System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a state of a child and an atmosphere of a class observed by a user using a terminal; means for transmitting the input data to a server and storing the input data in a database by the server; means for analyzing the stored data by the server and generating an optimal childcare plan by referring to a childcare guidance manual; and means for transmitting the generated childcare plan to the terminal of the user and allowing the user to confirm and correct the childcare plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Childcare workers currently have to handle a large amount of administrative work in addition to their daily childcare duties. This has increased the workload of childcare workers, resulting in a lack of direct care for children. Furthermore, the quality of childcare plans and growth records can vary between veteran and new childcare workers. There is a need for a method to reduce this workload and standardize the quality of childcare plans. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to input observations of children's behavior and the atmosphere of the class using a terminal, and a means for transmitting the input data to a server, which then stores the data in a database. The system also includes a means for the server to analyze the stored data and generate an optimal childcare plan by referring to childcare teaching guidelines, and a means for transmitting the generated childcare plan to the user's terminal so that the user can review and modify it. The system further includes a means for transmitting the user's feedback and modifications back to the server and storing them in the database, and a means for the server to store the final childcare plan and growth records in the database and provide them as needed. This reduces the workload of childcare workers and makes it possible to standardize the quality of childcare plans.

[0006] "Users" are childcare workers who use this system to input information such as the state of children and the atmosphere in the class.

[0007] A "terminal" is an electronic device used by a user to input and confirm information, and includes a personal computer, tablet, smartphone, etc.

[0008] The "server" is a central management system that receives data sent by users, stores it in a database, and runs the generative AI model.

[0009] The "database" is an information management system installed in a server for storing input information and generated childcare plans and growth records.

[0010] A "generative AI model" is an artificial intelligence system that includes an algorithm that analyzes input data and automatically generates an optimal childcare plan by referencing childcare guidelines.

[0011] A "childcare plan" is a document that lists the most appropriate childcare activities based on the child's growth and the class situation.

[0012] A "growth record" is a document that records a child's daily growth and progress and is used to report to parents and auditors.

[0013] The Childcare Guidelines are an official document that sets out guidelines and standards for carrying out childcare activities, and serve as a reference when creating a childcare plan.

[0014] "Feedback" is the process by which the user makes corrections or offers opinions on the generated childcare plan or growth record.

[0015] The "audit system" is an evaluation system for verifying the accuracy and legality of childcare activities and related records, and refers to childcare plans and growth records as necessary. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention relates to a system for reducing the amount of paperwork that childcare workers perform in their daily work and improving the quality of childcare. This system features a user using a terminal to input information about the children's behavior and the atmosphere in the class, which is then sent to a server, where it is saved and analyzed, and childcare plans and growth records are automatically generated.

[0038] Program processing

[0039] 1. Enter information

[0040] Device: The user inputs information about the child's behavior and the atmosphere in the classroom via a device (PC, tablet, smartphone, etc.). For example, they can input information such as, "Today, A-kun was playing with blocks during free play."

[0041] 2. Data transmission and storage

[0042] Terminal: The entered information is sent to the server using the HTTPS protocol.

[0043] Server: Stores the received information in a database, including a timestamp and the entered user ID.

[0044] 3. Data analysis and automatic generation of childcare plans

[0045] Server: Retrieves stored information from the database and analyzes it with a generative AI model. For example, by referencing past behavioral data and classroom situation data, it determines that "A-kun is interested in cooperating with other children."

[0046] Generative AI model: Refers to childcare guidelines and automatically generates optimal childcare plans based on the analysis results. For example, it creates a childcare plan such as, "Next week, plan an activity that incorporates cooperative play for A-kun."

[0047] 4. Send and review the generated documents

[0048] Server: Sends the generated childcare plan and growth record to the terminal.

[0049] Terminal: The user checks the document through the terminal and makes corrections as necessary. At this stage, the user can make corrections such as "increase free play time and incorporate cooperative activities."

[0050] 5. Feedback and Resave

[0051] Terminal: The user sends the modified information back to the server.

[0052] Server: The corrected information is restored to the database. This restored information is used for audits and parental reporting.

[0053] 6. Database updates and audit support

[0054] Server: The final childcare plan and growth record are saved in a database and provided to the parent portal and audit system as needed. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[0055] Example

[0056] As a concrete example, suppose a nursery teacher inputs into their device, "Today during free play, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The generative AI model analyzes the information, references the childcare guidelines, and concludes that "A-kun is suited to activities that strengthen group activities." As a result, the nursery school plan includes the following: "Next week, A-kun's activities will include increasing cooperative play in groups." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary adjustments.

[0057] In this way, this system can significantly reduce the workload of childcare workers and improve the quality of childcare planning.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] User input of information

[0061] The user logs in to the device and accesses a screen where they can input the observed behavior of the children and the atmosphere of the class. For example, they can input, "Today, A-kun was playing with blocks during free play."

[0062] The terminal prepares the entered information to be sent to the server in an appropriate format.

[0063] Step 2:

[0064] Sending data

[0065] The device securely transmits the entered information to the server using the HTTPS protocol, which receives the data and prepares it for storage in a database.

[0066] Step 3:

[0067] Data storage

[0068] The server stores the received information in a database. The stored data includes metadata such as a timestamp and user ID. For example, it might be stored in the format "2023-10-05 14:30:00, A-kun was playing with blocks during free play time."

[0069] Step 4:

[0070] Data Acquisition

[0071] The server retrieves the latest information and historical trend data from the database, which is used in the subsequent analysis process.

[0072] Step 5:

[0073] Data analysis

[0074] The server uses a generative AI model to analyze the accumulated data. For example, by referencing past behavioral data and classroom situation data, it can draw the conclusion that "Kid A is interested in collaborating with other children."

[0075] Step 6:

[0076] Generate childcare plans

[0077] The generative AI model automatically creates an optimal childcare plan based on the analysis results and references childcare guidelines. For example, it might generate a childcare plan that states, "For A-kun, plan an activity that incorporates cooperative play next week."

[0078] Step 7:

[0079] Sending generated documents

[0080] The server sends the generated childcare plan to the terminal, where the user can check it.

[0081] Step 8:

[0082] User verification and correction

[0083] The user can check the generated childcare plan on the device and make any necessary changes, such as "increase free play time and incorporate cooperative activities."

[0084] Step 9:

[0085] Sending correction data

[0086] The terminal sends the corrected information back to the server, which receives the corrected data and updates the database.

[0087] Step 10:

[0088] Save the final data

[0089] The server stores the final revised childcare plan and progress records in a database, which is used for audits and reporting to parents.

[0090] Step 11:

[0091] Audit and Reporting

[0092] The server then provides the final data stored in the database to a parent portal or audit system as needed, allowing parents to check, for example, "A-kun's growth record."

[0093] Through these steps, the system can reduce the workload of childcare workers and provide high-quality childcare plans.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] Today's childcare workers are overwhelmed with a large amount of administrative work, such as observing children, creating childcare plans, and reporting to parents, which hinders the improvement of childcare quality. There is a need to reduce this enormous workload and allow childcare workers to devote more time to actual childcare activities. It is also important to streamline the process of creating childcare plans and growth records and improve their accuracy and consistency.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes a means for a user to input observations of children's behavior and the atmosphere of the class using a terminal, a means for transmitting the input data to the server using HTTPS protocol and for the server to store it in a database, a means for the server to analyze the stored data using a generative AI model and generate an optimal childcare plan by referring to the childcare teaching guidelines, and a means for transmitting the generated childcare plan and growth record in PDF or Word format to the user's terminal so that the user can check and modify them. This reduces the workload of childcare workers and improves the quality of childcare plans.

[0099] "Users" refers to nursery teachers and other childcare staff who use the system to input information about children's behavior and the atmosphere in the classroom.

[0100] "Terminal" refers to electronic devices used by users, such as computers, tablets, and smartphones.

[0101] "Observation" refers to the process of carefully watching and recording children's behavior and the atmosphere in the classroom.

[0102] "Classroom atmosphere" refers to the general situation and environment within the classroom at a particular time.

[0103] "Means of input" refers to the method of registering observed data into the system using a terminal.

[0104] The "HTTPS protocol" refers to a communications protocol for securely transmitting data over the Internet.

[0105] "Server" refers to a central processing unit for receiving, storing and analyzing data sent by users.

[0106] "Database" refers to a system that manages various data such as observation data, childcare plans, and growth records stored on a server.

[0107] A "generative AI model" refers to an artificial intelligence model that automatically generates childcare plans and growth records based on childcare guidelines and past data.

[0108] "Childcare guidelines" refers to documents that set out standards and guidelines regarding childcare activities and children's development.

[0109] A "childcare plan" is a document that includes a specific plan of activities to support children's growth and development.

[0110] A "growth record" refers to a document that records children's developmental process and learning progress.

[0111] "PDF and Word formats" refer to file formats for saving and transferring childcare plans and growth records.

[0112] "Means for review and correction" refers to a method by which a user can review the submitted childcare plan or growth record and change the contents as necessary.

[0113] This invention relates to a system for reducing the amount of paperwork that childcare workers have to do in their daily work and improving the quality of childcare. In this system, users use their terminals to input information about the children's behavior and the atmosphere in the class, and the data is sent to a server, where it is saved and analyzed, and childcare plans and growth records are automatically generated.

[0114] First, the user uses a device (PC, tablet, smartphone, etc.) to input details about the children's behavior and the atmosphere in the class. For example, they can input information such as "Mr. A was playing with blocks during free play today" or "Today, outdoor play time was canceled due to rain." This information is encrypted using the HTTPS protocol and sent to the server after ensuring security.

[0115] The server then stores the received information in a database, including the child's behavior, timestamps, and the user ID entered, for later analysis and auditing.

[0116] The server retrieves the saved data from the database and passes it to the generative AI model. This generative AI model has been trained in advance on childcare guidelines and past data, and analyzes the input data. For example, the generative AI model might determine that "A-kun often actively interacts with other children," and generate a specific childcare plan such as, "Next week, plan childcare activities that include a lot of cooperative play."

[0117] The generated childcare plan and growth record are converted into PDF or Word format and sent from the server to the user's device. The user can review the documents on their device and make any necessary changes. For example, they can make a change such as "extending free play time from 30 minutes to 45 minutes."

[0118] Any information corrected by the user is sent back to the server and the corrected information is restored to the database for future audits and parental reporting.

[0119] Finally, the server stores the final childcare plan and growth record in a database and provides them to the parent portal and audit system. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[0120] As a concrete example, suppose a nursery teacher inputs into their device, "Today during free playtime, A-kun enjoyed playing dominoes with B-chan." This information is encrypted and sent to the server, where it is stored in a database. The generative AI model analyzes the input data and, referring to the childcare guidelines, concludes that "activities that strengthen group activities are appropriate for A-kun." As a result, the nursery school plan will state, "Next week, activities for A-kun will involve increasing group cooperative play." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary adjustments.

[0121] An example of a prompt for the generative AI model would be, "Today, during free play, A-kun cooperated with the other children and enjoyed playing a domino game. Based on this data, please suggest a childcare plan for A-kun next week."

[0122] In this way, this system can significantly reduce the workload of childcare workers and improve the quality of childcare planning.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1:

[0125] Enter information

[0126] User: The user uses a device to input details about the children's behavior and the atmosphere in the class. For example, they can input information such as, "Today, A-kun was playing with blocks during free play."

[0127] Input: Child behavior information, class atmosphere, timestamp, user ID.

[0128] Output: The input data is stored in the terminal.

[0129] Step 2:

[0130] Sending data

[0131] Terminal: Data entered by the user is encrypted using the HTTPS protocol and sent to the server.

[0132] Input: Data stored on the device.

[0133] Output: The encrypted data is sent to the server using the HTTPS protocol.

[0134] Step 3:

[0135] Data storage

[0136] Server: Receives the encrypted data and stores it in a database, along with the timestamp, user ID, data type, etc.

[0137] Input: Encrypted data sent over the HTTPS protocol.

[0138] Output: Structured data stored in a database.

[0139] Step 4:

[0140] Data analysis

[0141] Server: Retrieves the stored data from the database and passes it to the generative AI model, which has already learned the childcare curriculum and past data.

[0142] Input: Observation data retrieved from the database.

[0143] Output: The analysis results produced by the generative AI model.

[0144] Step 5:

[0145] Automatic generation of childcare plans

[0146] Generative AI model: Analyzes observation data and generates optimal childcare plans based on childcare guidelines. For example, based on the analysis result that "A-kun often actively interacts with other children," it creates a specific childcare plan such as "Plan childcare activities next week that include a lot of cooperative play."

[0147] Input: Analysis results, childcare guidelines.

[0148] Output: The generated childcare plan.

[0149] Step 6:

[0150] Submitting a childcare plan

[0151] Server: The generated childcare plan and growth record are sent to the user's device, where the file format is converted to PDF or Word document format.

[0152] Input: The generated childcare plan.

[0153] Output: The childcare plan sent to the user's device in PDF or Word format.

[0154] Step 7:

[0155] Check and correct

[0156] User: Checks the childcare plan and growth record sent via the device and makes any necessary changes. For example, the user can make specific changes such as extending free play time from 30 minutes to 45 minutes.

[0157] Input: User feedback and corrections.

[0158] Output: Revised childcare plan.

[0159] Step 8:

[0160] Resend and save corrected data

[0161] Terminal: The user sends the modified information back to the server.

[0162] Server: The resent modified data is saved back into the database.

[0163] Enter: Revised Child Care Plan.

[0164] Output: The modified data resaved to the database.

[0165] Step 9:

[0166] Database updates and audit support

[0167] Server: Stores the final childcare plan and growth records in a database and provides them to the parent portal and audit system as needed.

[0168] Input: Final childcare plan, growth record.

[0169] Output: Final data provided to parent portal and audit systems.

[0170] (Application example 1)

[0171] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0172] In childcare operations and factory operation management, large amounts of data entry and management work are required, which has led to problems with reduced work efficiency. In addition, data analysis takes time, making it difficult to create appropriate childcare and maintenance plans. In particular, creating plans and records manually takes time and effort, and is prone to human error.

[0173] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0174] In this invention, the server

[0175] 1. A means for users to input observations of children's behavior and the atmosphere of the class using a device;

[0176] 2. A means for transmitting the input data to the server and for the server to store it in a database;

[0177] 3. A means for the server to analyze the stored data and generate an optimal childcare plan by referring to the childcare teaching guidelines;

[0178] 4. A means for transmitting the generated childcare plan to the user's terminal so that the user can check and modify it;

[0179] 5. A means for a user to input the observed operating status and maintenance notes of factory equipment using a terminal;

[0180] 6. A means for the server to analyze the operational status data based on the input information and generate a preventive maintenance plan;

[0181] 7. A means for transmitting the generated preventive maintenance plan to a user's terminal so that the user can check and modify it;

[0182] This automates the large amount of data entry and analysis work involved in childcare operations and factory management, making it possible to quickly and efficiently generate optimal plans and maintenance plans.

[0183] "User terminals" refer to machines, such as computers, tablets, and smartphones, used by childcare workers and factory workers.

[0184] "Means for inputting observed behavior of children and classroom atmosphere" refers to an interface or software that allows childcare workers to input children's behavior and classroom conditions into a terminal.

[0185] "Means for transmitting data to a server and having the server store it in a database" refers to the process and device for transmitting information entered from a terminal to a server via a network, and for the server to record it in a database.

[0186] The "means of analyzing data and generating a childcare plan by referring to the childcare curriculum guidelines" refers to the process and program in which the server analyzes the information in the database using machine learning algorithms and generative AI models, and automatically creates an educational plan based on childcare standards.

