system

Abstract

We provide a system that responds quickly and appropriately to parents' questions and needs, and contributes to resolving the problem of waiting lists for childcare. [Solution] The specific processing unit 290 of the data processing device 12 in the system performs a reception process to receive questions from parents, an analysis process to analyze the content of the questions received by the reception process, a provision process to provide appropriate advice or resources based on the content of the questions analyzed by the analysis process, a collection process to collect past data or the current situation, a prediction process to analyze the data collected by the collection process to predict the future number of children on waiting lists, a planning process to make a facility expansion plan based on the prediction results obtained by the prediction process, and a proposal process to propose appropriate childcare facilities based on the needs analyzed by the analysis process.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to respond quickly and appropriately to parents' questions and needs, and have not contributed sufficiently to solving the problem of children on waiting lists.

[0005] The system according to the embodiment aims to respond promptly and appropriately to the questions and needs of parents and to contribute to resolving the problem of children on waiting lists. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a collection unit, a prediction unit, a planning unit, and a proposal unit. The reception unit receives questions from parents. The analysis unit analyzes the questions received by the reception unit. The provision unit provides appropriate advice or resources based on the questions analyzed by the analysis unit. The collection unit collects past data or the current situation. The prediction unit analyzes the data collected by the collection unit to predict the future number of children on waiting lists. The planning unit makes a facility expansion plan based on the prediction results obtained by the prediction unit. The proposal unit proposes appropriate childcare facilities based on the needs analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can respond quickly and appropriately to the questions and needs of parents, and contribute to resolving the problem of children on waiting lists. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses generative AI to provide tools to contribute to eliminating the problem of children on waiting lists. This system includes an AI chatbot that provides support tools and resources for home-based childcare, a tool that predicts the number of children on waiting lists and develops childcare facility expansion plans, and a tool that matches parents' needs with childcare facilities. Specifically, we first develop an AI chatbot that provides support tools and resources for home-based childcare. This chatbot allows parents to ask questions and ask questions about home-based childcare and provides appropriate advice and resources. We then develop a tool that predicts the number of children on waiting lists and develops childcare facility expansion plans. This tool can predict the future number of children on waiting lists based on past data and current conditions and develop necessary facility expansion plans. We also develop a tool that matches parents' needs with childcare facilities. This tool suggests childcare facilities that match parents' requirements and requirements when they input their desired childcare facility conditions and needs. Combining these tools can contribute to eliminating the problem of children on waiting lists and reduce the burden on parents. For example, an AI chatbot that provides support tools and resources for home-based childcare can be divided into a reception unit that accepts questions from parents, an analysis unit that analyzes the questions, and a provision unit that provides appropriate advice and resources. Next, the waiting list prediction tool can be divided into a collection section that collects past data and current conditions, a prediction section that analyzes the collected data to predict the future number of children on waiting lists, and a planning section that creates facility expansion plans based on the prediction results.Furthermore, the matching tool between parental needs and childcare facilities can be divided into a reception section that inputs parents' needs, an analysis section that analyzes the input needs, and a proposal section that proposes appropriate childcare facilities based on the analysis results.Each of these sections is interrelated, with the information received by the reception section being analyzed by the analysis section, and the provision section and proposal section providing appropriate information and facilities.In this way, the system can contribute to eliminating the waiting list problem and reduce the burden on parents.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a collection unit, a prediction unit, a planning unit, and a proposal unit. The reception unit receives questions from parents. Questions from parents include, but are not limited to, questions about childcare, information about childcare facilities, and contact information for experts. The reception unit allows parents to input questions using, for example, a smartphone or a computer. The reception unit can also receive questions using voice input. For example, the parent dictates a question using a microphone and converts it into text data using voice recognition technology. The analysis unit analyzes the question received by the reception unit. The analysis unit analyzes the question using, for example, natural language processing technology and extracts information for providing appropriate advice and resources. For example, the analysis unit breaks down the question into keywords and searches a database for related information. The analysis unit can also use an algorithm to understand the context of the question and generate an appropriate answer. The provision unit provides appropriate advice and resources based on the question analyzed by the analysis unit. The provision unit provides, for example, parenting tips and advice, a list of childcare facilities, and contact information for experts. For example, the providing unit automatically displays relevant information depending on the content of a question asked by a parent. The providing unit can also provide information in a format desired by the parent. For example, the information is provided in text format, image format, video format, or the like. The collecting unit collects past data and the current situation. The collecting unit collects data such as the past number of children on waiting lists, parent survey results, and local demographics. For example, the collecting unit obtains data from government or local government databases. The collecting unit can also conduct questionnaire surveys of parents and collect data to understand the current situation. The predicting unit analyzes the data collected by the collecting unit to predict the future number of children on waiting lists. The predicting unit predicts the future number of children on waiting lists, for example, using a statistical model or a machine learning algorithm. For example, the predicting unit analyzes trends in the future number of children on waiting lists based on past data. The predicting unit can also predict the future number of children on waiting lists taking the current situation into consideration. The planning unit makes facility expansion plans based on the prediction results obtained by the predicting unit.The planning unit makes plans for, for example, the construction of new childcare facilities, the renovation of existing facilities, and the hiring of additional staff. For example, the planning unit determines the number and size of facilities needed based on the prediction results. The planning unit can also prioritize facility expansion plans based on the prediction results. The proposal unit includes a reception unit that inputs parents' needs, an analysis unit that analyzes the input needs, and a proposal unit that proposes appropriate childcare facilities based on the analysis results. The proposal unit can, for example, input the conditions and needs of childcare facilities desired by parents. Examples of such needs include extending childcare hours, providing special care, and enhancing educational programs. The analysis unit analyzes the input needs and extracts information for proposing appropriate childcare facilities. The proposal unit proposes childcare facilities that meet the parents' needs based on the analysis results. For example, the proposal unit displays a list of childcare facilities that meet the parents' desired conditions. The proposal unit can also provide detailed information about childcare facilities that meet the parents' desired conditions. As a result, the system according to the embodiment can respond to parents' questions and needs and contribute to eliminating the problem of children on waiting lists.

[0030] The provision unit can provide information or resources related to childcare. The provision unit provides information and resources such as a list of childcare facilities, childcare advice, and contact information for experts. For example, the provision unit automatically displays related information depending on the content of a question asked by a parent. The provision unit can also provide information in a format desired by the parent. For example, the information can be provided in text format, image format, video format, etc. This can reduce the burden on parents by providing information and resources related to childcare. Some or all of the above-mentioned processing in the provision unit may be performed using, or without, AI, for example. For example, the provision unit can input the content of a parent's question into a generation AI, which can generate appropriate advice and resources.

[0031] The prediction unit can predict the future number of children on waiting lists based on past data or the current situation. The prediction unit predicts the future number of children on waiting lists based on data such as the past number of children on waiting lists, parent survey results, and local demographics. For example, the prediction unit predicts the future number of children on waiting lists using a statistical model or a machine learning algorithm. For example, the prediction unit analyzes trends in the future number of children on waiting lists based on past data. The prediction unit can also predict the future number of children on waiting lists taking the current situation into consideration. This allows appropriate facility expansion plans to be made by predicting the future number of children on waiting lists. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs collected data into a generation AI, which can then predict the future number of children on waiting lists.

[0032] The planning unit can create a facility expansion plan based on the prediction results. The planning unit creates plans, for example, for the construction of new childcare facilities, the renovation of existing facilities, and the addition of staff. For example, the planning unit determines the number and size of facilities required based on the prediction results. The planning unit can also determine the priority of facility expansion plans based on the prediction results. In this way, creating facility expansion plans can contribute to eliminating childcare waiting lists. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the prediction results into a generation AI, which can then create a facility expansion plan.

[0033] The suggestion unit can suggest childcare facilities that meet the needs of the parents. For example, the suggestion unit can input the conditions and needs of the childcare facility desired by the parents. For example, these may include needs such as extending childcare hours, providing special care, and enhancing educational programs. The analysis unit analyzes the input needs and extracts information for suggesting appropriate childcare facilities. The suggestion unit suggests childcare facilities that meet the needs of the parents based on the analysis results. For example, the suggestion unit displays a list of childcare facilities that meet the conditions desired by the parents. The suggestion unit can also provide detailed information about childcare facilities that meet the conditions desired by the parents. This reduces the burden on the parents by suggesting childcare facilities that meet the needs of the parents. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input the needs of the parents to a generation AI, which then suggests appropriate childcare facilities.

[0034] The reception unit can analyze the parent's past question history and select the optimal reception method. For example, the reception unit prioritizes suggesting question formats that the parent has frequently used in the past. For example, the reception unit stores the parent's past question history in a database and identifies the frequently used question formats. The reception unit can also select the most efficient reception method from the parent's past question history. For example, the reception unit analyzes the parent's past question history and automatically selects the reception method that the parent preferred. Furthermore, the reception unit can dynamically adjust the optimal reception method based on the parent's past question history. For example, the reception unit analyzes the parent's question history in real time and suggests the optimal reception method. In this way, the optimal reception method can be selected by analyzing the parent's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's question history data into a generation AI, which can select the optimal reception method.

[0035] When receiving questions or needs, the reception unit can filter them based on the guardian's current living situation and areas of interest. The reception unit, for example, prioritizes receiving related questions and needs based on the guardian's current living situation. For example, the reception unit stores the guardian's living situation in a database and identifies related questions and needs. The reception unit can also filter related questions and needs based on the guardian's areas of interest. For example, the reception unit analyzes the guardian's areas of interest and prioritizes receiving related questions and needs. Furthermore, the reception unit can suggest optimal questions and needs based on the guardian's living situation and areas of interest. For example, the reception unit analyzes the guardian's living situation and areas of interest in real time and suggests optimal questions and needs. This enables more appropriate responses by filtering questions and needs based on the guardian's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the guardian's living situation and areas of interest into a generation AI, which can then suggest optimal questions and needs.

