system

Abstract

We provide the system. [Solution] A means of receiving data on the skills, work preferences, and geographical conditions of childcare workers or caregivers as input, Means for acquiring and updating regional demand data and facility data, A means for calculating the suitability of personnel and facilities and performing matching using the received input data and acquired demand data, A means of notifying personnel and facilities of the matching results, Means of providing appropriate career information to personnel, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The high turnover rate of childcare workers and the shortage of childcare workers are serious problems in a specific region. Furthermore, there is a mismatch between demand and supply between childcare workers and childcare facilities, and the lack of an appropriate career path is reducing the motivation of childcare workers. These problems are not only causing a decline in the quality of childcare but also deteriorating the childcare environment in families.

Means for Solving the Problems

[0005] This invention provides means for collecting skill data, work preference data, and geographical condition data of childcare workers, and for acquiring and updating regional childcare needs data and facility data based on this data. Furthermore, it includes means for calculating the degree of suitability between childcare workers and childcare facilities and performing matching. This makes it possible to notify childcare workers and childcare facilities of the optimal matching results and provide childcare workers with information on suitable careers. In addition, it aims to solve the problem by generating proposals for the placement of childcare facilities to the government based on the supply situation and demand forecast of childcare workers and presenting them through a dashboard accessible to the government.

[0006] "Childcare worker skill data" refers to information that indicates a childcare worker's experience, professional skills, qualifications, and other job-related abilities.

[0007] "Work preference data" refers to information about the working conditions desired by childcare workers, including working hours, work location, and salary structure.

[0008] "Geographical conditions data" refers to information about the areas in which childcare workers can work, and includes data indicating geographical location such as address and commuting distance.

[0009] "Childcare needs data" refers to information that indicates the demand for childcare in a specific area, and includes data such as birth rates and the utilization status of local childcare facilities.

[0010] "Facility data" refers to information about childcare facilities, including their location, size, and the services they provide.

[0011] "Fit" is an indicator that shows how well a childcare worker's skills, desired working conditions, and geographical location match the requirements of the childcare facility.

[0012] "Matching" is the process of comparing the conditions of both childcare workers and childcare facilities and selecting the best combination in order to establish the optimal employment relationship between them.

[0013] "Notification" refers to the act of communicating the matching results and proposals to childcare workers and childcare facilities.

[0014] "Career-related information" refers to data that includes guidelines and suggestions necessary for childcare workers to grow in their work and achieve their future professional goals.

[0015] "Supply status" refers to information regarding the number and capabilities of childcare workers in a particular area, and indicates the supply capacity of childcare workers in that area.

[0016] "Demand forecasting" refers to data that predicts future demand for childcare workers and estimates the need for childcare services in a given area.

[0017] A "dashboard" refers to an interface that allows government agencies to visually review information and support decision-making regarding the placement of childcare facilities. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Further, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] This invention is a system that supports the optimal matching of childcare workers and childcare facilities, and is built with a server as its main component. This system aggregates childcare workers' skill data, work preference data, and geographical preference data, and uses a database to keep local childcare needs data and facility data up to date. The server also calculates the degree of suitability between childcare workers and childcare facilities based on this data and provides appropriate matching results.

[0040] The server analyzes recruitment information received from childcare facilities and matches it with the individual working conditions entered by childcare workers. Furthermore, the server aims to reduce the turnover rate of childcare workers by providing them with optimal career information. Based on the supply situation and demand forecast compiled by the server, it becomes possible to optimize the placement of childcare facilities as a proposal to the government.

[0041] On the other hand, the terminal functions as a user interface for childcare workers, providing a screen where they can input their skills and desired conditions. When a childcare worker updates their information using this terminal, the server receives this information and updates the database, enabling more precise matching.

[0042] For example, if a childcare worker user enters "I want to work 30 hours a week in Tokyo" into their profile, the server analyzes data on childcare facilities in Tokyo and identifies the facility that best matches this condition. The terminal then presents this identified facility information to the childcare worker, making it available for application. The server also analyzes the supply situation and demand forecast for childcare workers in Tokyo for the government, generates a report indicating whether the establishment of new childcare facilities would be beneficial, and presents it as a suggestion on the government's dashboard.

[0043] This system enables efficient matching of childcare workers with childcare facilities, allowing for flexible responses to the childcare needs of each region.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server periodically accesses the government's statistical database API to retrieve regional demographic and birth rate data. Based on this, it updates the database with regional childcare needs.

[0047] Step 2:

[0048] The user, a childcare worker, uses the terminal's UI to input their skill data, work preferences, and geographical conditions. This input data is then sent from the terminal to the server as the childcare worker's profile.

[0049] Step 3:

[0050] The server receives profile data sent from childcare workers and stores / updates it in the database. Next, it analyzes each childcare worker's input data and local childcare facility data, and runs a matching algorithm.

[0051] Step 4:

[0052] The server calculates the degree of compatibility between the two parties and lists the most suitable daycare centers. Based on this matching result, it prepares to send notifications to the childcare workers and childcare facilities.

[0053] Step 5:

[0054] The terminal notifies the childcare worker of the matching results from the server and displays detailed information about the candidate childcare facilities on the screen. This includes information such as working conditions, salary, and work environment.

[0055] Step 6:

[0056] The user, a childcare worker, selects a childcare facility of interest from the presented candidates and makes a decision to apply. This selection information is sent from the terminal to the server.

[0057] Step 7:

[0058] The server verifies the application information of childcare workers, notifies the relevant childcare facilities, and subsequently provides necessary information regarding the childcare workers' career paths.

[0059] Step 8:

[0060] The server generates proposals for the placement of childcare facilities to the government based on the local childcare worker supply situation and future demand forecasts, and presents them visually on a dashboard.

[0061] This will enable continuous and effective matching of childcare workers with childcare facilities.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Effective matching of childcare workers with childcare facilities requires balancing supply and demand, which varies from region to region. Current technology makes it difficult to quickly and accurately match the individual requirements of childcare workers with the requirements of facilities, and administrative bodies lack sufficient information for optimal placement of childcare facilities in their areas. This contributes to high turnover rates among childcare workers and hinders the efficient operation of facilities.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for receiving childcare worker job competency data, work preference data, and location condition data as input; means for acquiring and updating regional childcare demand data and facility information data; and means for calculating the degree of suitability between childcare workers and childcare facilities and performing matching using the received input information and acquired childcare demand data. This enables rapid and highly accurate matching of childcare workers and childcare facilities, allowing for flexible responses to regional childcare needs and appropriate facility placement proposals to the government.

[0067] "Job competency data" refers to the knowledge, skills, and experience that childcare workers possess, and is used to evaluate their abilities and suitability for their jobs.

[0068] "Work preference data" refers to data that shows the working conditions desired by childcare workers, such as working hours, work location, employment type, etc.

[0069] "Location condition data" refers to data that represents the geographical conditions that childcare workers consider, such as commuting distance and mode of transportation.

[0070] "Childcare demand data" refers to data that shows the demand for and need for childcare in a specific area.

[0071] "Facility information data" refers to data that includes basic information about childcare facilities, such as the facility's location, the services it provides, and the age range of children it accepts.

[0072] "Calculating the degree of fit" is the process of comparing the desired conditions of childcare workers with the conditions of childcare facilities and quantifying the degree to which they match.

[0073] "Implementing a matching process" refers to the process of connecting highly suitable childcare workers with childcare facilities.

[0074] "Feedback" refers to opinions and reactions obtained from users, and is used as information for improving and adjusting the system.

[0075] "Tuning a machine learning algorithm" is the process of modifying the algorithm's parameters based on feedback to improve the accuracy of data analysis.

[0076] This invention is a system that supports the optimal matching of childcare workers and childcare facilities, and uses a server as its main component. The server aggregates childcare workers' job competency data, work preference data, and location condition data, and stores this data in a database. For example, Presto or PostgreSQL can be used as the database system. This ensures that each childcare worker's data is always kept up-to-date.

[0077] The server has the functionality to acquire and update regional childcare demand data and facility information data. This data shows the childcare needs and facility capacity for each region, enabling short-term and long-term matching between childcare workers and facilities. The data is updated in real time via a Web API and used to improve the accuracy of the analysis.

[0078] The server uses machine learning algorithms to calculate the degree of fit between childcare workers and childcare facilities. For example, software such as Scikit-learn and TENSORFLOW® are used to perform efficient data analysis. This allows for a comparison of the characteristics of individual childcare workers and facilities, and identifies combinations with a high degree of fit.

[0079] Childcare workers, as users, input their job skills data and work preference data through a terminal. This terminal can be a common device such as a smartphone or personal computer. The data entered by the childcare workers is sent to a server and used in the matching process.

[0080] For example, if a childcare worker user enters their preference for working 20 hours a week in a major metropolitan area via a terminal, the server uses that data to identify suitable childcare facilities and sends the matching results to the childcare worker. The terminal displays these results visually to the childcare worker, allowing them to smoothly proceed with the application process if necessary.

[0081] Furthermore, the server continuously collects feedback and uses it to refine its machine learning algorithms. This feedback, provided by childcare workers, is used to improve the quality of matching.

[0082] An example of a prompt for the generating AI model is a text input such as, "Generate a list of the most suitable childcare facilities based on the childcare worker's desired work location." In this way, the system implementing the invention provides advanced functionality to support the optimized matching of childcare workers and childcare facilities.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server collects data from childcare workers and childcare facilities. Specifically, it receives childcare workers' job competency data, work preference data, and location condition data entered via terminals. This input data is sent to the server in JSON format and stored in the database. A data format check process is performed to verify the integrity and type of the data.

[0086] Step 2:

[0087] The server retrieves and updates regional childcare demand data and facility information data. This data is periodically collected from external data providers and sent to the server via API. The server analyzes the retrieved data and updates the database to reflect the latest regional conditions.

[0088] Step 3:

[0089] The server uses the received job competency data and childcare demand data to calculate the degree of fit between childcare workers and childcare facilities. Specifically, the data calculation involves comparing the skill sets of childcare workers with the skill sets required by the facilities, and scoring the degree of match using the Scikit-learn library. This score is obtained as output, and the compatible combinations are listed.

[0090] Step 4:

[0091] The server performs a matching process based on the calculated suitability score and sends the best matching result to the childcare worker. In this process, the matching result is sent to the terminal via a RESTful API. The terminal displays the received information to the childcare worker and presents detailed information about the childcare facilities they can apply to.

[0092] Step 5:

[0093] Childcare workers, as users, make selections based on the presented matching results and either apply or make detailed inquiries. These selections are also sent to the server and recorded as feedback in the system. This feedback is used to improve the accuracy of subsequent analyses and to adjust the algorithms.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] Currently, in many sectors of the human resources industry, particularly in childcare and elderly care, there is insufficient matching of suitable personnel with appropriate facilities. This hinders improvements in the quality of facilities and services, leading to problems such as high employee turnover and excessive workload. Therefore, there is a need for a personnel-facility matching system optimized for each region.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for receiving ability data, work preference data, and geographical condition data of childcare workers or caregivers as input; means for acquiring and updating regional demand data and facility data; and means for calculating the suitability of personnel to facilities and performing matching using the received input data and acquired demand data. This enables efficient and accurate matching of personnel to facilities.

[0099] "Competency data" refers to information that quantifies or categorizes the skills and experience of personnel.

[0100] "Work preference data" refers to information about the working conditions and work style that employees desire.

[0101] "Geographical conditions data" refers to data related to the location information and geographical constraints of personnel and facilities.

[0102] "Demand data" refers to data that indicates the need for services or personnel in a specific region or facility.

[0103] "Facility data" refers to information about a specific facility, such as its size, equipment, and location.

[0104] "Fit" is an indicator that shows how well the requirements for personnel and facilities match.

[0105] "Matching" is the process of determining the most suitable combination between personnel and facilities.

[0106] This invention is a system for optimally matching childcare workers and caregivers with their respective facilities. The server is built using Python and Django, and utilizes PostgreSQL as its database management system. Through a user interface accessible from smartphones and PCs, individuals can input their skills data, work preferences, and geographical location data. The server receives this input data and compares it with regional demand data and facility data.

[0107] The server retrieves data and calculates the degree of fit by performing calculations using Python. This allows it to provide matching results to terminals, indicating the optimal facility for the user (the personnel). Furthermore, to generate proposals for government agencies, the server analyzes aggregated supply and demand forecast data and produces a report on the validity of new facility placements.

[0108] For example, if a caregiver enters "I would like to work 20 hours of night shifts per week in Osaka City," the server analyzes regional data and identifies facilities that meet the criteria. The identified facility information is then notified to the caregiver, enabling quick and appropriate employment.

[0109] A concrete example of a prompt message for a generating AI model is: "Please provide the conditions and data necessary for optimal matching for a caregiver in Osaka City who desires 20 hours of night shifts per week, and generate a list of suitable facilities." This allows the system to support effective matching.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] Users input their skills data, work preferences, and geographical location data using a terminal. This input data is then sent from the terminal to the server. Crucially, the terminal verifies the accuracy of the entered data and ensures its reliable transfer to the server.