[0187] "Means for sending the generated childcare plan to the user's terminal for confirmation and modification" refers to software or an interface that sends the childcare plan created by the server back to the terminal and allows the childcare worker to confirm and edit its contents.

[0188] The "means for inputting the operating status and maintenance notes of observed factory equipment" refers to an interface or software that allows factory workers to input information about the operating status and maintenance inspection of equipment into a terminal.

[0189] The "means for analyzing operational status data and generating preventive maintenance plans" refers to a process and program that analyzes operational data collected by a server, predicts future maintenance needs using machine learning algorithms, and automatically generates specific work plans.

[0190] "Means for sending the generated preventive maintenance plan to the user's terminal for confirmation and modification" refers to software or an interface that sends the maintenance plan created by the server to the terminal and allows factory workers to confirm and edit its contents.

[0191] This invention is a system that reduces the user's workload and efficiently generates childcare plans and maintenance plans. This system consists of a series of processes in which the user inputs observation data using a terminal, the server receives and analyzes the data, generates an optimal plan, and sends it back to the terminal.

[0192] Specifically, nursery teachers use their devices to input information about the children's behavior and the atmosphere in the classroom. For example, they input information such as, "During free play today, A-kun and B-chan were enjoying a game of dominoes." This information is sent to the server using the HTTPS protocol. The server then stores the information in a database. The stored information also includes a timestamp and the user ID that was entered.

[0193] Next, the server retrieves the stored information from the database and analyzes it using a generative AI model. For example, by referencing past behavioral data and classroom situation data, it may determine that "Mr. A is interested in cooperating with other children." The generative AI model then references childcare guidelines and automatically generates an optimal childcare plan based on the analysis results. For example, it creates a childcare plan such as, "Next week, plan an activity for Mr. A that incorporates cooperative play."

[0194] The generated childcare plan is sent from the server to the terminal, where the user can review the document and make any necessary corrections. For example, a correction could be to "increase free play time and incorporate cooperative activities." This corrected information is sent back to the server and re-stored in the database. This re-stored information is used for audits and reports to parents. It can also be provided to parent portals and auditing systems.

[0195] Meanwhile, factory workers use their terminals to input the observed operating status of factory equipment and maintenance notes. For example, they might input "Robot_1 was operating normally today." This information is also sent to the server using the HTTPS protocol. The server stores the information in a database and analyzes the operating status data to generate a preventive maintenance plan. For example, the operating status data can be used to predict future maintenance needs, generating a plan such as "Robot_1 requires maintenance next week." This plan is then sent from the server to the terminal, where the factory worker can review it and make any necessary adjustments.

[0196] Specific hardware used includes PCs, tablets, smartphones, etc. Software used includes Python, Flask, Pandas, Scikit-learn, Requests, etc. By combining these, users can efficiently input data, and the generative AI model can automatically generate, revise, and manage optimal plans based on that data.

[0197] As a concrete example, a user inputs into a device, "Today during free playtime, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The generative AI model analyzes this information and concludes that "A-kun is interested in cooperating with other children." The childcare plan then includes the following: "Next week, we will increase group cooperative play as an activity for A-kun." This plan is then sent from the server to the device, where the user can review it and make any necessary adjustments.

[0198] Example prompt sentence:

[0199] "Robot_1 was running fine today. What should next week's preventive maintenance include?"

[0200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0201] Step 1:

[0202] Users use their devices to input observations of children's behavior, the atmosphere in the classroom, or the operating status of factory equipment and maintenance notes. For example, consider the case where a nursery teacher inputs, "During free play today, A-kun and B-chan were enjoying a game of dominoes." A timestamp and user ID are automatically assigned to the input data.

[0203] Step 2:

[0204] The device sends the input data to the server using the HTTPS protocol. This communication ensures security and protects the integrity of the data. The input data consists of information such as the observation, a timestamp, and a user ID.

[0205] Step 3:

[0206] The server saves the received data to the database. This save operation converts the data format (e.g., from JSON to table format) and stores it in the database in the appropriate format. The saved data is used in the subsequent analysis process.

[0207] Step 4:

[0208] The server retrieves the stored information from the database and analyzes it using a generative AI model. Specifically, it combines past behavioral data and classroom situation data to create the next childcare plan while referring to the childcare curriculum. This analysis involves data frame manipulation using Pandas and machine learning models using Scikit-learn.

[0209] Step 5:

[0210] The server's AI model automatically generates an optimal childcare plan based on the analysis results. For example, it creates a childcare plan such as, "For A-kun, plan an activity that incorporates cooperative play next week." This generation process references childcare instruction standards and past data, and automatically generates the plan using a Python script.

[0211] Step 6:

[0212] The server sends the generated care plan or maintenance plan to the device. The user can review the plan and make any necessary modifications. This operation includes editing functions on a front-end interface (e.g., a web application or a mobile app).

[0213] Step 7:

[0214] The user sends the revised information back to the server. The device sends the revised data to the server using the HTTPS protocol. This data transmission causes the revised plan to be saved in the database again.

[0215] Step 8:

[0216] The server stores the final childcare plan and maintenance plan in a database and provides them to the parent portal and audit system when required. This process involves retrieving the output from the database in an appropriate format and providing it through a specific user interface or API.

[0217] The above steps allow for an efficient execution of a series of processes, from data entry to analysis, plan generation, feedback resubmission, and final data storage and provision.

[0218] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0219] This invention relates to a system that reduces the amount of paperwork that childcare workers do in their daily work and improves the quality of childcare. This system has the function of allowing users to input information about the children's behavior and the atmosphere of the class using a terminal, sending the data to a server, saving it, and analyzing it, and automatically generating optimal childcare plans and growth records. In addition, by combining it with an emotion engine that recognizes the user's emotions, it is possible to create more personalized childcare plans.

[0220] Program processing

[0221] 1. Information input and emotion recognition

[0222] Device: The user logs in to the device and accesses a screen where they can enter information about their child's behavior and the atmosphere in the class. For example, they might enter, "Today, A-kun was playing with blocks during free play."

[0223] Emotion engine: Analyzes the user's emotions when they input. For example, recognize the user's stress or satisfaction level from voice input or text analysis.

[0224] 2. Data transmission and storage

[0225] Terminal: The input information and sentiment analysis results are sent to the server using the HTTPS protocol.

[0226] Server: The received information is stored in a database, including the timestamp, the entered user ID, and the sentiment analysis results.

[0227] 3. Data analysis and automatic generation of childcare plans

[0228] Server: Retrieves stored information from the database and analyzes it with a generative AI model.

[0229] Generative AI model: Refers to childcare guidelines and automatically generates an optimal childcare plan based on the analysis results. The analysis results of the emotion engine are also taken into account to adjust the contents of the plan. For example, if the user is feeling stressed, the activities will be adjusted to reduce those emotions.

[0230] 4. Send and review the generated documents

[0231] Server: Sends the generated childcare plan and growth record to the terminal.

[0232] Terminal: The user checks this and makes corrections as necessary. For example, they may make corrections such as "increase free play time and incorporate cooperative activities."

[0233] 5. Feedback and Resave

[0234] Terminal: The user sends the modified information back to the server.

[0235] Server: The corrected information is restored to the database. This restored information is used for audits and parental reporting.

[0236] 6. Database updates and audit support

[0237] Server: The final childcare plan and growth record are saved in a database. If necessary, the server provides them to the parent portal or audit system. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[0238] Example

[0239] As a concrete example, suppose a nursery teacher inputs into their device, "During free play today, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The emotion engine analyzes the emotion (e.g., satisfaction or stress) felt by the user when they entered the information and adds it to the data. The generative AI model analyzes the information and, referring to the childcare guidelines, determines that "A-kun is interested in cooperating with other children." As a result, the nursery school plan includes the following: "Next week, we will increase group cooperative play as an activity for A-kun." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary revisions.

[0240] In this way, this system can significantly reduce the workload of childcare workers and provide high-quality childcare plans. In addition, by taking the user's emotions into consideration, it can realize more personalized plans and provide a better environment for both childcare workers and children.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] User input and emotion recognition

[0244] The user (childcare worker) logs in to the device and inputs the behavior of the children they observed and the atmosphere of the class. The emotion engine also analyzes the user's voice and input speed to recognize the user's emotions. For example, if a user inputs "Mr. A was playing with blocks during free play today," the emotion engine simultaneously detects the teacher's level of satisfaction.

[0245] Step 2:

[0246] Sending data and adding emotion data

[0247] The device sends the input information and recognized emotional data, including the stress level and satisfaction level of the childcare worker, to the server using the HTTPS protocol.

[0248] Step 3:

[0249] Data storage

[0250] The server stores the received information in a database. The stored data includes a timestamp, the entered user ID, and the results of sentiment analysis. For example, it might be stored as "2023-10-05 14:30:00, A-kun was playing with blocks during free play, and the childcare worker was very satisfied."

[0251] Step 4:

[0252] Data Acquisition

[0253] The server retrieves the stored information from the database, including past behavioral data and classroom situation data to use the necessary information for analysis, including emotional data.

[0254] Step 5:

[0255] Data analysis

[0256] The server analyzes the data using a generative AI model. Data from the emotion engine is also incorporated into the analysis process. The result is that "Kid A is interested in cooperating with other children," and the conclusion is that "the nursery teachers are highly satisfied with the cooperative play activities."

[0257] Step 6:

[0258] Generate childcare plans

[0259] The generative AI model automatically generates an optimal childcare plan based on the analysis results and childcare teaching guidelines. The content of the plan is further adjusted based on emotional data. For example, it might state, "Next week, we will plan an activity for A-kun that incorporates cooperative play. We will also specifically incorporate activities that will give childcare workers a high level of satisfaction."

[0260] Step 7:

[0261] Sending generated documents

[0262] The server sends the generated childcare plan to the terminal, where the user can check it.

[0263] Step 8:

[0264] User verification and correction

[0265] The user can check the generated childcare plan on the device and make any necessary changes, such as "increase free play time and incorporate cooperative activities."

[0266] Step 9:

[0267] Sending correction data

[0268] The terminal sends the corrected information back to the server, which receives the corrected data and updates the database.

[0269] Step 10:

[0270] Save the final data

[0271] The server stores the final revised childcare plan and progress records in a database, which is used for audits and reporting to parents.

[0272] Step 11:

[0273] Audit and Reporting

[0274] The server then provides the final data stored in the database to a parent portal or audit system as needed, allowing parents to check, for example, "A-kun's growth record."

[0275] Through this series of steps, the system reduces the workload of childcare workers and provides more personalized childcare plans using the emotion engine, thereby improving the quality of childcare and childcare worker satisfaction.

[0276] Example 2

[0277] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0278] To improve the quality of childcare, it is necessary to provide a system that allows childcare workers to create childcare plans and growth records effectively and efficiently without being overwhelmed by the large amount of paperwork in their daily work. There is also a challenge in creating more personalized childcare plans by taking into account the emotional state of childcare workers.

[0279] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes emotion recognition means for analyzing input data and recognizing the user's emotion, means for transmitting the input data and the recognized emotion data to the server and storing them in a database, and generation AI model means for analyzing the stored data and generating an optimal childcare plan by referring to the childcare teaching guidelines. This makes it possible to recognize and analyze emotions from data input by childcare workers and automatically generate a more personalized childcare plan efficiently based on the results.

[0280] "Users" refer to childcare workers and educators who operate the devices to input information about the children's behavior and the atmosphere in the class.

[0281] "Terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0282] "Emotion recognition" refers to the process of analyzing data entered by a user, as well as the voice and facial expressions used when entering data, to identify the user's emotional state.

[0283] "Database" refers to a data storage system where the server stores user input data and analysis results.

[0284] "Server" refers to a computer system that receives, stores, and analyzes input data and transmits the generated childcare plan to the user.

[0285] "Generative AI model" refers to an artificial intelligence model that automatically generates optimal childcare plans based on childcare guidelines, stored data, and emotion analysis results.

[0286] "Childcare plan" refers to a document that details the education and activities of children.

[0287] A "growth record" refers to a document that records in detail the development and growth process of children.

[0288] A "parent portal" refers to a web system that allows parents to view their children's growth records and childcare plans online.

[0289] "Audit system" refers to a system for auditing the contents of childcare plans and growth records.

[0290] This invention relates to a system that reduces the amount of paperwork that childcare workers do in their daily work and improves the quality of childcare. This system has the function of allowing users to input information about the children's behavior and the atmosphere in the class using a terminal, sending the data to a server, saving it, and analyzing it, and automatically generating optimal childcare plans and growth records. Furthermore, by combining it with an emotion recognition engine that recognizes the user's emotions, it is possible to create more personalized childcare plans.

[0291] The specific hardware of this system includes the devices used by users (e.g., computers, smartphones, tablets, etc.) and the server that sends, receives, stores, and analyzes data. The software includes an emotion recognition engine, a generative AI model, and a database management system.

[0292] First, the user logs in to the device and launches a dedicated application. The user then accesses a form within the application and enters text about the child's condition and the atmosphere in the class. For example, the user might enter, "During free play today, A-kun enjoyed playing dominoes with B-chan."

[0293] Next, the emotion recognition engine analyzes the user's emotions from their voice and facial expressions. This is done using algorithms that analyze voice input and text. For example, it analyzes keywords such as "happy" to identify a feeling of satisfaction.

[0294] The device then sends the input information and the emotion analysis results to the server using the HTTPS protocol. The server then stores the received data in a database. The stored information includes a timestamp, user ID, input content, and emotion analysis results.

[0295] The server periodically crawls the stored data and sends the information that needs to be analyzed to the generative AI model.

[0296] "The nursery teacher input that A-kun enjoyed playing dominoes with B-chan during free play today. Based on this, please use the emotion recognition engine to analyze the teacher's level of satisfaction and generate an optimal childcare plan based on the childcare teaching guidelines."

[0297] The generative AI model analyzes the prompt sentence and automatically generates an optimal childcare plan by referring to the childcare curriculum guidelines. For example, it might state, "Next week, increase cooperative group play as an activity for A-kun."

[0298] The generated childcare plan is sent from the server to the user's device, where the user can review it and make any necessary changes. Any changes made by the user are sent back to the server and re-saved in the database. This re-saved information is used for audits and reporting to parents.

[0299] Finally, the server stores the final childcare plan and growth records in a database and provides them to a parent portal and audit system, allowing parents to view their child's growth records through a web portal.

[0300] In this way, this system can significantly reduce the workload of childcare workers and provide high-quality childcare plans. In addition, by taking the user's emotions into consideration, it can realize more personalized plans and provide a better environment for both childcare workers and children.

[0301] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0302] Step 1:

[0303] The user logs in to the device and launches the dedicated application. The user accesses the form within the application and enters, for example, "During free play time today, Mr. A enjoyed playing a game of dominoes with Ms. B." This form is made up of text boxes, allowing the user to enter any text they like. Once they have finished entering the information, they click the submit button.

[0304] Input: Text data about children's behavior and classroom atmosphere

[0305] Output: Input data after the submit button is pressed

[0306] Step 2:

[0307] The device receives the input text data and uses an emotion recognition engine to analyze the user's emotions. This analysis includes voice input and facial expression recognition, and can also identify emotional states from text data. For example, it can extract positive and negative keywords and quantify the user's stress level and satisfaction.

[0308] Input: Text data entered by the user

[0309] Output: Emotion analysis results (quantified stress level and satisfaction)

[0310] Step 3:

[0311] The device sends the text data and sentiment analysis results to the server using the HTTPS protocol, where the data is encrypted and securely transmitted, including a timestamp and user ID.