[0036] When receiving questions or needs, the reception unit can prioritize receiving highly relevant information by taking into account the guardian's geographical location information. The reception unit, for example, prioritizes receiving related questions or needs based on the guardian's current location. For example, the reception unit stores the guardian's geographical location information in a database and identifies related questions or needs. The reception unit can also suggest optimal questions or needs by taking the guardian's geographical location information into account. For example, the reception unit analyzes the guardian's geographical location information in real time and suggests optimal questions or needs. Furthermore, the reception unit can filter highly relevant information based on the guardian's geographical location information. For example, the reception unit prioritizes displaying relevant information based on the guardian's geographical location information. In this way, highly relevant information can be prioritized by taking the guardian's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the guardian's geographical location information to a generation AI, which can then suggest highly relevant information.

[0037] The reception unit can analyze the parent's social media activity and receive relevant information when receiving a question or need. The reception unit, for example, analyzes the parent's social media activity and prioritizes receiving relevant questions and needs. For example, the reception unit stores the parent's social media activity in a database and identifies relevant questions and needs. The reception unit can also suggest optimal questions and needs based on the parent's social media activity. For example, the reception unit analyzes the parent's social media activity in real time and suggests optimal questions and needs. The reception unit can also filter highly relevant information taking the parent's social media activity into consideration. For example, the reception unit prioritizes displaying relevant information based on the parent's social media activity. In this way, by analyzing the parent's social media activity, relevant information can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's social media activity data to a generation AI, which then suggests highly relevant information.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the question or need during analysis. For example, the analysis unit performs a detailed analysis on questions or needs with high importance. For example, the analysis unit stores the importance of questions and needs in a database and identifies those with high importance. The analysis unit can also perform a concise analysis on questions or needs with low importance. For example, the analysis unit dynamically adjusts the level of detail of the analysis based on the importance of the question or need. Furthermore, the analysis unit can analyze the importance of questions and needs in real time and determine the optimal level of detail of the analysis. For example, the analysis unit determines the priority of the analysis based on the importance of the question or need. This allows for adjusting the level of detail of the analysis based on the importance of the question or need, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input importance data of questions and needs to a generation AI, which can then adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the question or need. For example, the analysis unit applies a specialized analysis algorithm to questions related to childcare. For example, the analysis unit stores the categories of questions and needs in a database and selects an analysis algorithm according to the category. The analysis unit can also apply an appropriate analysis algorithm to questions related to resource provision. For example, the analysis unit selects the optimal analysis algorithm depending on the category of the question or need. Furthermore, the analysis unit can analyze the categories of questions and needs in real time and dynamically apply the optimal analysis algorithm. For example, the analysis unit determines the analysis priority based on the category of the question or need. This allows for applying the optimal analysis algorithm depending on the category of the question or need, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input question and need category data into a generation AI, which then applies the optimal analysis algorithm.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time when the questions and needs were submitted. The analysis unit, for example, prioritizes analysis of recently submitted questions and needs. For example, the analysis unit stores the time when the questions and needs were submitted in a database and identifies the most recently submitted questions and needs. The analysis unit can also postpone older questions and needs. For example, the analysis unit dynamically adjusts the analysis priority based on the time when the questions and needs were submitted. Furthermore, the analysis unit can analyze the time when the questions and needs were submitted in real time and determine the optimal analysis priority. For example, the analysis unit determines the analysis priority based on the time when the questions and needs were submitted. This enables more appropriate analysis results to be provided by determining the analysis priority based on the time when the questions and needs were submitted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the time when the questions and needs were submitted to a generation AI, which then determines the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of questions and needs. The analysis unit, for example, prioritizes analysis of highly relevant questions and needs. For example, the analysis unit stores the relevance of questions and needs in a database and identifies those with high relevance. The analysis unit can also postpone less relevant questions and needs. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of questions and needs. Furthermore, the analysis unit can analyze the relevance of questions and needs in real time and determine the optimal order of analysis. For example, the analysis unit determines the order of analysis based on the relevance of questions and needs. In this way, adjusting the order of analysis based on the relevance of questions and needs can provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of questions and needs to a generation AI, which can adjust the order of analysis.

[0042] The providing unit can adjust the level of detail of the information or resources provided based on the importance of the information or resources when providing the information. The providing unit, for example, provides detailed information or resources with high importance. For example, the providing unit stores the importance of the information or resources in a database and identifies those with high importance. The providing unit can also provide concise information or resources with low importance. For example, the providing unit dynamically adjusts the level of detail of the information or resources provided based on the importance of the information or resources. Furthermore, the providing unit can analyze the importance of the information or resources in real time and determine the optimal level of detail of the information or resources provided. For example, the providing unit determines the priority of the information or resources provided based on the importance of the information or resources. This enables more appropriate information to be provided by adjusting the level of detail of the information or resources based on the importance of the information or resources. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the information or resources to a generating AI, which can then adjust the level of detail of the information or resources provided.

[0043] The providing unit can apply different provision algorithms depending on the category of information or resources when providing the information. For example, the providing unit applies a specialized provision algorithm to information related to childcare. For example, the providing unit stores categories of information or resources in a database and selects a provision algorithm depending on the category. The providing unit can also apply an appropriate provision algorithm to information related to resource provision. For example, the providing unit selects an optimal provision algorithm depending on the category of information or resources. Furthermore, the providing unit can analyze the categories of information or resources in real time and dynamically apply the optimal provision algorithm. For example, the providing unit determines a provision priority based on the category of information or resources. This enables more appropriate information to be provided by applying the optimal provision algorithm depending on the category of information or resources. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input category data of information or resources to a generation AI, which can then apply the optimal provision algorithm.

[0044] The providing unit can determine the priority of provision based on the time of submission of information or resources at the time of provision. The providing unit, for example, prioritizes providing information or resources that have been provided recently. For example, the providing unit stores the time of submission of information or resources in a database and identifies the most recently submitted information or resources. The providing unit can also postpone information or resources that have been submitted older. For example, the providing unit dynamically adjusts the priority of provision based on the time of submission of information or resources. Furthermore, the providing unit can analyze the time of submission of information or resources in real time and determine the optimal priority of provision. For example, the providing unit determines the priority of provision based on the time of submission of information or resources. This enables more appropriate information provision by determining the priority of provision based on the time of submission of information or resources. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of submission of information or resources to a generation AI, which can then determine the priority of provision.

[0045] The providing unit can adjust the order of provision based on the relevance of the information and resources when providing them. The providing unit, for example, prioritizes providing highly relevant information and resources. For example, the providing unit stores the relevance of the information and resources in a database and identifies those with high relevance. The providing unit can also postpone less relevant information and resources. For example, the providing unit dynamically adjusts the order of provision based on the relevance of the information and resources. Furthermore, the providing unit can analyze the relevance of the information and resources in real time and determine an optimal order of provision. For example, the providing unit determines the order of provision based on the relevance of the information and resources. This enables more appropriate information provision by adjusting the order of provision based on the relevance of the information and resources. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the information and resources to a generation AI, which can adjust the order of provision.

[0046] The collection unit can analyze past data collection history and select the optimal collection method. For example, the collection unit prioritizes selecting data collection methods that were effective in the past. For example, the collection unit stores past data collection history in a database and identifies collection methods that were effective. The collection unit can also select the most efficient collection method from the past data collection history. For example, the collection unit analyzes past data collection history and automatically selects the optimal collection method. Furthermore, the collection unit can analyze past data collection history in real time and dynamically adjust the optimal collection method. For example, the collection unit determines collection priorities based on the past data collection history. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data collection history data to a generation AI, which can select the optimal collection method.

[0047] The collection unit can filter data based on the current situation and areas of interest when collecting data. The collection unit, for example, prioritizes collecting relevant data based on the current situation. For example, the collection unit stores the parent's current situation in a database and identifies relevant data. The collection unit can also filter relevant data based on the parent's areas of interest. For example, the collection unit analyzes the parent's areas of interest and prioritizes collecting relevant data. Furthermore, the collection unit can suggest optimal data based on the parent's current situation and areas of interest. For example, the collection unit analyzes the parent's current situation and areas of interest in real time and suggests optimal data. This enables more appropriate data collection by filtering data based on the current situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the parent's current situation and areas of interest to a generation AI, which then suggests optimal data.

[0048] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. The collection unit, for example, prioritizes collecting relevant data based on the guardian's current location. For example, the collection unit stores the guardian's geographical location information in a database and identifies relevant data. The collection unit can also suggest optimal data by taking the geographical location information into consideration. For example, the collection unit analyzes the guardian's geographical location information in real time and suggests optimal data. Furthermore, the collection unit can filter highly relevant data based on the geographical location information. For example, the collection unit prioritizes displaying relevant data based on the guardian's geographical location information. In this way, highly relevant data can be preferentially collected by taking the geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the guardian's geographical location information data to a generation AI, which then suggests highly relevant data.

[0049] The collection unit can analyze social media activity and collect relevant data when collecting data. The collection unit, for example, analyzes the parent's social media activity and prioritizes collecting relevant data. For example, the collection unit stores the parent's social media activity in a database and identifies relevant data. The collection unit can also suggest optimal data based on the social media activity. For example, the collection unit analyzes the parent's social media activity in real time and suggests optimal data. The collection unit can also filter highly relevant data taking the social media activity into consideration. For example, the collection unit prioritizes displaying relevant data based on the parent's social media activity. In this way, relevant data can be preferentially collected by analyzing the social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the parent's social media activity data to a generation AI, which then suggests highly relevant data.

[0050] The prediction unit can adjust the level of detail of the prediction based on the importance of the data during prediction. For example, the prediction unit performs a detailed prediction for data with high importance. For example, the prediction unit stores the importance of data in a database and identifies data with high importance. The prediction unit can also perform a concise prediction for data with low importance. For example, the prediction unit dynamically adjusts the level of detail of the prediction according to the importance of the data. Furthermore, the prediction unit can analyze the importance of data in real time and determine the optimal level of detail of the prediction. For example, the prediction unit determines the priority of the prediction based on the importance of the data. As a result, by adjusting the level of detail of the prediction based on the importance of the data, more appropriate prediction results can be provided. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data importance of the data to a generation AI, which can then adjust the level of detail of the prediction.