[0113] Step 2:

[0114] The server saves the received data to a PostgreSQL database. Based on the input data, it retrieves regional demand data and facility data from the database. The server efficiently extracts the necessary data using database queries.

[0115] Step 3:

[0116] The server calculates the degree of fit based on the acquired data. It utilizes Python computation to compare the user's desired conditions with the facility's conditions and calculate the degree of fit. This allows the server to identify the facility that best matches the user's requirements.

[0117] Step 4:

[0118] The server sends the calculated matching results to the user's device. The user can then view a list of highly suitable facilities on their device. The output includes the name of the facility, details, and contact information.

[0119] Step 5:

[0120] The server analyzes supply and demand forecast data using a generative AI model. Based on this, it generates a report indicating whether a new facility placement would be beneficial. This report is then proposed to the government as needed.

[0121] Step 6:

[0122] The server provides an interface so that the government can review the proposed report and take necessary actions. Users can obtain more detailed analysis results using prompts directed to the generated AI model.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention aims to provide support that takes into account the emotions of childcare workers by incorporating an emotion engine into a system that optimizes the matching of childcare workers with childcare facilities. This system is server-centric and includes data input means for creating profiles based on the skills, work preferences, and geographical conditions of childcare workers.

[0125] The server retrieves data on childcare facilities and local childcare needs, and runs a matching algorithm that calculates the degree of fit by combining it with input data from childcare workers. During this process, the server uses an emotion engine to analyze emotional expressions in the text obtained from the childcare workers' input data, identifying their current emotional state. Based on this information, the server dynamically adjusts the parameters of the matching algorithm to obtain more appropriate matching results.

[0126] For example, if a user, a childcare worker, inputs "I want to take on new challenges, but I'm currently feeling a bit tired," the emotion engine recognizes the emotional states of "challenge" and "fatigue." Based on these emotional states, the server prioritizes matching the user with a work environment that allows them to acquire new skills within a manageable scope.

[0127] The device notifies childcare workers of the matching results and provides information on suitable childcare facilities. Furthermore, based on analysis results using an emotion engine, it presents a support plan required at the childcare facility selected by the childcare worker.

[0128] In addition, the server statistically processes continuously collected sentiment data for government agencies and makes suggestions aimed at improving the placement of childcare facilities and the user experience. This is presented on a dashboard accessible to government agencies. In this way, the present invention enables the provision of detailed services to childcare workers and contributes to improving the working environment throughout the childcare industry.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user, a childcare worker, enters information about their skills, work preferences, and geographical conditions through the terminal's interface. This input includes free-form text describing their current emotional state.

[0132] Step 2:

[0133] The terminal sends the entered information to the server. The server receives this information and saves it as new data in the childcare worker's profile database.

[0134] Step 3:

[0135] The server extracts text data from the childcare worker's input and performs sentiment analysis using an emotion engine. The analysis results identify the type and intensity of the emotion.

[0136] Step 4:

[0137] The server retrieves regional childcare needs data and childcare facility data, and updates this data to the latest state.

[0138] Step 5:

[0139] The server executes a matching algorithm based on the childcare worker's profile and emotion analysis results. This algorithm dynamically adjusts the matching criteria according to the emotional state to select the most suitable childcare facility.

[0140] Step 6:

[0141] The server prepares to notify childcare workers and related childcare facilities of the matching results.

[0142] Step 7:

[0143] The terminal displays the matching results sent from the server to the childcare worker. The childcare worker accesses this information to find the childcare facility that best matches their criteria.

[0144] Step 8:

[0145] Based on the childcare worker's selection, the server presents necessary support plans for childcare facilities. Along with this information, detailed career information, including suggestions for the work environment, is provided.

[0146] Step 9:

[0147] The server continuously analyzes sentiment data collected by the sentiment engine and generates suggestions for childcare facility placement for the government. These suggestions are displayed on a dashboard accessible to the government.

[0148] This entire process enables more personalized matching and career support that takes user emotions into consideration.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0151] Appropriate matching of childcare workers with childcare facilities requires consideration not only of the worker's skills and geographical location, but also their emotional state. However, traditional systems have struggled to reflect emotional changes in real time, posing challenges in providing a workplace environment where childcare workers can work without undue burden. Furthermore, accurately grasping the supply and demand balance across the entire childcare industry and providing appropriate information to the government has not been easy.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for receiving childcare worker's expertise data, work preference data, and location condition data as input; means for analyzing the emotional state from the input data and acquiring emotional information; and means for dynamically adjusting the suitability of childcare workers and childcare facilities and performing matching based on the acquired emotional information. This enables the provision of a work environment that is in line with the emotional state of childcare workers, thereby realizing manageable working conditions, improving the supply and demand balance in the childcare industry, and allowing for accurate proposals to be made to the government.

[0154] "Specialized knowledge data" refers to information about the skills, qualifications, and experience possessed by childcare workers.

[0155] "Work preference data" refers to information regarding the working conditions, working hours, and work location that childcare workers desire.

[0156] "Location-based data" refers to geographical information regarding the childcare worker's place of residence and the area in which they can work.

[0157] "Emotional state" refers to the psychological and emotional state extracted from text data entered by childcare workers.

[0158] "Emotional information" refers to data that includes results regarding the psychological state and emotional expression of childcare workers, as analyzed by the emotion engine.

[0159] "Fit" is an indicator that shows the degree to which a childcare worker's profile matches the requirements of a childcare facility.

[0160] "Dynamic adjustment" refers to the system's ability to flexibly change the parameters of its matching algorithm based on real-time changing data.

[0161] "Supply and demand balance" refers to the equilibrium between the number of childcare workers in the childcare industry and the number of childcare facilities that need them.

[0162] A "proposal" is information that compiles advice and recommended measures given to the government regarding the placement and policies of childcare facilities.

[0163] This invention is a system for effectively matching childcare workers with childcare facilities, and by incorporating an emotion analysis function, it provides an optimal work environment that takes into account the emotional state of childcare workers. This system is built around a server and is implemented as follows.

[0164] The server has a data collection interface for receiving specialized knowledge data, work preference data, and location condition data entered by childcare workers. This received data is stored in a database management system such as MySQL® or PostgreSQL.

[0165] Next, the server utilizes an emotion analysis engine to perform natural language processing and analyze the user's input text. This engine could potentially utilize the OpenAI® API. Based on the analyzed emotional state, a matching algorithm is executed to dynamically adjust the suitability between childcare workers and childcare facilities. Here, the algorithm reflects the emotional information and updates the suitability in real time.

[0166] For example, if a user, a childcare worker, inputs "I want to take on new challenges, but I'm currently feeling a little tired," the emotion analysis engine will acquire emotional information such as "challenge" and "fatigue." Based on this, the server will take this emotional state into consideration and prioritize selecting a work environment that allows the childcare worker to acquire new skills within a reasonable range.

[0167] The matching results are notified to childcare workers via a terminal. The terminal also provides childcare workers with support plans based on sentiment analysis and appropriate advice. In addition, the server analyzes sentiment data collected for the government and supply and demand data for workers to generate suggestions regarding the placement of childcare facilities. These suggestions are displayed through a dashboard accessible to the government.

[0168] An example of a prompt to the generating AI model is, "Please tell me how to suggest the optimal work environment based on the sentiment analysis results." Based on such prompts, the system will make appropriate suggestions.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user inputs expertise data, work preference data, and location condition data using a terminal. This input data is sent to the server. The server receives this data and stores it in a database. Specifically, the server receives the input data in JSON format and registers it in the database using an SQL query.

[0172] Step 2:

[0173] The server activates an emotion analysis engine based on the received input data and extracts the emotional state from the user's input text. This process uses the OpenAI API. The input is text data entered by the childcare worker, and the output is the analyzed emotion label. Specifically, the server sends the text data to the API and stores the received emotion information as internal data.

[0174] Step 3:

[0175] The server retrieves data on childcare facilities and local childcare needs from external APIs. This allows the system to incorporate the latest facility information and demand data. The retrieved data is processed by an algorithm within the server, which dynamically calculates the degree of suitability based on the emotional state of childcare workers. This calculation result is output as the matching result.

[0176] Step 4:

[0177] The device receives matching results sent from the server and notifies the childcare worker. Specifically, the device presents the visualized results to the childcare worker through the application's user interface. Simultaneously, suggestions and support plans based on sentiment analysis are also displayed.

[0178] Step 5:

[0179] The server performs statistical analysis based on collected sentiment data and supply / demand data, and generates suggestions for improving the placement of childcare facilities for the government. These suggestions are provided to the government through a dashboard. The server regularly updates the dashboard to display the latest analysis results.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0182] In today's supply industry, there is a challenge in achieving appropriate matching that considers not only workers' abilities and preferences but also their emotional aspects. Existing systems that ignore diverse working conditions and individual emotional states make it difficult to increase productivity and job satisfaction. Against this backdrop, there is a need to realize a matching process that takes workers' emotions into account and provides them with an appropriate environment.

[0183] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0184] In this invention, the server includes means for receiving worker skill information, work preference information, and location condition information as input; means for acquiring and updating regional supply needs information and facility information; means for calculating the degree of suitability between workers and supply facilities and performing matching using the received input information and acquired supply needs information; means for analyzing emotional expressions in the worker's input information using an expression analysis device and identifying emotional states; and means for dynamically adjusting the setting values ​​of the matching algorithm based on the identified emotional states. This enables optimal matching that takes into account the worker's emotions.

[0185] "Workers" refer to employees who possess specific skills and qualifications and work at supply facilities based on designated location conditions and work preferences.

[0186] "Skills information" refers to information about the specialized knowledge, skills, and qualifications possessed by workers.

[0187] "Work preference information" refers to information about working conditions such as the working hours and work style desired by the worker.

[0188] "Location information" refers to information regarding the geographical location of workers and supply facilities.

[0189] "Supply needs information" refers to information regarding the balance of labor supply and demand in each region, as well as the demand for personnel with specific skills.

[0190] "Facility information" refers to detailed data about supply facilities that are intended for workers to travel to, including geographical location information and facility needs.

[0191] "Emotional expression" refers to words or phrases related to emotions included in the worker's input information.

[0192] "Emotional state" refers to the mental condition and mood of a worker, and is analyzed from their emotional expressions.

[0193] A "representation analysis device" refers to a device or software that analyzes emotional expressions from input information such as text and identifies the emotional state of a worker.

[0194] A "matching algorithm" refers to a computational method for calculating the degree of compatibility between workers and facilities based on the worker's input information and the facility's needs, and for selecting the optimal combination.

[0195] "Dynamically adjusting settings" means changing the parameters of the matching algorithm in real time based on the detected emotional state.

[0196] The system for implementing this invention mainly consists of a server and user terminals. The server is equipped with means for receiving worker skill information, work preference information, and location condition information as input. It also has means for acquiring regional supply needs information and facility information and updating them to the latest status. This makes it possible to calculate the degree of suitability between workers and supply facilities and perform optimal matching.

[0197] The server is equipped with an expression analysis device that analyzes emotional expressions in the worker's input information and identifies their emotional state. Based on the analysis results, it has a function to dynamically adjust the settings of the matching algorithm between the worker and the facility. This makes it possible to provide a more appropriate environment that takes the worker's emotions into consideration.

[0198] The user's terminal notifies the worker of the matching results and displays information on suitable supply facilities. It also has a means of providing appropriate occupational information to the worker, taking into account their identified emotional state. This information provision supports workers in effectively building their careers.

[0199] As a concrete example, a worker might input information into a terminal indicating they have skills in "medical care" and "cooking," prefer a "daytime shift," and describe their emotional state as "seeking new challenges but somewhat fatigued." Based on this input, the server prioritizes matching the worker with a workplace environment that allows them to acquire new skills at a comfortable pace.

[0200] An example of a prompt for a generating AI model is, "List the most suitable supply facilities based on the worker's latest profile data and emotional state." This is an important means of using more advanced technology to enable matching that accurately captures the psychological needs of workers.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The server receives worker skill information, work preference information, and location information as input from the user's terminal. This is used to initialize the user's profile data. The input information is stored in a database and used for subsequent processing.

[0204] Step 2:

[0205] The server retrieves regional supply needs information and facility information from an external database and updates it to the latest status. The retrieved information is organized through a data processing program and stored as supply facility profile data.

[0206] Step 3:

[0207] The server calculates the degree of fit using the stored worker profile data and supply facility profile data. A matching algorithm is then executed to calculate the fit score for each worker and supply facility. The calculated scores are output as a matching candidate list.

[0208] Step 4:

[0209] The server analyzes emotional expressions within the worker's input information. An expression analysis device is used to identify the worker's emotional state. This information is used to dynamically adjust the settings of the matching algorithm. The results of the emotional identification are stored internally.

