and have the generating AI perform the information filtering.

[0037] The data collection unit can prioritize the collection of relevant information based on the geographical location information of parents when collecting information on childcare facilities. For example, the data collection unit can prioritize the collection of information on childcare facilities close to the parents' home. For example, the data collection unit can prioritize the collection of information on childcare facilities close to the parents' workplace. For example, the data collection unit can prioritize the collection of information on childcare facilities along the parents' commute route. By considering geographical location information, the data collection unit can provide parents with information that is highly relevant to them. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the parents' geographical location data into a generating AI and have the generating AI perform the collection of relevant information.

[0038] The data collection unit can analyze parents' social media activity and collect relevant information when collecting information on childcare facilities. For example, the data collection unit can collect information on childcare facilities that parents follow on social media. For example, the data collection unit can collect information on childcare facilities that parents have shown interest in on social media. For example, the data collection unit can collect information on childcare facilities that parents have shared on social media. By analyzing social media activity, the data collection unit can provide information relevant to parents. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input parents' social media activity data into a generating AI and have the generating AI collect relevant information.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the childcare facilities. For example, the analysis unit can perform a detailed analysis for childcare facilities of high importance, and a simplified analysis for childcare facilities of low importance. For example, the analysis unit can prioritize the analysis of childcare facilities of high importance based on the parents' preferences. By adjusting the level of detail of the analysis based on the importance of the childcare facilities, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input childcare facility importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply an appropriate analysis algorithm during analysis, depending on the category of the childcare facility. For example, the analysis unit can apply different analysis algorithms based on educational policies. For example, the analysis unit can apply different analysis algorithms based on the facility's equipment. For example, the analysis unit can apply different analysis algorithms based on the qualifications and experience of childcare workers. By applying different analysis algorithms depending on the category of the childcare facility, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input childcare facility category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the collection timing of childcare facility data. For example, the analysis unit may prioritize the analysis of recently collected information on childcare facilities. For example, the analysis unit may lower the priority of older information. For example, the analysis unit may determine priorities based on parents' preferences, regardless of the collection timing. By determining the priority of analysis based on the collection timing of childcare facilities, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input childcare facility collection timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0042] The analysis unit can adjust the order of analysis based on the relevance of childcare facilities during the analysis process. For example, the analysis unit may prioritize analyzing childcare facilities that are most relevant to the parents' preferences. For example, the analysis unit may postpone the analysis of less relevant childcare facilities. For example, the analysis unit may prioritize analyzing childcare facilities that are highly relevant based on the parents' preferences. By adjusting the order of analysis based on the relevance of childcare facilities, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of childcare facilities into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The proposal unit can adjust the level of detail in its proposals based on the importance of the childcare facilities. For example, the proposal unit will provide detailed proposals for highly important childcare facilities, and simplified proposals for less important facilities. The proposal unit will also prioritize proposing highly important childcare facilities based on the parents' preferences. By adjusting the level of detail in proposals based on the importance of the childcare facilities, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input childcare facility importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0044] The proposal unit can apply an appropriate proposal algorithm depending on the category of the childcare facility when making a proposal. For example, the proposal unit can apply a different proposal algorithm based on the educational policy. For example, the proposal unit can apply a different proposal algorithm based on the facility's equipment. For example, the proposal unit can apply a different proposal algorithm based on the qualifications and experience of the childcare workers. By applying different proposal algorithms depending on the category of the childcare facility, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input childcare facility category data into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0045] The proposal unit can determine the priority of proposals based on the timing of data collection for childcare facilities. For example, the proposal unit may prioritize information on childcare facilities that have been recently collected. For example, the proposal unit may lower the priority of older information. For example, the proposal unit may determine the priority based on the parents' preferences, regardless of the data collection timing. This allows for the provision of more appropriate proposals by determining the priority of proposals based on the timing of data collection for childcare facilities. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the timing of data collection for childcare facilities into a generating AI and have the generating AI perform the determination of proposal priorities.

[0046] The proposal unit can adjust the order of proposals based on the relevance of the childcare facilities. For example, the proposal unit will prioritize proposing childcare facilities that are most relevant to the parents' preferences. For example, the proposal unit will postpone proposing childcare facilities that are less relevant. For example, the proposal unit will prioritize proposing childcare facilities that are highly relevant based on the parents' preferences. By adjusting the order of proposals based on the relevance of the childcare facilities, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance data of childcare facilities into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0047] The update unit can predict current rules based on past legal amendment data during updates. For example, the update unit can predict future trends in legal amendments based on past legal amendment data. For example, the update unit can analyze past legal amendment data and reflect it in current rules. For example, the update unit can predict the latest rules by referring to past legal amendment data. This allows the update unit to predict current rules and provide more appropriate information by referring to past legal amendment data. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past legal amendment data into a generating AI and have the generating AI perform predictions of current rules.