[0051] The prediction unit can apply different prediction algorithms depending on the data category during prediction. For example, the prediction unit applies a specialized prediction algorithm to data related to childcare. For example, the prediction unit stores the data category in a database and selects a prediction algorithm depending on the category. The prediction unit can also apply an appropriate prediction algorithm to data related to resource provision. For example, the prediction unit selects an optimal prediction algorithm depending on the data category. Furthermore, the prediction unit can analyze the data category in real time and dynamically apply the optimal prediction algorithm. For example, the prediction unit determines a prediction priority based on the data category. This allows for applying the optimal prediction algorithm depending on the data category, thereby providing more appropriate prediction results. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data category data to a generation AI, which then applies the optimal prediction algorithm.

[0052] The prediction unit can determine the priority of predictions based on the time of data submission during prediction. The prediction unit, for example, prioritizes predictions for recently submitted data. For example, the prediction unit stores the time of data submission in a database and identifies data submitted more recently. The prediction unit can also postpone data submitted more recently. For example, the prediction unit dynamically adjusts the priority of predictions based on the time of data submission. Furthermore, the prediction unit can analyze the time of data submission in real time and determine the optimal priority of predictions. For example, the prediction unit determines the priority of predictions based on the time of data submission. In this way, by determining the priority of predictions based on the time of data submission, more appropriate prediction results can be provided. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the time of data submission to a generation AI, which can then determine the priority of predictions.

[0053] The prediction unit can adjust the order of predictions based on the relevance of the data during prediction. The prediction unit, for example, prioritizes predicting highly relevant data. For example, the prediction unit stores the relevance of the data in a database and identifies highly relevant data. The prediction unit can also postpone less relevant data. For example, the prediction unit dynamically adjusts the order of predictions based on the relevance of the data. Furthermore, the prediction unit can analyze the relevance of the data in real time and determine the optimal order of predictions. For example, the prediction unit determines the order of predictions based on the relevance of the data. In this way, by adjusting the order of predictions based on the relevance of the data, more appropriate prediction results can be provided. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the relevance of the data to a generation AI, which can then adjust the order of predictions.

[0054] The planning unit can adjust the level of detail of the plan based on the importance of the prediction results during planning. For example, the planning unit creates a detailed plan for a highly important prediction result. For example, the planning unit stores the importance of the prediction results in a database and identifies the most important ones. The planning unit can also create a concise plan for a less important prediction result. For example, the planning unit dynamically adjusts the level of detail of the plan depending on the importance of the prediction result. Furthermore, the planning unit can analyze the importance of the prediction results in real time and determine the optimal level of detail of the plan. For example, the planning unit determines the priority of the plan based on the importance of the prediction result. As a result, a more appropriate plan can be provided by adjusting the level of detail of the plan based on the importance of the prediction result. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input importance data of the prediction results to a generation AI, which can then adjust the level of detail of the plan.

[0055] The planning unit can apply different planning algorithms depending on the category of the prediction result during planning. For example, the planning unit applies a specialized planning algorithm to prediction results related to childcare. For example, the planning unit stores the category of the prediction result in a database and selects a planning algorithm according to the category. The planning unit can also apply an appropriate planning algorithm to prediction results related to resource provision. For example, the planning unit selects an optimal planning algorithm depending on the category of the prediction result. Furthermore, the planning unit can analyze the category of the prediction result in real time and dynamically apply the optimal planning algorithm. For example, the planning unit determines the priority of plans based on the category of the prediction result. This allows for applying the optimal planning algorithm depending on the category of the prediction result, thereby providing a more appropriate plan. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input category data of the prediction result to a generation AI, which then applies the optimal planning algorithm.

[0056] During planning, the planning unit can determine the priority of plans based on the submission time of prediction results. The planning unit, for example, prioritizes the most recently submitted prediction results. For example, the planning unit stores the submission time of prediction results in a database and identifies the most recently submitted prediction results. The planning unit can also postpone the oldest submitted prediction results. For example, the planning unit dynamically adjusts the priority of plans based on the submission time of prediction results. Furthermore, the planning unit can analyze the submission time of prediction results in real time and determine the optimal priority of plans. For example, the planning unit determines the priority of plans based on the submission time of prediction results. This makes it possible to provide more appropriate plans by determining the priority of plans based on the submission time of prediction results. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input data on the submission time of prediction results to a generation AI, which can then determine the priority of plans.

[0057] The planning unit can adjust the order of plans based on the relevance of the prediction results during planning. The planning unit, for example, prioritizes highly relevant prediction results when planning. For example, the planning unit stores the relevance of the prediction results in a database and identifies highly relevant results. The planning unit can also postpone less relevant prediction results. For example, the planning unit dynamically adjusts the order of plans based on the relevance of the prediction results. Furthermore, the planning unit can analyze the relevance of the prediction results in real time and determine the optimal order of plans. For example, the planning unit determines the order of plans based on the relevance of the prediction results. As a result, a more appropriate plan can be provided by adjusting the order of plans based on the relevance of the prediction results. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input relevance data of the prediction results to a generation AI, which can then adjust the order of plans.

[0058] The suggestion unit can adjust the level of detail of the proposal based on the importance of the childcare facility when making a proposal. The suggestion unit, for example, makes a detailed proposal for a childcare facility with a high importance. For example, the suggestion unit stores the importance of childcare facilities in a database and identifies childcare facilities with a high importance. The suggestion unit can also make a concise proposal for a childcare facility with a low importance. For example, the suggestion unit dynamically adjusts the level of detail of the proposal according to the importance of the childcare facility. Furthermore, the suggestion unit can analyze the importance of the childcare facility in real time and determine an optimal level of detail of the proposal. For example, the suggestion unit determines the priority of the proposal based on the importance of the childcare facility. As a result, by adjusting the level of detail of the proposal based on the importance of the childcare facility, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of the childcare facility to a generation AI, which can adjust the level of detail of the proposal.

[0059] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the childcare facility. For example, the proposal unit applies a specialized proposal algorithm to childcare facilities. For example, the proposal unit stores the categories of childcare facilities in a database and selects a proposal algorithm depending on the category. The proposal unit can also apply an appropriate proposal algorithm to facilities related to resource provision. For example, the proposal unit selects an optimal proposal algorithm depending on the category of the childcare facility. Furthermore, the proposal unit can analyze the category of the childcare facility in real time and dynamically apply the optimal proposal algorithm. For example, the proposal unit determines a priority of proposals based on the category of the childcare facility. This allows for more appropriate proposals to be provided by applying the optimal proposal algorithm depending on the category of the childcare facility. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input category data of the childcare facility to a generation AI, which can then apply the optimal proposal algorithm.

[0060] When making a proposal, the suggestion unit can determine the priority of the proposals based on the submission date of the childcare facilities. The suggestion unit, for example, prioritizes the proposals of childcare facilities that have been submitted recently. For example, the suggestion unit stores the submission date of the childcare facilities in a database and identifies childcare facilities with the most recent submission date. The suggestion unit can also postpone childcare facilities with the oldest submission date. For example, the suggestion unit dynamically adjusts the priority of the proposals based on the submission date of the childcare facilities. Furthermore, the suggestion unit can analyze the submission date of the childcare facilities in real time and determine the optimal priority of the proposals. For example, the suggestion unit determines the priority of the proposals based on the submission date of the childcare facilities. This makes it possible to provide more appropriate proposals by determining the priority of the proposals based on the submission date of the childcare facilities. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input submission date data of childcare facilities into a generation AI, which can then determine the priority of the proposals.

[0061] The suggestion unit can adjust the order of suggestions based on the relevance of the childcare facilities when making suggestions. The suggestion unit, for example, prioritizes suggesting highly relevant childcare facilities. For example, the suggestion unit stores the relevance of childcare facilities in a database and identifies those with high relevance. The suggestion unit can also postpone suggesting less relevant childcare facilities. For example, the suggestion unit dynamically adjusts the order of suggestions based on the relevance of the childcare facilities. Furthermore, the suggestion unit can analyze the relevance of childcare facilities in real time and determine an optimal order of suggestions. For example, the suggestion unit determines the order of suggestions based on the relevance of the childcare facilities. As a result, more appropriate suggestions can be provided by adjusting the order of suggestions based on the relevance of the childcare facilities. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of childcare facilities to a generation AI, which can adjust the order of suggestions.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The reception unit can analyze the parent's past question history and select the optimal reception method. For example, it can preferentially suggest question formats that the parent has frequently used in the past. Furthermore, the reception unit can also select the most efficient reception method from the parent's past question history. For example, it can analyze the parent's past question history and automatically select the reception method that the parent has preferred. In this way, the optimal reception method can be selected by analyzing the parent's past question history.

[0064] The prediction unit can adjust the level of detail of the prediction based on the importance of the data when making a prediction. For example, a detailed prediction can be made for data with high importance. A simple prediction can also be made for data with low importance. Furthermore, the importance of the data can be analyzed in real time to determine the optimal level of detail of the prediction. By adjusting the level of detail of the prediction based on the importance of the data, more appropriate prediction results can be provided.

[0065] The suggestion unit can adjust the level of detail of the proposal based on the importance of the childcare facility when making the proposal. For example, a detailed proposal can be made for a childcare facility with a high importance. Also, a concise proposal can be made for a childcare facility with a low importance. Furthermore, the importance of the childcare facility can be analyzed in real time to determine the optimal level of detail of the proposal. As a result, by adjusting the level of detail of the proposal based on the importance of the childcare facility, more appropriate proposals can be provided.