[0210] Step 5:

[0211] The server adjusts the matching algorithm settings based on the identified emotional state. After adjustment, it recalculates the fit score and selects the optimal supply facility, taking into account the worker's current psychological state. A new list of matching candidates is then generated.

[0212] Step 6:

[0213] The terminal notifies the user of the latest list of matching candidates received from the server and displays information on the most suitable supply facilities. It also provides career advice based on the user's emotional state to support them in making a choice with confidence. Once the information has been provided, the user can confirm their selection and proceed to the next action.

[0214] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0230] This invention is a system that supports the optimal matching of childcare workers and childcare facilities, and is built with a server as its main component. This system aggregates childcare workers' skill data, work preference data, and geographical preference data, and uses a database to keep local childcare needs data and facility data up to date. The server also calculates the degree of suitability between childcare workers and childcare facilities based on this data and provides appropriate matching results.

[0231] The server analyzes recruitment information received from childcare facilities and matches it with the individual working conditions entered by childcare workers. Furthermore, the server aims to reduce the turnover rate of childcare workers by providing them with optimal career information. Based on the supply situation and demand forecast compiled by the server, it becomes possible to optimize the placement of childcare facilities as a proposal to the government.

[0232] On the other hand, the terminal functions as a user interface for childcare workers, providing a screen where they can input their skills and desired conditions. When a childcare worker updates their information using this terminal, the server receives this information and updates the database, enabling more precise matching.

[0233] For example, if a childcare worker user enters "I want to work 30 hours a week in Tokyo" into their profile, the server analyzes data on childcare facilities in Tokyo and identifies the facility that best matches this condition. The terminal then presents this identified facility information to the childcare worker, making it available for application. The server also analyzes the supply situation and demand forecast for childcare workers in Tokyo for the government, generates a report indicating whether the establishment of new childcare facilities would be beneficial, and presents it as a suggestion on the government's dashboard.

[0234] This system enables efficient matching of childcare workers with childcare facilities, allowing for flexible responses to the childcare needs of each region.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The server periodically accesses the government's statistical database API to retrieve regional demographic and birth rate data. Based on this, it updates the database with regional childcare needs.

[0238] Step 2:

[0239] The user, a childcare worker, uses the terminal's UI to input their skill data, work preferences, and geographical conditions. This input data is then sent from the terminal to the server as the childcare worker's profile.

[0240] Step 3:

[0241] The server receives profile data sent from childcare workers and stores / updates it in the database. Next, it analyzes each childcare worker's input data and local childcare facility data, and runs a matching algorithm.

[0242] Step 4:

[0243] The server calculates the degree of compatibility between the two parties and lists the most suitable daycare centers. Based on this matching result, it prepares to send notifications to the childcare workers and childcare facilities.

[0244] Step 5:

[0245] The terminal notifies the childcare worker of the matching results from the server and displays detailed information about the candidate childcare facilities on the screen. This includes information such as working conditions, salary, and work environment.

[0246] Step 6:

[0247] The user, a childcare worker, selects a childcare facility of interest from the presented candidates and makes a decision to apply. This selection information is sent from the terminal to the server.

[0248] Step 7:

[0249] The server verifies the application information of childcare workers, notifies the relevant childcare facilities, and subsequently provides necessary information regarding the childcare workers' career paths.

[0250] Step 8:

[0251] The server generates proposals for the placement of childcare facilities to the government based on the local childcare worker supply situation and future demand forecasts, and presents them visually on a dashboard.

[0252] This will enable continuous and effective matching of childcare workers with childcare facilities.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0255] Effective matching of childcare workers with childcare facilities requires balancing supply and demand, which varies from region to region. Current technology makes it difficult to quickly and accurately match the individual requirements of childcare workers with the requirements of facilities, and administrative bodies lack sufficient information for optimal placement of childcare facilities in their areas. This contributes to high turnover rates among childcare workers and hinders the efficient operation of facilities.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes means for receiving childcare worker job competency data, work preference data, and location condition data as input; means for acquiring and updating regional childcare demand data and facility information data; and means for calculating the degree of suitability between childcare workers and childcare facilities and performing matching using the received input information and acquired childcare demand data. This enables rapid and highly accurate matching of childcare workers and childcare facilities, allowing for flexible responses to regional childcare needs and appropriate facility placement proposals to the government.

[0258] "Job competency data" refers to the knowledge, skills, and experience that childcare workers possess, and is used to evaluate their abilities and suitability for their jobs.

[0259] "Work preference data" refers to data that shows the working conditions desired by childcare workers, such as working hours, work location, employment type, etc.

[0260] "Location condition data" refers to data that represents the geographical conditions that childcare workers consider, such as commuting distance and mode of transportation.

[0261] "Childcare demand data" refers to data that shows the demand for and need for childcare in a specific area.

[0262] "Facility information data" refers to data that includes basic information about childcare facilities, such as the facility's location, the services it provides, and the age range of children it accepts.

[0263] "Calculating the degree of fit" is the process of comparing the desired conditions of childcare workers with the conditions of childcare facilities and quantifying the degree to which they match.

[0264] "Implementing a matching process" refers to the process of connecting highly suitable childcare workers with childcare facilities.

[0265] "Feedback" refers to opinions and reactions obtained from users, and is used as information for improving and adjusting the system.

[0266] "Tuning a machine learning algorithm" is the process of modifying the algorithm's parameters based on feedback to improve the accuracy of data analysis.

[0267] This invention is a system that supports the optimal matching of childcare workers and childcare facilities, and uses a server as its main component. The server aggregates childcare workers' job competency data, work preference data, and location condition data, and stores this data in a database. For example, Presto or PostgreSQL can be used as the database system. This ensures that each childcare worker's data is always kept up-to-date.

[0268] The server has the functionality to acquire and update regional childcare demand data and facility information data. This data shows the childcare needs and facility capacity for each region, enabling short-term and long-term matching between childcare workers and facilities. The data is updated in real time via a Web API and used to improve the accuracy of the analysis.

[0269] The server uses machine learning algorithms to calculate the degree of fit between childcare workers and childcare facilities. For example, software such as Scikit-learn and TensorFlow are used to perform efficient data analysis. This allows for a comparison of the characteristics of individual childcare workers and facilities, and identifies highly compatible combinations.

[0270] Childcare workers, as users, input their job skills data and work preference data through a terminal. This terminal can be a common device such as a smartphone or personal computer. The data entered by the childcare workers is sent to a server and used in the matching process.

[0271] For example, if a childcare worker user enters their preference for working 20 hours a week in a major metropolitan area via a terminal, the server uses that data to identify suitable childcare facilities and sends the matching results to the childcare worker. The terminal displays these results visually to the childcare worker, allowing them to smoothly proceed with the application process if necessary.

[0272] Furthermore, the server continuously collects feedback and uses it to refine its machine learning algorithms. This feedback, provided by childcare workers, is used to improve the quality of matching.

[0273] An example of a prompt for the generating AI model is a text input such as, "Generate a list of the most suitable childcare facilities based on the childcare worker's desired work location." In this way, the system implementing the invention provides advanced functionality to support the optimized matching of childcare workers and childcare facilities.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The server collects data from childcare workers and childcare facilities. Specifically, it receives childcare workers' job competency data, work preference data, and location condition data entered via terminals. This input data is sent to the server in JSON format and stored in the database. A data format check process is performed to verify the integrity and type of the data.

[0277] Step 2:

[0278] The server retrieves and updates regional childcare demand data and facility information data. This data is periodically collected from external data providers and sent to the server via API. The server analyzes the retrieved data and updates the database to reflect the latest regional conditions.

[0279] Step 3:

[0280] The server uses the received job competency data and childcare demand data to calculate the degree of fit between childcare workers and childcare facilities. Specifically, the data calculation involves comparing the skill sets of childcare workers with the skill sets required by the facilities, and scoring the degree of match using the Scikit-learn library. This score is obtained as output, and the compatible combinations are listed.

[0281] Step 4:

[0282] The server performs a matching process based on the calculated suitability score and sends the best matching result to the childcare worker. In this process, the matching result is sent to the terminal via a RESTful API. The terminal displays the received information to the childcare worker and presents detailed information about the childcare facilities they can apply to.

[0283] Step 5:

[0284] The user, a childcare worker, makes a selection based on the presented matching results and applies or makes a detailed inquiry. This selection result is also sent to the server and recorded as feedback in the system. This feedback is used to improve the analysis accuracy and adjust the algorithm later.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] Currently, in many talent industries, especially in the childcare and nursing care fields, the proper matching of talents and facilities is not being carried out sufficiently. For this reason, the improvement of the quality of facilities and services is hindered, and the turnover and excessive burden of talents have become problems. Therefore, there is a demand for the provision of a talent and facility matching system optimized for each region.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for receiving, as input, the ability data, work preference data, and geographical condition data of childcare workers or nursing care workers, means for acquiring and updating the demand data and facility data by region, and means for calculating the compatibility between talents and facilities and performing matching using the received input data and the acquired demand data. As a result, efficient and accurate matching between talents and facilities becomes possible. [[ID=2X]]

[0290] "Ability data" is information obtained by quantifying or categorizing the skills and experiences of talents.

[0291] "Work preference data" is information regarding the work conditions and working styles desired by talents.

[0292] "Geographical conditions data" refers to data related to the location information and geographical constraints of personnel and facilities.

[0293] "Demand data" refers to data that indicates the need for services or personnel in a specific region or facility.

[0294] "Facility data" refers to information about a specific facility, such as its size, equipment, and location.

[0295] "Fit" is an indicator that shows how well the requirements for personnel and facilities match.

[0296] "Matching" is the process of determining the most suitable combination between personnel and facilities.

[0297] This invention is a system for optimally matching childcare workers and caregivers with their respective facilities. The server is built using Python and Django, and utilizes PostgreSQL as its database management system. Through a user interface accessible from smartphones and PCs, individuals can input their skills data, work preferences, and geographical location data. The server receives this input data and compares it with regional demand data and facility data.

[0298] The server retrieves data and calculates the degree of fit by performing calculations using Python. This allows it to provide matching results to terminals, indicating the optimal facility for the user (the personnel). Furthermore, to generate proposals for government agencies, the server analyzes aggregated supply and demand forecast data and produces a report on the validity of new facility placements.

[0299] For example, if a caregiver enters "I would like to work 20 hours of night shifts per week in Osaka City," the server analyzes regional data and identifies facilities that meet the criteria. The identified facility information is then notified to the caregiver, enabling quick and appropriate employment.

[0300] As a specific example of a prompt sentence for a generative AI model, there is one that says, "Please present the conditions and data for optimal matching for a nurse who wishes to work night shifts for 20 hours a week in Osaka City, and generate a list of appropriate facilities." This enables the system to support effective matching.

[0301] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0302] Step 1:

[0303] The user uses a terminal to input their ability data, work preference data, and geographical condition data. The input data is transmitted from the terminal to the server. Here, it is important to confirm on the terminal whether the input data is accurate and transfer it to the server reliably.

[0304] Step 2:

[0305] The server saves the received data in a PostgreSQL database. Based on the input data, demand data and facility data by region are retrieved from the database. The server efficiently extracts the necessary data using a database query.

[0306] Step 3:

[0307] The server calculates a fitness level based on the retrieved data. Utilizing Python's arithmetic processing, the server compares the user's desired conditions with the facility's conditions to calculate the fitness level. This enables the identification of the facility that best matches the user's conditions.

[0308] Step 4:

[0309] The server transmits the calculated matching result to the terminal. The user can view a list of facilities with a high fitness level on the terminal. As the output here, the corresponding facility name, details, contact information, etc. are provided.

[0310] Step 5:

[0311] The server analyzes supply and demand forecast data using a generative AI model. Based on this, it generates a report indicating whether a new facility placement would be beneficial. This report is then proposed to the government as needed.

[0312] Step 6:

[0313] The server provides an interface so that the government can review the proposed report and take necessary actions. Users can obtain more detailed analysis results using prompts directed to the generated AI model.

[0314] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0315] This invention aims to provide support that takes into account the emotions of childcare workers by incorporating an emotion engine into a system that optimizes the matching of childcare workers with childcare facilities. This system is server-centric and includes data input means for creating profiles based on the skills, work preferences, and geographical conditions of childcare workers.

[0316] The server retrieves data on childcare facilities and local childcare needs, and runs a matching algorithm that calculates the degree of fit by combining it with input data from childcare workers. During this process, the server uses an emotion engine to analyze emotional expressions in the text obtained from the childcare workers' input data, identifying their current emotional state. Based on this information, the server dynamically adjusts the parameters of the matching algorithm to obtain more appropriate matching results.

[0317] For example, if a user, a childcare worker, inputs "I want to take on new challenges, but I'm currently feeling a bit tired," the emotion engine recognizes the emotional states of "challenge" and "fatigue." Based on these emotional states, the server prioritizes matching the user with a work environment that allows them to acquire new skills within a manageable scope.