[0312] Input: Text data and sentiment analysis results

[0313] Output: Data sent to the server

[0314] Step 4:

[0315] The server stores the received data in a database, which includes the timestamp, user ID, input content, and sentiment analysis results. Specifically, it inserts the data into the database using an SQL query.

[0316] Input: Submitted text data and sentiment analysis results

[0317] Output: Data stored in the database

[0318] Step 5:

[0319] The server periodically crawls the database to retrieve data that needs to be analyzed. This includes the latest input data and emotion analysis results. The server generates a prompt for the generative AI model based on the retrieved data and sends the data. For example, the prompt might read, "The nursery teacher input that A-kun enjoyed playing dominoes with B-chan during free play today. Based on this, please use the emotion recognition engine to analyze the nursery teacher's level of satisfaction and generate an optimal childcare plan based on the childcare guidance guidelines."

[0320] Input: Latest data retrieved from the database

[0321] Output: Generated prompt statement

[0322] Step 6:

[0323] The generative AI model receives prompts, analyzes the data, and automatically generates an optimal childcare plan while referencing childcare guidelines. For example, based on the data, it generates a suggestion such as "Increase group cooperative play as an activity for A-kun next week." The plan is then generated as text data using specialized algorithms.

[0324] Input: prompt statement

[0325] Output: Generated childcare plan text

[0326] Step 7:

[0327] The server then sends the generated childcare plan to the user's device. The data is sent securely using the HTTPS protocol. This includes the PDF file of the document and text data.

[0328] Input: Generated childcare plan text

[0329] Output: Childcare plan sent to the user's device

[0330] Step 8:

[0331] The user can review the childcare plan sent on their device and make any necessary changes. They can use a text editor to make changes such as "increase free play time and incorporate cooperative activities." Once the changes are complete, they can click the resend button.

[0332] Input: Generated childcare plan

[0333] Output: Revised childcare plan

[0334] Step 9:

[0335] The device then sends the revised childcare plan back to the server, again using the HTTPS protocol, so the data is transmitted securely.

[0336] Input: Revised Childcare Plan

[0337] Output: Corrected data sent to the server

[0338] Step 10:

[0339] The server then re-stores the revised childcare plan in the database. Storing both the original and revised data is useful for future audits and verification. Specifically, the server uses SQL queries to update the data.

[0340] Input: Correction data sent to the server

[0341] Output: Corrected data stored in the database

[0342] Step 11:

[0343] The server stores the final childcare plan and progress records in a database and provides them when needed. This stored data is used for audits and reporting to parents, and is also provided to the parent portal and audit system.

[0344] Input: Final childcare plan and growth record

[0345] Output: Final data stored in the database

[0346] (Application example 2)

[0347] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0348] In the past, improving the work efficiency of factory robots required a lot of clerical work, placing a heavy burden on operators. It was also difficult to generate optimal work plans and maintenance schedules that took into account the robot's operating status and the operator's emotional state. Given these circumstances, there is a need for an efficient and flexible management system that reduces the burden on operators.

[0349] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input the robot's operation and work environment observed using a terminal; means for transmitting the input data to the server and for the server to store it in a database; means including an emotion recognition engine that recognizes the user's emotion from voice input and text analysis; means for transmitting the user's emotion analysis results together with the data to the server; means for the generative AI model to adjust the optimal work plan taking into account the emotion analysis results; means for transmitting the generated work plan and maintenance schedule to the user's terminal so that the user can check and modify them; and means for transmitting the user's feedback and modifications back to the server and storing them in a database. This reduces the operator's workload and enables the generation of an optimal work plan that takes into account the robot's operation status and the operator's emotions.

[0350] A "terminal" is a device or equipment operated by a user, and is used to input information about the robot's operation and working environment.

[0351] An "emotion recognition engine" is a technology that analyzes a user's emotions through voice input and text analysis and obtains the results.

[0352] A "generative AI model" is an artificial intelligence algorithm used to analyze data and automatically generate optimal work plans and maintenance schedules.

[0353] A "work plan" is a document that describes plans for the operation and maintenance schedule of factory robots, and is created by a generative AI model.

[0354] A "maintenance schedule" is a schedule that shows a plan for regular maintenance, inspection, and repair of factory robots.

[0355] A "database" is a system that systematically stores data received by a server so that it can be analyzed and searched as needed.

[0356] "User" refers to an operator or manager who manages and operates a factory robot, and is a person who uses a terminal to input, check, and modify information.

[0357] "Server" means a computer system that receives, stores, and analyzes data sent by users.

[0358] "Feedback" refers to opinions and corrections provided by users after using the system, and is information that is re-stored in the database.

[0359] The present invention relates to a system for managing factory robots and improving work efficiency. How to implement this system will be described below in detail.

[0360] First, the factory robot operator (user) inputs information about the robot's operating status and work environment using a terminal (e.g., a smartphone or head-mounted display). This terminal is equipped with an emotion recognition engine and can recognize the user's emotions through voice input and text analysis, and obtain analysis results.

[0361] The device then transmits the input information and sentiment analysis results to a server, which stores the received data in a database and later analyzes it using a generative AI model.

[0362] The generative AI model used by the server automatically generates optimal work plans and maintenance schedules based on input data and the results of emotion analysis, and adjusts the plans by taking into account the operating status of factory robots and the emotional state of operators.

[0363] The generated work plans and maintenance schedules are sent from the server to the terminal, where the user can review them and make any necessary corrections. The user's feedback and corrections are then sent back to the server and saved in the database. This process reduces the operator's workload and enables efficient and flexible management.

[0364] As a concrete example, consider the case where an operator inputs into a terminal, "Robot A was operating normally on the assembly line today." This information is sent to the server and stored in a database. The emotion recognition engine analyzes the operator's emotions at the time of input, such as satisfaction, and adds the results to the data. The generative AI model analyzes this data and determines that "the next maintenance will be on schedule and there will be no problems." This plan is then sent from the server to the operator's terminal, where the operator confirms it.

[0365] Here are some example prompts that the generative AI model uses to parse:

[0366] Please analyze the following robot status and generate an optimal maintenance schedule:

[0367] Robot A was running fine in assembly line today.

[0368] This system will enable the generation of optimal work plans that take into account the operating status of factory robots and the emotions of operators, which will likely significantly reduce the workload of operators.

[0369] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0370] Step 1:

[0371] The user uses a terminal to input information about the robot's operation and working environment. For example, the user might input "Robot A was operating normally on the assembly line today." At this time, the emotion recognition engine operates through voice input or text to recognize the user's emotions. The input data includes the "robot's operation status" and the user's "voice" or "text." The emotion recognition engine analyzes this input data and generates emotion analysis results.

[0372] Step 2:

[0373] The terminal sends the input information and the emotion analysis results to the server. The input data here is the "robot's operating status" and the "emotion analysis results." Secure data transfer is achieved by sending this to the server using the HTTPS protocol. The server then stores the received data in a database.

[0374] Step 3:

[0375] The server retrieves the information stored in the database and analyzes it using the generative AI model. The input data is the data stored in the database. The generative AI model generates optimal work plans and maintenance schedules based on the robot's operating status and the results of user emotion analysis. For example, as a result of the analysis, a work plan may be generated that states, "There will be no problem if the next maintenance is performed according to the regular schedule."

[0376] Step 4:

[0377] The server sends the generated work plan and maintenance schedule to the terminal. Here, the input data is the "work plan" generated by the generative AI model. The server sends this work plan to the terminal so that the user can check it. The terminal displays the received data, and the user can check the contents.

[0378] Step 5:

[0379] The user checks the work plan and maintenance schedule and makes any necessary corrections. For example, they input specific corrections such as "changing the date and time of the next maintenance." The corrected data is sent again from the terminal to the server. At this time, the "corrected work plan" is sent to the server as input data.

[0380] Step 6:

[0381] The server saves the revised information in a database and updates the final work plan and maintenance schedule. The input data is the "revised work plan." This database update maintains an accurate record that can later be used for audits and further analysis.

[0382] Based on the above processing steps, it is possible to generate optimal work plans that take into account the operating status of factory robots and the emotions of operators. This system reduces the workload of operators and enables flexible and efficient factory management.

[0383] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0384] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0385] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0386] [Second embodiment]

[0387] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0388] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0389] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0390] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0391] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0393] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0394] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0395] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0396] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0397] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0398] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0399] This invention relates to a system for reducing the amount of paperwork that childcare workers perform in their daily work and improving the quality of childcare. This system features a user using a terminal to input information about the children's behavior and the atmosphere in the class, which is then sent to a server, where it is saved and analyzed, and childcare plans and growth records are automatically generated.

[0400] Program processing

[0401] 1. Enter information

[0402] Device: The user inputs information about the child's behavior and the atmosphere in the classroom via a device (PC, tablet, smartphone, etc.). For example, they can input information such as, "Today, A-kun was playing with blocks during free play."

[0403] 2. Data transmission and storage

[0404] Terminal: The entered information is sent to the server using the HTTPS protocol.

[0405] Server: Stores the received information in a database, including a timestamp and the entered user ID.

[0406] 3. Data analysis and automatic generation of childcare plans

[0407] Server: Retrieves stored information from the database and analyzes it with a generative AI model. For example, by referencing past behavioral data and classroom situation data, it determines that "A-kun is interested in cooperating with other children."

[0408] Generative AI model: Refers to childcare guidelines and automatically generates optimal childcare plans based on the analysis results. For example, it creates a childcare plan such as, "Next week, plan an activity that incorporates cooperative play for A-kun."

[0409] 4. Send and review the generated documents

[0410] Server: Sends the generated childcare plan and growth record to the terminal.

[0411] Terminal: The user checks the document through the terminal and makes corrections as necessary. At this stage, the user can make corrections such as "increase free play time and incorporate cooperative activities."

[0412] 5. Feedback and Resave

[0413] Terminal: The user sends the modified information back to the server.

[0414] Server: The corrected information is restored to the database. This restored information is used for audits and parental reporting.

[0415] 6. Database updates and audit support

[0416] Server: The final childcare plan and growth record are saved in a database and provided to the parent portal and audit system as needed. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[0417] Example

[0418] As a concrete example, suppose a nursery teacher inputs into their device, "Today during free play, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The generative AI model analyzes the information, references the childcare guidelines, and concludes that "A-kun is suited to activities that strengthen group activities." As a result, the nursery school plan includes the following: "Next week, A-kun's activities will include increasing cooperative play in groups." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary adjustments.

[0419] In this way, this system can significantly reduce the workload of childcare workers and improve the quality of childcare planning.

[0420] The processing flow will be explained below.

[0421] Step 1:

[0422] User input of information

[0423] The user logs in to the device and accesses a screen where they can input the observed behavior of the children and the atmosphere of the class. For example, they can input, "Today, A-kun was playing with blocks during free play."

[0424] The terminal prepares the entered information to be sent to the server in an appropriate format.

[0425] Step 2:

[0426] Sending data

[0427] The device securely transmits the entered information to the server using the HTTPS protocol, which receives the data and prepares it for storage in a database.

[0428] Step 3:

[0429] Data storage

[0430] The server stores the received information in a database. The stored data includes metadata such as a timestamp and user ID. For example, it might be stored in the format "2023-10-05 14:30:00, A-kun was playing with blocks during free play time."

[0431] Step 4:

[0432] Data Acquisition

[0433] The server retrieves the latest information and historical trend data from the database, which is used in the subsequent analysis process.

[0434] Step 5:

[0435] Data analysis

[0436] The server uses a generative AI model to analyze the accumulated data. For example, by referencing past behavioral data and classroom situation data, it can draw the conclusion that "Kid A is interested in collaborating with other children."

[0437] Step 6:

[0438] Generate childcare plans

[0439] The generative AI model automatically creates an optimal childcare plan based on the analysis results and references childcare guidelines. For example, it might generate a childcare plan that states, "For A-kun, plan an activity that incorporates cooperative play next week."

[0440] Step 7:

[0441] Sending generated documents

[0442] The server sends the generated childcare plan to the terminal, where the user can check it.

[0443] Step 8:

[0444] User verification and correction

[0445] The user can check the generated childcare plan on the device and make any necessary changes, such as "increase free play time and incorporate cooperative activities."

[0446] Step 9:

[0447] Sending correction data

[0448] The terminal sends the corrected information back to the server, which receives the corrected data and updates the database.

[0449] Step 10:

[0450] Save the final data

[0451] The server stores the final revised childcare plan and progress records in a database, which is used for audits and reporting to parents.

[0452] Step 11:

[0453] Audit and Reporting

[0454] The server then provides the final data stored in the database to a parent portal or audit system as needed, allowing parents to check, for example, "A-kun's growth record."

[0455] Through these steps, the system can reduce the workload of childcare workers and provide high-quality childcare plans.

[0456] Example 1

[0457] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0458] Today's childcare workers are overwhelmed with a large amount of administrative work, such as observing children, creating childcare plans, and reporting to parents, which hinders the improvement of childcare quality. There is a need to reduce this enormous workload and allow childcare workers to devote more time to actual childcare activities. It is also important to streamline the process of creating childcare plans and growth records and improve their accuracy and consistency.

[0459] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0460] In this invention, the server includes a means for a user to input observations of children's behavior and the atmosphere of the class using a terminal, a means for transmitting the input data to the server using HTTPS protocol and for the server to store it in a database, a means for the server to analyze the stored data using a generative AI model and generate an optimal childcare plan by referring to the childcare teaching guidelines, and a means for transmitting the generated childcare plan and growth record in PDF or Word format to the user's terminal so that the user can check and modify them. This reduces the workload of childcare workers and improves the quality of childcare plans.

[0461] "Users" refers to nursery teachers and other childcare staff who use the system to input information about children's behavior and the atmosphere in the classroom.

[0462] "Terminal" refers to electronic devices used by users, such as computers, tablets, and smartphones.

[0463] "Observation" refers to the process of carefully watching and recording children's behavior and the atmosphere in the classroom.

[0464] "Classroom atmosphere" refers to the general situation and environment within the classroom at a particular time.

[0465] "Means of input" refers to the method of registering observed data into the system using a terminal.

[0466] The "HTTPS protocol" refers to a communications protocol for securely transmitting data over the Internet.

[0467] "Server" refers to a central processing unit for receiving, storing and analyzing data sent by users.

[0468] "Database" refers to a system that manages various data such as observation data, childcare plans, and growth records stored on a server.

[0469] A "generative AI model" refers to an artificial intelligence model that automatically generates childcare plans and growth records based on childcare guidelines and past data.

[0470] "Childcare guidelines" refers to documents that set out standards and guidelines regarding childcare activities and children's development.

[0471] A "childcare plan" is a document that includes a specific plan of activities to support children's growth and development.

[0472] A "growth record" refers to a document that records children's developmental process and learning progress.

[0473] "PDF and Word formats" refer to file formats for saving and transferring childcare plans and growth records.

[0474] "Means for review and correction" refers to a method by which a user can review the submitted childcare plan or growth record and change the contents as necessary.

[0475] This invention relates to a system for reducing the amount of paperwork that childcare workers have to do in their daily work and improving the quality of childcare. In this system, users use their terminals to input information about the children's behavior and the atmosphere in the class, and the data is sent to a server, where it is saved and analyzed, and childcare plans and growth records are automatically generated.

[0476] First, the user uses a device (PC, tablet, smartphone, etc.) to input details about the children's behavior and the atmosphere in the class. For example, they can input information such as "Mr. A was playing with blocks during free play today" or "Today, outdoor play time was canceled due to rain." This information is encrypted using the HTTPS protocol and sent to the server after ensuring security.

[0477] The server then stores the received information in a database, including the child's behavior, timestamps, and the user ID entered, for later analysis and auditing.