[0066] The prediction unit can apply different prediction algorithms depending on the data category when making predictions. For example, a specialized prediction algorithm can be applied to data related to childcare. Alternatively, an appropriate prediction algorithm can be applied to data related to resource provision. Furthermore, the data category can be analyzed in real time and the optimal prediction algorithm can be dynamically applied. This allows for more appropriate prediction results to be provided by applying the optimal prediction algorithm depending on the data category.

[0067] When planning, the planning department can determine the priority of plans based on the time when prediction results were submitted. For example, it can prioritize the most recently submitted prediction results. It can also postpone prediction results that were submitted earlier. Furthermore, it can analyze the time when prediction results are submitted in real time and determine the optimal priority of plans. This makes it possible to provide more appropriate plans by prioritizing plans based on the time when prediction results were submitted.

[0068] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, relevant data can be collected preferentially based on the guardian's current location. The collection unit can also suggest optimal data by taking geographical location information into consideration. Furthermore, highly relevant data can be filtered based on geographical location information. This allows highly relevant data to be collected preferentially by taking geographical location information into consideration.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit accepts questions from parents. Parents can input questions using a smartphone or computer, or they can input questions by voice. For example, parents can dictate their questions using a microphone, and the questions are converted into text data using voice recognition technology. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis unit uses natural language processing technology to analyze the question and extract information to provide appropriate advice and resources. For example, it breaks down the question into keywords and searches a database for related information. It can also use algorithms to understand the context of the question and generate an appropriate answer. Step 3: The provider provides appropriate advice and resources based on the questions analyzed by the analyzer. For example, it provides parenting tips and advice, a list of childcare facilities, contact information for experts, etc. The provider can also provide information in the format desired by the parent. For example, it provides information in text, image, video, etc. Step 4: The collection department collects past data and the current situation. For example, they collect data such as the number of children on waiting lists in the past, the results of parent surveys, and local demographics. In addition to obtaining data from government and local government databases, the collection department can also conduct parent surveys to gather data to understand the current situation. Step 5: The prediction unit analyzes the data collected by the collection unit and predicts the future number of children on waiting lists. The prediction unit uses statistical models and machine learning algorithms to predict the future number of children on waiting lists. For example, the prediction unit analyzes the future trend of the number of children on waiting lists based on past data and makes a prediction taking into account the current situation. Step 6: The Planning Department makes facility expansion plans based on the forecast results obtained by the Forecasting Department. For example, they make plans for the construction of new childcare facilities, the renovation of existing facilities, and the increase in staff. Based on the forecast results, they determine the number and size of facilities needed and set priorities for facility expansion plans. Step 7: The proposal unit consists of a reception unit that inputs the parents' needs, an analysis unit that analyzes the input needs, and a unit that proposes appropriate childcare facilities based on the analysis results. Parents input the conditions and needs of the childcare facility they desire, and the analysis unit analyzes them and proposes appropriate childcare facilities. For example, it provides a list of childcare facilities that meet the parents' desired conditions and provides detailed information.

[0071] (Example 2) A system according to an embodiment of the present invention uses generative AI to provide tools to contribute to eliminating the problem of children on waiting lists. This system includes an AI chatbot that provides support tools and resources for home-based childcare, a tool that predicts the number of children on waiting lists and develops childcare facility expansion plans, and a tool that matches parents' needs with childcare facilities. Specifically, we first develop an AI chatbot that provides support tools and resources for home-based childcare. This chatbot allows parents to ask questions and ask questions about home-based childcare and provides appropriate advice and resources. We then develop a tool that predicts the number of children on waiting lists and develops childcare facility expansion plans. This tool can predict the future number of children on waiting lists based on past data and current conditions and develop necessary facility expansion plans. We also develop a tool that matches parents' needs with childcare facilities. This tool suggests childcare facilities that match parents' requirements and requirements when they input their desired childcare facility conditions and needs. Combining these tools can contribute to eliminating the problem of children on waiting lists and reduce the burden on parents. For example, an AI chatbot that provides support tools and resources for home-based childcare can be divided into a reception unit that accepts questions from parents, an analysis unit that analyzes the questions, and a provision unit that provides appropriate advice and resources. Next, the waiting list prediction tool can be divided into a collection section that collects past data and current conditions, a prediction section that analyzes the collected data to predict the future number of children on waiting lists, and a planning section that creates facility expansion plans based on the prediction results.Furthermore, the matching tool between parental needs and childcare facilities can be divided into a reception section that inputs parents' needs, an analysis section that analyzes the input needs, and a proposal section that proposes appropriate childcare facilities based on the analysis results.Each of these sections is interrelated, with the information received by the reception section being analyzed by the analysis section, and the provision section and proposal section providing appropriate information and facilities.In this way, the system can contribute to eliminating the waiting list problem and reduce the burden on parents.

[0072] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a collection unit, a prediction unit, a planning unit, and a proposal unit. The reception unit receives questions from parents. Questions from parents include, but are not limited to, questions about childcare, information about childcare facilities, and contact information for experts. The reception unit allows parents to input questions using, for example, a smartphone or a computer. The reception unit can also receive questions using voice input. For example, the parent dictates a question using a microphone and converts it into text data using voice recognition technology. The analysis unit analyzes the question received by the reception unit. The analysis unit analyzes the question using, for example, natural language processing technology and extracts information for providing appropriate advice and resources. For example, the analysis unit breaks down the question into keywords and searches a database for related information. The analysis unit can also use an algorithm to understand the context of the question and generate an appropriate answer. The provision unit provides appropriate advice and resources based on the question analyzed by the analysis unit. The provision unit provides, for example, parenting tips and advice, a list of childcare facilities, and contact information for experts. For example, the providing unit automatically displays relevant information depending on the content of a question asked by a parent. The providing unit can also provide information in a format desired by the parent. For example, the information is provided in text format, image format, video format, or the like. The collecting unit collects past data and the current situation. The collecting unit collects data such as the past number of children on waiting lists, parent survey results, and local demographics. For example, the collecting unit obtains data from government or local government databases. The collecting unit can also conduct questionnaire surveys of parents and collect data to understand the current situation. The predicting unit analyzes the data collected by the collecting unit to predict the future number of children on waiting lists. The predicting unit predicts the future number of children on waiting lists, for example, using a statistical model or a machine learning algorithm. For example, the predicting unit analyzes trends in the future number of children on waiting lists based on past data. The predicting unit can also predict the future number of children on waiting lists taking the current situation into consideration. The planning unit makes facility expansion plans based on the prediction results obtained by the predicting unit.The planning unit makes plans for, for example, the construction of new childcare facilities, the renovation of existing facilities, and the hiring of additional staff. For example, the planning unit determines the number and size of facilities needed based on the prediction results. The planning unit can also prioritize facility expansion plans based on the prediction results. The proposal unit includes a reception unit that inputs parents' needs, an analysis unit that analyzes the input needs, and a proposal unit that proposes appropriate childcare facilities based on the analysis results. The proposal unit can, for example, input the conditions and needs of childcare facilities desired by parents. Examples of such needs include extending childcare hours, providing special care, and enhancing educational programs. The analysis unit analyzes the input needs and extracts information for proposing appropriate childcare facilities. The proposal unit proposes childcare facilities that meet the parents' needs based on the analysis results. For example, the proposal unit displays a list of childcare facilities that meet the parents' desired conditions. The proposal unit can also provide detailed information about childcare facilities that meet the parents' desired conditions. As a result, the system according to the embodiment can respond to parents' questions and needs and contribute to eliminating the problem of children on waiting lists.

[0073] The provision unit can provide information or resources related to childcare. The provision unit provides information and resources such as a list of childcare facilities, childcare advice, and contact information for experts. For example, the provision unit automatically displays related information depending on the content of a question asked by a parent. The provision unit can also provide information in a format desired by the parent. For example, the information can be provided in text format, image format, video format, etc. This can reduce the burden on parents by providing information and resources related to childcare. Some or all of the above-mentioned processing in the provision unit may be performed using, or without, AI, for example. For example, the provision unit can input the content of a parent's question into a generation AI, which can generate appropriate advice and resources.

[0074] The prediction unit can predict the future number of children on waiting lists based on past data or the current situation. The prediction unit predicts the future number of children on waiting lists based on data such as the past number of children on waiting lists, parent survey results, and local demographics. For example, the prediction unit predicts the future number of children on waiting lists using a statistical model or a machine learning algorithm. For example, the prediction unit analyzes trends in the future number of children on waiting lists based on past data. The prediction unit can also predict the future number of children on waiting lists taking the current situation into consideration. This allows appropriate facility expansion plans to be made by predicting the future number of children on waiting lists. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs collected data into a generation AI, which can then predict the future number of children on waiting lists.

[0075] The planning unit can create a facility expansion plan based on the prediction results. The planning unit creates plans, for example, for the construction of new childcare facilities, the renovation of existing facilities, and the addition of staff. For example, the planning unit determines the number and size of facilities required based on the prediction results. The planning unit can also determine the priority of facility expansion plans based on the prediction results. In this way, creating facility expansion plans can contribute to eliminating childcare waiting lists. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the prediction results into a generation AI, which can then create a facility expansion plan.

[0076] The suggestion unit can suggest childcare facilities that meet the needs of the parents. For example, the suggestion unit can input the conditions and needs of the childcare facility desired by the parents. For example, these may include needs such as extending childcare hours, providing special care, and enhancing educational programs. The analysis unit analyzes the input needs and extracts information for suggesting appropriate childcare facilities. The suggestion unit suggests childcare facilities that meet the needs of the parents based on the analysis results. For example, the suggestion unit displays a list of childcare facilities that meet the conditions desired by the parents. The suggestion unit can also provide detailed information about childcare facilities that meet the conditions desired by the parents. This reduces the burden on the parents by suggesting childcare facilities that meet the needs of the parents. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input the needs of the parents to a generation AI, which then suggests appropriate childcare facilities.