[0318] The device notifies childcare workers of the matching results and provides information on suitable childcare facilities. Furthermore, based on analysis results using an emotion engine, it presents a support plan required at the childcare facility selected by the childcare worker.

[0319] In addition, the server statistically processes continuously collected sentiment data for government agencies and makes suggestions aimed at improving the placement of childcare facilities and the user experience. This is presented on a dashboard accessible to government agencies. In this way, the present invention enables the provision of detailed services to childcare workers and contributes to improving the working environment throughout the childcare industry.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The user, a childcare worker, enters information about their skills, work preferences, and geographical conditions through the terminal's interface. This input includes free-form text describing their current emotional state.

[0323] Step 2:

[0324] The terminal sends the entered information to the server. The server receives this information and saves it as new data in the childcare worker's profile database.

[0325] Step 3:

[0326] The server extracts text data from the childcare worker's input and performs sentiment analysis using an emotion engine. The analysis results identify the type and intensity of the emotion.

[0327] Step 4:

[0328] The server retrieves regional childcare needs data and childcare facility data, and updates this data to the latest state.

[0329] Step 5:

[0330] The server executes a matching algorithm based on the childcare worker's profile and emotion analysis results. This algorithm dynamically adjusts the matching criteria according to the emotional state to select the most suitable childcare facility.

[0331] Step 6:

[0332] The server prepares to notify childcare workers and related childcare facilities of the matching results.

[0333] Step 7:

[0334] The terminal displays the matching results sent from the server to the childcare worker. The childcare worker accesses this information to find the childcare facility that best matches their criteria.

[0335] Step 8:

[0336] Based on the childcare worker's selection, the server presents necessary support plans for childcare facilities. Along with this information, detailed career information, including suggestions for the work environment, is provided.

[0337] Step 9:

[0338] The server continuously analyzes sentiment data collected by the sentiment engine and generates suggestions for childcare facility placement for the government. These suggestions are displayed on a dashboard accessible to the government.

[0339] This entire process enables more personalized matching and career support that takes user emotions into consideration.

[0340] (Example 2)

[0341] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0342] Appropriate matching of childcare workers with childcare facilities requires consideration not only of the worker's skills and geographical location, but also their emotional state. However, traditional systems have struggled to reflect emotional changes in real time, posing challenges in providing a workplace environment where childcare workers can work without undue burden. Furthermore, accurately grasping the supply and demand balance across the entire childcare industry and providing appropriate information to the government has not been easy.

[0343] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0344] In this invention, the server includes means for receiving childcare worker's expertise data, work preference data, and location condition data as input; means for analyzing the emotional state from the input data and acquiring emotional information; and means for dynamically adjusting the suitability of childcare workers and childcare facilities and performing matching based on the acquired emotional information. This enables the provision of a work environment that is in line with the emotional state of childcare workers, thereby realizing manageable working conditions, improving the supply and demand balance in the childcare industry, and allowing for accurate proposals to be made to the government.

[0345] "Specialized knowledge data" refers to information about the skills, qualifications, and experience possessed by childcare workers.

[0346] "Work preference data" refers to information regarding the working conditions, working hours, and work location that childcare workers desire.

[0347] "Location-based data" refers to geographical information regarding the childcare worker's place of residence and the area in which they can work.

[0348] "Emotional state" refers to the psychological and emotional state extracted from text data entered by childcare workers.

[0349] "Emotional information" refers to data that includes results regarding the psychological state and emotional expression of childcare workers, as analyzed by the emotion engine.

[0350] "Fit" is an indicator that shows the degree to which a childcare worker's profile matches the requirements of a childcare facility.

[0351] "Dynamic adjustment" refers to the system's ability to flexibly change the parameters of its matching algorithm based on real-time changing data.

[0352] "Supply and demand balance" refers to the equilibrium between the number of childcare workers in the childcare industry and the number of childcare facilities that need them.

[0353] A "proposal" is information that compiles advice and recommended measures given to the government regarding the placement and policies of childcare facilities.

[0354] This invention is a system for effectively matching childcare workers with childcare facilities, and by incorporating an emotion analysis function, it provides an optimal work environment that takes into account the emotional state of childcare workers. This system is built around a server and is implemented as follows.

[0355] The server has a data collection interface for receiving specialized knowledge data, work preference data, and location condition data entered by childcare workers. This received data is stored in a database management system such as MySQL or PostgreSQL.

[0356] Next, the server utilizes an emotion analysis engine to perform natural language processing and analyze the user's input text. This engine could potentially utilize the OpenAI API. Based on the analyzed emotional state, a matching algorithm is executed to dynamically adjust the suitability between childcare workers and childcare facilities. Here, the algorithm reflects the emotional information and updates the suitability in real time.

[0357] For example, if a user, a childcare worker, inputs "I want to take on new challenges, but I'm currently feeling a little tired," the emotion analysis engine will acquire emotional information such as "challenge" and "fatigue." Based on this, the server will take this emotional state into consideration and prioritize selecting a work environment that allows the childcare worker to acquire new skills within a reasonable range.

[0358] The matching results are notified to childcare workers via a terminal. The terminal also provides childcare workers with support plans based on sentiment analysis and appropriate advice. In addition, the server analyzes sentiment data collected for the government and supply and demand data for workers to generate suggestions regarding the placement of childcare facilities. These suggestions are displayed through a dashboard accessible to the government.

[0359] An example of a prompt to the generating AI model is, "Please tell me how to suggest the optimal work environment based on the sentiment analysis results." Based on such prompts, the system will make appropriate suggestions.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The user inputs expertise data, work preference data, and location condition data using a terminal. This input data is sent to the server. The server receives this data and stores it in a database. Specifically, the server receives the input data in JSON format and registers it in the database using an SQL query.

[0363] Step 2:

[0364] The server activates an emotion analysis engine based on the received input data and extracts the emotional state from the user's input text. This process uses the OpenAI API. The input is text data entered by the childcare worker, and the output is the analyzed emotion label. Specifically, the server sends the text data to the API and stores the received emotion information as internal data.

[0365] Step 3:

[0366] The server retrieves data on childcare facilities and local childcare needs from external APIs. This allows the system to incorporate the latest facility information and demand data. The retrieved data is processed by an algorithm within the server, which dynamically calculates the degree of suitability based on the emotional state of childcare workers. This calculation result is output as the matching result.

[0367] Step 4:

[0368] The device receives matching results sent from the server and notifies the childcare worker. Specifically, the device presents the visualized results to the childcare worker through the application's user interface. Simultaneously, suggestions and support plans based on sentiment analysis are also displayed.

[0369] Step 5:

[0370] The server performs statistical analysis based on collected sentiment data and supply / demand data, and generates suggestions for improving the placement of childcare facilities for the government. These suggestions are provided to the government through a dashboard. The server regularly updates the dashboard to display the latest analysis results.

[0371] (Application Example 2)

[0372] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0373] In today's supply industry, there is a challenge in achieving appropriate matching that considers not only workers' abilities and preferences but also their emotional aspects. Existing systems that ignore diverse working conditions and individual emotional states make it difficult to increase productivity and job satisfaction. Against this backdrop, there is a need to realize a matching process that takes workers' emotions into account and provides them with an appropriate environment.

[0374] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0375] In this invention, the server includes means for receiving worker skill information, work preference information, and location condition information as input; means for acquiring and updating regional supply needs information and facility information; means for calculating the degree of suitability between workers and supply facilities and performing matching using the received input information and acquired supply needs information; means for analyzing emotional expressions in the worker's input information using an expression analysis device and identifying emotional states; and means for dynamically adjusting the setting values ​​of the matching algorithm based on the identified emotional states. This enables optimal matching that takes into account the worker's emotions.

[0376] "Workers" refer to employees who possess specific skills and qualifications and work at supply facilities based on designated location conditions and work preferences.

[0377] "Skills information" refers to information about the specialized knowledge, skills, and qualifications possessed by workers.

[0378] "Work preference information" refers to information about working conditions such as the working hours and work style desired by the worker.

[0379] "Location information" refers to information regarding the geographical location of workers and supply facilities.

[0380] "Supply needs information" refers to information regarding the balance of labor supply and demand in each region, as well as the demand for personnel with specific skills.

[0381] "Facility information" refers to detailed data about supply facilities that are intended for workers to travel to, including geographical location information and facility needs.

[0382] "Emotional expression" refers to words or phrases related to emotions included in the worker's input information.

[0383] "Emotional state" refers to the mental condition and mood of a worker, and is analyzed from their emotional expressions.

[0384] A "representation analysis device" refers to a device or software that analyzes emotional expressions from input information such as text and identifies the emotional state of a worker.

[0385] A "matching algorithm" refers to a computational method for calculating the degree of compatibility between workers and facilities based on the worker's input information and the facility's needs, and for selecting the optimal combination.

[0386] "Dynamically adjusting settings" means changing the parameters of the matching algorithm in real time based on the detected emotional state.

[0387] The system for implementing this invention mainly consists of a server and user terminals. The server is equipped with means for receiving worker skill information, work preference information, and location condition information as input. It also has means for acquiring regional supply needs information and facility information and updating them to the latest status. This makes it possible to calculate the degree of suitability between workers and supply facilities and perform optimal matching.

[0388] The server is equipped with an expression analysis device that analyzes emotional expressions in the worker's input information and identifies their emotional state. Based on the analysis results, it has a function to dynamically adjust the settings of the matching algorithm between the worker and the facility. This makes it possible to provide a more appropriate environment that takes the worker's emotions into consideration.

[0389] The user's terminal notifies the worker of the matching results and displays information on suitable supply facilities. It also has a means of providing appropriate occupational information to the worker, taking into account their identified emotional state. This information provision supports workers in effectively building their careers.

[0390] As a concrete example, a worker might input information into a terminal indicating they have skills in "medical care" and "cooking," prefer a "daytime shift," and describe their emotional state as "seeking new challenges but somewhat fatigued." Based on this input, the server prioritizes matching the worker with a workplace environment that allows them to acquire new skills at a comfortable pace.

[0391] An example of a prompt for a generating AI model is, "List the most suitable supply facilities based on the worker's latest profile data and emotional state." This is an important means of using more advanced technology to enable matching that accurately captures the psychological needs of workers.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The server receives worker skill information, work preference information, and location information as input from the user's terminal. This is used to initialize the user's profile data. The input information is stored in a database and used for subsequent processing.

[0395] Step 2:

[0396] The server retrieves regional supply needs information and facility information from an external database and updates it to the latest status. The retrieved information is organized through a data processing program and stored as supply facility profile data.

[0397] Step 3:

[0398] The server calculates the degree of fit using the stored worker profile data and supply facility profile data. A matching algorithm is then executed to calculate the fit score for each worker and supply facility. The calculated scores are output as a matching candidate list.

[0399] Step 4:

[0400] The server analyzes emotional expressions within the worker's input information. An expression analysis device is used to identify the worker's emotional state. This information is used to dynamically adjust the settings of the matching algorithm. The results of the emotional identification are stored internally.

[0401] Step 5:

[0402] The server adjusts the matching algorithm settings based on the identified emotional state. After adjustment, it recalculates the fit score and selects the optimal supply facility, taking into account the worker's current psychological state. A new list of matching candidates is then generated.

[0403] Step 6:

[0404] The terminal notifies the user of the latest list of matching candidates received from the server and displays information on the most suitable supply facilities. It also provides career advice based on the user's emotional state to support them in making a choice with confidence. Once the information has been provided, the user can confirm their selection and proceed to the next action.

[0405] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0406] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0407] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0411] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0412] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0413] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0414] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0415] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0416] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0417] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0418] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0419] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0420] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0421] This invention is a system that supports the optimal matching of childcare workers and childcare facilities, and is built with a server as its main component. This system aggregates childcare workers' skill data, work preference data, and geographical preference data, and uses a database to keep local childcare needs data and facility data up to date. The server also calculates the degree of suitability between childcare workers and childcare facilities based on this data and provides appropriate matching results.

[0422] The server analyzes recruitment information received from childcare facilities and matches it with the individual working conditions entered by childcare workers. Furthermore, the server aims to reduce the turnover rate of childcare workers by providing them with optimal career information. Based on the supply situation and demand forecast compiled by the server, it becomes possible to optimize the placement of childcare facilities as a proposal to the government.

[0423] On the other hand, the terminal functions as a user interface for childcare workers, providing a screen where they can input their skills and desired conditions. When a childcare worker updates their information using this terminal, the server receives this information and updates the database, enabling more precise matching.

[0424] For example, if a childcare worker user enters "I want to work 30 hours a week in Tokyo" into their profile, the server analyzes data on childcare facilities in Tokyo and identifies the facility that best matches this condition. The terminal then presents this identified facility information to the childcare worker, making it available for application. The server also analyzes the supply situation and demand forecast for childcare workers in Tokyo for the government, generates a report indicating whether the establishment of new childcare facilities would be beneficial, and presents it as a suggestion on the government's dashboard.