[0478] The server retrieves the saved data from the database and passes it to the generative AI model. This generative AI model has been trained in advance on childcare guidelines and past data, and analyzes the input data. For example, the generative AI model might determine that "A-kun often actively interacts with other children," and generate a specific childcare plan such as, "Next week, plan childcare activities that include a lot of cooperative play."

[0479] The generated childcare plan and growth record are converted into PDF or Word format and sent from the server to the user's device. The user can review the documents on their device and make any necessary changes. For example, they can make a change such as "extending free play time from 30 minutes to 45 minutes."

[0480] Any information corrected by the user is sent back to the server and the corrected information is restored to the database for future audits and parental reporting.

[0481] Finally, the server stores the final childcare plan and growth record in a database and provides them to the parent portal and audit system. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[0482] As a concrete example, suppose a nursery teacher inputs into their device, "Today during free playtime, A-kun enjoyed playing dominoes with B-chan." This information is encrypted and sent to the server, where it is stored in a database. The generative AI model analyzes the input data and, referring to the childcare guidelines, concludes that "activities that strengthen group activities are appropriate for A-kun." As a result, the nursery school plan will state, "Next week, activities for A-kun will involve increasing group cooperative play." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary adjustments.

[0483] An example of a prompt for the generative AI model would be, "Today, during free play, A-kun cooperated with the other children and enjoyed playing a domino game. Based on this data, please suggest a childcare plan for A-kun next week."

[0484] In this way, this system can significantly reduce the workload of childcare workers and improve the quality of childcare planning.

[0485] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0486] Step 1:

[0487] Enter information

[0488] User: The user uses a device to input details about the children's behavior and the atmosphere in the class. For example, they can input information such as, "Today, A-kun was playing with blocks during free play."

[0489] Input: Child behavior information, class atmosphere, timestamp, user ID.

[0490] Output: The input data is stored in the terminal.

[0491] Step 2:

[0492] Sending data

[0493] Terminal: Data entered by the user is encrypted using the HTTPS protocol and sent to the server.

[0494] Input: Data stored on the device.

[0495] Output: The encrypted data is sent to the server using the HTTPS protocol.

[0496] Step 3:

[0497] Data storage

[0498] Server: Receives the encrypted data and stores it in a database, along with the timestamp, user ID, data type, etc.

[0499] Input: Encrypted data sent over the HTTPS protocol.

[0500] Output: Structured data stored in a database.

[0501] Step 4:

[0502] Data analysis

[0503] Server: Retrieves the stored data from the database and passes it to the generative AI model, which has already learned the childcare curriculum and past data.

[0504] Input: Observation data retrieved from the database.

[0505] Output: The analysis results produced by the generative AI model.

[0506] Step 5:

[0507] Automatic generation of childcare plans

[0508] Generative AI model: Analyzes observation data and generates optimal childcare plans based on childcare guidelines. For example, based on the analysis result that "A-kun often actively interacts with other children," it creates a specific childcare plan such as "Plan childcare activities next week that include a lot of cooperative play."

[0509] Input: Analysis results, childcare guidelines.

[0510] Output: The generated childcare plan.

[0511] Step 6:

[0512] Submitting a childcare plan

[0513] Server: The generated childcare plan and growth record are sent to the user's device, where the file format is converted to PDF or Word document format.

[0514] Input: The generated childcare plan.

[0515] Output: The childcare plan sent to the user's device in PDF or Word format.

[0516] Step 7:

[0517] Check and correct

[0518] User: Checks the childcare plan and growth record sent via the device and makes any necessary changes. For example, the user can make specific changes such as extending free play time from 30 minutes to 45 minutes.

[0519] Input: User feedback and corrections.

[0520] Output: Revised childcare plan.

[0521] Step 8:

[0522] Resend and save corrected data

[0523] Terminal: The user sends the modified information back to the server.

[0524] Server: The resent modified data is saved back into the database.

[0525] Enter: Revised Child Care Plan.

[0526] Output: The modified data resaved to the database.

[0527] Step 9:

[0528] Database updates and audit support

[0529] Server: Stores the final childcare plan and growth records in a database and provides them to the parent portal and audit system as needed.

[0530] Input: Final childcare plan, growth record.

[0531] Output: Final data provided to parent portal and audit systems.

[0532] (Application example 1)

[0533] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0534] In childcare operations and factory operation management, large amounts of data entry and management work are required, which has led to problems with reduced work efficiency. In addition, data analysis takes time, making it difficult to create appropriate childcare and maintenance plans. In particular, creating plans and records manually takes time and effort, and is prone to human error.

[0535] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0536] In this invention, the server

[0537] 1. A means for users to input observations of children's behavior and the atmosphere of the class using a device;

[0538] 2. A means for transmitting the input data to the server and for the server to store it in a database;

[0539] 3. A means for the server to analyze the stored data and generate an optimal childcare plan by referring to the childcare teaching guidelines;

[0540] 4. A means for transmitting the generated childcare plan to the user's terminal so that the user can check and modify it;

[0541] 5. A means for a user to input the observed operating status and maintenance notes of factory equipment using a terminal;

[0542] 6. A means for the server to analyze the operational status data based on the input information and generate a preventive maintenance plan;

[0543] 7. A means for transmitting the generated preventive maintenance plan to a user's terminal so that the user can check and modify it;

[0544] This automates the large amount of data entry and analysis work involved in childcare operations and factory management, making it possible to quickly and efficiently generate optimal plans and maintenance plans.

[0545] "User terminals" refer to machines, such as computers, tablets, and smartphones, used by childcare workers and factory workers.

[0546] "Means for inputting observed behavior of children and classroom atmosphere" refers to an interface or software that allows childcare workers to input children's behavior and classroom conditions into a terminal.

[0547] "Means for transmitting data to a server and having the server store it in a database" refers to the process and device for transmitting information entered from a terminal to a server via a network, and for the server to record it in a database.

[0548] The "means of analyzing data and generating a childcare plan by referring to the childcare curriculum guidelines" refers to the process and program in which the server analyzes the information in the database using machine learning algorithms and generative AI models, and automatically creates an educational plan based on childcare standards.

[0549] "Means for sending the generated childcare plan to the user's terminal for confirmation and modification" refers to software or an interface that sends the childcare plan created by the server back to the terminal and allows the childcare worker to confirm and edit its contents.

[0550] The "means for inputting the operating status and maintenance notes of observed factory equipment" refers to an interface or software that allows factory workers to input information about the operating status and maintenance inspection of equipment into a terminal.

[0551] The "means for analyzing operational status data and generating preventive maintenance plans" refers to a process and program that analyzes operational data collected by a server, predicts future maintenance needs using machine learning algorithms, and automatically generates specific work plans.

[0552] "Means for sending the generated preventive maintenance plan to the user's terminal for confirmation and modification" refers to software or an interface that sends the maintenance plan created by the server to the terminal and allows factory workers to confirm and edit its contents.

[0553] This invention is a system that reduces the user's workload and efficiently generates childcare plans and maintenance plans. This system consists of a series of processes in which the user inputs observation data using a terminal, the server receives and analyzes the data, generates an optimal plan, and sends it back to the terminal.

[0554] Specifically, nursery teachers use their devices to input information about the children's behavior and the atmosphere in the classroom. For example, they input information such as, "During free play today, A-kun and B-chan were enjoying a game of dominoes." This information is sent to the server using the HTTPS protocol. The server then stores the information in a database. The stored information also includes a timestamp and the user ID that was entered.

[0555] Next, the server retrieves the stored information from the database and analyzes it using a generative AI model. For example, by referencing past behavioral data and classroom situation data, it may determine that "Mr. A is interested in cooperating with other children." The generative AI model then references childcare guidelines and automatically generates an optimal childcare plan based on the analysis results. For example, it creates a childcare plan such as, "Next week, plan an activity for Mr. A that incorporates cooperative play."

[0556] The generated childcare plan is sent from the server to the terminal, where the user can review the document and make any necessary corrections. For example, a correction could be to "increase free play time and incorporate cooperative activities." This corrected information is sent back to the server and re-stored in the database. This re-stored information is used for audits and reports to parents. It can also be provided to parent portals and auditing systems.

[0557] Meanwhile, factory workers use their terminals to input the observed operating status of factory equipment and maintenance notes. For example, they might input "Robot_1 was operating normally today." This information is also sent to the server using the HTTPS protocol. The server stores the information in a database and analyzes the operating status data to generate a preventive maintenance plan. For example, the operating status data can be used to predict future maintenance needs, generating a plan such as "Robot_1 requires maintenance next week." This plan is then sent from the server to the terminal, where the factory worker can review it and make any necessary adjustments.

[0558] Specific hardware used includes PCs, tablets, smartphones, etc. Software used includes Python, Flask, Pandas, Scikit-learn, Requests, etc. By combining these, users can efficiently input data, and the generative AI model can automatically generate, revise, and manage optimal plans based on that data.

[0559] As a concrete example, a user inputs into a device, "Today during free playtime, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The generative AI model analyzes this information and concludes that "A-kun is interested in cooperating with other children." The childcare plan then includes the following: "Next week, we will increase group cooperative play as an activity for A-kun." This plan is then sent from the server to the device, where the user can review it and make any necessary adjustments.

[0560] Example prompt sentence:

[0561] "Robot_1 was running fine today. What should next week's preventive maintenance include?"

[0562] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0563] Step 1:

[0564] Users use their devices to input observations of children's behavior, the atmosphere in the classroom, or the operating status of factory equipment and maintenance notes. For example, consider the case where a nursery teacher inputs, "During free play today, A-kun and B-chan were enjoying a game of dominoes." A timestamp and user ID are automatically assigned to the input data.

[0565] Step 2:

[0566] The device sends the input data to the server using the HTTPS protocol. This communication ensures security and protects the integrity of the data. The input data consists of information such as the observation, a timestamp, and a user ID.

[0567] Step 3:

[0568] The server saves the received data to the database. This save operation converts the data format (e.g., from JSON to table format) and stores it in the database in the appropriate format. The saved data is used in the subsequent analysis process.

[0569] Step 4:

[0570] The server retrieves the stored information from the database and analyzes it using a generative AI model. Specifically, it combines past behavioral data and classroom situation data to create the next childcare plan while referring to the childcare curriculum. This analysis involves data frame manipulation using Pandas and machine learning models using Scikit-learn.

[0571] Step 5:

[0572] The server's AI model automatically generates an optimal childcare plan based on the analysis results. For example, it creates a childcare plan such as, "For A-kun, plan an activity that incorporates cooperative play next week." This generation process references childcare instruction standards and past data, and automatically generates the plan using a Python script.

[0573] Step 6:

[0574] The server sends the generated care plan or maintenance plan to the device. The user can review the plan and make any necessary modifications. This operation includes editing functions on a front-end interface (e.g., a web application or a mobile app).

[0575] Step 7:

[0576] The user sends the revised information back to the server. The device sends the revised data to the server using the HTTPS protocol. This data transmission causes the revised plan to be saved in the database again.

[0577] Step 8:

[0578] The server stores the final childcare plan and maintenance plan in a database and provides them to the parent portal and audit system when required. This process involves retrieving the output from the database in an appropriate format and providing it through a specific user interface or API.

[0579] The above steps allow for an efficient execution of a series of processes, from data entry to analysis, plan generation, feedback resubmission, and final data storage and provision.

[0580] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0581] This invention relates to a system that reduces the amount of paperwork that childcare workers do in their daily work and improves the quality of childcare. This system has the function of allowing users to input information about the children's behavior and the atmosphere of the class using a terminal, sending the data to a server, saving it, and analyzing it, and automatically generating optimal childcare plans and growth records. In addition, by combining it with an emotion engine that recognizes the user's emotions, it is possible to create more personalized childcare plans.

[0582] Program processing

[0583] 1. Information input and emotion recognition

[0584] Device: The user logs in to the device and accesses a screen where they can enter information about their child's behavior and the atmosphere in the class. For example, they might enter, "Today, A-kun was playing with blocks during free play."

[0585] Emotion engine: Analyzes the user's emotions when they input. For example, recognize the user's stress or satisfaction level from voice input or text analysis.

[0586] 2. Data transmission and storage

[0587] Terminal: The input information and sentiment analysis results are sent to the server using the HTTPS protocol.

[0588] Server: The received information is stored in a database, including the timestamp, the entered user ID, and the sentiment analysis results.

[0589] 3. Data analysis and automatic generation of childcare plans

[0590] Server: Retrieves stored information from the database and analyzes it with a generative AI model.

[0591] Generative AI model: Refers to childcare guidelines and automatically generates an optimal childcare plan based on the analysis results. The analysis results of the emotion engine are also taken into account to adjust the contents of the plan. For example, if the user is feeling stressed, the activities will be adjusted to reduce those emotions.

[0592] 4. Send and review the generated documents

[0593] Server: Sends the generated childcare plan and growth record to the terminal.

[0594] Terminal: The user checks this and makes corrections as necessary. For example, they may make corrections such as "increase free play time and incorporate cooperative activities."

[0595] 5. Feedback and Resave

[0596] Terminal: The user sends the modified information back to the server.

[0597] Server: The corrected information is restored to the database. This restored information is used for audits and parental reporting.

[0598] 6. Database updates and audit support

[0599] Server: The final childcare plan and growth record are saved in a database. If necessary, the server provides them to the parent portal or audit system. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[0600] Example

[0601] As a concrete example, suppose a nursery teacher inputs into their device, "During free play today, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The emotion engine analyzes the emotion (e.g., satisfaction or stress) felt by the user when they entered the information and adds it to the data. The generative AI model analyzes the information and, referring to the childcare guidelines, determines that "A-kun is interested in cooperating with other children." As a result, the nursery school plan includes the following: "Next week, we will increase group cooperative play as an activity for A-kun." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary revisions.

[0602] In this way, this system can significantly reduce the workload of childcare workers and provide high-quality childcare plans. In addition, by taking the user's emotions into consideration, it can realize more personalized plans and provide a better environment for both childcare workers and children.

[0603] The processing flow will be explained below.

[0604] Step 1:

[0605] User input and emotion recognition

[0606] The user (childcare worker) logs in to the device and inputs the behavior of the children they observed and the atmosphere of the class. The emotion engine also analyzes the user's voice and input speed to recognize the user's emotions. For example, if a user inputs "Mr. A was playing with blocks during free play today," the emotion engine simultaneously detects the teacher's level of satisfaction.

[0607] Step 2:

[0608] Sending data and adding emotion data

[0609] The device sends the input information and recognized emotional data, including the stress level and satisfaction level of the childcare worker, to the server using the HTTPS protocol.

[0610] Step 3:

[0611] Data storage

[0612] The server stores the received information in a database. The stored data includes a timestamp, the entered user ID, and the results of sentiment analysis. For example, it might be stored as "2023-10-05 14:30:00, A-kun was playing with blocks during free play, and the childcare worker was very satisfied."

[0613] Step 4:

[0614] Data Acquisition

[0615] The server retrieves the stored information from the database, including past behavioral data and classroom situation data to use the necessary information for analysis, including emotional data.

[0616] Step 5:

[0617] Data analysis

[0618] The server analyzes the data using a generative AI model. Data from the emotion engine is also incorporated into the analysis process. The result is that "Kid A is interested in cooperating with other children," and the conclusion is that "the nursery teachers are highly satisfied with the cooperative play activities."

[0619] Step 6:

[0620] Generate childcare plans

[0621] The generative AI model automatically generates an optimal childcare plan based on the analysis results and childcare teaching guidelines. The content of the plan is further adjusted based on emotional data. For example, it might state, "Next week, we will plan an activity for A-kun that incorporates cooperative play. We will also specifically incorporate activities that will give childcare workers a high level of satisfaction."