[0077] The reception unit can estimate the parent's emotions and adjust the timing of receiving questions and needs based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reception unit may receive questions and needs during times when the parent is able to relax. For example, the reception unit may use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. Furthermore, if the parent is busy, the reception unit may prioritize receiving questions and needs that can be completed in a short time. For example, the reception unit may determine the optimal reception timing taking into account the parent's schedule. Furthermore, if the parent is relaxed, the reception unit may receive detailed questions and needs. For example, the reception unit may dynamically adjust the method of receiving questions and needs depending on the parent's emotional state. This allows for more appropriate responses by adjusting the timing of receiving questions and needs according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the parent's emotional data into the generation AI, which may then adjust the timing of receiving questions and needs.

[0078] The reception unit can analyze the parent's past question history and select the optimal reception method. For example, the reception unit prioritizes suggesting question formats that the parent has frequently used in the past. For example, the reception unit stores the parent's past question history in a database and identifies the frequently used question formats. The reception unit can also select the most efficient reception method from the parent's past question history. For example, the reception unit analyzes the parent's past question history and automatically selects the reception method that the parent preferred. Furthermore, the reception unit can dynamically adjust the optimal reception method based on the parent's past question history. For example, the reception unit analyzes the parent's question history in real time and suggests the optimal reception method. In this way, the optimal reception method can be selected by analyzing the parent's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's question history data into a generation AI, which can select the optimal reception method.

[0079] When receiving questions or needs, the reception unit can filter them based on the guardian's current living situation and areas of interest. The reception unit, for example, prioritizes receiving related questions and needs based on the guardian's current living situation. For example, the reception unit stores the guardian's living situation in a database and identifies related questions and needs. The reception unit can also filter related questions and needs based on the guardian's areas of interest. For example, the reception unit analyzes the guardian's areas of interest and prioritizes receiving related questions and needs. Furthermore, the reception unit can suggest optimal questions and needs based on the guardian's living situation and areas of interest. For example, the reception unit analyzes the guardian's living situation and areas of interest in real time and suggests optimal questions and needs. This enables more appropriate responses by filtering questions and needs based on the guardian's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the guardian's living situation and areas of interest into a generation AI, which can then suggest optimal questions and needs.

[0080] The reception unit can estimate the parent's emotions and prioritize the questions and needs to be received based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reception unit prioritizes more urgent questions and needs. For example, the reception unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. Furthermore, if the parent is relaxed, the reception unit can prioritize detailed questions and needs. For example, the reception unit dynamically adjusts the priority of questions and needs according to the parent's emotional state. Furthermore, if the parent is busy, the reception unit can prioritize questions and needs that can be completed in a short time. For example, the reception unit determines the optimal priority of questions and needs taking into account the parent's schedule. This enables more appropriate responses by prioritizing questions and needs according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the parent's emotional data into the generation AI, which may then determine the priority of questions and needs.

[0081] When receiving questions or needs, the reception unit can prioritize receiving highly relevant information by taking into account the guardian's geographical location information. The reception unit, for example, prioritizes receiving related questions or needs based on the guardian's current location. For example, the reception unit stores the guardian's geographical location information in a database and identifies related questions or needs. The reception unit can also suggest optimal questions or needs by taking the guardian's geographical location information into account. For example, the reception unit analyzes the guardian's geographical location information in real time and suggests optimal questions or needs. Furthermore, the reception unit can filter highly relevant information based on the guardian's geographical location information. For example, the reception unit prioritizes displaying relevant information based on the guardian's geographical location information. In this way, highly relevant information can be prioritized by taking the guardian's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the guardian's geographical location information to a generation AI, which can then suggest highly relevant information.

[0082] The reception unit can analyze the parent's social media activity and receive relevant information when receiving a question or need. The reception unit, for example, analyzes the parent's social media activity and prioritizes receiving relevant questions and needs. For example, the reception unit stores the parent's social media activity in a database and identifies relevant questions and needs. The reception unit can also suggest optimal questions and needs based on the parent's social media activity. For example, the reception unit analyzes the parent's social media activity in real time and suggests optimal questions and needs. The reception unit can also filter highly relevant information taking the parent's social media activity into consideration. For example, the reception unit prioritizes displaying relevant information based on the parent's social media activity. In this way, by analyzing the parent's social media activity, relevant information can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's social media activity data to a generation AI, which then suggests highly relevant information.

[0083] The analysis unit can estimate the parent's emotions and adjust the analysis presentation method based on the estimated parent's emotions. For example, if the parent is stressed, the analysis unit uses a simple and easy-to-understand presentation method. For example, the analysis unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. Furthermore, the analysis unit can provide detailed analysis results if the parent is relaxed. For example, the analysis unit dynamically adjusts the analysis presentation method according to the parent's emotional state. Furthermore, if the parent is busy, the analysis unit can provide concise analysis results that focus on the main points. For example, the analysis unit determines the optimal analysis presentation method taking into account the parent's schedule. By adjusting the analysis presentation method 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 a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the parent's emotional data into the generation AI, which can then adjust the way the analysis is expressed.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the question or need during analysis. For example, the analysis unit performs a detailed analysis on questions or needs with high importance. For example, the analysis unit stores the importance of questions and needs in a database and identifies those with high importance. The analysis unit can also perform a concise analysis on questions or needs with low importance. For example, the analysis unit dynamically adjusts the level of detail of the analysis based on the importance of the question or need. Furthermore, the analysis unit can analyze the importance of questions and needs in real time and determine the optimal level of detail of the analysis. For example, the analysis unit determines the priority of the analysis based on the importance of the question or need. This allows for adjusting the level of detail of the analysis based on the importance of the question or need, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input importance data of questions and needs to a generation AI, which can then adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the question or need. For example, the analysis unit applies a specialized analysis algorithm to questions related to childcare. For example, the analysis unit stores the categories of questions and needs in a database and selects an analysis algorithm according to the category. The analysis unit can also apply an appropriate analysis algorithm to questions related to resource provision. For example, the analysis unit selects the optimal analysis algorithm depending on the category of the question or need. Furthermore, the analysis unit can analyze the categories of questions and needs in real time and dynamically apply the optimal analysis algorithm. For example, the analysis unit determines the analysis priority based on the category of the question or need. This allows for applying the optimal analysis algorithm depending on the category of the question or need, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input question and need category data into a generation AI, which then applies the optimal analysis algorithm.

[0086] The analysis unit can estimate the parent's emotions and adjust the length of the analysis based on the estimated parent's emotions. For example, if the parent is stressed, the analysis unit provides a short and concise analysis result. For example, the analysis unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. The analysis unit can also provide a detailed analysis result if the parent is relaxed. For example, the analysis unit dynamically adjusts the length of the analysis according to the parent's emotional state. Furthermore, if the parent is busy, the analysis unit can provide a concise analysis result. For example, the analysis unit determines the optimal length of the analysis taking into account the parent's schedule. This allows for adjusting the length of the analysis according to the parent's emotions, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the parent's emotional data into the generation AI, which can then adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the analysis priority based on the time when the questions and needs were submitted. The analysis unit, for example, prioritizes analysis of recently submitted questions and needs. For example, the analysis unit stores the time when the questions and needs were submitted in a database and identifies the most recently submitted questions and needs. The analysis unit can also postpone older questions and needs. For example, the analysis unit dynamically adjusts the analysis priority based on the time when the questions and needs were submitted. Furthermore, the analysis unit can analyze the time when the questions and needs were submitted in real time and determine the optimal analysis priority. For example, the analysis unit determines the analysis priority based on the time when the questions and needs were submitted. This enables more appropriate analysis results to be provided by determining the analysis priority based on the time when the questions and needs were submitted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the time when the questions and needs were submitted to a generation AI, which then determines the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of questions and needs. The analysis unit, for example, prioritizes analysis of highly relevant questions and needs. For example, the analysis unit stores the relevance of questions and needs in a database and identifies those with high relevance. The analysis unit can also postpone less relevant questions and needs. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of questions and needs. Furthermore, the analysis unit can analyze the relevance of questions and needs in real time and determine the optimal order of analysis. For example, the analysis unit determines the order of analysis based on the relevance of questions and needs. In this way, adjusting the order of analysis based on the relevance of questions and needs can provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of questions and needs to a generation AI, which can adjust the order of analysis.

[0089] The providing unit can estimate the parent's emotions and adjust the presentation method of the information and resources to be provided based on the estimated parent's emotions. For example, if the parent is feeling stressed, the providing unit can provide simple and easy-to-understand information. For example, the providing unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. Furthermore, the providing unit can provide detailed information if the parent is relaxed. For example, the providing unit dynamically adjusts the presentation method of the information and resources according to the parent's emotional state. Furthermore, if the parent is busy, the providing unit can provide concise information that focuses on the main points. For example, the providing unit determines the optimal presentation method of the information and resources taking into account the parent's schedule. This enables more appropriate information to be provided by adjusting the presentation method of the information and resources according to the parent's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input emotional data of the guardian into the generating AI, and the generating AI may adjust the way information and resources are presented.

[0090] The providing unit can adjust the level of detail of the information or resources provided based on the importance of the information or resources when providing the information. The providing unit, for example, provides detailed information or resources with high importance. For example, the providing unit stores the importance of the information or resources in a database and identifies those with high importance. The providing unit can also provide concise information or resources with low importance. For example, the providing unit dynamically adjusts the level of detail of the information or resources provided based on the importance of the information or resources. Furthermore, the providing unit can analyze the importance of the information or resources in real time and determine the optimal level of detail of the information or resources provided. For example, the providing unit determines the priority of the information or resources provided based on the importance of the information or resources. This enables more appropriate information to be provided by adjusting the level of detail of the information or resources based on the importance of the information or resources. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the information or resources to a generating AI, which can then adjust the level of detail of the information or resources provided.