[0425] This system enables efficient matching of childcare workers with childcare facilities, allowing for flexible responses to the childcare needs of each region.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] The server periodically accesses the government's statistical database API to retrieve regional demographic and birth rate data. Based on this, it updates the database with regional childcare needs.

[0429] Step 2:

[0430] The user, a childcare worker, uses the terminal's UI to input their skill data, work preferences, and geographical conditions. This input data is then sent from the terminal to the server as the childcare worker's profile.

[0431] Step 3:

[0432] The server receives profile data sent from childcare workers and stores / updates it in the database. Next, it analyzes each childcare worker's input data and local childcare facility data, and runs a matching algorithm.

[0433] Step 4:

[0434] The server calculates the degree of compatibility between the two parties and lists the most suitable daycare centers. Based on this matching result, it prepares to send notifications to the childcare workers and childcare facilities.

[0435] Step 5:

[0436] The terminal notifies the childcare worker of the matching results from the server and displays detailed information about the candidate childcare facilities on the screen. This includes information such as working conditions, salary, and work environment.

[0437] Step 6:

[0438] The user, a childcare worker, selects a childcare facility of interest from the presented candidates and makes a decision to apply. This selection information is sent from the terminal to the server.

[0439] Step 7:

[0440] The server verifies the application information of childcare workers, notifies the relevant childcare facilities, and subsequently provides necessary information regarding the childcare workers' career paths.

[0441] Step 8:

[0442] The server generates proposals for the placement of childcare facilities to the government based on the local childcare worker supply situation and future demand forecasts, and presents them visually on a dashboard.

[0443] This will enable continuous and effective matching of childcare workers with childcare facilities.

[0444] (Example 1)

[0445] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0446] Effective matching of childcare workers with childcare facilities requires balancing supply and demand, which varies from region to region. Current technology makes it difficult to quickly and accurately match the individual requirements of childcare workers with the requirements of facilities, and administrative bodies lack sufficient information for optimal placement of childcare facilities in their areas. This contributes to high turnover rates among childcare workers and hinders the efficient operation of facilities.

[0447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0448] In this invention, the server includes means for receiving childcare worker job competency data, work preference data, and location condition data as input; means for acquiring and updating regional childcare demand data and facility information data; and means for calculating the degree of suitability between childcare workers and childcare facilities and performing matching using the received input information and acquired childcare demand data. This enables rapid and highly accurate matching of childcare workers and childcare facilities, allowing for flexible responses to regional childcare needs and appropriate facility placement proposals to the government.

[0449] "Job competency data" refers to the knowledge, skills, and experience that childcare workers possess, and is used to evaluate their abilities and suitability for their jobs.

[0450] "Work preference data" refers to data that shows the working conditions desired by childcare workers, such as working hours, work location, employment type, etc.

[0451] "Location condition data" refers to data that represents the geographical conditions that childcare workers consider, such as commuting distance and mode of transportation.

[0452] "Childcare demand data" refers to data that shows the demand for and need for childcare in a specific area.

[0453] "Facility information data" refers to data that includes basic information about childcare facilities, such as the facility's location, the services it provides, and the age range of children it accepts.

[0454] "Calculating the degree of fit" is the process of comparing the desired conditions of childcare workers with the conditions of childcare facilities and quantifying the degree to which they match.

[0455] "Implementing a matching process" refers to the process of connecting highly suitable childcare workers with childcare facilities.

[0456] "Feedback" refers to opinions and reactions obtained from users, and is used as information for improving and adjusting the system.

[0457] "Tuning a machine learning algorithm" is the process of modifying the algorithm's parameters based on feedback to improve the accuracy of data analysis.

[0458] This invention is a system that supports the optimal matching of childcare workers and childcare facilities, and uses a server as its main component. The server aggregates childcare workers' job competency data, work preference data, and location condition data, and stores this data in a database. For example, Presto or PostgreSQL can be used as the database system. This ensures that each childcare worker's data is always kept up-to-date.

[0459] The server has the functionality to acquire and update regional childcare demand data and facility information data. This data shows the childcare needs and facility capacity for each region, enabling short-term and long-term matching between childcare workers and facilities. The data is updated in real time via a Web API and used to improve the accuracy of the analysis.

[0460] The server uses machine learning algorithms to calculate the degree of fit between childcare workers and childcare facilities. For example, software such as Scikit-learn and TensorFlow are used to perform efficient data analysis. This allows for a comparison of the characteristics of individual childcare workers and facilities, and identifies highly compatible combinations.

[0461] Childcare workers, as users, input their job skills data and work preference data through a terminal. This terminal can be a common device such as a smartphone or personal computer. The data entered by the childcare workers is sent to a server and used in the matching process.

[0462] For example, if a childcare worker user enters their preference for working 20 hours a week in a major metropolitan area via a terminal, the server uses that data to identify suitable childcare facilities and sends the matching results to the childcare worker. The terminal displays these results visually to the childcare worker, allowing them to smoothly proceed with the application process if necessary.

[0463] Furthermore, the server continuously collects feedback and uses it to refine its machine learning algorithms. This feedback, provided by childcare workers, is used to improve the quality of matching.

[0464] An example of a prompt for the generating AI model is a text input such as, "Generate a list of the most suitable childcare facilities based on the childcare worker's desired work location." In this way, the system implementing the invention provides advanced functionality to support the optimized matching of childcare workers and childcare facilities.

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1:

[0467] The server collects data from childcare workers and childcare facilities. Specifically, it receives childcare workers' job competency data, work preference data, and location condition data entered via terminals. This input data is sent to the server in JSON format and stored in the database. A data format check process is performed to verify the integrity and type of the data.

[0468] Step 2:

[0469] The server retrieves and updates regional childcare demand data and facility information data. This data is periodically collected from external data providers and sent to the server via API. The server analyzes the retrieved data and updates the database to reflect the latest regional conditions.

[0470] Step 3:

[0471] The server uses the received job competency data and childcare demand data to calculate the degree of fit between childcare workers and childcare facilities. Specifically, the data calculation involves comparing the skill sets of childcare workers with the skill sets required by the facilities, and scoring the degree of match using the Scikit-learn library. This score is obtained as output, and the compatible combinations are listed.

[0472] Step 4:

[0473] The server performs a matching process based on the calculated suitability score and sends the best matching result to the childcare worker. In this process, the matching result is sent to the terminal via a RESTful API. The terminal displays the received information to the childcare worker and presents detailed information about the childcare facilities they can apply to.

[0474] Step 5:

[0475] Childcare workers, as users, make selections based on the presented matching results and either apply or make detailed inquiries. These selections are also sent to the server and recorded as feedback in the system. This feedback is used to improve the accuracy of subsequent analyses and to adjust the algorithms.

[0476] (Application Example 1)

[0477] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0478] Currently, in many sectors of the human resources industry, particularly in childcare and elderly care, there is insufficient matching of suitable personnel with appropriate facilities. This hinders improvements in the quality of facilities and services, leading to problems such as high employee turnover and excessive workload. Therefore, there is a need for a personnel-facility matching system optimized for each region.

[0479] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0480] In this invention, the server includes means for receiving ability data, work preference data, and geographical condition data of childcare workers or caregivers as input; means for acquiring and updating regional demand data and facility data; and means for calculating the suitability of personnel to facilities and performing matching using the received input data and acquired demand data. This enables efficient and accurate matching of personnel to facilities.

[0481] "Competency data" refers to information that quantifies or categorizes the skills and experience of personnel.

[0482] "Work preference data" refers to information about the working conditions and work style that employees desire.

[0483] "Geographical conditions data" refers to data related to the location information and geographical constraints of personnel and facilities.

[0484] "Demand data" refers to data that indicates the need for services or personnel in a specific region or facility.

[0485] "Facility data" refers to information about a specific facility, such as its size, equipment, and location.

[0486] "Fit" is an indicator that shows how well the requirements for personnel and facilities match.

[0487] "Matching" is the process of determining the most suitable combination between personnel and facilities.

[0488] This invention is a system for optimally matching childcare workers and caregivers with their respective facilities. The server is built using Python and Django, and utilizes PostgreSQL as its database management system. Through a user interface accessible from smartphones and PCs, individuals can input their skills data, work preferences, and geographical location data. The server receives this input data and compares it with regional demand data and facility data.

[0489] The server retrieves data and calculates the degree of fit by performing calculations using Python. This allows it to provide matching results to terminals, indicating the optimal facility for the user (the personnel). Furthermore, to generate proposals for government agencies, the server analyzes aggregated supply and demand forecast data and produces a report on the validity of new facility placements.

[0490] For example, if a caregiver enters "I would like to work 20 hours of night shifts per week in Osaka City," the server analyzes regional data and identifies facilities that meet the criteria. The identified facility information is then notified to the caregiver, enabling quick and appropriate employment.

[0491] A concrete example of a prompt message for a generating AI model is: "Please provide the conditions and data necessary for optimal matching for a caregiver in Osaka City who desires 20 hours of night shifts per week, and generate a list of suitable facilities." This allows the system to support effective matching.

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] Users input their skills data, work preferences, and geographical location data using a terminal. This input data is then sent from the terminal to the server. Crucially, the terminal verifies the accuracy of the entered data and ensures its reliable transfer to the server.

[0495] Step 2:

[0496] The server saves the received data to a PostgreSQL database. Based on the input data, it retrieves regional demand data and facility data from the database. The server efficiently extracts the necessary data using database queries.

[0497] Step 3:

[0498] The server calculates the degree of fit based on the acquired data. It utilizes Python computation to compare the user's desired conditions with the facility's conditions and calculate the degree of fit. This allows the server to identify the facility that best matches the user's requirements.

[0499] Step 4:

[0500] The server sends the calculated matching results to the user's device. The user can then view a list of highly suitable facilities on their device. The output includes the name of the facility, details, and contact information.

[0501] Step 5:

[0502] The server analyzes supply and demand forecast data using a generative AI model. Based on this, it generates a report indicating whether a new facility placement would be beneficial. This report is then proposed to the government as needed.

[0503] Step 6:

[0504] The server provides an interface so that the government can review the proposed report and take necessary actions. Users can obtain more detailed analysis results using prompts directed to the generated AI model.

[0505] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0506] This invention aims to provide support that takes into account the emotions of childcare workers by incorporating an emotion engine into a system that optimizes the matching of childcare workers with childcare facilities. This system is server-centric and includes data input means for creating profiles based on the skills, work preferences, and geographical conditions of childcare workers.

[0507] The server retrieves data on childcare facilities and local childcare needs, and runs a matching algorithm that calculates the degree of fit by combining it with input data from childcare workers. During this process, the server uses an emotion engine to analyze emotional expressions in the text obtained from the childcare workers' input data, identifying their current emotional state. Based on this information, the server dynamically adjusts the parameters of the matching algorithm to obtain more appropriate matching results.

[0508] For example, if a user, a childcare worker, inputs "I want to take on new challenges, but I'm currently feeling a bit tired," the emotion engine recognizes the emotional states of "challenge" and "fatigue." Based on these emotional states, the server prioritizes matching the user with a work environment that allows them to acquire new skills within a manageable scope.

[0509] The device notifies childcare workers of the matching results and provides information on suitable childcare facilities. Furthermore, based on analysis results using an emotion engine, it presents a support plan required at the childcare facility selected by the childcare worker.

[0510] In addition, the server statistically processes continuously collected sentiment data for government agencies and makes suggestions aimed at improving the placement of childcare facilities and the user experience. This is presented on a dashboard accessible to government agencies. In this way, the present invention enables the provision of detailed services to childcare workers and contributes to improving the working environment throughout the childcare industry.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] The user, a childcare worker, enters information about their skills, work preferences, and geographical conditions through the terminal's interface. This input includes free-form text describing their current emotional state.

[0514] Step 2:

[0515] The terminal sends the entered information to the server. The server receives this information and saves it as new data in the childcare worker's profile database.

[0516] Step 3:

[0517] The server extracts text data from the childcare worker's input and performs sentiment analysis using an emotion engine. The analysis results identify the type and intensity of the emotion.

[0518] Step 4:

[0519] The server retrieves regional childcare needs data and childcare facility data, and updates this data to the latest state.

[0520] Step 5:

[0521] The server executes a matching algorithm based on the childcare worker's profile and emotion analysis results. This algorithm dynamically adjusts the matching criteria according to the emotional state to select the most suitable childcare facility.

[0522] Step 6:

[0523] The server prepares to notify childcare workers and related childcare facilities of the matching results.

[0524] Step 7:

[0525] The terminal displays the matching results sent from the server to the childcare worker. The childcare worker accesses this information to find the childcare facility that best matches their criteria.

[0526] Step 8:

[0527] Based on the childcare worker's selection, the server presents necessary support plans for childcare facilities. Along with this information, detailed career information, including suggestions for the work environment, is provided.