[0048] The update unit can apply an appropriate update algorithm according to the rules of each region during the update process. For example, the update unit may apply an algorithm that performs frequent updates based on the rules of urban areas. For example, the update unit may apply an algorithm that performs periodic updates based on the rules of suburban areas. For example, the update unit may apply a customized update algorithm based on the rules of a specific region. This allows for the provision of more appropriate information by applying different update algorithms according to the rules of each region. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input region-specific rule data into a generating AI and have the generating AI execute the application of the update algorithm.

[0049] The update unit can adjust the order of updates based on the timing of collection of legal amendments and local rules during the update process. For example, the update unit may prioritize updating recently collected legal amendment information. For example, the update unit may lower the priority of updating older information. For example, the update unit may determine the order of updates based on the preferences of guardians, regardless of the collection timing. This allows for the provision of more appropriate information by adjusting the order of updates based on the timing of collection of legal amendments and local rules. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit may input data on the timing of collection of legal amendments and local rules into a generating AI and have the generating AI perform the adjustment of the update order.

[0050] The update unit can improve the accuracy of updates by referring to relevant legal amendments and regional rule literature during the update process. For example, the update unit can refer to the latest legal amendment literature to provide accurate information. For example, the update unit can refer to regional rule literature to provide region-specific information. For example, the update unit can refer to past legal amendment literature to predict future trends in legal amendments. This allows the update unit to improve the accuracy of updates and provide more appropriate information by referring to relevant literature. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input relevant legal amendment and regional rule literature data into a generating AI and have the generating AI perform the update accuracy improvement.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] Kids School Selector can analyze parents' past selection history and suggest appropriate school visit schedules. For example, it can analyze the characteristics of childcare facilities parents have previously chosen and prioritize scheduling visits to similar facilities. It can also prioritize the visit methods parents have used in the past (online, in-person, etc.). Furthermore, it can schedule visits at specific time slots based on parents' past selection history. In this way, by analyzing past selection history, it can provide parents with the most optimal school visit schedule.

[0053] Kids School Selector can adjust visit schedules based on parents' current circumstances and areas of interest. For example, it can schedule visits to financially suitable childcare facilities based on parents' current income. It can also prioritize visits to relevant facilities based on parents' areas of interest (educational philosophy, facility facilities, etc.). Furthermore, it can schedule visits to conveniently located childcare facilities based on parents' living circumstances (working hours, commute distance, etc.). By adjusting visit schedules based on parents' circumstances and areas of interest, it can provide a more appropriate visit experience.

[0054] Kids School Selector can adjust visit schedules based on parents' geographical location. For example, it can prioritize visits to childcare facilities close to parents' homes, workplaces, and even along their commute routes. By considering geographical location, it can provide parents with a more relevant visit schedule.

[0055] Kids School Selector can analyze parents' social media activity and suggest relevant childcare facility visit schedules. For example, it can schedule visits to childcare facilities that parents follow on social media, facilities that parents have shown interest in on social media, and facilities that parents have shared on social media. This allows for the provision of relevant visit schedules to parents by analyzing their social media activity.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection department collects information on childcare facilities. The collection department collects information on childcare facilities based on, for example, the preferences entered by parents. The collected information includes the location of the facility, the services offered, fees, and reputation. The collection department collects publicly available information from the internet, for example. The collection department can also collect information from the official websites of childcare facilities and review sites. Furthermore, the collection department can collect information from brochures and informational materials of childcare facilities. For example, the collection department uses internet search engines to collect information on childcare facilities. They collect information such as the services offered, fees, and reputation from the official websites of childcare facilities. They collect parent ratings and opinions from review sites. Step 2: The analysis department analyzes the information collected by the data collection department. For example, the analysis department identifies the best childcare facility that matches the parents' desired conditions based on the collected information. For example, the analysis department proposes the best childcare facility by considering dynamic conditions such as income, commuting distance, city rules, and legal changes. For example, the analysis department identifies economically suitable childcare facilities based on income. For example, it identifies convenient childcare facilities based on commuting distance. For example, it identifies appropriate childcare facilities based on city rules and legal changes. Step 3: The proposal department proposes the most suitable childcare facility based on the analysis results obtained by the analysis department. For example, the proposal department proposes the most suitable childcare facility that matches the parents' desired conditions. For example, the proposal department proposes the most suitable childcare facility considering dynamic conditions such as income, commuting distance, city rules, and legal revisions. For example, the proposal department proposes an economically suitable childcare facility based on income. For example, it proposes a convenient childcare facility based on commuting distance. For example, it proposes an appropriate childcare facility based on city rules and legal revisions. Step 4: The update section reflects legal changes and local rules in real time. The update section instantly reflects legal changes and local rules, for example. The update section collects information such as the content of legal changes, the effective date, and the scope of impact, and reflects it in the system. It updates the information of childcare facilities based on local rules.