[0091] The providing unit can apply different provision algorithms depending on the category of information or resources when providing the information. For example, the providing unit applies a specialized provision algorithm to information related to childcare. For example, the providing unit stores categories of information or resources in a database and selects a provision algorithm depending on the category. The providing unit can also apply an appropriate provision algorithm to information related to resource provision. For example, the providing unit selects an optimal provision algorithm depending on the category of information or resources. Furthermore, the providing unit can analyze the categories of information or resources in real time and dynamically apply the optimal provision algorithm. For example, the providing unit determines a provision priority based on the category of information or resources. This enables more appropriate information to be provided by applying the optimal provision algorithm depending on the category of information or resources. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input category data of information or resources to a generation AI, which can then apply the optimal provision algorithm.

[0092] The providing unit can estimate the guardian's emotions and determine the priority of information and resources to be provided based on the estimated guardian's emotions. For example, if the guardian is feeling stressed, the providing unit prioritizes information and resources that are more urgent. For example, the providing unit can use facial expression recognition technology or voice analysis technology to estimate the guardian's emotions. The providing unit can also prioritize detailed information and resources if the guardian is relaxed. For example, the providing unit dynamically adjusts the priority of information and resources according to the guardian's emotional state. Furthermore, if the guardian is busy, the providing unit can prioritize information and resources that can be completed in a short time. For example, the providing unit determines the priority of optimal information and resources taking the guardian's schedule into consideration. This enables more appropriate information to be provided by prioritizing information and resources according to the guardian's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the parent's emotional data into the generating AI, which may then determine the priority of information and resources.

[0093] The providing unit can determine the priority of provision based on the time of submission of information or resources at the time of provision. The providing unit, for example, prioritizes providing information or resources that have been provided recently. For example, the providing unit stores the time of submission of information or resources in a database and identifies the most recently submitted information or resources. The providing unit can also postpone information or resources that have been submitted older. For example, the providing unit dynamically adjusts the priority of provision based on the time of submission of information or resources. Furthermore, the providing unit can analyze the time of submission of information or resources in real time and determine the optimal priority of provision. For example, the providing unit determines the priority of provision based on the time of submission of information or resources. This enables more appropriate information provision by determining the priority of provision based on the time of submission of information or resources. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of submission of information or resources to a generation AI, which can then determine the priority of provision.

[0094] The providing unit can adjust the order of provision based on the relevance of the information and resources when providing them. The providing unit, for example, prioritizes providing highly relevant information and resources. For example, the providing unit stores the relevance of the information and resources in a database and identifies those with high relevance. The providing unit can also postpone less relevant information and resources. For example, the providing unit dynamically adjusts the order of provision based on the relevance of the information and resources. Furthermore, the providing unit can analyze the relevance of the information and resources in real time and determine an optimal order of provision. For example, the providing unit determines the order of provision based on the relevance of the information and resources. This enables more appropriate information provision by adjusting the order of provision based on the relevance of the information and resources. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the information and resources to a generation AI, which can adjust the order of provision.

[0095] The collection unit can estimate the parent's emotions and adjust the timing of data collection based on the estimated parent's emotions. For example, if the parent is feeling stressed, the collection unit collects data during a time when the parent is able to relax. For example, the collection unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. Furthermore, if the parent is busy, the collection unit can prioritize data collection that can be completed in a short time. For example, the collection unit can determine the optimal collection timing taking the parent's schedule into consideration. Furthermore, if the parent is relaxed, the collection unit can collect detailed data. For example, the collection unit dynamically adjusts the timing of data collection according to the parent's emotional state. This enables more appropriate data collection by adjusting the timing of data collection according to the parent's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the parent's emotional data into the generation AI, and the generation AI can adjust the timing of data collection.

[0096] The collection unit can analyze past data collection history and select the optimal collection method. For example, the collection unit prioritizes selecting data collection methods that were effective in the past. For example, the collection unit stores past data collection history in a database and identifies collection methods that were effective. The collection unit can also select the most efficient collection method from the past data collection history. For example, the collection unit analyzes past data collection history and automatically selects the optimal collection method. Furthermore, the collection unit can analyze past data collection history in real time and dynamically adjust the optimal collection method. For example, the collection unit determines collection priorities based on the past data collection history. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data collection history data to a generation AI, which can select the optimal collection method.

[0097] The collection unit can filter data based on the current situation and areas of interest when collecting data. The collection unit, for example, prioritizes collecting relevant data based on the current situation. For example, the collection unit stores the parent's current situation in a database and identifies relevant data. The collection unit can also filter relevant data based on the parent's areas of interest. For example, the collection unit analyzes the parent's areas of interest and prioritizes collecting relevant data. Furthermore, the collection unit can suggest optimal data based on the parent's current situation and areas of interest. For example, the collection unit analyzes the parent's current situation and areas of interest in real time and suggests optimal data. This enables more appropriate data collection by filtering data based on the current situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the parent's current situation and areas of interest to a generation AI, which then suggests optimal data.

[0098] The collection unit can estimate the guardian's emotions and determine the priority of data to be collected based on the estimated guardian's emotions. For example, if the guardian is stressed, the collection unit prioritizes collecting data with high urgency. For example, the collection unit can use facial expression recognition technology or voice analysis technology to estimate the guardian's emotions. The collection unit can also prioritize collecting detailed data if the guardian is relaxed. For example, the collection unit dynamically adjusts the priority of data to be collected according to the guardian's emotional state. Furthermore, if the guardian is busy, the collection unit can prioritize data that can be collected in a short time. For example, the collection unit determines the optimal priority of data taking the guardian's schedule into consideration. This enables more appropriate data collection by prioritizing data to be collected according to the guardian's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input emotional data of the guardian into the generation AI and determine the priority of the data to be collected by the generation AI.

[0099] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. The collection unit, for example, prioritizes collecting relevant data based on the guardian's current location. For example, the collection unit stores the guardian's geographical location information in a database and identifies relevant data. The collection unit can also suggest optimal data by taking the geographical location information into consideration. For example, the collection unit analyzes the guardian's geographical location information in real time and suggests optimal data. Furthermore, the collection unit can filter highly relevant data based on the geographical location information. For example, the collection unit prioritizes displaying relevant data based on the guardian's geographical location information. In this way, highly relevant data can be preferentially collected by taking the geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the guardian's geographical location information data to a generation AI, which then suggests highly relevant data.

[0100] The collection unit can analyze social media activity and collect relevant data when collecting data. The collection unit, for example, analyzes the parent's social media activity and prioritizes collecting relevant data. For example, the collection unit stores the parent's social media activity in a database and identifies relevant data. The collection unit can also suggest optimal data based on the social media activity. For example, the collection unit analyzes the parent's social media activity in real time and suggests optimal data. The collection unit can also filter highly relevant data taking the social media activity into consideration. For example, the collection unit prioritizes displaying relevant data based on the parent's social media activity. In this way, relevant data can be preferentially collected by analyzing the social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the parent's social media activity data to a generation AI, which then suggests highly relevant data.

[0101] The prediction unit can estimate the parent's emotions and adjust the way the prediction is expressed based on the estimated parent's emotions. For example, if the parent is stressed, the prediction unit provides a simple and easy-to-understand prediction result. For example, the prediction unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. The prediction unit can also provide a detailed prediction result if the parent is relaxed. For example, the prediction unit dynamically adjusts the way the prediction is expressed depending on the parent's emotional state. Furthermore, if the parent is busy, the prediction unit can provide a concise prediction result that focuses on the main points. For example, the prediction unit determines the optimal way to express the prediction taking into account the parent's schedule. This allows for adjusting the way the prediction is expressed depending on the parent's emotions, thereby providing a more appropriate prediction result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, an AI, or without using an AI. For example, the prediction unit can input the parent's emotional data into the generation AI, which can then adjust the way the prediction is expressed.

[0102] The prediction unit can adjust the level of detail of the prediction based on the importance of the data during prediction. For example, the prediction unit performs a detailed prediction for data with high importance. For example, the prediction unit stores the importance of data in a database and identifies data with high importance. The prediction unit can also perform a concise prediction for data with low importance. For example, the prediction unit dynamically adjusts the level of detail of the prediction according to the importance of the data. Furthermore, the prediction unit can analyze the importance of data in real time and determine the optimal level of detail of the prediction. For example, the prediction unit determines the priority of the prediction based on the importance of the data. As a result, by adjusting the level of detail of the prediction based on the importance of the data, more appropriate prediction results can be provided. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data importance of the data to a generation AI, which can then adjust the level of detail of the prediction.

[0103] The prediction unit can apply different prediction algorithms depending on the data category during prediction. For example, the prediction unit applies a specialized prediction algorithm to data related to childcare. For example, the prediction unit stores the data category in a database and selects a prediction algorithm depending on the category. The prediction unit can also apply an appropriate prediction algorithm to data related to resource provision. For example, the prediction unit selects an optimal prediction algorithm depending on the data category. Furthermore, the prediction unit can analyze the data category in real time and dynamically apply the optimal prediction algorithm. For example, the prediction unit determines a prediction priority based on the data category. This allows for applying the optimal prediction algorithm depending on the data category, thereby providing more appropriate prediction results. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data category data to a generation AI, which then applies the optimal prediction algorithm.

[0104] The prediction unit can estimate the parent's emotions and determine the priority of predictions based on the estimated parent's emotions. For example, if the parent is stressed, the prediction unit prioritizes predictions with high urgency. For example, the prediction unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. The prediction unit can also prioritize detailed predictions if the parent is relaxed. For example, the prediction unit dynamically adjusts the priority of predictions according to the parent's emotional state. Furthermore, if the parent is busy, the prediction unit can prioritize predictions that can be completed in a short time. For example, the prediction unit determines the optimal priority of predictions taking the parent's schedule into consideration. This allows for more appropriate prediction results to be provided by prioritizing predictions according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit can be performed using, for example, an AI, or without an AI. For example, the prediction unit can input the parent's emotional data into the generation AI, which can then determine the priority of the predictions.