[0528] Step 9:

[0529] The server continuously analyzes sentiment data collected by the sentiment engine and generates suggestions for childcare facility placement for the government. These suggestions are displayed on a dashboard accessible to the government.

[0530] This entire process enables more personalized matching and career support that takes user emotions into consideration.

[0531] (Example 2)

[0532] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0533] Appropriate matching of childcare workers with childcare facilities requires consideration not only of the worker's skills and geographical location, but also their emotional state. However, traditional systems have struggled to reflect emotional changes in real time, posing challenges in providing a workplace environment where childcare workers can work without undue burden. Furthermore, accurately grasping the supply and demand balance across the entire childcare industry and providing appropriate information to the government has not been easy.

[0534] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0535] In this invention, the server includes means for receiving childcare worker's expertise data, work preference data, and location condition data as input; means for analyzing the emotional state from the input data and acquiring emotional information; and means for dynamically adjusting the suitability of childcare workers and childcare facilities and performing matching based on the acquired emotional information. This enables the provision of a work environment that is in line with the emotional state of childcare workers, thereby realizing manageable working conditions, improving the supply and demand balance in the childcare industry, and allowing for accurate proposals to be made to the government.

[0536] "Specialized knowledge data" refers to information about the skills, qualifications, and experience possessed by childcare workers.

[0537] "Work preference data" refers to information regarding the working conditions, working hours, and work location that childcare workers desire.

[0538] "Location-based data" refers to geographical information regarding the childcare worker's place of residence and the area in which they can work.

[0539] "Emotional state" refers to the psychological and emotional state extracted from text data entered by childcare workers.

[0540] "Emotional information" refers to data that includes results regarding the psychological state and emotional expression of childcare workers, as analyzed by the emotion engine.

[0541] "Fit" is an indicator that shows the degree to which a childcare worker's profile matches the requirements of a childcare facility.

[0542] "Dynamic adjustment" refers to the system's ability to flexibly change the parameters of its matching algorithm based on real-time changing data.

[0543] "Supply and demand balance" refers to the equilibrium between the number of childcare workers in the childcare industry and the number of childcare facilities that need them.

[0544] A "proposal" is information that compiles advice and recommended measures given to the government regarding the placement and policies of childcare facilities.

[0545] This invention is a system for effectively matching childcare workers with childcare facilities, and by incorporating an emotion analysis function, it provides an optimal work environment that takes into account the emotional state of childcare workers. This system is built around a server and is implemented as follows.

[0546] The server has a data collection interface for receiving specialized knowledge data, work preference data, and location condition data entered by childcare workers. This received data is stored in a database management system such as MySQL or PostgreSQL.

[0547] Next, the server utilizes an emotion analysis engine to perform natural language processing and analyze the user's input text. This engine could potentially utilize the OpenAI API. Based on the analyzed emotional state, a matching algorithm is executed to dynamically adjust the suitability between childcare workers and childcare facilities. Here, the algorithm reflects the emotional information and updates the suitability in real time.

[0548] For example, if a user, a childcare worker, inputs "I want to take on new challenges, but I'm currently feeling a little tired," the emotion analysis engine will acquire emotional information such as "challenge" and "fatigue." Based on this, the server will take this emotional state into consideration and prioritize selecting a work environment that allows the childcare worker to acquire new skills within a reasonable range.

[0549] The matching results are notified to childcare workers via a terminal. The terminal also provides childcare workers with support plans based on sentiment analysis and appropriate advice. In addition, the server analyzes sentiment data collected for the government and supply and demand data for workers to generate suggestions regarding the placement of childcare facilities. These suggestions are displayed through a dashboard accessible to the government.

[0550] An example of a prompt to the generating AI model is, "Please tell me how to suggest the optimal work environment based on the sentiment analysis results." Based on such prompts, the system will make appropriate suggestions.

[0551] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0552] Step 1:

[0553] The user inputs expertise data, work preference data, and location condition data using a terminal. This input data is sent to the server. The server receives this data and stores it in a database. Specifically, the server receives the input data in JSON format and registers it in the database using an SQL query.

[0554] Step 2:

[0555] The server activates an emotion analysis engine based on the received input data and extracts the emotional state from the user's input text. This process uses the OpenAI API. The input is text data entered by the childcare worker, and the output is the analyzed emotion label. Specifically, the server sends the text data to the API and stores the received emotion information as internal data.

[0556] Step 3:

[0557] The server retrieves data on childcare facilities and local childcare needs from external APIs. This allows the system to incorporate the latest facility information and demand data. The retrieved data is processed by an algorithm within the server, which dynamically calculates the degree of suitability based on the emotional state of childcare workers. This calculation result is output as the matching result.

[0558] Step 4:

[0559] The device receives matching results sent from the server and notifies the childcare worker. Specifically, the device presents the visualized results to the childcare worker through the application's user interface. Simultaneously, suggestions and support plans based on sentiment analysis are also displayed.

[0560] Step 5:

[0561] The server performs statistical analysis based on collected sentiment data and supply / demand data, and generates suggestions for improving the placement of childcare facilities for the government. These suggestions are provided to the government through a dashboard. The server regularly updates the dashboard to display the latest analysis results.

[0562] (Application Example 2)

[0563] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0564] In today's supply industry, there is a challenge in achieving appropriate matching that considers not only workers' abilities and preferences but also their emotional aspects. Existing systems that ignore diverse working conditions and individual emotional states make it difficult to increase productivity and job satisfaction. Against this backdrop, there is a need to realize a matching process that takes workers' emotions into account and provides them with an appropriate environment.

[0565] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0566] In this invention, the server includes means for receiving worker skill information, work preference information, and location condition information as input; means for acquiring and updating regional supply needs information and facility information; means for calculating the degree of suitability between workers and supply facilities and performing matching using the received input information and acquired supply needs information; means for analyzing emotional expressions in the worker's input information using an expression analysis device and identifying emotional states; and means for dynamically adjusting the setting values ​​of the matching algorithm based on the identified emotional states. This enables optimal matching that takes into account the worker's emotions.

[0567] "Workers" refer to employees who possess specific skills and qualifications and work at supply facilities based on designated location conditions and work preferences.

[0568] "Skills information" refers to information about the specialized knowledge, skills, and qualifications possessed by workers.

[0569] "Work preference information" refers to information about working conditions such as the working hours and work style desired by the worker.

[0570] "Location information" refers to information regarding the geographical location of workers and supply facilities.

[0571] "Supply needs information" refers to information regarding the balance of labor supply and demand in each region, as well as the demand for personnel with specific skills.

[0572] "Facility information" refers to detailed data about supply facilities that are intended for workers to travel to, including geographical location information and facility needs.

[0573] "Emotional expression" refers to words or phrases related to emotions included in the worker's input information.

[0574] "Emotional state" refers to the mental condition and mood of a worker, and is analyzed from their emotional expressions.

[0575] A "representation analysis device" refers to a device or software that analyzes emotional expressions from input information such as text and identifies the emotional state of a worker.

[0576] A "matching algorithm" refers to a computational method for calculating the degree of compatibility between workers and facilities based on the worker's input information and the facility's needs, and for selecting the optimal combination.

[0577] "Dynamically adjusting settings" means changing the parameters of the matching algorithm in real time based on the detected emotional state.

[0578] The system for implementing this invention mainly consists of a server and user terminals. The server is equipped with means for receiving worker skill information, work preference information, and location condition information as input. It also has means for acquiring regional supply needs information and facility information and updating them to the latest status. This makes it possible to calculate the degree of suitability between workers and supply facilities and perform optimal matching.

[0579] The server is equipped with an expression analysis device that analyzes emotional expressions in the worker's input information and identifies their emotional state. Based on the analysis results, it has a function to dynamically adjust the settings of the matching algorithm between the worker and the facility. This makes it possible to provide a more appropriate environment that takes the worker's emotions into consideration.

[0580] The user's terminal notifies the worker of the matching results and displays information on suitable supply facilities. It also has a means of providing appropriate occupational information to the worker, taking into account their identified emotional state. This information provision supports workers in effectively building their careers.

[0581] As a concrete example, a worker might input information into a terminal indicating they have skills in "medical care" and "cooking," prefer a "daytime shift," and describe their emotional state as "seeking new challenges but somewhat fatigued." Based on this input, the server prioritizes matching the worker with a workplace environment that allows them to acquire new skills at a comfortable pace.

[0582] An example of a prompt for a generating AI model is, "List the most suitable supply facilities based on the worker's latest profile data and emotional state." This is an important means of using more advanced technology to enable matching that accurately captures the psychological needs of workers.

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The server receives worker skill information, work preference information, and location information as input from the user's terminal. This is used to initialize the user's profile data. The input information is stored in a database and used for subsequent processing.

[0586] Step 2:

[0587] The server retrieves regional supply needs information and facility information from an external database and updates it to the latest status. The retrieved information is organized through a data processing program and stored as supply facility profile data.

[0588] Step 3:

[0589] The server calculates the degree of fit using the stored worker profile data and supply facility profile data. A matching algorithm is then executed to calculate the fit score for each worker and supply facility. The calculated scores are output as a matching candidate list.

[0590] Step 4:

[0591] The server analyzes emotional expressions within the worker's input information. An expression analysis device is used to identify the worker's emotional state. This information is used to dynamically adjust the settings of the matching algorithm. The results of the emotional identification are stored internally.

[0592] Step 5:

[0593] The server adjusts the matching algorithm settings based on the identified emotional state. After adjustment, it recalculates the fit score and selects the optimal supply facility, taking into account the worker's current psychological state. A new list of matching candidates is then generated.

[0594] Step 6:

[0595] The terminal notifies the user of the latest list of matching candidates received from the server and displays information on the most suitable supply facilities. It also provides career advice based on the user's emotional state to support them in making a choice with confidence. Once the information has been provided, the user can confirm their selection and proceed to the next action.

[0596] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0597] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0598] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0599] [Fourth Embodiment]

[0600] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0601] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0602] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0603] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0604] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0605] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0606] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0607] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0608] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0609] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0610] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0611] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0612] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0613] This invention is a system that supports the optimal matching of childcare workers and childcare facilities, and is built with a server as its main component. This system aggregates childcare workers' skill data, work preference data, and geographical preference data, and uses a database to keep local childcare needs data and facility data up to date. The server also calculates the degree of suitability between childcare workers and childcare facilities based on this data and provides appropriate matching results.

[0614] The server analyzes recruitment information received from childcare facilities and matches it with the individual working conditions entered by childcare workers. Furthermore, the server aims to reduce the turnover rate of childcare workers by providing them with optimal career information. Based on the supply situation and demand forecast compiled by the server, it becomes possible to optimize the placement of childcare facilities as a proposal to the government.

[0615] On the other hand, the terminal functions as a user interface for childcare workers, providing a screen where they can input their skills and desired conditions. When a childcare worker updates their information using this terminal, the server receives this information and updates the database, enabling more precise matching.

[0616] For example, if a childcare worker user enters "I want to work 30 hours a week in Tokyo" into their profile, the server analyzes data on childcare facilities in Tokyo and identifies the facility that best matches this condition. The terminal then presents this identified facility information to the childcare worker, making it available for application. The server also analyzes the supply situation and demand forecast for childcare workers in Tokyo for the government, generates a report indicating whether the establishment of new childcare facilities would be beneficial, and presents it as a suggestion on the government's dashboard.

[0617] This system enables efficient matching of childcare workers with childcare facilities, allowing for flexible responses to the childcare needs of each region.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The server periodically accesses the government's statistical database API to retrieve regional demographic and birth rate data. Based on this, it updates the database with regional childcare needs.

[0621] Step 2:

[0622] The user, a childcare worker, uses the terminal's UI to input their skill data, work preferences, and geographical conditions. This input data is then sent from the terminal to the server as the childcare worker's profile.

[0623] Step 3:

[0624] The server receives profile data sent from childcare workers and stores / updates it in the database. Next, it analyzes each childcare worker's input data and local childcare facility data, and runs a matching algorithm.

[0625] Step 4:

[0626] The server calculates the degree of compatibility between the two parties and lists the most suitable daycare centers. Based on this matching result, it prepares to send notifications to the childcare workers and childcare facilities.

[0627] Step 5:

[0628] The terminal notifies the childcare worker of the matching results from the server and displays detailed information about the candidate childcare facilities on the screen. This includes information such as working conditions, salary, and work environment.

[0629] Step 6:

[0630] The user, a childcare worker, selects a childcare facility of interest from the presented candidates and makes a decision to apply. This selection information is sent from the terminal to the server.

[0631] Step 7:

[0632] The server verifies the application information of childcare workers, notifies the relevant childcare facilities, and subsequently provides necessary information regarding the childcare workers' career paths.

[0633] Step 8:

[0634] The server generates proposals for the placement of childcare facilities to the government based on the local childcare worker supply situation and future demand forecasts, and presents them visually on a dashboard.