[0058] (Example of form 2) The Kids School Selector, according to an embodiment of the present invention, is a service that utilizes an AI agent to solve the complex problems that parents face when choosing a kindergarten or nursery school. Specifically, it consists of the following steps. First, the AI ​​agent automatically collects information on each childcare facility and proposes the most suitable facility that matches the desired conditions entered by the parents. Next, it also considers dynamic conditions such as income, commuting distance, city rules, and legal revisions in real time, and provides support to increase the chances of admission. Furthermore, by inputting the child's interests and characteristics, as well as the parents' educational philosophy, the AI ​​agent predicts future learning milestones and career aptitudes, and proposes the most suitable childcare facility and educational plan based on this. This allows parents to understand their child's future aptitudes and make the best choice of path, aiming for a society where children can enjoy education and growth that is better suited to them. By providing the optimal educational environment, future generations are expected to grow up healthier, resulting in an improvement in the overall well-being and productivity of society. For example, the AI ​​agent collects information on childcare facilities based on the desired conditions entered by the parents. The collected information includes facility location, services offered, fees, and reputation. Next, the AI ​​agent analyzes the collected information to identify the optimal childcare facility that matches the parents' preferences. For example, it considers dynamic factors such as parents' income, commuting distance, city rules, and legal changes to suggest the best facility. Furthermore, the AI ​​agent proposes an optimal educational plan considering the child's interests and characteristics, as well as the parents' educational philosophy. For instance, it considers the child's learning style, hobbies, personality, parents' educational policies, values, and goals to predict future learning milestones and career aptitudes, and proposes the most suitable childcare facility and educational plan. This allows parents to understand their child's future aptitudes and make optimal career choices, aiming to create a society where children can enjoy a more personalized education and growth. Providing an optimal educational environment is expected to lead to healthier development for future generations, resulting in increased overall societal well-being and productivity.This allows Kids School Selector to provide a system that solves the complex problems parents face when choosing a kindergarten or daycare center.

[0059] The Kids School Selector according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and an update unit. The collection unit collects information on childcare facilities. For example, the collection unit collects information on childcare facilities based on the desired conditions entered by parents. The collected information includes the location of the facility, the services offered, fees, and reputation. For example, the collection unit collects publicly available information from the internet. The collection unit can also collect information from the official websites of childcare facilities and review sites. Furthermore, the collection unit can also collect information from brochures and informational materials of childcare facilities. For example, the collection unit collects information on childcare facilities using an internet search engine. It collects information such as the services offered, fees, and reputation from the official websites of childcare facilities. It collects parent evaluations and opinions from review sites. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit identifies the optimal childcare facility that matches the parents' desired conditions based on the collected information. For example, the analysis unit suggests the optimal childcare facility by considering dynamic conditions such as income, commuting distance, city rules, and legal changes. For example, the analysis unit identifies an economically suitable childcare facility based on income status. The system identifies convenient childcare facilities based on commuting distance. It identifies appropriate childcare facilities based on city rules and legal revisions. The proposal department proposes the optimal childcare facilities based on the analysis results obtained by the analysis department. The proposal department proposes the optimal childcare facilities that match the parents' desired conditions, for example. The proposal department proposes the optimal childcare facilities considering dynamic conditions such as income, commuting distance, city rules, and legal revisions, for example. The proposal department proposes economically suitable childcare facilities based on income, for example. It proposes convenient childcare facilities based on commuting distance. It proposes appropriate childcare facilities based on city rules and legal revisions. The update department reflects legal revisions and local rules in real time. The update department reflects legal revisions and local rules immediately, for example. The update department collects information such as the content, effective date, and scope of impact of legal revisions and reflects it in the system, for example. It updates childcare facility information based on local rules. As a result, the Kids School Selector according to this embodiment can provide a system to solve the complex problems that parents face when choosing a kindergarten or nursery school.