[0622] Step 7:

[0623] Sending generated documents

[0624] The server sends the generated childcare plan to the terminal, where the user can check it.

[0625] Step 8:

[0626] User verification and correction

[0627] The user can check the generated childcare plan on the device and make any necessary changes, such as "increase free play time and incorporate cooperative activities."

[0628] Step 9:

[0629] Sending correction data

[0630] The terminal sends the corrected information back to the server, which receives the corrected data and updates the database.

[0631] Step 10:

[0632] Save the final data

[0633] The server stores the final revised childcare plan and progress records in a database, which is used for audits and reporting to parents.

[0634] Step 11:

[0635] Audit and Reporting

[0636] The server then provides the final data stored in the database to a parent portal or audit system as needed, allowing parents to check, for example, "A-kun's growth record."

[0637] Through this series of steps, the system reduces the workload of childcare workers and provides more personalized childcare plans using the emotion engine, thereby improving the quality of childcare and childcare worker satisfaction.

[0638] Example 2

[0639] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0640] To improve the quality of childcare, it is necessary to provide a system that allows childcare workers to create childcare plans and growth records effectively and efficiently without being overwhelmed by the large amount of paperwork in their daily work. There is also a challenge in creating more personalized childcare plans by taking into account the emotional state of childcare workers.

[0641] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes emotion recognition means for analyzing input data and recognizing the user's emotion, means for transmitting the input data and the recognized emotion data to the server and storing them in a database, and generation AI model means for analyzing the stored data and generating an optimal childcare plan by referring to the childcare teaching guidelines. This makes it possible to recognize and analyze emotions from data input by childcare workers and automatically generate a more personalized childcare plan efficiently based on the results.

[0642] "Users" refer to childcare workers and educators who operate the devices to input information about the children's behavior and the atmosphere in the class.

[0643] "Terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0644] "Emotion recognition" refers to the process of analyzing data entered by a user, as well as the voice and facial expressions used when entering data, to identify the user's emotional state.

[0645] "Database" refers to a data storage system where the server stores user input data and analysis results.

[0646] "Server" refers to a computer system that receives, stores, and analyzes input data and transmits the generated childcare plan to the user.

[0647] "Generative AI model" refers to an artificial intelligence model that automatically generates optimal childcare plans based on childcare guidelines, stored data, and emotion analysis results.

[0648] "Childcare plan" refers to a document that details the education and activities of children.

[0649] A "growth record" refers to a document that records in detail the development and growth process of children.

[0650] A "parent portal" refers to a web system that allows parents to view their children's growth records and childcare plans online.

[0651] "Audit system" refers to a system for auditing the contents of childcare plans and growth records.

[0652] This invention relates to a system that reduces the amount of paperwork that childcare workers do in their daily work and improves the quality of childcare. This system has the function of allowing users to input information about the children's behavior and the atmosphere in the class using a terminal, sending the data to a server, saving it, and analyzing it, and automatically generating optimal childcare plans and growth records. Furthermore, by combining it with an emotion recognition engine that recognizes the user's emotions, it is possible to create more personalized childcare plans.

[0653] The specific hardware of this system includes the devices used by users (e.g., computers, smartphones, tablets, etc.) and the server that sends, receives, stores, and analyzes data. The software includes an emotion recognition engine, a generative AI model, and a database management system.

[0654] First, the user logs in to the device and launches a dedicated application. The user then accesses a form within the application and enters text about the child's condition and the atmosphere in the class. For example, the user might enter, "During free play today, A-kun enjoyed playing dominoes with B-chan."

[0655] Next, the emotion recognition engine analyzes the user's emotions from their voice and facial expressions. This is done using algorithms that analyze voice input and text. For example, it analyzes keywords such as "happy" to identify a feeling of satisfaction.

[0656] The device then sends the input information and the emotion analysis results to the server using the HTTPS protocol. The server then stores the received data in a database. The stored information includes a timestamp, user ID, input content, and emotion analysis results.

[0657] The server periodically crawls the stored data and sends the information that needs to be analyzed to the generative AI model.

[0658] "The nursery teacher input that A-kun enjoyed playing dominoes with B-chan during free play today. Based on this, please use the emotion recognition engine to analyze the teacher's level of satisfaction and generate an optimal childcare plan based on the childcare teaching guidelines."

[0659] The generative AI model analyzes the prompt sentence and automatically generates an optimal childcare plan by referring to the childcare curriculum guidelines. For example, it might state, "Next week, increase cooperative group play as an activity for A-kun."

[0660] The generated childcare plan is sent from the server to the user's device, where the user can review it and make any necessary changes. Any changes made by the user are sent back to the server and re-saved in the database. This re-saved information is used for audits and reporting to parents.

[0661] Finally, the server stores the final childcare plan and growth records in a database and provides them to a parent portal and audit system, allowing parents to view their child's growth records through a web portal.

[0662] In this way, this system can significantly reduce the workload of childcare workers and provide high-quality childcare plans. In addition, by taking the user's emotions into consideration, it can realize more personalized plans and provide a better environment for both childcare workers and children.

[0663] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0664] Step 1:

[0665] The user logs in to the device and launches the dedicated application. The user accesses the form within the application and enters, for example, "During free play time today, Mr. A enjoyed playing a game of dominoes with Ms. B." This form is made up of text boxes, allowing the user to enter any text they like. Once they have finished entering the information, they click the submit button.

[0666] Input: Text data about children's behavior and classroom atmosphere

[0667] Output: Input data after the submit button is pressed

[0668] Step 2:

[0669] The device receives the input text data and uses an emotion recognition engine to analyze the user's emotions. This analysis includes voice input and facial expression recognition, and can also identify emotional states from text data. For example, it can extract positive and negative keywords and quantify the user's stress level and satisfaction.

[0670] Input: Text data entered by the user

[0671] Output: Emotion analysis results (quantified stress level and satisfaction)

[0672] Step 3:

[0673] The device sends the text data and sentiment analysis results to the server using the HTTPS protocol, where the data is encrypted and securely transmitted, including a timestamp and user ID.

[0674] Input: Text data and sentiment analysis results

[0675] Output: Data sent to the server

[0676] Step 4:

[0677] The server stores the received data in a database, which includes the timestamp, user ID, input content, and sentiment analysis results. Specifically, it inserts the data into the database using an SQL query.

[0678] Input: Submitted text data and sentiment analysis results

[0679] Output: Data stored in the database

[0680] Step 5:

[0681] The server periodically crawls the database to retrieve data that needs to be analyzed. This includes the latest input data and emotion analysis results. The server generates a prompt for the generative AI model based on the retrieved data and sends the data. For example, the prompt might read, "The nursery teacher input that A-kun enjoyed playing dominoes with B-chan during free play today. Based on this, please use the emotion recognition engine to analyze the nursery teacher's level of satisfaction and generate an optimal childcare plan based on the childcare guidance guidelines."

[0682] Input: Latest data retrieved from the database

[0683] Output: Generated prompt statement

[0684] Step 6:

[0685] The generative AI model receives prompts, analyzes the data, and automatically generates an optimal childcare plan while referencing childcare guidelines. For example, based on the data, it generates a suggestion such as "Increase group cooperative play as an activity for A-kun next week." The plan is then generated as text data using specialized algorithms.

[0686] Input: prompt statement

[0687] Output: Generated childcare plan text

[0688] Step 7:

[0689] The server then sends the generated childcare plan to the user's device. The data is sent securely using the HTTPS protocol. This includes the PDF file of the document and text data.

[0690] Input: Generated childcare plan text

[0691] Output: Childcare plan sent to the user's device

[0692] Step 8:

[0693] The user can review the childcare plan sent on their device and make any necessary changes. They can use a text editor to make changes such as "increase free play time and incorporate cooperative activities." Once the changes are complete, they can click the resend button.

[0694] Input: Generated childcare plan

[0695] Output: Revised childcare plan

[0696] Step 9:

[0697] The device then sends the revised childcare plan back to the server, again using the HTTPS protocol, so the data is transmitted securely.

[0698] Input: Revised Childcare Plan

[0699] Output: Corrected data sent to the server

[0700] Step 10:

[0701] The server then re-stores the revised childcare plan in the database. Storing both the original and revised data is useful for future audits and verification. Specifically, the server uses SQL queries to update the data.

[0702] Input: Correction data sent to the server

[0703] Output: Corrected data stored in the database

[0704] Step 11:

[0705] The server stores the final childcare plan and progress records in a database and provides them when needed. This stored data is used for audits and reporting to parents, and is also provided to the parent portal and audit system.

[0706] Input: Final childcare plan and growth record

[0707] Output: Final data stored in the database

[0708] (Application example 2)

[0709] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0710] In the past, improving the work efficiency of factory robots required a lot of clerical work, placing a heavy burden on operators. It was also difficult to generate optimal work plans and maintenance schedules that took into account the robot's operating status and the operator's emotional state. Given these circumstances, there is a need for an efficient and flexible management system that reduces the burden on operators.

[0711] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input the robot's operation and work environment observed using a terminal; means for transmitting the input data to the server and for the server to store it in a database; means including an emotion recognition engine that recognizes the user's emotion from voice input and text analysis; means for transmitting the user's emotion analysis results together with the data to the server; means for the generative AI model to adjust the optimal work plan taking into account the emotion analysis results; means for transmitting the generated work plan and maintenance schedule to the user's terminal so that the user can check and modify them; and means for transmitting the user's feedback and modifications back to the server and storing them in a database. This reduces the operator's workload and enables the generation of an optimal work plan that takes into account the robot's operation status and the operator's emotions.

[0712] A "terminal" is a device or equipment operated by a user, and is used to input information about the robot's operation and working environment.

[0713] An "emotion recognition engine" is a technology that analyzes a user's emotions through voice input and text analysis and obtains the results.

[0714] A "generative AI model" is an artificial intelligence algorithm used to analyze data and automatically generate optimal work plans and maintenance schedules.

[0715] A "work plan" is a document that describes plans for the operation and maintenance schedule of factory robots, and is created by a generative AI model.

[0716] A "maintenance schedule" is a schedule that shows a plan for regular maintenance, inspection, and repair of factory robots.

[0717] A "database" is a system that systematically stores data received by a server so that it can be analyzed and searched as needed.

[0718] "User" refers to an operator or manager who manages and operates a factory robot, and is a person who uses a terminal to input, check, and modify information.

[0719] "Server" means a computer system that receives, stores, and analyzes data sent by users.

[0720] "Feedback" refers to opinions and corrections provided by users after using the system, and is information that is re-stored in the database.

[0721] The present invention relates to a system for managing factory robots and improving work efficiency. How to implement this system will be described below in detail.

[0722] First, the factory robot operator (user) inputs information about the robot's operating status and work environment using a terminal (e.g., a smartphone or head-mounted display). This terminal is equipped with an emotion recognition engine and can recognize the user's emotions through voice input and text analysis, and obtain analysis results.

[0723] The device then transmits the input information and sentiment analysis results to a server, which stores the received data in a database and later analyzes it using a generative AI model.

[0724] The generative AI model used by the server automatically generates optimal work plans and maintenance schedules based on input data and the results of emotion analysis, and adjusts the plans by taking into account the operating status of factory robots and the emotional state of operators.

[0725] The generated work plans and maintenance schedules are sent from the server to the terminal, where the user can review them and make any necessary corrections. The user's feedback and corrections are then sent back to the server and saved in the database. This process reduces the operator's workload and enables efficient and flexible management.

[0726] As a concrete example, consider the case where an operator inputs into a terminal, "Robot A was operating normally on the assembly line today." This information is sent to the server and stored in a database. The emotion recognition engine analyzes the operator's emotions at the time of input, such as satisfaction, and adds the results to the data. The generative AI model analyzes this data and determines that "the next maintenance will be on schedule and there will be no problems." This plan is then sent from the server to the operator's terminal, where the operator confirms it.

[0727] Here are some example prompts that the generative AI model uses to parse:

[0728] Please analyze the following robot status and generate an optimal maintenance schedule:

[0729] Robot A was running fine in assembly line today.

[0730] This system will enable the generation of optimal work plans that take into account the operating status of factory robots and the emotions of operators, which will likely significantly reduce the workload of operators.

[0731] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0732] Step 1:

[0733] The user uses a terminal to input information about the robot's operation and working environment. For example, the user might input "Robot A was operating normally on the assembly line today." At this time, the emotion recognition engine operates through voice input or text to recognize the user's emotions. The input data includes the "robot's operation status" and the user's "voice" or "text." The emotion recognition engine analyzes this input data and generates emotion analysis results.

[0734] Step 2:

[0735] The terminal sends the input information and the emotion analysis results to the server. The input data here is the "robot's operating status" and the "emotion analysis results." Secure data transfer is achieved by sending this to the server using the HTTPS protocol. The server then stores the received data in a database.

[0736] Step 3:

[0737] The server retrieves the information stored in the database and analyzes it using the generative AI model. The input data is the data stored in the database. The generative AI model generates optimal work plans and maintenance schedules based on the robot's operating status and the results of user emotion analysis. For example, as a result of the analysis, a work plan may be generated that states, "There will be no problem if the next maintenance is performed according to the regular schedule."

[0738] Step 4:

[0739] The server sends the generated work plan and maintenance schedule to the terminal. Here, the input data is the "work plan" generated by the generative AI model. The server sends this work plan to the terminal so that the user can check it. The terminal displays the received data, and the user can check the contents.

[0740] Step 5:

[0741] The user checks the work plan and maintenance schedule and makes any necessary corrections. For example, they input specific corrections such as "changing the date and time of the next maintenance." The corrected data is sent again from the terminal to the server. At this time, the "corrected work plan" is sent to the server as input data.

[0742] Step 6:

[0743] The server saves the revised information in a database and updates the final work plan and maintenance schedule. The input data is the "revised work plan." This database update maintains an accurate record that can later be used for audits and further analysis.

[0744] Based on the above processing steps, it is possible to generate optimal work plans that take into account the operating status of factory robots and the emotions of operators. This system reduces the workload of operators and enables flexible and efficient factory management.

[0745] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0746] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0747] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0748] [Third embodiment]

[0749] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0750] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0751] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0752] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0753] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0754] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0755] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0756] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0757] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0758] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0759] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0760] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0761] This invention relates to a system for reducing the amount of paperwork that childcare workers perform in their daily work and improving the quality of childcare. This system features a user using a terminal to input information about the children's behavior and the atmosphere in the class, which is then sent to a server, where it is saved and analyzed, and childcare plans and growth records are automatically generated.

[0762] Program processing

[0763] 1. Enter information

[0764] Device: The user inputs information about the child's behavior and the atmosphere in the classroom via a device (PC, tablet, smartphone, etc.). For example, they can input information such as, "Today, A-kun was playing with blocks during free play."

[0765] 2. Data transmission and storage

[0766] Terminal: The entered information is sent to the server using the HTTPS protocol.

[0767] Server: Stores the received information in a database, including a timestamp and the entered user ID.

[0768] 3. Data analysis and automatic generation of childcare plans

[0769] Server: Retrieves stored information from the database and analyzes it with a generative AI model. For example, by referencing past behavioral data and classroom situation data, it determines that "A-kun is interested in cooperating with other children."

[0770] Generative AI model: Refers to childcare guidelines and automatically generates optimal childcare plans based on the analysis results. For example, it creates a childcare plan such as, "Next week, plan an activity that incorporates cooperative play for A-kun."

[0771] 4. Send and review the generated documents

[0772] Server: Sends the generated childcare plan and growth record to the terminal.

[0773] Terminal: The user checks the document through the terminal and makes corrections as necessary. At this stage, the user can make corrections such as "increase free play time and incorporate cooperative activities."