[0635] This will enable continuous and effective matching of childcare workers with childcare facilities.

[0636] (Example 1)

[0637] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] Effective matching of childcare workers with childcare facilities requires balancing supply and demand, which varies from region to region. Current technology makes it difficult to quickly and accurately match the individual requirements of childcare workers with the requirements of facilities, and administrative bodies lack sufficient information for optimal placement of childcare facilities in their areas. This contributes to high turnover rates among childcare workers and hinders the efficient operation of facilities.

[0639] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0640] In this invention, the server includes means for receiving childcare worker job competency data, work preference data, and location condition data as input; means for acquiring and updating regional childcare demand data and facility information data; and means for calculating the degree of suitability between childcare workers and childcare facilities and performing matching using the received input information and acquired childcare demand data. This enables rapid and highly accurate matching of childcare workers and childcare facilities, allowing for flexible responses to regional childcare needs and appropriate facility placement proposals to the government.

[0641] "Job competency data" refers to the knowledge, skills, and experience that childcare workers possess, and is used to evaluate their abilities and suitability for their jobs.

[0642] "Work preference data" refers to data that shows the working conditions desired by childcare workers, such as working hours, work location, employment type, etc.

[0643] "Location condition data" refers to data that represents the geographical conditions that childcare workers consider, such as commuting distance and mode of transportation.

[0644] "Childcare demand data" refers to data that shows the demand for and need for childcare in a specific area.

[0645] "Facility information data" refers to data that includes basic information about childcare facilities, such as the facility's location, the services it provides, and the age range of children it accepts.

[0646] "Calculating the degree of fit" is the process of comparing the desired conditions of childcare workers with the conditions of childcare facilities and quantifying the degree to which they match.

[0647] "Implementing a matching process" refers to the process of connecting highly suitable childcare workers with childcare facilities.

[0648] "Feedback" refers to opinions and reactions obtained from users, and is used as information for improving and adjusting the system.

[0649] "Tuning a machine learning algorithm" is the process of modifying the algorithm's parameters based on feedback to improve the accuracy of data analysis.

[0650] This invention is a system that supports the optimal matching of childcare workers and childcare facilities, and uses a server as its main component. The server aggregates childcare workers' job competency data, work preference data, and location condition data, and stores this data in a database. For example, Presto or PostgreSQL can be used as the database system. This ensures that each childcare worker's data is always kept up-to-date.

[0651] The server has the functionality to acquire and update regional childcare demand data and facility information data. This data shows the childcare needs and facility capacity for each region, enabling short-term and long-term matching between childcare workers and facilities. The data is updated in real time via a Web API and used to improve the accuracy of the analysis.

[0652] The server uses machine learning algorithms to calculate the degree of fit between childcare workers and childcare facilities. For example, software such as Scikit-learn and TensorFlow are used to perform efficient data analysis. This allows for a comparison of the characteristics of individual childcare workers and facilities, and identifies highly compatible combinations.

[0653] Childcare workers, as users, input their job skills data and work preference data through a terminal. This terminal can be a common device such as a smartphone or personal computer. The data entered by the childcare workers is sent to a server and used in the matching process.

[0654] For example, if a childcare worker user enters their preference for working 20 hours a week in a major metropolitan area via a terminal, the server uses that data to identify suitable childcare facilities and sends the matching results to the childcare worker. The terminal displays these results visually to the childcare worker, allowing them to smoothly proceed with the application process if necessary.

[0655] Furthermore, the server continuously collects feedback and uses it to refine its machine learning algorithms. This feedback, provided by childcare workers, is used to improve the quality of matching.

[0656] An example of a prompt for the generating AI model is a text input such as, "Generate a list of the most suitable childcare facilities based on the childcare worker's desired work location." In this way, the system implementing the invention provides advanced functionality to support the optimized matching of childcare workers and childcare facilities.

[0657] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0658] Step 1:

[0659] The server collects data from childcare workers and childcare facilities. Specifically, it receives childcare workers' job competency data, work preference data, and location condition data entered via terminals. This input data is sent to the server in JSON format and stored in the database. A data format check process is performed to verify the integrity and type of the data.

[0660] Step 2:

[0661] The server retrieves and updates regional childcare demand data and facility information data. This data is periodically collected from external data providers and sent to the server via API. The server analyzes the retrieved data and updates the database to reflect the latest regional conditions.

[0662] Step 3:

[0663] The server uses the received job competency data and childcare demand data to calculate the degree of fit between childcare workers and childcare facilities. Specifically, the data calculation involves comparing the skill sets of childcare workers with the skill sets required by the facilities, and scoring the degree of match using the Scikit-learn library. This score is obtained as output, and the compatible combinations are listed.

[0664] Step 4:

[0665] The server performs a matching process based on the calculated suitability score and sends the best matching result to the childcare worker. In this process, the matching result is sent to the terminal via a RESTful API. The terminal displays the received information to the childcare worker and presents detailed information about the childcare facilities they can apply to.

[0666] Step 5:

[0667] Childcare workers, as users, make selections based on the presented matching results and either apply or make detailed inquiries. These selections are also sent to the server and recorded as feedback in the system. This feedback is used to improve the accuracy of subsequent analyses and to adjust the algorithms.

[0668] (Application Example 1)

[0669] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0670] Currently, in many sectors of the human resources industry, particularly in childcare and elderly care, there is insufficient matching of suitable personnel with appropriate facilities. This hinders improvements in the quality of facilities and services, leading to problems such as high employee turnover and excessive workload. Therefore, there is a need for a personnel-facility matching system optimized for each region.

[0671] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0672] In this invention, the server includes means for receiving ability data, work preference data, and geographical condition data of childcare workers or caregivers as input; means for acquiring and updating regional demand data and facility data; and means for calculating the suitability of personnel to facilities and performing matching using the received input data and acquired demand data. This enables efficient and accurate matching of personnel to facilities.

[0673] "Competency data" refers to information that quantifies or categorizes the skills and experience of personnel.

[0674] "Work preference data" refers to information about the working conditions and work style that employees desire.

[0675] "Geographical conditions data" refers to data related to the location information and geographical constraints of personnel and facilities.

[0676] "Demand data" refers to data that indicates the need for services or personnel in a specific region or facility.

[0677] "Facility data" refers to information about a specific facility, such as its size, equipment, and location.

[0678] "Fit" is an indicator that shows how well the requirements for personnel and facilities match.

[0679] "Matching" is the process of determining the most suitable combination between personnel and facilities.

[0680] This invention is a system for optimally matching childcare workers and caregivers with their respective facilities. The server is built using Python and Django, and utilizes PostgreSQL as its database management system. Through a user interface accessible from smartphones and PCs, individuals can input their skills data, work preferences, and geographical location data. The server receives this input data and compares it with regional demand data and facility data.

[0681] The server retrieves data and calculates the degree of fit by performing calculations using Python. This allows it to provide matching results to terminals, indicating the optimal facility for the user (the personnel). Furthermore, to generate proposals for government agencies, the server analyzes aggregated supply and demand forecast data and produces a report on the validity of new facility placements.

[0682] For example, if a caregiver enters "I would like to work 20 hours of night shifts per week in Osaka City," the server analyzes regional data and identifies facilities that meet the criteria. The identified facility information is then notified to the caregiver, enabling quick and appropriate employment.

[0683] A concrete example of a prompt message for a generating AI model is: "Please provide the conditions and data necessary for optimal matching for a caregiver in Osaka City who desires 20 hours of night shifts per week, and generate a list of suitable facilities." This allows the system to support effective matching.

[0684] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0685] Step 1:

[0686] Users input their skills data, work preferences, and geographical location data using a terminal. This input data is then sent from the terminal to the server. Crucially, the terminal verifies the accuracy of the entered data and ensures its reliable transfer to the server.

[0687] Step 2:

[0688] The server saves the received data to a PostgreSQL database. Based on the input data, it retrieves regional demand data and facility data from the database. The server efficiently extracts the necessary data using database queries.

[0689] Step 3:

[0690] The server calculates the degree of fit based on the acquired data. It utilizes Python computation to compare the user's desired conditions with the facility's conditions and calculate the degree of fit. This allows the server to identify the facility that best matches the user's requirements.

[0691] Step 4:

[0692] The server sends the calculated matching results to the user's device. The user can then view a list of highly suitable facilities on their device. The output includes the name of the facility, details, and contact information.

[0693] Step 5:

[0694] The server analyzes supply and demand forecast data using a generative AI model. Based on this, it generates a report indicating whether a new facility placement would be beneficial. This report is then proposed to the government as needed.

[0695] Step 6:

[0696] The server provides an interface so that the government can review the proposed report and take necessary actions. Users can obtain more detailed analysis results using prompts directed to the generated AI model.

[0697] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0698] This invention aims to provide support that takes into account the emotions of childcare workers by incorporating an emotion engine into a system that optimizes the matching of childcare workers with childcare facilities. This system is server-centric and includes data input means for creating profiles based on the skills, work preferences, and geographical conditions of childcare workers.

[0699] The server retrieves data on childcare facilities and local childcare needs, and runs a matching algorithm that calculates the degree of fit by combining it with input data from childcare workers. During this process, the server uses an emotion engine to analyze emotional expressions in the text obtained from the childcare workers' input data, identifying their current emotional state. Based on this information, the server dynamically adjusts the parameters of the matching algorithm to obtain more appropriate matching results.

[0700] For example, if a user, a childcare worker, inputs "I want to take on new challenges, but I'm currently feeling a bit tired," the emotion engine recognizes the emotional states of "challenge" and "fatigue." Based on these emotional states, the server prioritizes matching the user with a work environment that allows them to acquire new skills within a manageable scope.

[0701] The device notifies childcare workers of the matching results and provides information on suitable childcare facilities. Furthermore, based on analysis results using an emotion engine, it presents a support plan required at the childcare facility selected by the childcare worker.

[0702] In addition, the server statistically processes continuously collected sentiment data for government agencies and makes suggestions aimed at improving the placement of childcare facilities and the user experience. This is presented on a dashboard accessible to government agencies. In this way, the present invention enables the provision of detailed services to childcare workers and contributes to improving the working environment throughout the childcare industry.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] The user, a childcare worker, enters information about their skills, work preferences, and geographical conditions through the terminal's interface. This input includes free-form text describing their current emotional state.

[0706] Step 2:

[0707] The terminal sends the entered information to the server. The server receives this information and saves it as new data in the childcare worker's profile database.

[0708] Step 3:

[0709] The server extracts text data from the childcare worker's input and performs sentiment analysis using an emotion engine. The analysis results identify the type and intensity of the emotion.

[0710] Step 4:

[0711] The server retrieves regional childcare needs data and childcare facility data, and updates this data to the latest state.

[0712] Step 5:

[0713] The server executes a matching algorithm based on the childcare worker's profile and emotion analysis results. This algorithm dynamically adjusts the matching criteria according to the emotional state to select the most suitable childcare facility.

[0714] Step 6:

[0715] The server prepares to notify childcare workers and related childcare facilities of the matching results.

[0716] Step 7:

[0717] The terminal displays the matching results sent from the server to the childcare worker. The childcare worker accesses this information to find the childcare facility that best matches their criteria.

[0718] Step 8:

[0719] Based on the childcare worker's selection, the server presents necessary support plans for childcare facilities. Along with this information, detailed career information, including suggestions for the work environment, is provided.

[0720] Step 9:

[0721] The server continuously analyzes sentiment data collected by the sentiment engine and generates suggestions for childcare facility placement for the government. These suggestions are displayed on a dashboard accessible to the government.

[0722] This entire process enables more personalized matching and career support that takes user emotions into consideration.

[0723] (Example 2)

[0724] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] Appropriate matching of childcare workers with childcare facilities requires consideration not only of the worker's skills and geographical location, but also their emotional state. However, traditional systems have struggled to reflect emotional changes in real time, posing challenges in providing a workplace environment where childcare workers can work without undue burden. Furthermore, accurately grasping the supply and demand balance across the entire childcare industry and providing appropriate information to the government has not been easy.

[0726] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0727] In this invention, the server includes means for receiving childcare worker's expertise data, work preference data, and location condition data as input; means for analyzing the emotional state from the input data and acquiring emotional information; and means for dynamically adjusting the suitability of childcare workers and childcare facilities and performing matching based on the acquired emotional information. This enables the provision of a work environment that is in line with the emotional state of childcare workers, thereby realizing manageable working conditions, improving the supply and demand balance in the childcare industry, and allowing for accurate proposals to be made to the government.

[0728] "Specialized knowledge data" refers to information about the skills, qualifications, and experience possessed by childcare workers.

[0729] "Work preference data" refers to information regarding the working conditions, working hours, and work location that childcare workers desire.

[0730] "Location-based data" refers to geographical information regarding the childcare worker's place of residence and the area in which they can work.

[0731] "Emotional state" refers to the psychological and emotional state extracted from text data entered by childcare workers.