[0060] The data collection department collects information on childcare facilities. For example, it collects information on childcare facilities based on the preferences entered by parents. The collected information includes the facility's location, services offered, fees, and reputation. The data collection department also collects publicly available information from the internet. Specifically, it uses internet search engines to collect information on childcare facilities. It collects information such as services offered, fees, and reputation from the official websites of childcare facilities. It collects parent ratings and opinions from review sites. Furthermore, the data collection department can also collect information from brochures and informational materials of childcare facilities. For example, the official websites of childcare facilities often contain an overview of the facility, the programs offered, fee structures, photos of the facility, and staff introductions. By collecting this information, it is possible to provide parents with the detailed information they seek. In addition, review sites contain ratings and opinions from parents who have actually used the facilities, making them an important source of information for understanding the reputation and reliability of facilities. The data collection department centrally manages this information and can link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis department and the proposal department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The Analysis Department analyzes the information collected by the Data Collection Department. For example, based on the collected information, the Analysis Department identifies the optimal childcare facility that matches the parents' desired conditions. Specifically, it proposes the optimal childcare facility by considering dynamic conditions such as income, commuting distance, city rules, and legal changes. For example, it identifies economically suitable childcare facilities based on income. It identifies convenient childcare facilities based on commuting distance. It identifies appropriate childcare facilities based on city rules and legal changes. The Analysis Department uses AI to analyze the collected data and identify the childcare facility that best suits the parents' desired conditions. The AI ​​performs complex calculations based on the collected data to identify childcare facilities that match the parents' desired conditions. For example, the AI ​​identifies the optimal childcare facility by considering dynamic conditions such as income, commuting distance, city rules, and legal changes. This allows the Analysis Department to quickly and accurately analyze the collected data and identify the childcare facility that best suits the parents' desired conditions. Furthermore, the Analysis Department can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past usage and evaluation data of childcare facilities, the system can predict fluctuations in the demand and supply of childcare facilities in specific areas and time periods, and formulate future countermeasures. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0062] The Proposal Department proposes the most suitable childcare facilities based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes the most suitable childcare facilities that match the parents' desired conditions. Specifically, it proposes the most suitable childcare facilities by considering dynamic conditions such as income, commuting distance, city rules, and legal revisions. For example, it proposes economically suitable childcare facilities based on income. For example, it proposes convenient childcare facilities based on commuting distance. For example, it proposes appropriate childcare facilities based on city rules and legal revisions. The Proposal Department uses AI to propose the childcare facilities that best suit the parents' desired conditions based on the analysis results. The AI ​​performs complex calculations to identify childcare facilities that match the parents' desired conditions based on the collected data. For example, the AI ​​identifies the most suitable childcare facilities by considering dynamic conditions such as income, commuting distance, city rules, and legal revisions. This allows the Proposal Department to quickly and accurately propose the childcare facilities that best suit the parents' desired conditions. Furthermore, the Proposal Department can collect feedback from parents and continuously improve the accuracy and effectiveness of its proposals. For example, it can review and improve its proposals based on feedback from parents who have used the proposed childcare facilities. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For example, it can use email, SMS, and app notifications in combination to ensure that important information is delivered reliably. This allows the proposal department to quickly and reliably propose the most suitable childcare facilities to parents, thereby improving parental satisfaction.

[0063] The update department reflects legal revisions and regional rules in real time. For example, the update department immediately reflects legal revisions and regional rules. Specifically, it collects information such as the content of legal revisions, the effective date, and the scope of impact, and reflects it in the system. It updates information on childcare facilities based on regional rules. For example, if a legal revision is made, the update department quickly collects the content and reflects it in the system. This allows parents to select childcare facilities based on the latest information. Furthermore, by updating information on childcare facilities based on regional rules, it can provide appropriate information tailored to the characteristics and requirements of each region. The update department uses AI to automatically detect legal revisions and changes in regional rules and reflect them in the system. The AI ​​monitors publicly available information on the internet and official announcements to detect legal revisions and rule changes. This allows the update department to quickly and accurately reflect legal revisions and rule changes, improving the reliability and security of the entire system. In addition, the update department can collect feedback from parents and use it to improve the system. For example, it improves the system's functions and information provision methods based on opinions and requests from parents. This allows the update department to always provide the latest information and meet the needs of parents.

[0064] The proposal department can propose appropriate educational plans based on the child's interests and characteristics, and the parents' educational philosophy. For example, the proposal department proposes the optimal educational plan by considering the child's interests and characteristics and the parents' educational philosophy. For example, the proposal department considers the child's learning style, hobbies, personality, parents' educational policies, values, and goals to predict future learning milestones and career aptitudes, and proposes the most suitable childcare facilities and educational plans. For example, the proposal department proposes appropriate educational plans based on the child's interests and characteristics. For example, the proposal department proposes appropriate educational plans based on the parents' educational philosophy. This allows the department to predict the child's future learning milestones and career aptitudes, and propose the optimal educational plan.

[0065] The data collection unit can estimate the emotions of parents and adjust the timing of information collection from childcare facilities based on the estimated emotions. For example, if a parent is stressed, the data collection unit reduces the frequency of information collection and collects only essential information. For example, if a parent is relaxed, the data collection unit collects detailed information and provides a wide range of options. For example, if a parent is in a hurry, the data collection unit quickly collects information and makes immediate suggestions. This allows for more appropriate information collection by adjusting the timing of information collection according to the parents' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0066] The data collection unit can analyze parents' past selection history and select appropriate information collection methods. For example, the data collection unit can analyze the characteristics of childcare facilities previously chosen by parents and prioritize collecting information on similar facilities. For example, the data collection unit can prioritize using information collection methods (online, telephone, etc.) that parents have used in the past. For example, the data collection unit can collect information at specific time periods based on parents' past selection history. By analyzing past selection history, the data collection unit can provide parents with the most suitable information collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input parents' past selection history data into a generating AI and have the generating AI select the information collection method.