[0774] 5. Feedback and Resave

[0775] Terminal: The user sends the modified information back to the server.

[0776] Server: The corrected information is restored to the database. This restored information is used for audits and parental reporting.

[0777] 6. Database updates and audit support

[0778] Server: The final childcare plan and growth record are saved in a database and provided to the parent portal and audit system as needed. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[0779] Example

[0780] As a concrete example, suppose a nursery teacher inputs into their device, "Today during free play, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The generative AI model analyzes the information, references the childcare guidelines, and concludes that "A-kun is suited to activities that strengthen group activities." As a result, the nursery school plan includes the following: "Next week, A-kun's activities will include increasing cooperative play in groups." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary adjustments.

[0781] In this way, this system can significantly reduce the workload of childcare workers and improve the quality of childcare planning.

[0782] The processing flow will be explained below.

[0783] Step 1:

[0784] User input of information

[0785] The user logs in to the device and accesses a screen where they can input the observed behavior of the children and the atmosphere of the class. For example, they can input, "Today, A-kun was playing with blocks during free play."

[0786] The terminal prepares the entered information to be sent to the server in an appropriate format.

[0787] Step 2:

[0788] Sending data

[0789] The device securely transmits the entered information to the server using the HTTPS protocol, which receives the data and prepares it for storage in a database.

[0790] Step 3:

[0791] Data storage

[0792] The server stores the received information in a database. The stored data includes metadata such as a timestamp and user ID. For example, it might be stored in the format "2023-10-05 14:30:00, A-kun was playing with blocks during free play time."

[0793] Step 4:

[0794] Data Acquisition

[0795] The server retrieves the latest information and historical trend data from the database, which is used in the subsequent analysis process.

[0796] Step 5:

[0797] Data analysis

[0798] The server uses a generative AI model to analyze the accumulated data. For example, by referencing past behavioral data and classroom situation data, it can draw the conclusion that "Kid A is interested in collaborating with other children."

[0799] Step 6:

[0800] Generate childcare plans

[0801] The generative AI model automatically creates an optimal childcare plan based on the analysis results and references childcare guidelines. For example, it might generate a childcare plan that states, "For A-kun, plan an activity that incorporates cooperative play next week."

[0802] Step 7:

[0803] Sending generated documents

[0804] The server sends the generated childcare plan to the terminal, where the user can check it.

[0805] Step 8:

[0806] User verification and correction

[0807] The user can check the generated childcare plan on the device and make any necessary changes, such as "increase free play time and incorporate cooperative activities."

[0808] Step 9:

[0809] Sending correction data

[0810] The terminal sends the corrected information back to the server, which receives the corrected data and updates the database.

[0811] Step 10:

[0812] Save the final data

[0813] The server stores the final revised childcare plan and progress records in a database, which is used for audits and reporting to parents.

[0814] Step 11:

[0815] Audit and Reporting

[0816] The server then provides the final data stored in the database to a parent portal or audit system as needed, allowing parents to check, for example, "A-kun's growth record."

[0817] Through these steps, the system can reduce the workload of childcare workers and provide high-quality childcare plans.

[0818] Example 1

[0819] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0820] Today's childcare workers are overwhelmed with a large amount of administrative work, such as observing children, creating childcare plans, and reporting to parents, which hinders the improvement of childcare quality. There is a need to reduce this enormous workload and allow childcare workers to devote more time to actual childcare activities. It is also important to streamline the process of creating childcare plans and growth records and improve their accuracy and consistency.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0822] In this invention, the server includes a means for a user to input observations of children's behavior and the atmosphere of the class using a terminal, a means for transmitting the input data to the server using HTTPS protocol and for the server to store it in a database, a means for the server to analyze the stored data using a generative AI model and generate an optimal childcare plan by referring to the childcare teaching guidelines, and a means for transmitting the generated childcare plan and growth record in PDF or Word format to the user's terminal so that the user can check and modify them. This reduces the workload of childcare workers and improves the quality of childcare plans.

[0823] "Users" refers to nursery teachers and other childcare staff who use the system to input information about children's behavior and the atmosphere in the classroom.

[0824] "Terminal" refers to electronic devices used by users, such as computers, tablets, and smartphones.

[0825] "Observation" refers to the process of carefully watching and recording children's behavior and the atmosphere in the classroom.

[0826] "Classroom atmosphere" refers to the general situation and environment within the classroom at a particular time.

[0827] "Means of input" refers to the method of registering observed data into the system using a terminal.

[0828] The "HTTPS protocol" refers to a communications protocol for securely transmitting data over the Internet.

[0829] "Server" refers to a central processing unit for receiving, storing and analyzing data sent by users.

[0830] "Database" refers to a system that manages various data such as observation data, childcare plans, and growth records stored on a server.

[0831] A "generative AI model" refers to an artificial intelligence model that automatically generates childcare plans and growth records based on childcare guidelines and past data.

[0832] "Childcare guidelines" refers to documents that set out standards and guidelines regarding childcare activities and children's development.

[0833] A "childcare plan" is a document that includes a specific plan of activities to support children's growth and development.

[0834] A "growth record" refers to a document that records children's developmental process and learning progress.

[0835] "PDF and Word formats" refer to file formats for saving and transferring childcare plans and growth records.

[0836] "Means for review and correction" refers to a method by which a user can review the submitted childcare plan or growth record and change the contents as necessary.

[0837] This invention relates to a system for reducing the amount of paperwork that childcare workers have to do in their daily work and improving the quality of childcare. In this system, users use their terminals to input information about the children's behavior and the atmosphere in the class, and the data is sent to a server, where it is saved and analyzed, and childcare plans and growth records are automatically generated.

[0838] First, the user uses a device (PC, tablet, smartphone, etc.) to input details about the children's behavior and the atmosphere in the class. For example, they can input information such as "Mr. A was playing with blocks during free play today" or "Today, outdoor play time was canceled due to rain." This information is encrypted using the HTTPS protocol and sent to the server after ensuring security.

[0839] The server then stores the received information in a database, including the child's behavior, timestamps, and the user ID entered, for later analysis and auditing.

[0840] The server retrieves the saved data from the database and passes it to the generative AI model. This generative AI model has been trained in advance on childcare guidelines and past data, and analyzes the input data. For example, the generative AI model might determine that "A-kun often actively interacts with other children," and generate a specific childcare plan such as, "Next week, plan childcare activities that include a lot of cooperative play."

[0841] The generated childcare plan and growth record are converted into PDF or Word format and sent from the server to the user's device. The user can review the documents on their device and make any necessary changes. For example, they can make a change such as "extending free play time from 30 minutes to 45 minutes."

[0842] Any information corrected by the user is sent back to the server and the corrected information is restored to the database for future audits and parental reporting.

[0843] Finally, the server stores the final childcare plan and growth record in a database and provides them to the parent portal and audit system. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[0844] As a concrete example, suppose a nursery teacher inputs into their device, "Today during free playtime, A-kun enjoyed playing dominoes with B-chan." This information is encrypted and sent to the server, where it is stored in a database. The generative AI model analyzes the input data and, referring to the childcare guidelines, concludes that "activities that strengthen group activities are appropriate for A-kun." As a result, the nursery school plan will state, "Next week, activities for A-kun will involve increasing group cooperative play." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary adjustments.

[0845] An example of a prompt for the generative AI model would be, "Today, during free play, A-kun cooperated with the other children and enjoyed playing a domino game. Based on this data, please suggest a childcare plan for A-kun next week."

[0846] In this way, this system can significantly reduce the workload of childcare workers and improve the quality of childcare planning.

[0847] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0848] Step 1:

[0849] Enter information

[0850] User: The user uses a device to input details about the children's behavior and the atmosphere in the class. For example, they can input information such as, "Today, A-kun was playing with blocks during free play."

[0851] Input: Child behavior information, class atmosphere, timestamp, user ID.

[0852] Output: The input data is stored in the terminal.

[0853] Step 2:

[0854] Sending data

[0855] Terminal: Data entered by the user is encrypted using the HTTPS protocol and sent to the server.

[0856] Input: Data stored on the device.

[0857] Output: The encrypted data is sent to the server using the HTTPS protocol.

[0858] Step 3:

[0859] Data storage

[0860] Server: Receives the encrypted data and stores it in a database, along with the timestamp, user ID, data type, etc.

[0861] Input: Encrypted data sent over the HTTPS protocol.

[0862] Output: Structured data stored in a database.

[0863] Step 4:

[0864] Data analysis

[0865] Server: Retrieves the stored data from the database and passes it to the generative AI model, which has already learned the childcare curriculum and past data.

[0866] Input: Observation data retrieved from the database.

[0867] Output: The analysis results produced by the generative AI model.

[0868] Step 5:

[0869] Automatic generation of childcare plans

[0870] Generative AI model: Analyzes observation data and generates optimal childcare plans based on childcare guidelines. For example, based on the analysis result that "A-kun often actively interacts with other children," it creates a specific childcare plan such as "Plan childcare activities next week that include a lot of cooperative play."

[0871] Input: Analysis results, childcare guidelines.

[0872] Output: The generated childcare plan.

[0873] Step 6:

[0874] Submitting a childcare plan

[0875] Server: The generated childcare plan and growth record are sent to the user's device, where the file format is converted to PDF or Word document format.

[0876] Input: The generated childcare plan.

[0877] Output: The childcare plan sent to the user's device in PDF or Word format.

[0878] Step 7:

[0879] Check and correct

[0880] User: Checks the childcare plan and growth record sent via the device and makes any necessary changes. For example, the user can make specific changes such as extending free play time from 30 minutes to 45 minutes.

[0881] Input: User feedback and corrections.

[0882] Output: Revised childcare plan.

[0883] Step 8:

[0884] Resend and save corrected data

[0885] Terminal: The user sends the modified information back to the server.

[0886] Server: The resent modified data is saved back into the database.

[0887] Enter: Revised Child Care Plan.

[0888] Output: The modified data resaved to the database.

[0889] Step 9:

[0890] Database updates and audit support

[0891] Server: Stores the final childcare plan and growth records in a database and provides them to the parent portal and audit system as needed.

[0892] Input: Final childcare plan, growth record.

[0893] Output: Final data provided to parent portal and audit systems.

[0894] (Application example 1)

[0895] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0896] In childcare operations and factory operation management, large amounts of data entry and management work are required, which has led to problems with reduced work efficiency. In addition, data analysis takes time, making it difficult to create appropriate childcare and maintenance plans. In particular, creating plans and records manually takes time and effort, and is prone to human error.

[0897] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0898] In this invention, the server

[0899] 1. A means for users to input observations of children's behavior and the atmosphere of the class using a device;

[0900] 2. A means for transmitting the input data to the server and for the server to store it in a database;

[0901] 3. A means for the server to analyze the stored data and generate an optimal childcare plan by referring to the childcare teaching guidelines;

[0902] 4. A means for transmitting the generated childcare plan to the user's terminal so that the user can check and modify it;

[0903] 5. A means for a user to input the observed operating status and maintenance notes of factory equipment using a terminal;

[0904] 6. A means for the server to analyze the operational status data based on the input information and generate a preventive maintenance plan;

[0905] 7. A means for transmitting the generated preventive maintenance plan to a user's terminal so that the user can check and modify it;

[0906] This automates the large amount of data entry and analysis work involved in childcare operations and factory management, making it possible to quickly and efficiently generate optimal plans and maintenance plans.

[0907] "User terminals" refer to machines, such as computers, tablets, and smartphones, used by childcare workers and factory workers.

[0908] "Means for inputting observed behavior of children and classroom atmosphere" refers to an interface or software that allows childcare workers to input children's behavior and classroom conditions into a terminal.

[0909] "Means for transmitting data to a server and having the server store it in a database" refers to the process and device for transmitting information entered from a terminal to a server via a network, and for the server to record it in a database.

[0910] The "means of analyzing data and generating a childcare plan by referring to the childcare curriculum guidelines" refers to the process and program in which the server analyzes the information in the database using machine learning algorithms and generative AI models, and automatically creates an educational plan based on childcare standards.

[0911] "Means for sending the generated childcare plan to the user's terminal for confirmation and modification" refers to software or an interface that sends the childcare plan created by the server back to the terminal and allows the childcare worker to confirm and edit its contents.

[0912] The "means for inputting the operating status and maintenance notes of observed factory equipment" refers to an interface or software that allows factory workers to input information about the operating status and maintenance inspection of equipment into a terminal.

[0913] The "means for analyzing operational status data and generating preventive maintenance plans" refers to a process and program that analyzes operational data collected by a server, predicts future maintenance needs using machine learning algorithms, and automatically generates specific work plans.

[0914] "Means for sending the generated preventive maintenance plan to the user's terminal for confirmation and modification" refers to software or an interface that sends the maintenance plan created by the server to the terminal and allows factory workers to confirm and edit its contents.

[0915] This invention is a system that reduces the user's workload and efficiently generates childcare plans and maintenance plans. This system consists of a series of processes in which the user inputs observation data using a terminal, the server receives and analyzes the data, generates an optimal plan, and sends it back to the terminal.

[0916] Specifically, nursery teachers use their devices to input information about the children's behavior and the atmosphere in the classroom. For example, they input information such as, "During free play today, A-kun and B-chan were enjoying a game of dominoes." This information is sent to the server using the HTTPS protocol. The server then stores the information in a database. The stored information also includes a timestamp and the user ID that was entered.

[0917] Next, the server retrieves the stored information from the database and analyzes it using a generative AI model. For example, by referencing past behavioral data and classroom situation data, it may determine that "Mr. A is interested in cooperating with other children." The generative AI model then references childcare guidelines and automatically generates an optimal childcare plan based on the analysis results. For example, it creates a childcare plan such as, "Next week, plan an activity for Mr. A that incorporates cooperative play."

[0918] The generated childcare plan is sent from the server to the terminal, where the user can review the document and make any necessary corrections. For example, a correction could be to "increase free play time and incorporate cooperative activities." This corrected information is sent back to the server and re-stored in the database. This re-stored information is used for audits and reports to parents. It can also be provided to parent portals and auditing systems.

[0919] Meanwhile, factory workers use their terminals to input the observed operating status of factory equipment and maintenance notes. For example, they might input "Robot_1 was operating normally today." This information is also sent to the server using the HTTPS protocol. The server stores the information in a database and analyzes the operating status data to generate a preventive maintenance plan. For example, the operating status data can be used to predict future maintenance needs, generating a plan such as "Robot_1 requires maintenance next week." This plan is then sent from the server to the terminal, where the factory worker can review it and make any necessary adjustments.

[0920] Specific hardware used includes PCs, tablets, smartphones, etc. Software used includes Python, Flask, Pandas, Scikit-learn, Requests, etc. By combining these, users can efficiently input data, and the generative AI model can automatically generate, revise, and manage optimal plans based on that data.

[0921] As a concrete example, a user inputs into a device, "Today during free playtime, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The generative AI model analyzes this information and concludes that "A-kun is interested in cooperating with other children." The childcare plan then includes the following: "Next week, we will increase group cooperative play as an activity for A-kun." This plan is then sent from the server to the device, where the user can review it and make any necessary adjustments.

[0922] Example prompt sentence:

[0923] "Robot_1 was running fine today. What should next week's preventive maintenance include?"

[0924] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0925] Step 1:

[0926] Users use their devices to input observations of children's behavior, the atmosphere in the classroom, or the operating status of factory equipment and maintenance notes. For example, consider the case where a nursery teacher inputs, "During free play today, A-kun and B-chan were enjoying a game of dominoes." A timestamp and user ID are automatically assigned to the input data.

[0927] Step 2:

[0928] The device sends the input data to the server using the HTTPS protocol. This communication ensures security and protects the integrity of the data. The input data consists of information such as the observation, a timestamp, and a user ID.