[0105] The prediction unit can determine the priority of predictions based on the time of data submission during prediction. The prediction unit, for example, prioritizes predictions for recently submitted data. For example, the prediction unit stores the time of data submission in a database and identifies data submitted more recently. The prediction unit can also postpone data submitted more recently. For example, the prediction unit dynamically adjusts the priority of predictions based on the time of data submission. Furthermore, the prediction unit can analyze the time of data submission in real time and determine the optimal priority of predictions. For example, the prediction unit determines the priority of predictions based on the time of data submission. In this way, by determining the priority of predictions based on the time of data submission, more appropriate prediction results can be provided. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the time of data submission to a generation AI, which can then determine the priority of predictions.

[0106] The prediction unit can adjust the order of predictions based on the relevance of the data during prediction. The prediction unit, for example, prioritizes predicting highly relevant data. For example, the prediction unit stores the relevance of the data in a database and identifies highly relevant data. The prediction unit can also postpone less relevant data. For example, the prediction unit dynamically adjusts the order of predictions based on the relevance of the data. Furthermore, the prediction unit can analyze the relevance of the data in real time and determine the optimal order of predictions. For example, the prediction unit determines the order of predictions based on the relevance of the data. In this way, by adjusting the order of predictions based on the relevance of the data, more appropriate prediction results can be provided. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the relevance of the data to a generation AI, which can then adjust the order of predictions.

[0107] The planning unit can estimate the parent's emotions and adjust the way the plan is presented based on the estimated parent's emotions. For example, if the parent is stressed, the planning unit provides a simple and easy-to-understand plan. For example, the planning unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. Furthermore, the planning unit can provide a detailed plan if the parent is relaxed. For example, the planning unit dynamically adjusts the way the plan is presented depending on the parent's emotional state. Furthermore, if the parent is busy, the planning unit can provide a concise plan that focuses on the main points. For example, the planning unit determines the optimal way to present the plan taking into account the parent's schedule. This allows for a more appropriate plan to be provided by adjusting the way the plan is presented depending on the parent's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the planning unit can be performed using, for example, an AI, or without an AI. For example, the planning department can input the parent's emotional data into the generation AI, which can then adjust the way the plan is expressed.

[0108] The planning unit can adjust the level of detail of the plan based on the importance of the prediction results during planning. For example, the planning unit creates a detailed plan for a highly important prediction result. For example, the planning unit stores the importance of the prediction results in a database and identifies the most important ones. The planning unit can also create a concise plan for a less important prediction result. For example, the planning unit dynamically adjusts the level of detail of the plan depending on the importance of the prediction result. Furthermore, the planning unit can analyze the importance of the prediction results in real time and determine the optimal level of detail of the plan. For example, the planning unit determines the priority of the plan based on the importance of the prediction result. As a result, a more appropriate plan can be provided by adjusting the level of detail of the plan based on the importance of the prediction result. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input importance data of the prediction results to a generation AI, which can then adjust the level of detail of the plan.

[0109] The planning unit can apply different planning algorithms depending on the category of the prediction result during planning. For example, the planning unit applies a specialized planning algorithm to prediction results related to childcare. For example, the planning unit stores the category of the prediction result in a database and selects a planning algorithm according to the category. The planning unit can also apply an appropriate planning algorithm to prediction results related to resource provision. For example, the planning unit selects an optimal planning algorithm depending on the category of the prediction result. Furthermore, the planning unit can analyze the category of the prediction result in real time and dynamically apply the optimal planning algorithm. For example, the planning unit determines the priority of plans based on the category of the prediction result. This allows for applying the optimal planning algorithm depending on the category of the prediction result, thereby providing a more appropriate plan. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input category data of the prediction result to a generation AI, which then applies the optimal planning algorithm.

[0110] The planning unit can estimate the parent's emotions and prioritize plans based on the estimated parent's emotions. For example, if the parent is stressed, the planning unit prioritizes plans with high urgency. For example, the planning unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. Furthermore, the planning unit can prioritize detailed plans if the parent is relaxed. For example, the planning unit dynamically adjusts the priority of plans according to the parent's emotional state. Furthermore, if the parent is busy, the planning unit can prioritize plans that can be completed in a short time. For example, the planning unit determines the priority of optimal plans taking into account the parent's schedule. Thus, by prioritizing plans according to the parent's emotions, a more appropriate plan can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the planning unit can be performed using, for example, AI, or without AI. For example, the planning department can input the parent's emotional data into the generation AI, which can then determine the priorities of the plan.

[0111] During planning, the planning unit can determine the priority of plans based on the submission time of prediction results. The planning unit, for example, prioritizes the most recently submitted prediction results. For example, the planning unit stores the submission time of prediction results in a database and identifies the most recently submitted prediction results. The planning unit can also postpone the oldest submitted prediction results. For example, the planning unit dynamically adjusts the priority of plans based on the submission time of prediction results. Furthermore, the planning unit can analyze the submission time of prediction results in real time and determine the optimal priority of plans. For example, the planning unit determines the priority of plans based on the submission time of prediction results. This makes it possible to provide more appropriate plans by determining the priority of plans based on the submission time of prediction results. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input data on the submission time of prediction results to a generation AI, which can then determine the priority of plans.

[0112] The planning unit can adjust the order of plans based on the relevance of the prediction results during planning. The planning unit, for example, prioritizes highly relevant prediction results when planning. For example, the planning unit stores the relevance of the prediction results in a database and identifies highly relevant results. The planning unit can also postpone less relevant prediction results. For example, the planning unit dynamically adjusts the order of plans based on the relevance of the prediction results. Furthermore, the planning unit can analyze the relevance of the prediction results in real time and determine the optimal order of plans. For example, the planning unit determines the order of plans based on the relevance of the prediction results. As a result, a more appropriate plan can be provided by adjusting the order of plans based on the relevance of the prediction results. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input relevance data of the prediction results to a generation AI, which can then adjust the order of plans.

[0113] The suggestion unit can estimate the parent's emotions and adjust the way the suggestions are expressed based on the estimated parent's emotions. For example, if the parent is stressed, the suggestion unit can provide simple and easy-to-understand suggestions. For example, the suggestion unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. Furthermore, the suggestion unit can provide detailed suggestions if the parent is relaxed. For example, the suggestion unit dynamically adjusts the way the suggestions are expressed depending on the parent's emotional state. Furthermore, if the parent is busy, the suggestion unit can provide concise suggestions that focus on the main points. For example, the suggestion unit can determine the optimal way to express the suggestions taking into account the parent's schedule. This allows for more appropriate suggestions to be provided by adjusting the way the suggestions are expressed based on the parent's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the parent's emotional data into the generation AI, which can then adjust the way the suggestion is expressed.

[0114] The suggestion unit can adjust the level of detail of the proposal based on the importance of the childcare facility when making a proposal. The suggestion unit, for example, makes a detailed proposal for a childcare facility with a high importance. For example, the suggestion unit stores the importance of childcare facilities in a database and identifies childcare facilities with a high importance. The suggestion unit can also make a concise proposal for a childcare facility with a low importance. For example, the suggestion unit dynamically adjusts the level of detail of the proposal according to the importance of the childcare facility. Furthermore, the suggestion unit can analyze the importance of the childcare facility in real time and determine an optimal level of detail of the proposal. For example, the suggestion unit determines the priority of the proposal based on the importance of the childcare facility. As a result, by adjusting the level of detail of the proposal based on the importance of the childcare facility, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of the childcare facility to a generation AI, which can adjust the level of detail of the proposal.

[0115] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the childcare facility. For example, the proposal unit applies a specialized proposal algorithm to childcare facilities. For example, the proposal unit stores the categories of childcare facilities in a database and selects a proposal algorithm depending on the category. The proposal unit can also apply an appropriate proposal algorithm to facilities related to resource provision. For example, the proposal unit selects an optimal proposal algorithm depending on the category of the childcare facility. Furthermore, the proposal unit can analyze the category of the childcare facility in real time and dynamically apply the optimal proposal algorithm. For example, the proposal unit determines a priority of proposals based on the category of the childcare facility. This allows for more appropriate proposals to be provided by applying the optimal proposal algorithm depending on the category of the childcare facility. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input category data of the childcare facility to a generation AI, which can then apply the optimal proposal algorithm.

[0116] The suggestion unit can estimate the parent's emotions and prioritize suggestions based on the estimated parent's emotions. For example, if the parent is stressed, the suggestion unit prioritizes suggestions with high urgency. For example, the suggestion unit can use facial expression recognition technology or voice analysis technology to estimate the parent's emotions. Furthermore, the suggestion unit can prioritize detailed suggestions if the parent is relaxed. For example, the suggestion unit dynamically adjusts the priority of suggestions according to the parent's emotional state. Furthermore, if the parent is busy, the suggestion unit can prioritize suggestions that can be completed in a short time. For example, the suggestion unit determines the optimal priority of suggestions taking into account the parent's schedule. Thus, by prioritizing suggestions according to the parent's emotions, more appropriate suggestions can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the parent's emotional data into the generation AI, which can then determine the priority of the suggestions.

[0117] When making a proposal, the suggestion unit can determine the priority of the proposals based on the submission date of the childcare facilities. The suggestion unit, for example, prioritizes the proposals of childcare facilities that have been submitted recently. For example, the suggestion unit stores the submission date of the childcare facilities in a database and identifies childcare facilities with the most recent submission date. The suggestion unit can also postpone childcare facilities with the oldest submission date. For example, the suggestion unit dynamically adjusts the priority of the proposals based on the submission date of the childcare facilities. Furthermore, the suggestion unit can analyze the submission date of the childcare facilities in real time and determine the optimal priority of the proposals. For example, the suggestion unit determines the priority of the proposals based on the submission date of the childcare facilities. This makes it possible to provide more appropriate proposals by determining the priority of the proposals based on the submission date of the childcare facilities. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input submission date data of childcare facilities into a generation AI, which can then determine the priority of the proposals.