[0067] The data collection unit can filter information on childcare facilities based on the parents' current living situation and areas of interest. For example, the data collection unit can collect information on economically suitable childcare facilities based on the parents' current income. For example, the data collection unit can prioritize the collection of relevant information based on the parents' areas of interest (educational policies, facility facilities, etc.). For example, the data collection unit can collect information on conveniently located childcare facilities based on the parents' living situation (working hours, commuting distance, etc.). By filtering information based on the parents' living situation and areas of interest, more appropriate information can be provided. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input parents' living situation data into a generating AI and have the generating AI perform the information filtering.

[0068] The data collection unit can estimate the emotions of parents and determine the priority of childcare facility information to collect based on the estimated emotions. For example, if a parent is feeling anxious, the data collection unit will prioritize collecting information on reliable childcare facilities. For example, if a parent is agitated, the data collection unit will prioritize collecting information on new childcare facilities. For example, if a parent is calm, the data collection unit will prioritize collecting information on childcare facilities that include detailed information. This allows for the provision of more appropriate information by prioritizing information according to the emotions of parents. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0069] The data collection unit can prioritize the collection of relevant information based on the geographical location information of parents when collecting information on childcare facilities. For example, the data collection unit can prioritize the collection of information on childcare facilities close to the parents' home. For example, the data collection unit can prioritize the collection of information on childcare facilities close to the parents' workplace. For example, the data collection unit can prioritize the collection of information on childcare facilities along the parents' commute route. By considering geographical location information, the data collection unit can provide parents with information that is highly relevant to them. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the parents' geographical location data into a generating AI and have the generating AI perform the collection of relevant information.

[0070] The data collection unit can analyze parents' social media activity and collect relevant information when collecting information on childcare facilities. For example, the data collection unit can collect information on childcare facilities that parents follow on social media. For example, the data collection unit can collect information on childcare facilities that parents have shown interest in on social media. For example, the data collection unit can collect information on childcare facilities that parents have shared on social media. By analyzing social media activity, the data collection unit can provide information relevant to parents. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input parents' social media activity data into a generating AI and have the generating AI collect relevant information.

[0071] The analysis unit can estimate the parent's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the parent is feeling anxious, the analysis unit will use a reassuring presentation. For example, if the parent is excited, the analysis unit will use an engaging presentation. For example, if the parent is calm, the analysis unit will use a presentation that includes detailed information. By adjusting the presentation of the analysis according to the parent's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input parent's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the childcare facilities. For example, the analysis unit can perform a detailed analysis for childcare facilities of high importance, and a simplified analysis for childcare facilities of low importance. For example, the analysis unit can prioritize the analysis of childcare facilities of high importance based on the parents' preferences. By adjusting the level of detail of the analysis based on the importance of the childcare facilities, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input childcare facility importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0073] The analysis unit can apply an appropriate analysis algorithm during analysis, depending on the category of the childcare facility. For example, the analysis unit can apply different analysis algorithms based on educational policies. For example, the analysis unit can apply different analysis algorithms based on the facility's equipment. For example, the analysis unit can apply different analysis algorithms based on the qualifications and experience of childcare workers. By applying different analysis algorithms depending on the category of the childcare facility, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input childcare facility category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0074] The analysis unit can estimate the parent's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the parent is in a hurry, the analysis unit will perform a short, concise analysis. If the parent is relaxed, the analysis unit will perform a detailed analysis. If the parent is excited, the analysis unit will perform a visually stimulating analysis. By adjusting the length of the analysis according to the parent's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input parent's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The analysis unit can determine the priority of analysis based on the collection timing of childcare facility data. For example, the analysis unit may prioritize the analysis of recently collected information on childcare facilities. For example, the analysis unit may lower the priority of older information. For example, the analysis unit may determine priorities based on parents' preferences, regardless of the collection timing. By determining the priority of analysis based on the collection timing of childcare facilities, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input childcare facility collection timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0076] The analysis unit can adjust the order of analysis based on the relevance of childcare facilities during the analysis process. For example, the analysis unit may prioritize analyzing childcare facilities that are most relevant to the parents' preferences. For example, the analysis unit may postpone the analysis of less relevant childcare facilities. For example, the analysis unit may prioritize analyzing childcare facilities that are highly relevant based on the parents' preferences. By adjusting the order of analysis based on the relevance of childcare facilities, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of childcare facilities into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0077] The suggestion unit can estimate the parent's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the parent is feeling anxious, the suggestion unit will use a reassuring presentation. For example, if the parent is excited, the suggestion unit will use an engaging presentation. For example, if the parent is calm, the suggestion unit will use a presentation that includes detailed information. By adjusting the presentation of the suggestion according to the parent's emotions, the suggestion unit can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The proposal unit can adjust the level of detail in its proposals based on the importance of the childcare facilities. For example, the proposal unit will provide detailed proposals for highly important childcare facilities, and simplified proposals for less important facilities. The proposal unit will also prioritize proposing highly important childcare facilities based on the parents' preferences. By adjusting the level of detail in proposals based on the importance of the childcare facilities, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input childcare facility importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0079] The proposal unit can apply an appropriate proposal algorithm depending on the category of the childcare facility when making a proposal. For example, the proposal unit can apply a different proposal algorithm based on the educational policy. For example, the proposal unit can apply a different proposal algorithm based on the facility's equipment. For example, the proposal unit can apply a different proposal algorithm based on the qualifications and experience of the childcare workers. By applying different proposal algorithms depending on the category of the childcare facility, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input childcare facility category data into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0080] The suggestion unit can estimate the parent's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the parent is in a hurry, the suggestion unit will provide a short, concise suggestion. If the parent is relaxed, the suggestion unit will provide a detailed suggestion. If the parent is excited, the suggestion unit will provide a visually stimulating suggestion. By adjusting the length of the suggestion according to the parent's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The proposal unit can determine the priority of proposals based on the timing of data collection for childcare facilities. For example, the proposal unit may prioritize information on childcare facilities that have been recently collected. For example, the proposal unit may lower the priority of older information. For example, the proposal unit may determine the priority based on the parents' preferences, regardless of the data collection timing. This allows for the provision of more appropriate proposals by determining the priority of proposals based on the timing of data collection for childcare facilities. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the timing of data collection for childcare facilities into a generating AI and have the generating AI perform the determination of proposal priorities.