[0929] Step 3:

[0930] The server saves the received data to the database. This save operation converts the data format (e.g., from JSON to table format) and stores it in the database in the appropriate format. The saved data is used in the subsequent analysis process.

[0931] Step 4:

[0932] The server retrieves the stored information from the database and analyzes it using a generative AI model. Specifically, it combines past behavioral data and classroom situation data to create the next childcare plan while referring to the childcare curriculum. This analysis involves data frame manipulation using Pandas and machine learning models using Scikit-learn.

[0933] Step 5:

[0934] The server's AI model automatically generates an optimal childcare plan based on the analysis results. For example, it creates a childcare plan such as, "For A-kun, plan an activity that incorporates cooperative play next week." This generation process references childcare instruction standards and past data, and automatically generates the plan using a Python script.

[0935] Step 6:

[0936] The server sends the generated care plan or maintenance plan to the device. The user can review the plan and make any necessary modifications. This operation includes editing functions on a front-end interface (e.g., a web application or a mobile app).

[0937] Step 7:

[0938] The user sends the revised information back to the server. The device sends the revised data to the server using the HTTPS protocol. This data transmission causes the revised plan to be saved in the database again.

[0939] Step 8:

[0940] The server stores the final childcare plan and maintenance plan in a database and provides them to the parent portal and audit system when required. This process involves retrieving the output from the database in an appropriate format and providing it through a specific user interface or API.

[0941] The above steps allow for an efficient execution of a series of processes, from data entry to analysis, plan generation, feedback resubmission, and final data storage and provision.

[0942] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0943] This invention relates to a system that reduces the amount of paperwork that childcare workers do in their daily work and improves the quality of childcare. This system has the function of allowing users to input information about the children's behavior and the atmosphere of the class using a terminal, sending the data to a server, saving it, and analyzing it, and automatically generating optimal childcare plans and growth records. In addition, by combining it with an emotion engine that recognizes the user's emotions, it is possible to create more personalized childcare plans.

[0944] Program processing

[0945] 1. Information input and emotion recognition

[0946] Device: The user logs in to the device and accesses a screen where they can enter information about their child's behavior and the atmosphere in the class. For example, they might enter, "Today, A-kun was playing with blocks during free play."

[0947] Emotion engine: Analyzes the user's emotions when they input. For example, recognize the user's stress or satisfaction level from voice input or text analysis.

[0948] 2. Data transmission and storage

[0949] Terminal: The input information and sentiment analysis results are sent to the server using the HTTPS protocol.

[0950] Server: The received information is stored in a database, including the timestamp, the entered user ID, and the sentiment analysis results.

[0951] 3. Data analysis and automatic generation of childcare plans

[0952] Server: Retrieves stored information from the database and analyzes it with a generative AI model.

[0953] Generative AI model: Refers to childcare guidelines and automatically generates an optimal childcare plan based on the analysis results. The analysis results of the emotion engine are also taken into account to adjust the contents of the plan. For example, if the user is feeling stressed, the activities will be adjusted to reduce those emotions.

[0954] 4. Send and review the generated documents

[0955] Server: Sends the generated childcare plan and growth record to the terminal.

[0956] Terminal: The user checks this and makes corrections as necessary. For example, they may make corrections such as "increase free play time and incorporate cooperative activities."

[0957] 5. Feedback and Resave

[0958] Terminal: The user sends the modified information back to the server.

[0959] Server: The corrected information is restored to the database. This restored information is used for audits and parental reporting.

[0960] 6. Database updates and audit support

[0961] Server: The final childcare plan and growth record are saved in a database. If necessary, the server provides them to the parent portal or audit system. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[0962] Example

[0963] As a concrete example, suppose a nursery teacher inputs into their device, "During free play today, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The emotion engine analyzes the emotion (e.g., satisfaction or stress) felt by the user when they entered the information and adds it to the data. The generative AI model analyzes the information and, referring to the childcare guidelines, determines that "A-kun is interested in cooperating with other children." As a result, the nursery school plan includes the following: "Next week, we will increase group cooperative play as an activity for A-kun." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary revisions.

[0964] In this way, this system can significantly reduce the workload of childcare workers and provide high-quality childcare plans. In addition, by taking the user's emotions into consideration, it can realize more personalized plans and provide a better environment for both childcare workers and children.

[0965] The processing flow will be explained below.

[0966] Step 1:

[0967] User input and emotion recognition

[0968] The user (childcare worker) logs in to the device and inputs the behavior of the children they observed and the atmosphere of the class. The emotion engine also analyzes the user's voice and input speed to recognize the user's emotions. For example, if a user inputs "Mr. A was playing with blocks during free play today," the emotion engine simultaneously detects the teacher's level of satisfaction.

[0969] Step 2:

[0970] Sending data and adding emotion data

[0971] The device sends the input information and recognized emotional data, including the stress level and satisfaction level of the childcare worker, to the server using the HTTPS protocol.

[0972] Step 3:

[0973] Data storage

[0974] The server stores the received information in a database. The stored data includes a timestamp, the entered user ID, and the results of sentiment analysis. For example, it might be stored as "2023-10-05 14:30:00, A-kun was playing with blocks during free play, and the childcare worker was very satisfied."

[0975] Step 4:

[0976] Data Acquisition

[0977] The server retrieves the stored information from the database, including past behavioral data and classroom situation data to use the necessary information for analysis, including emotional data.

[0978] Step 5:

[0979] Data analysis

[0980] The server analyzes the data using a generative AI model. Data from the emotion engine is also incorporated into the analysis process. The result is that "Kid A is interested in cooperating with other children," and the conclusion is that "the nursery teachers are highly satisfied with the cooperative play activities."

[0981] Step 6:

[0982] Generate childcare plans

[0983] The generative AI model automatically generates an optimal childcare plan based on the analysis results and childcare teaching guidelines. The content of the plan is further adjusted based on emotional data. For example, it might state, "Next week, we will plan an activity for A-kun that incorporates cooperative play. We will also specifically incorporate activities that will give childcare workers a high level of satisfaction."

[0984] Step 7:

[0985] Sending generated documents

[0986] The server sends the generated childcare plan to the terminal, where the user can check it.

[0987] Step 8:

[0988] User verification and correction

[0989] The user can check the generated childcare plan on the device and make any necessary changes, such as "increase free play time and incorporate cooperative activities."

[0990] Step 9:

[0991] Sending correction data

[0992] The terminal sends the corrected information back to the server, which receives the corrected data and updates the database.

[0993] Step 10:

[0994] Save the final data

[0995] The server stores the final revised childcare plan and progress records in a database, which is used for audits and reporting to parents.

[0996] Step 11:

[0997] Audit and Reporting

[0998] The server then provides the final data stored in the database to a parent portal or audit system as needed, allowing parents to check, for example, "A-kun's growth record."

[0999] Through this series of steps, the system reduces the workload of childcare workers and provides more personalized childcare plans using the emotion engine, thereby improving the quality of childcare and childcare worker satisfaction.

[1000] Example 2

[1001] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1002] To improve the quality of childcare, it is necessary to provide a system that allows childcare workers to create childcare plans and growth records effectively and efficiently without being overwhelmed by the large amount of paperwork in their daily work. There is also a challenge in creating more personalized childcare plans by taking into account the emotional state of childcare workers.

[1003] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes emotion recognition means for analyzing input data and recognizing the user's emotion, means for transmitting the input data and the recognized emotion data to the server and storing them in a database, and generation AI model means for analyzing the stored data and generating an optimal childcare plan by referring to the childcare teaching guidelines. This makes it possible to recognize and analyze emotions from data input by childcare workers and automatically generate a more personalized childcare plan efficiently based on the results.

[1004] "Users" refer to childcare workers and educators who operate the devices to input information about the children's behavior and the atmosphere in the class.

[1005] "Terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[1006] "Emotion recognition" refers to the process of analyzing data entered by a user, as well as the voice and facial expressions used when entering data, to identify the user's emotional state.

[1007] "Database" refers to a data storage system where the server stores user input data and analysis results.

[1008] "Server" refers to a computer system that receives, stores, and analyzes input data and transmits the generated childcare plan to the user.

[1009] "Generative AI model" refers to an artificial intelligence model that automatically generates optimal childcare plans based on childcare guidelines, stored data, and emotion analysis results.

[1010] "Childcare plan" refers to a document that details the education and activities of children.

[1011] A "growth record" refers to a document that records in detail the development and growth process of children.

[1012] A "parent portal" refers to a web system that allows parents to view their children's growth records and childcare plans online.

[1013] "Audit system" refers to a system for auditing the contents of childcare plans and growth records.

[1014] This invention relates to a system that reduces the amount of paperwork that childcare workers do in their daily work and improves the quality of childcare. This system has the function of allowing users to input information about the children's behavior and the atmosphere in the class using a terminal, sending the data to a server, saving it, and analyzing it, and automatically generating optimal childcare plans and growth records. Furthermore, by combining it with an emotion recognition engine that recognizes the user's emotions, it is possible to create more personalized childcare plans.

[1015] The specific hardware of this system includes the devices used by users (e.g., computers, smartphones, tablets, etc.) and the server that sends, receives, stores, and analyzes data. The software includes an emotion recognition engine, a generative AI model, and a database management system.

[1016] First, the user logs in to the device and launches a dedicated application. The user then accesses a form within the application and enters text about the child's condition and the atmosphere in the class. For example, the user might enter, "During free play today, A-kun enjoyed playing dominoes with B-chan."

[1017] Next, the emotion recognition engine analyzes the user's emotions from their voice and facial expressions. This is done using algorithms that analyze voice input and text. For example, it analyzes keywords such as "happy" to identify a feeling of satisfaction.

[1018] The device then sends the input information and the emotion analysis results to the server using the HTTPS protocol. The server then stores the received data in a database. The stored information includes a timestamp, user ID, input content, and emotion analysis results.

[1019] The server periodically crawls the stored data and sends the information that needs to be analyzed to the generative AI model.

[1020] "The nursery teacher input that A-kun enjoyed playing dominoes with B-chan during free play today. Based on this, please use the emotion recognition engine to analyze the teacher's level of satisfaction and generate an optimal childcare plan based on the childcare teaching guidelines."

[1021] The generative AI model analyzes the prompt sentence and automatically generates an optimal childcare plan by referring to the childcare curriculum guidelines. For example, it might state, "Next week, increase cooperative group play as an activity for A-kun."

[1022] The generated childcare plan is sent from the server to the user's device, where the user can review it and make any necessary changes. Any changes made by the user are sent back to the server and re-saved in the database. This re-saved information is used for audits and reporting to parents.

[1023] Finally, the server stores the final childcare plan and growth records in a database and provides them to a parent portal and audit system, allowing parents to view their child's growth records through a web portal.

[1024] In this way, this system can significantly reduce the workload of childcare workers and provide high-quality childcare plans. In addition, by taking the user's emotions into consideration, it can realize more personalized plans and provide a better environment for both childcare workers and children.

[1025] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1026] Step 1:

[1027] The user logs in to the device and launches the dedicated application. The user accesses the form within the application and enters, for example, "During free play time today, Mr. A enjoyed playing a game of dominoes with Ms. B." This form is made up of text boxes, allowing the user to enter any text they like. Once they have finished entering the information, they click the submit button.

[1028] Input: Text data about children's behavior and classroom atmosphere

[1029] Output: Input data after the submit button is pressed

[1030] Step 2:

[1031] The device receives the input text data and uses an emotion recognition engine to analyze the user's emotions. This analysis includes voice input and facial expression recognition, and can also identify emotional states from text data. For example, it can extract positive and negative keywords and quantify the user's stress level and satisfaction.

[1032] Input: Text data entered by the user

[1033] Output: Emotion analysis results (quantified stress level and satisfaction)

[1034] Step 3:

[1035] The device sends the text data and sentiment analysis results to the server using the HTTPS protocol, where the data is encrypted and securely transmitted, including a timestamp and user ID.

[1036] Input: Text data and sentiment analysis results

[1037] Output: Data sent to the server

[1038] Step 4:

[1039] The server stores the received data in a database, which includes the timestamp, user ID, input content, and sentiment analysis results. Specifically, it inserts the data into the database using an SQL query.

[1040] Input: Submitted text data and sentiment analysis results

[1041] Output: Data stored in the database

[1042] Step 5:

[1043] The server periodically crawls the database to retrieve data that needs to be analyzed. This includes the latest input data and emotion analysis results. The server generates a prompt for the generative AI model based on the retrieved data and sends the data. For example, the prompt might read, "The nursery teacher input that A-kun enjoyed playing dominoes with B-chan during free play today. Based on this, please use the emotion recognition engine to analyze the nursery teacher's level of satisfaction and generate an optimal childcare plan based on the childcare guidance guidelines."

[1044] Input: Latest data retrieved from the database

[1045] Output: Generated prompt statement

[1046] Step 6:

[1047] The generative AI model receives prompts, analyzes the data, and automatically generates an optimal childcare plan while referencing childcare guidelines. For example, based on the data, it generates a suggestion such as "Increase group cooperative play as an activity for A-kun next week." The plan is then generated as text data using specialized algorithms.

[1048] Input: prompt statement

[1049] Output: Generated childcare plan text

[1050] Step 7:

[1051] The server then sends the generated childcare plan to the user's device. The data is sent securely using the HTTPS protocol. This includes the PDF file of the document and text data.

[1052] Input: Generated childcare plan text

[1053] Output: Childcare plan sent to the user's device

[1054] Step 8:

[1055] The user can review the childcare plan sent on their device and make any necessary changes. They can use a text editor to make changes such as "increase free play time and incorporate cooperative activities." Once the changes are complete, they can click the resend button.

[1056] Input: Generated childcare plan

[1057] Output: Revised childcare plan

[1058] Step 9:

[1059] The device then sends the revised childcare plan back to the server, again using the HTTPS protocol, so the data is transmitted securely.

[1060] Input: Revised Childcare Plan

[1061] Output: Corrected data sent to the server

[1062] Step 10:

[1063] The server then re-stores the revised childcare plan in the database. Storing both the original and revised data is useful for future audits and verification. Specifically, the server uses SQL queries to update the data.

[1064] Input: Correction data sent to the server

[1065] Output: Corrected data stored in the database

[1066] Step 11:

[1067] The server stores the final childcare plan and progress records in a database and provides them when needed. This stored data is used for audits and reporting to parents, and is also provided to the parent portal and audit system.

[1068] Input: Final childcare plan and growth record

[1069] Output: Final data stored in the database

[1070] (Application example 2)

[1071] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1072] In the past, improving the work efficiency of factory robots required a lot of clerical work, placing a heavy burden on operators. It was also difficult to generate optimal work plans and maintenance schedules that took into account the robot's operating status and the operator's emotional state. Given these circumstances, there is a need for an efficient and flexible management system that reduces the burden on operators.

[1073] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input the robot's operation and work environment observed using a terminal; means for transmitting the input data to the server and for the server to store it in a database; means including an emotion recognition engine that recognizes the user's emotion from voice input and text analysis; means for transmitting the user's emotion analysis results together with the data to the server; means for the generative AI model to adjust the optimal work plan taking into account the emotion analysis results; means for transmitting the generated work plan and maintenance schedule to the user's terminal so that the user can check and modify them; and means for transmitting the user's feedback and modifications back to the server and storing them in a database. This reduces the operator's workload and enables the generation of an optimal work plan that takes into account the robot's operation status and the operator's emotions.

[1074] A "terminal" is a device or equipment operated by a user, and is used to input information about the robot's operation and working environment.

[1075] An "emotion recognition engine" is a technology that analyzes a user's emotions through voice input and text analysis and obtains the results.