[0732] "Emotional information" refers to data that includes results regarding the psychological state and emotional expression of childcare workers, as analyzed by the emotion engine.

[0733] "Fit" is an indicator that shows the degree to which a childcare worker's profile matches the requirements of a childcare facility.

[0734] "Dynamic adjustment" refers to the system's ability to flexibly change the parameters of its matching algorithm based on real-time changing data.

[0735] "Supply and demand balance" refers to the equilibrium between the number of childcare workers in the childcare industry and the number of childcare facilities that need them.

[0736] A "proposal" is information that compiles advice and recommended measures given to the government regarding the placement and policies of childcare facilities.

[0737] This invention is a system for effectively matching childcare workers with childcare facilities, and by incorporating an emotion analysis function, it provides an optimal work environment that takes into account the emotional state of childcare workers. This system is built around a server and is implemented as follows.

[0738] The server has a data collection interface for receiving specialized knowledge data, work preference data, and location condition data entered by childcare workers. This received data is stored in a database management system such as MySQL or PostgreSQL.

[0739] Next, the server utilizes an emotion analysis engine to perform natural language processing and analyze the user's input text. This engine could potentially utilize the OpenAI API. Based on the analyzed emotional state, a matching algorithm is executed to dynamically adjust the suitability between childcare workers and childcare facilities. Here, the algorithm reflects the emotional information and updates the suitability in real time.

[0740] For example, if a user, a childcare worker, inputs "I want to take on new challenges, but I'm currently feeling a little tired," the emotion analysis engine will acquire emotional information such as "challenge" and "fatigue." Based on this, the server will take this emotional state into consideration and prioritize selecting a work environment that allows the childcare worker to acquire new skills within a reasonable range.

[0741] The matching results are notified to childcare workers via a terminal. The terminal also provides childcare workers with support plans based on sentiment analysis and appropriate advice. In addition, the server analyzes sentiment data collected for the government and supply and demand data for workers to generate suggestions regarding the placement of childcare facilities. These suggestions are displayed through a dashboard accessible to the government.

[0742] An example of a prompt to the generating AI model is, "Please tell me how to suggest the optimal work environment based on the sentiment analysis results." Based on such prompts, the system will make appropriate suggestions.

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The user inputs expertise data, work preference data, and location condition data using a terminal. This input data is sent to the server. The server receives this data and stores it in a database. Specifically, the server receives the input data in JSON format and registers it in the database using an SQL query.

[0746] Step 2:

[0747] The server activates an emotion analysis engine based on the received input data and extracts the emotional state from the user's input text. This process uses the OpenAI API. The input is text data entered by the childcare worker, and the output is the analyzed emotion label. Specifically, the server sends the text data to the API and stores the received emotion information as internal data.

[0748] Step 3:

[0749] The server retrieves data on childcare facilities and local childcare needs from external APIs. This allows the system to incorporate the latest facility information and demand data. The retrieved data is processed by an algorithm within the server, which dynamically calculates the degree of suitability based on the emotional state of childcare workers. This calculation result is output as the matching result.

[0750] Step 4:

[0751] The device receives matching results sent from the server and notifies the childcare worker. Specifically, the device presents the visualized results to the childcare worker through the application's user interface. Simultaneously, suggestions and support plans based on sentiment analysis are also displayed.

[0752] Step 5:

[0753] The server performs statistical analysis based on collected sentiment data and supply / demand data, and generates suggestions for improving the placement of childcare facilities for the government. These suggestions are provided to the government through a dashboard. The server regularly updates the dashboard to display the latest analysis results.

[0754] (Application Example 2)

[0755] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] In today's supply industry, there is a challenge in achieving appropriate matching that considers not only workers' abilities and preferences but also their emotional aspects. Existing systems that ignore diverse working conditions and individual emotional states make it difficult to increase productivity and job satisfaction. Against this backdrop, there is a need to realize a matching process that takes workers' emotions into account and provides them with an appropriate environment.

[0757] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0758] In this invention, the server includes means for receiving worker skill information, work preference information, and location condition information as input; means for acquiring and updating regional supply needs information and facility information; means for calculating the degree of suitability between workers and supply facilities and performing matching using the received input information and acquired supply needs information; means for analyzing emotional expressions in the worker's input information using an expression analysis device and identifying emotional states; and means for dynamically adjusting the setting values ​​of the matching algorithm based on the identified emotional states. This enables optimal matching that takes into account the worker's emotions.

[0759] "Workers" refer to employees who possess specific skills and qualifications and work at supply facilities based on designated location conditions and work preferences.

[0760] "Skills information" refers to information about the specialized knowledge, skills, and qualifications possessed by workers.

[0761] "Work preference information" refers to information about working conditions such as the working hours and work style desired by the worker.

[0762] "Location information" refers to information regarding the geographical location of workers and supply facilities.

[0763] "Supply needs information" refers to information regarding the balance of labor supply and demand in each region, as well as the demand for personnel with specific skills.

[0764] "Facility information" refers to detailed data about supply facilities that are intended for workers to travel to, including geographical location information and facility needs.

[0765] "Emotional expression" refers to words or phrases related to emotions included in the worker's input information.

[0766] "Emotional state" refers to the mental condition and mood of a worker, and is analyzed from their emotional expressions.

[0767] A "representation analysis device" refers to a device or software that analyzes emotional expressions from input information such as text and identifies the emotional state of a worker.

[0768] A "matching algorithm" refers to a computational method for calculating the degree of compatibility between workers and facilities based on the worker's input information and the facility's needs, and for selecting the optimal combination.

[0769] "Dynamically adjusting settings" means changing the parameters of the matching algorithm in real time based on the detected emotional state.

[0770] The system for implementing this invention mainly consists of a server and user terminals. The server is equipped with means for receiving worker skill information, work preference information, and location condition information as input. It also has means for acquiring regional supply needs information and facility information and updating them to the latest status. This makes it possible to calculate the degree of suitability between workers and supply facilities and perform optimal matching.

[0771] The server is equipped with an expression analysis device that analyzes emotional expressions in the worker's input information and identifies their emotional state. Based on the analysis results, it has a function to dynamically adjust the settings of the matching algorithm between the worker and the facility. This makes it possible to provide a more appropriate environment that takes the worker's emotions into consideration.

[0772] The user's terminal notifies the worker of the matching results and displays information on suitable supply facilities. It also has a means of providing appropriate occupational information to the worker, taking into account their identified emotional state. This information provision supports workers in effectively building their careers.

[0773] As a concrete example, a worker might input information into a terminal indicating they have skills in "medical care" and "cooking," prefer a "daytime shift," and describe their emotional state as "seeking new challenges but somewhat fatigued." Based on this input, the server prioritizes matching the worker with a workplace environment that allows them to acquire new skills at a comfortable pace.

[0774] An example of a prompt for a generating AI model is, "List the most suitable supply facilities based on the worker's latest profile data and emotional state." This is an important means of using more advanced technology to enable matching that accurately captures the psychological needs of workers.

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The server receives worker skill information, work preference information, and location information as input from the user's terminal. This is used to initialize the user's profile data. The input information is stored in a database and used for subsequent processing.

[0778] Step 2:

[0779] The server retrieves regional supply needs information and facility information from an external database and updates it to the latest status. The retrieved information is organized through a data processing program and stored as supply facility profile data.

[0780] Step 3:

[0781] The server calculates the degree of fit using the stored worker profile data and supply facility profile data. A matching algorithm is then executed to calculate the fit score for each worker and supply facility. The calculated scores are output as a matching candidate list.

[0782] Step 4:

[0783] The server analyzes emotional expressions within the worker's input information. An expression analysis device is used to identify the worker's emotional state. This information is used to dynamically adjust the settings of the matching algorithm. The results of the emotional identification are stored internally.

[0784] Step 5:

[0785] The server adjusts the matching algorithm settings based on the identified emotional state. After adjustment, it recalculates the fit score and selects the optimal supply facility, taking into account the worker's current psychological state. A new list of matching candidates is then generated.

[0786] Step 6:

[0787] The terminal notifies the user of the latest list of matching candidates received from the server and displays information on the most suitable supply facilities. It also provides career advice based on the user's emotional state to support them in making a choice with confidence. Once the information has been provided, the user can confirm their selection and proceed to the next action.

[0788] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0789] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0790] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0791] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0792] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0793] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0794] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0795] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0796] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0797] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0798] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0799] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0800] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0801] 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.

[0802] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0803] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0804] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0805] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0806] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0807] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0808] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0809] The following is further disclosed regarding the embodiments described above.

[0810] (Claim 1)

[0811] A means of receiving childcare worker skill data, work preference data, and geographical preference data as input,

[0812] A means of acquiring and updating regional childcare needs data and facility data,

[0813] A means for calculating the degree of suitability between childcare workers and childcare facilities and performing matching using the received input data and acquired childcare needs data,

[0814] A means of notifying childcare workers and childcare facilities of the matching results,

[0815] Means of providing childcare workers with appropriate career information,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, which generates proposals to the government regarding the placement of childcare facilities based on the supply situation and demand forecast of childcare workers.

[0819] (Claim 3)

[0820] The system according to claim 1, which displays suggestions for the placement of childcare facilities through a dashboard accessible to the government.

[0821] "Example 1"

[0822] (Claim 1)

[0823] A means of receiving childcare worker's job competence data, work preference data, and location condition data as input,

[0824] A means for acquiring and updating regional childcare demand data and facility information data,

[0825] A means of calculating the suitability of childcare workers and childcare facilities using the received input information and acquired childcare demand data, and performing matching.

[0826] A means of notifying childcare workers and childcare institutions of the matching results,

[0827] Means of providing childcare workers with appropriate information about their profession,

[0828] A means of collecting feedback and adjusting machine learning algorithms to improve analysis accuracy,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, which generates proposals to administrative agencies regarding the placement of childcare facilities based on the supply situation and demand forecast of childcare workers.

[0832] (Claim 3)

[0833] The system according to claim 1, which displays a proposal for the placement of childcare facilities through an operation screen accessible to administrative agencies.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] A means of receiving data on the skills, work preferences, and geographical conditions of childcare workers or caregivers as input,

[0837] Means for acquiring and updating regional demand data and facility data,

[0838] A means for calculating the suitability of personnel and facilities and performing matching using the received input data and acquired demand data,

[0839] A means of notifying personnel and facilities of the matching results,

[0840] Means of providing appropriate career information to personnel,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, which generates proposals to the government regarding the placement of facilities based on the supply situation and demand forecast of personnel.

[0844] (Claim 3)

[0845] The system according to claim 1, which displays facility layout proposals through an interface accessible to the government.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] A means of receiving childcare worker's professional knowledge data, work preference data, and location condition data as input,

[0849] A means of acquiring and updating regional childcare needs data and facility data,

[0850] A means of analyzing emotional states from input data and obtaining emotional information,

[0851] Based on the acquired emotional information, a means of dynamically adjusting and matching the suitability of childcare workers and childcare facilities,

[0852] A means of notifying childcare workers and childcare facilities of the matching results,

[0853] A means of providing childcare workers with appropriate information about their duties and proposing necessary support plans,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, which generates proposals to the government regarding the placement of childcare facilities based on the supply situation and demand forecast of childcare workers, and continuously provides statistically processed sentiment data.

[0857] (Claim 3)

[0858] The system according to claim 1, which displays childcare facility placement suggestions and analysis results based on sentiment data through a dashboard accessible to the government.

[0859] "Application example 2 when combining with an emotional engine"

[0860] (Claim 1)

[0861] A means of receiving worker skill information, work preference information, and location condition information as input,

[0862] Means for acquiring and updating regional supply needs information and facility information,

[0863] A means for calculating the degree of suitability between workers and supply facilities and performing matching using the received input information and acquired supply needs information,

[0864] A means for analyzing emotional expressions in the worker's input information using an expression analysis device and identifying the emotional state,

[0865] A means for dynamically adjusting the settings of the matching algorithm based on the identified emotional state,

[0866] Means for notifying workers and supply facilities of the matching results,

[0867] Means of providing workers with appropriate occupational information,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, which generates a proposal for the placement of supply facilities to a control organization based on the supply status of workers and demand forecasts.

[0871] (Claim 3)

[0872] The system according to claim 1, which displays a proposed supply facility layout through an operating screen accessible to a control body. [Explanation of Symbols]

[0873] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving data on the skills, work preferences, and geographical conditions of childcare workers or caregivers as input, Means for acquiring and updating regional demand data and facility data, A means for calculating the suitability of personnel and facilities and performing matching using the received input data and acquired demand data, A means of notifying personnel and facilities of the matching results, Means of providing appropriate career information to personnel, A system that includes this.

2. The system according to claim 1, which generates proposals to the government regarding the placement of facilities based on the supply situation and demand forecast of personnel.

3. The system according to claim 1, which displays facility layout proposals through an interface accessible to the government.

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