[0082] The proposal unit can adjust the order of proposals based on the relevance of the childcare facilities. For example, the proposal unit will prioritize proposing childcare facilities that are most relevant to the parents' preferences. For example, the proposal unit will postpone proposing childcare facilities that are less relevant. For example, the proposal unit will prioritize proposing childcare facilities that are highly relevant based on the parents' preferences. By adjusting the order of proposals based on the relevance of the childcare facilities, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance data of childcare facilities into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0083] The update unit can estimate the parent's emotions and adjust the frequency of updates to legal amendments and local rules based on the estimated parent's emotions. For example, if the parent is feeling anxious, the update unit will update frequently to provide the latest information. For example, if the parent is relaxed, the update unit will update regularly to provide stable information. For example, if the parent is in a hurry, the update unit will update quickly to provide the latest information immediately. In this way, by adjusting the update frequency according to the parent's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input parent's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The update unit can predict current rules based on past legal amendment data during updates. For example, the update unit can predict future trends in legal amendments based on past legal amendment data. For example, the update unit can analyze past legal amendment data and reflect it in current rules. For example, the update unit can predict the latest rules by referring to past legal amendment data. This allows the update unit to predict current rules and provide more appropriate information by referring to past legal amendment data. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past legal amendment data into a generating AI and have the generating AI perform predictions of current rules.

[0085] The update unit can apply an appropriate update algorithm according to the rules of each region during the update process. For example, the update unit may apply an algorithm that performs frequent updates based on the rules of urban areas. For example, the update unit may apply an algorithm that performs periodic updates based on the rules of suburban areas. For example, the update unit may apply a customized update algorithm based on the rules of a specific region. This allows for the provision of more appropriate information by applying different update algorithms according to the rules of each region. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input region-specific rule data into a generating AI and have the generating AI execute the application of the update algorithm.

[0086] The update unit can estimate the parent's emotions and determine update priorities based on the estimated emotions. For example, if the parent is feeling anxious, the update unit will prioritize updating important information. If the parent is relaxed, the update unit will update the overall information. If the parent is in a hurry, the update unit will immediately update the information that is needed. This allows for the provision of more appropriate information by prioritizing updates according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The update unit can adjust the order of updates based on the timing of collection of legal amendments and local rules during the update process. For example, the update unit may prioritize updating recently collected legal amendment information. For example, the update unit may lower the priority of updating older information. For example, the update unit may determine the order of updates based on the preferences of guardians, regardless of the collection timing. This allows for the provision of more appropriate information by adjusting the order of updates based on the timing of collection of legal amendments and local rules. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit may input data on the timing of collection of legal amendments and local rules into a generating AI and have the generating AI perform the adjustment of the update order.

[0088] The update unit can improve the accuracy of updates by referring to relevant legal amendments and regional rule literature during the update process. For example, the update unit can refer to the latest legal amendment literature to provide accurate information. For example, the update unit can refer to regional rule literature to provide region-specific information. For example, the update unit can refer to past legal amendment literature to predict future trends in legal amendments. This allows the update unit to improve the accuracy of updates and provide more appropriate information by referring to relevant literature. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input relevant legal amendment and regional rule literature data into a generating AI and have the generating AI perform the update accuracy improvement.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] Kids School Selector can estimate parents' emotions and adjust the childcare facility visit schedule based on those emotions. For example, if parents are feeling anxious, the frequency of visits can be reduced and only important facilities can be visited. If parents are relaxed, multiple facilities can be visited to provide a wider range of options. Furthermore, if parents are in a hurry, a visit schedule can be quickly created and visits can be conducted immediately. In this way, by adjusting the visit schedule according to parents' emotions, a more appropriate visit experience can be provided.