[0118] The suggestion unit can adjust the order of suggestions based on the relevance of the childcare facilities when making suggestions. The suggestion unit, for example, prioritizes suggesting highly relevant childcare facilities. For example, the suggestion unit stores the relevance of childcare facilities in a database and identifies those with high relevance. The suggestion unit can also postpone suggesting less relevant childcare facilities. For example, the suggestion unit dynamically adjusts the order of suggestions based on the relevance of the childcare facilities. Furthermore, the suggestion unit can analyze the relevance of childcare facilities in real time and determine an optimal order of suggestions. For example, the suggestion unit determines the order of suggestions based on the relevance of the childcare facilities. As a result, more appropriate suggestions can be provided by adjusting the order of suggestions based on the relevance of the childcare facilities. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of childcare facilities to a generation AI, which can adjust the order of suggestions. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, collection unit, prediction unit, planning unit, and proposal unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives questions from parents. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions. The provision unit is realized by the control unit 46A of the smart device 14 and provides appropriate advice and resources. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects past data and current conditions. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the future number of children on waiting lists. The planning unit is realized by the specific processing unit 290 of the data processing device 12 and makes a facility expansion plan. The proposal unit is realized by the control unit 46A of the smart device 14 and proposes childcare facilities that meet the needs of parents. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, collection unit, prediction unit, planning unit, and proposal unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives questions from parents. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the questions. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides appropriate advice and resources. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects past data and current conditions. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the future number of children on waiting lists. The planning unit is realized by the specific processing unit 290 of the data processing device 12 and makes a facility expansion plan. The proposal unit is realized by the control unit 46A of the smart glasses 214 and proposes childcare facilities that meet the needs of parents. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, collection unit, prediction unit, planning unit, and proposal unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset-type terminal 314 and receives questions from parents. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the content of the questions. The provision unit is implemented by the control unit 46A of the headset-type terminal 314 and provides appropriate advice and resources. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects past data and current conditions. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts the future number of children on waiting lists. The planning unit is implemented by the specific processing unit 290 of the data processing device 12 and develops a facility expansion plan. The proposal unit is implemented by the control unit 46A of the headset-type terminal 314 and proposes childcare facilities that meet the needs of parents. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, collection unit, prediction unit, planning unit, and proposal unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives questions from parents. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the questions. The provision unit is realized by the control unit 46A of the robot 414 and provides appropriate advice and resources. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects past data and current conditions. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the future number of children on waiting lists. The planning unit is realized by the specific processing unit 290 of the data processing device 12 and makes a facility expansion plan. The proposal unit is realized by the control unit 46A of the robot 414 and suggests childcare facilities that meet the needs of parents.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The reception unit can analyze the parent's past question history and select the optimal reception method. For example, it can preferentially suggest question formats that the parent has frequently used in the past. Furthermore, the reception unit can also select the most efficient reception method from the parent's past question history. For example, it can analyze the parent's past question history and automatically select the reception method that the parent has preferred. In this way, the optimal reception method can be selected by analyzing the parent's past question history.

[0121] The providing unit can estimate the parent's emotions and adjust the way in which information and resources are presented based on the estimated parent's emotions. For example, if the parent is feeling stressed, simple and easy-to-understand information can be provided. If the parent is relaxed, detailed information can be provided. Furthermore, if the parent is busy, concise information that focuses on the main points can be provided. In this way, by adjusting the way in which information and resources are presented according to the parent's emotions, more appropriate information can be provided.

[0122] The prediction unit can adjust the level of detail of the prediction based on the importance of the data when making a prediction. For example, a detailed prediction can be made for data with high importance. A simple prediction can also be made for data with low importance. Furthermore, the importance of the data can be analyzed in real time to determine the optimal level of detail of the prediction. By adjusting the level of detail of the prediction based on the importance of the data, more appropriate prediction results can be provided.

[0123] The planning unit can estimate the parent's emotions and adjust the way the plan is presented based on the estimated parent's emotions. For example, if the parent is feeling stressed, a simple and easy-to-understand plan can be provided. If the parent is relaxed, a detailed plan can be provided. Furthermore, if the parent is busy, a concise plan that focuses on the main points can be provided. In this way, by adjusting the way the plan is presented according to the parent's emotions, a more appropriate plan can be provided.

[0124] The suggestion unit can adjust the level of detail of the proposal based on the importance of the childcare facility when making the proposal. For example, a detailed proposal can be made for a childcare facility with a high importance. Also, a concise proposal can be made for a childcare facility with a low importance. Furthermore, the importance of the childcare facility can be analyzed in real time to determine the optimal level of detail of the proposal. As a result, by adjusting the level of detail of the proposal based on the importance of the childcare facility, more appropriate proposals can be provided.

[0125] The collection unit can estimate the parent's emotions and adjust the timing of data collection based on the estimated parent's emotions. For example, if the parent is feeling stressed, data can be collected during times when the parent is able to relax. Also, if the parent is busy, data collection that can be completed in a short time can be prioritized. Furthermore, if the parent is relaxed, detailed data can be collected. This allows for more appropriate data collection by adjusting the timing of data collection according to the parent's emotions.

[0126] The prediction unit can apply different prediction algorithms depending on the data category when making predictions. For example, a specialized prediction algorithm can be applied to data related to childcare. Alternatively, an appropriate prediction algorithm can be applied to data related to resource provision. Furthermore, the data category can be analyzed in real time and the optimal prediction algorithm can be dynamically applied. This allows for more appropriate prediction results to be provided by applying the optimal prediction algorithm depending on the data category.

[0127] When planning, the planning department can determine the priority of plans based on the time when prediction results were submitted. For example, it can prioritize the most recently submitted prediction results. It can also postpone prediction results that were submitted earlier. Furthermore, it can analyze the time when prediction results are submitted in real time and determine the optimal priority of plans. This makes it possible to provide more appropriate plans by prioritizing plans based on the time when prediction results were submitted.

[0128] The suggestion unit can estimate the parent's emotions and prioritize suggestions based on the estimated parent's emotions. For example, if the parent is feeling stressed, it can prioritize suggestions with high urgency. Also, if the parent is relaxed, it can prioritize detailed suggestions. Furthermore, if the parent is busy, it can prioritize suggestions that can be completed in a short time. In this way, by prioritizing suggestions according to the parent's emotions, it is possible to provide more appropriate suggestions.

[0129] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, relevant data can be collected preferentially based on the guardian's current location. The collection unit can also suggest optimal data by taking geographical location information into consideration. Furthermore, highly relevant data can be filtered based on geographical location information. This allows highly relevant data to be collected preferentially by taking geographical location information into consideration.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The reception unit accepts questions from parents. Parents can input questions using a smartphone or computer, or they can input questions by voice. For example, parents can dictate their questions using a microphone, and the questions are converted into text data using voice recognition technology. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis unit uses natural language processing technology to analyze the question and extract information to provide appropriate advice and resources. For example, it breaks down the question into keywords and searches a database for related information. It can also use algorithms to understand the context of the question and generate an appropriate answer. Step 3: The provider provides appropriate advice and resources based on the questions analyzed by the analyzer. For example, it provides parenting tips and advice, a list of childcare facilities, contact information for experts, etc. The provider can also provide information in the format desired by the parent. For example, it provides information in text, image, video, etc. Step 4: The collection department collects past data and the current situation. For example, they collect data such as the number of children on waiting lists in the past, the results of parent surveys, and local demographics. In addition to obtaining data from government and local government databases, the collection department can also conduct parent surveys to gather data to understand the current situation. Step 5: The prediction unit analyzes the data collected by the collection unit and predicts the future number of children on waiting lists. The prediction unit uses statistical models and machine learning algorithms to predict the future number of children on waiting lists. For example, the prediction unit analyzes the future trend of the number of children on waiting lists based on past data and makes a prediction taking into account the current situation. Step 6: The Planning Department makes facility expansion plans based on the forecast results obtained by the Forecasting Department. For example, they make plans for the construction of new childcare facilities, the renovation of existing facilities, and the increase in staff. Based on the forecast results, they determine the number and size of facilities needed and set priorities for facility expansion plans. Step 7: The proposal unit consists of a reception unit that inputs the parents' needs, an analysis unit that analyzes the input needs, and a unit that proposes appropriate childcare facilities based on the analysis results. Parents input the conditions and needs of the childcare facility they desire, and the analysis unit analyzes them and proposes appropriate childcare facilities. For example, it provides a list of childcare facilities that meet the parents' desired conditions and provides detailed information.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0134] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 7, a 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.

[0170] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0172] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0175] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0176] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0183] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0187] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0188] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0189] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0192] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0193] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0194] 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.

[0195] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0196] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0197] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0198] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0200] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0201] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0202] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0203] [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk to answer questions from parents, an analysis unit that analyzes the content of the question received by the reception unit; a providing unit that provides appropriate advice or resources based on the question content analyzed by the analyzing unit; a collection unit for collecting past data or current status; a prediction unit that analyzes the data collected by the collection unit and predicts the future number of children on waiting lists; a planning unit that makes a facility expansion plan based on the prediction result obtained by the prediction unit; A reception desk where parents can input their needs; an analysis unit that analyzes the needs input by the reception unit; a proposal unit that proposes appropriate childcare facilities based on the needs analyzed by the analysis unit; A system characterized by:

2. The providing unit Providing childcare information or resources 2. The system of claim 1.

3. The prediction unit Predict the future number of children on waiting lists based on past data or current circumstances 2. The system of claim 1.

4. The planning unit Plan facility expansion based on forecast results 2. The system of claim 1.

5. The proposal unit Proposing childcare facilities that meet the needs of parents 2. The system of claim 1.

6. The reception unit Estimate the parent's emotions and adjust the timing of accepting questions and needs based on the estimated parent's emotions 2. The system of claim 1.

7. The reception unit Analyze parents' past question history and select the most appropriate reception method 2. The system of claim 1.

8. The reception unit When receiving questions or needs, filter them based on the parent's current life situation and areas of interest 2. The system of claim 1.

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