[0091] Kids School Selector can analyze parents' past selection history and suggest appropriate school visit schedules. For example, it can analyze the characteristics of childcare facilities parents have previously chosen and prioritize scheduling visits to similar facilities. It can also prioritize the visit methods parents have used in the past (online, in-person, etc.). Furthermore, it can schedule visits at specific time slots based on parents' past selection history. In this way, by analyzing past selection history, it can provide parents with the most optimal school visit schedule.

[0092] Kids School Selector can adjust visit schedules based on parents' current circumstances and areas of interest. For example, it can schedule visits to financially suitable childcare facilities based on parents' current income. It can also prioritize visits to relevant facilities based on parents' areas of interest (educational philosophy, facility facilities, etc.). Furthermore, it can schedule visits to conveniently located childcare facilities based on parents' living circumstances (working hours, commute distance, etc.). By adjusting visit schedules based on parents' circumstances and areas of interest, it can provide a more appropriate visit experience.

[0093] Kids School Selector can adjust visit schedules based on parents' geographical location. For example, it can prioritize visits to childcare facilities close to parents' homes, workplaces, and even along their commute routes. By considering geographical location, it can provide parents with a more relevant visit schedule.

[0094] Kids School Selector can analyze parents' social media activity and suggest relevant childcare facility visit schedules. For example, it can schedule visits to childcare facilities that parents follow on social media, facilities that parents have shown interest in on social media, and facilities that parents have shared on social media. This allows for the provision of relevant visit schedules to parents by analyzing their social media activity.

[0095] Kids School Selector can estimate parents' emotions and prioritize visit schedules based on those emotions. For example, if parents are feeling anxious, it can prioritize visits to highly reliable childcare facilities. If parents are excited, it can prioritize visits to new childcare facilities. Furthermore, if parents are calm, it can prioritize visits to childcare facilities that offer detailed information. This allows for a more appropriate visit experience by prioritizing the visit schedule according to parents' emotions.

[0096] Kids School Selector can estimate parents' emotions and adjust the tour guidance based on those estimates. For example, if a parent is feeling anxious, a reassuring guidance method can be used. If a parent is excited, an engaging guidance method can be used. Furthermore, if a parent is calm, a guidance method including detailed information can be used. This allows for a more appropriate tour experience by adjusting the guidance method according to the parents' emotions.

[0097] The Kids School Selector can estimate parents' emotions and adjust the length of the visit based on those estimates. For example, if parents are in a hurry, it can provide a short, concise visit. If parents are relaxed, it can provide a more detailed visit. Furthermore, if parents are excited, it can provide a visually stimulating visit. By adjusting the length of the visit according to the parents' emotions, it can provide a more appropriate visit experience.

[0098] Kids School Selector can estimate parents' emotions and adjust the timing of school visits based on those estimates. For example, if parents are feeling stressed, the frequency of visits can be reduced, and only important facilities can be visited. If parents are relaxed, multiple facilities can be visited, offering a wider range of options. Furthermore, if parents are in a hurry, a visit schedule can be quickly created and the visit can be conducted immediately. This allows for a more appropriate school visit experience by adjusting the timing of visits according to parents' emotions.

[0099] Kids School Selector can estimate parents' emotions and adjust the feedback method during the visit based on those estimates. For example, if a parent is feeling anxious, it can use reassuring feedback. If a parent is excited, it can use engaging feedback. Furthermore, if a parent is calm, it can use feedback that includes detailed information. This allows for a more appropriate visit experience by adjusting the feedback method according to the parents' emotions.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The collection department collects information on childcare facilities. The collection department collects information on childcare facilities based on, for example, the preferences entered by parents. The collected information includes the location of the facility, the services offered, fees, and reputation. The collection department collects publicly available information from the internet, for example. The collection department can also collect information from the official websites of childcare facilities and review sites. Furthermore, the collection department can collect information from brochures and informational materials of childcare facilities. For example, the collection department uses internet search engines to collect information on childcare facilities. They collect information such as the services offered, fees, and reputation from the official websites of childcare facilities. They collect parent ratings and opinions from review sites. Step 2: The analysis department analyzes the information collected by the data collection department. For example, the analysis department identifies the best childcare facility that matches the parents' desired conditions based on the collected information. For example, the analysis department proposes the best childcare facility by considering dynamic conditions such as income, commuting distance, city rules, and legal changes. For example, the analysis department identifies economically suitable childcare facilities based on income. For example, it identifies convenient childcare facilities based on commuting distance. For example, it identifies appropriate childcare facilities based on city rules and legal changes. Step 3: The proposal department proposes the most suitable childcare facility based on the analysis results obtained by the analysis department. For example, the proposal department proposes the most suitable childcare facility that matches the parents' desired conditions. For example, the proposal department proposes the most suitable childcare facility considering dynamic conditions such as income, commuting distance, city rules, and legal revisions. For example, the proposal department proposes an economically suitable childcare facility based on income. For example, it proposes a convenient childcare facility based on commuting distance. For example, it proposes an appropriate childcare facility based on city rules and legal revisions. Step 4: The update section reflects legal changes and local rules in real time. The update section instantly reflects legal changes and local rules, for example. The update section collects information such as the content of legal changes, the effective date, and the scope of impact, and reflects it in the system. It updates the information of childcare facilities based on local rules.

[0102] 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.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information from publicly available information on the internet, official websites of childcare facilities, and review sites. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the optimal childcare facility that matches the parents' desired conditions based on the collected information. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal childcare facility based on the analysis results. The update unit is implemented by the identification processing unit 290 of the data processing unit 12 and reflects legal revisions and regional rules in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] 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.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] 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.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and update unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information from publicly available information on the internet, official websites of childcare facilities, and review sites. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies the optimal childcare facility that matches the parents' desired conditions based on the collected information. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes the optimal childcare facility based on the analysis results. The update unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and reflects legal revisions and regional rules in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] 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.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] 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.

[0129] 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.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and update unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information from publicly available information on the internet, official websites of childcare facilities, and review sites. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the optimal childcare facility that matches the parents' desired conditions based on the collected information. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal childcare facility based on the analysis results. The update unit is implemented by the identification processing unit 290 of the data processing unit 12 and reflects legal revisions and regional rules in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] 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.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] 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.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] 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.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and update unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information from publicly available information on the internet, official websites of childcare facilities, and review sites. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and identifies the optimal childcare facility that matches the parents' desired conditions based on the collected information. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes the optimal childcare facility based on the analysis results. The update unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and reflects legal revisions and regional rules in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] 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.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] 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.

[0158] 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.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) A collection department that collects information on childcare facilities, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes appropriate childcare facilities. It includes an update section that immediately reflects legal revisions and regional rules. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose an appropriate educational plan based on the child's interests and characteristics, as well as the parents' educational philosophy. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We estimate the emotions of parents and adjust the timing of information gathering from childcare facilities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze parents' past decision-making history and select appropriate information gathering methods. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When gathering information on childcare facilities, select information based on the parents' current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the feelings of parents and prioritize the information about childcare facilities to collect based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When gathering information on childcare facilities, we prioritize collecting relevant information based on the geographical location of parents. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When gathering information on childcare facilities, we analyze parents' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is We estimate the parents' emotions and adjust the way the analysis is presented based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During the analysis, adjust the details of the analysis based on the importance of the childcare facilities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During the analysis, apply the appropriate analytical algorithm according to the category of childcare facility. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Estimate the parents' emotions and adjust the length of the analysis based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the childcare facilities collected the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of childcare facilities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, We estimate the parents' emotions and adjust the way the proposal is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When submitting a proposal, adjust the details of the proposal based on the importance of the childcare facility. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting a proposal, apply the appropriate proposal algorithm according to the category of childcare facility. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, Estimate the parents' feelings and adjust the length of the suggestion based on the estimated parents' feelings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When submitting proposals, prioritize them based on the collection schedule of childcare facilities. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the childcare facilities. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned update unit is We estimate the feelings of parents and adjust the frequency of legal revisions and local rule updates based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned update unit is When updating, the system predicts current rules based on past legal amendment data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is During updates, the appropriate update algorithm is applied according to the rules specific to each region. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is The system estimates parental sentiment and determines update priorities based on the estimated parental sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is When updating, the update order will be adjusted based on the timing of legal changes and the collection of local rules. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is When updating, we improve the accuracy of the update based on relevant legal amendments and local rule literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information on childcare facilities, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes appropriate childcare facilities. It includes an update section that immediately reflects legal revisions and regional rules. A system characterized by the following features.

2. The aforementioned proposal section is, We propose an appropriate educational plan based on the child's interests and characteristics, as well as the parents' educational philosophy. The system according to feature 1.

3. The aforementioned collection unit is We estimate the emotions of parents and adjust the timing of information gathering from childcare facilities based on those estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze parents' past decision-making history and select appropriate information gathering methods. The system according to feature 1.

5. The aforementioned collection unit is When gathering information on childcare facilities, select information based on the parents' current living situation and areas of interest. The system according to feature 1.

6. The aforementioned collection unit is We estimate the feelings of parents and prioritize the information about childcare facilities to collect based on those estimated feelings. The system according to feature 1.

7. The aforementioned collection unit is When gathering information on childcare facilities, we prioritize collecting relevant information based on the geographical location of parents. The system according to feature 1.

8. The aforementioned collection unit is When gathering information on childcare facilities, we analyze parents' social media activity and collect relevant information. The system according to feature 1.

9. The aforementioned analysis unit is We estimate the parents' emotions and adjust the way the analysis is presented based on the estimated parents' emotions. The system according to feature 1.

10. The aforementioned analysis unit is During the analysis, adjust the details of the analysis based on the importance of the childcare facilities. The system according to feature 1.

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