[1076] A "generative AI model" is an artificial intelligence algorithm used to analyze data and automatically generate optimal work plans and maintenance schedules.

[1077] A "work plan" is a document that describes plans for the operation and maintenance schedule of factory robots, and is created by a generative AI model.

[1078] A "maintenance schedule" is a schedule that shows a plan for regular maintenance, inspection, and repair of factory robots.

[1079] A "database" is a system that systematically stores data received by a server so that it can be analyzed and searched as needed.

[1080] "User" refers to an operator or manager who manages and operates a factory robot, and is a person who uses a terminal to input, check, and modify information.

[1081] "Server" means a computer system that receives, stores, and analyzes data sent by users.

[1082] "Feedback" refers to opinions and corrections provided by users after using the system, and is information that is re-stored in the database.

[1083] The present invention relates to a system for managing factory robots and improving work efficiency. How to implement this system will be described below in detail.

[1084] First, the factory robot operator (user) inputs information about the robot's operating status and work environment using a terminal (e.g., a smartphone or head-mounted display). This terminal is equipped with an emotion recognition engine and can recognize the user's emotions through voice input and text analysis, and obtain analysis results.

[1085] The device then transmits the input information and sentiment analysis results to a server, which stores the received data in a database and later analyzes it using a generative AI model.

[1086] The generative AI model used by the server automatically generates optimal work plans and maintenance schedules based on input data and the results of emotion analysis, and adjusts the plans by taking into account the operating status of factory robots and the emotional state of operators.

[1087] The generated work plans and maintenance schedules are sent from the server to the terminal, where the user can review them and make any necessary corrections. The user's feedback and corrections are then sent back to the server and saved in the database. This process reduces the operator's workload and enables efficient and flexible management.

[1088] As a concrete example, consider the case where an operator inputs into a terminal, "Robot A was operating normally on the assembly line today." This information is sent to the server and stored in a database. The emotion recognition engine analyzes the operator's emotions at the time of input, such as satisfaction, and adds the results to the data. The generative AI model analyzes this data and determines that "the next maintenance will be on schedule and there will be no problems." This plan is then sent from the server to the operator's terminal, where the operator confirms it.

[1089] Here are some example prompts that the generative AI model uses to parse:

[1090] Please analyze the following robot status and generate an optimal maintenance schedule:

[1091] Robot A was running fine in assembly line today.

[1092] This system will enable the generation of optimal work plans that take into account the operating status of factory robots and the emotions of operators, which will likely significantly reduce the workload of operators.

[1093] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1094] Step 1:

[1095] The user uses a terminal to input information about the robot's operation and working environment. For example, the user might input "Robot A was operating normally on the assembly line today." At this time, the emotion recognition engine operates through voice input or text to recognize the user's emotions. The input data includes the "robot's operation status" and the user's "voice" or "text." The emotion recognition engine analyzes this input data and generates emotion analysis results.

[1096] Step 2:

[1097] The terminal sends the input information and the emotion analysis results to the server. The input data here is the "robot's operating status" and the "emotion analysis results." Secure data transfer is achieved by sending this to the server using the HTTPS protocol. The server then stores the received data in a database.

[1098] Step 3:

[1099] The server retrieves the information stored in the database and analyzes it using the generative AI model. The input data is the data stored in the database. The generative AI model generates optimal work plans and maintenance schedules based on the robot's operating status and the results of user emotion analysis. For example, as a result of the analysis, a work plan may be generated that states, "There will be no problem if the next maintenance is performed according to the regular schedule."

[1100] Step 4:

[1101] The server sends the generated work plan and maintenance schedule to the terminal. Here, the input data is the "work plan" generated by the generative AI model. The server sends this work plan to the terminal so that the user can check it. The terminal displays the received data, and the user can check the contents.

[1102] Step 5:

[1103] The user checks the work plan and maintenance schedule and makes any necessary corrections. For example, they input specific corrections such as "changing the date and time of the next maintenance." The corrected data is sent again from the terminal to the server. At this time, the "corrected work plan" is sent to the server as input data.

[1104] Step 6:

[1105] The server saves the revised information in a database and updates the final work plan and maintenance schedule. The input data is the "revised work plan." This database update maintains an accurate record that can later be used for audits and further analysis.

[1106] Based on the above processing steps, it is possible to generate optimal work plans that take into account the operating status of factory robots and the emotions of operators. This system reduces the workload of operators and enables flexible and efficient factory management.

[1107] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1109] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1110] [Fourth embodiment]

[1111] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1112] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1114] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1115] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1118] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1119] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1120] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1122] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1123] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1124] This invention relates to a system for reducing the amount of paperwork that childcare workers perform in their daily work and improving the quality of childcare. This system features a user using a terminal to input information about the children's behavior and the atmosphere in the class, which is then sent to a server, where it is saved and analyzed, and childcare plans and growth records are automatically generated.

[1125] Program processing

[1126] 1. Enter information

[1127] Device: The user inputs information about the child's behavior and the atmosphere in the classroom via a device (PC, tablet, smartphone, etc.). For example, they can input information such as, "Today, A-kun was playing with blocks during free play."

[1128] 2. Data transmission and storage

[1129] Terminal: The entered information is sent to the server using the HTTPS protocol.

[1130] Server: Stores the received information in a database, including a timestamp and the entered user ID.

[1131] 3. Data analysis and automatic generation of childcare plans

[1132] Server: Retrieves stored information from the database and analyzes it with a generative AI model. For example, by referencing past behavioral data and classroom situation data, it determines that "A-kun is interested in cooperating with other children."

[1133] Generative AI model: Refers to childcare guidelines and automatically generates optimal childcare plans based on the analysis results. For example, it creates a childcare plan such as, "Next week, plan an activity that incorporates cooperative play for A-kun."

[1134] 4. Send and review the generated documents

[1135] Server: Sends the generated childcare plan and growth record to the terminal.

[1136] Terminal: The user checks the document through the terminal and makes corrections as necessary. At this stage, the user can make corrections such as "increase free play time and incorporate cooperative activities."

[1137] 5. Feedback and Resave

[1138] Terminal: The user sends the modified information back to the server.

[1139] Server: The corrected information is restored to the database. This restored information is used for audits and parental reporting.

[1140] 6. Database updates and audit support

[1141] Server: The final childcare plan and growth record are saved in a database and provided to the parent portal and audit system as needed. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[1142] Example

[1143] As a concrete example, suppose a nursery teacher inputs into their device, "Today during free play, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The generative AI model analyzes the information, references the childcare guidelines, and concludes that "A-kun is suited to activities that strengthen group activities." As a result, the nursery school plan includes the following: "Next week, A-kun's activities will include increasing cooperative play in groups." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary adjustments.

[1144] In this way, this system can significantly reduce the workload of childcare workers and improve the quality of childcare planning.

[1145] The processing flow will be explained below.

[1146] Step 1:

[1147] User input of information

[1148] The user logs in to the device and accesses a screen where they can input the observed behavior of the children and the atmosphere of the class. For example, they can input, "Today, A-kun was playing with blocks during free play."

[1149] The terminal prepares the entered information to be sent to the server in an appropriate format.

[1150] Step 2:

[1151] Sending data

[1152] The device securely transmits the entered information to the server using the HTTPS protocol, which receives the data and prepares it for storage in a database.

[1153] Step 3:

[1154] Data storage

[1155] The server stores the received information in a database. The stored data includes metadata such as a timestamp and user ID. For example, it might be stored in the format "2023-10-05 14:30:00, A-kun was playing with blocks during free play time."

[1156] Step 4:

[1157] Data Acquisition

[1158] The server retrieves the latest information and historical trend data from the database, which is used in the subsequent analysis process.

[1159] Step 5:

[1160] Data analysis

[1161] The server uses a generative AI model to analyze the accumulated data. For example, by referencing past behavioral data and classroom situation data, it can draw the conclusion that "Kid A is interested in collaborating with other children."

[1162] Step 6:

[1163] Generate childcare plans

[1164] The generative AI model automatically creates an optimal childcare plan based on the analysis results and references childcare guidelines. For example, it might generate a childcare plan that states, "For A-kun, plan an activity that incorporates cooperative play next week."

[1165] Step 7:

[1166] Sending generated documents

[1167] The server sends the generated childcare plan to the terminal, where the user can check it.

[1168] Step 8:

[1169] User verification and correction

[1170] The user can check the generated childcare plan on the device and make any necessary changes, such as "increase free play time and incorporate cooperative activities."

[1171] Step 9:

[1172] Sending correction data

[1173] The terminal sends the corrected information back to the server, which receives the corrected data and updates the database.

[1174] Step 10:

[1175] Save the final data

[1176] The server stores the final revised childcare plan and progress records in a database, which is used for audits and reporting to parents.

[1177] Step 11:

[1178] Audit and Reporting

[1179] The server then provides the final data stored in the database to a parent portal or audit system as needed, allowing parents to check, for example, "A-kun's growth record."

[1180] Through these steps, the system can reduce the workload of childcare workers and provide high-quality childcare plans.

[1181] Example 1

[1182] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1183] Today's childcare workers are overwhelmed with a large amount of administrative work, such as observing children, creating childcare plans, and reporting to parents, which hinders the improvement of childcare quality. There is a need to reduce this enormous workload and allow childcare workers to devote more time to actual childcare activities. It is also important to streamline the process of creating childcare plans and growth records and improve their accuracy and consistency.

[1184] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1185] In this invention, the server includes a means for a user to input observations of children's behavior and the atmosphere of the class using a terminal, a means for transmitting the input data to the server using HTTPS protocol and for the server to store it in a database, a means for the server to analyze the stored data using a generative AI model and generate an optimal childcare plan by referring to the childcare teaching guidelines, and a means for transmitting the generated childcare plan and growth record in PDF or Word format to the user's terminal so that the user can check and modify them. This reduces the workload of childcare workers and improves the quality of childcare plans.

[1186] "Users" refers to nursery teachers and other childcare staff who use the system to input information about children's behavior and the atmosphere in the classroom.

[1187] "Terminal" refers to electronic devices used by users, such as computers, tablets, and smartphones.

[1188] "Observation" refers to the process of carefully watching and recording children's behavior and the atmosphere in the classroom.

[1189] "Classroom atmosphere" refers to the general situation and environment within the classroom at a particular time.

[1190] "Means of input" refers to the method of registering observed data into the system using a terminal.

[1191] The "HTTPS protocol" refers to a communications protocol for securely transmitting data over the Internet.

[1192] "Server" refers to a central processing unit for receiving, storing and analyzing data sent by users.

[1193] "Database" refers to a system that manages various data such as observation data, childcare plans, and growth records stored on a server.

[1194] A "generative AI model" refers to an artificial intelligence model that automatically generates childcare plans and growth records based on childcare guidelines and past data.

[1195] "Childcare guidelines" refers to documents that set out standards and guidelines regarding childcare activities and children's development.

[1196] A "childcare plan" is a document that includes a specific plan of activities to support children's growth and development.

[1197] A "growth record" refers to a document that records children's developmental process and learning progress.

[1198] "PDF and Word formats" refer to file formats for saving and transferring childcare plans and growth records.

[1199] "Means for review and correction" refers to a method by which a user can review the submitted childcare plan or growth record and change the contents as necessary.

[1200] This invention relates to a system for reducing the amount of paperwork that childcare workers have to do in their daily work and improving the quality of childcare. In this system, users use their terminals to input information about the children's behavior and the atmosphere in the class, and the data is sent to a server, where it is saved and analyzed, and childcare plans and growth records are automatically generated.

[1201] First, the user uses a device (PC, tablet, smartphone, etc.) to input details about the children's behavior and the atmosphere in the class. For example, they can input information such as "Mr. A was playing with blocks during free play today" or "Today, outdoor play time was canceled due to rain." This information is encrypted using the HTTPS protocol and sent to the server after ensuring security.

[1202] The server then stores the received information in a database, including the child's behavior, timestamps, and the user ID entered, for later analysis and auditing.

[1203] The server retrieves the saved data from the database and passes it to the generative AI model. This generative AI model has been trained in advance on childcare guidelines and past data, and analyzes the input data. For example, the generative AI model might determine that "A-kun often actively interacts with other children," and generate a specific childcare plan such as, "Next week, plan childcare activities that include a lot of cooperative play."

[1204] The generated childcare plan and growth record are converted into PDF or Word format and sent from the server to the user's device. The user can review the documents on their device and make any necessary changes. For example, they can make a change such as "extending free play time from 30 minutes to 45 minutes."

[1205] Any information corrected by the user is sent back to the server and the corrected information is restored to the database for future audits and parental reporting.

[1206] Finally, the server stores the final childcare plan and growth record in a database and provides them to the parent portal and audit system. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[1207] As a concrete example, suppose a nursery teacher inputs into their device, "Today during free playtime, A-kun enjoyed playing dominoes with B-chan." This information is encrypted and sent to the server, where it is stored in a database. The generative AI model analyzes the input data and, referring to the childcare guidelines, concludes that "activities that strengthen group activities are appropriate for A-kun." As a result, the nursery school plan will state, "Next week, activities for A-kun will involve increasing group cooperative play." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary adjustments.

[1208] An example of a prompt for the generative AI model would be, "Today, during free play, A-kun cooperated with the other children and enjoyed playing a domino game. Based on this data, please suggest a childcare plan for A-kun next week."

[1209] In this way, this system can significantly reduce the workload of childcare workers and improve the quality of childcare planning.

[1210] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1211] Step 1:

[1212] Enter information

[1213] User: The user uses a device to input details about the children's behavior and the atmosphere in the class. For example, they can input information such as, "Today, A-kun was playing with blocks during free play."

[1214] Input: Child behavior information, class atmosphere, timestamp, user ID.

[1215] Output: The input data is stored in the terminal.

[1216] Step 2:

[1217] Sending data

[1218] Terminal: Data entered by the user is encrypted using the HTTPS protocol and sent to the server.

[1219] Input: Data stored on the device.

[1220] Output: The encrypted data is sent to the server using the HTTPS protocol.

[1221] Step 3:

[1222] Data storage

[1223] Server: Receives the encrypted data and stores it in a database, along with the timestamp, user ID, data type, etc.

[1224] Input: Encrypted data sent over the HTTPS protocol.

[1225] Output: Structured data stored in a database.

[1226] Step 4:

[1227] Data analysis

[1228] Server: Retrieves the stored data from the database and passes it to the generative AI model, which has already learned the childcare curriculum and past data.

[1229] Input: Observation data retrieved from the database.

[1230] Output: The analysis results produced by the generative AI model.

[1231] Step 5:

[1232] Automatic generation of childcare plans

[1233] Generative AI model: Analyzes observation data and generates optimal childcare plans based on childcare guidelines. For example, based on the analysis result that "A-kun often actively interacts with other children," it creates a specific childcare plan such as "Plan childcare activities next week that include a lot of cooperative play."

[1234] Input: Analysis results, childcare guidelines.

[1235] Output: The generated childcare plan.

[1236] Step 6:

[1237] Submitting a childcare plan

[1238] Server: The generated childcare plan and growth record are sent to the user's device, where the file format is converted to PDF or Word document format.

[1239] Input: The generated childcare plan.

[1240] Output: The childcare plan sent to the user's device in PDF or Word format.

[1241] Step 7:

[1242] Check and correct

[1243] User: Checks the childcare plan and growth record sent via the device and makes any necessary changes. For example, the user can make specific changes such as extending free play time from 30 minutes to 45 minutes.

[1244] Input: User feedback and corrections.

[1245] Output: Revised childcare plan.

[1246] Step 8:

[1247] Resend and save corrected data

[1248] Terminal: The user sends the modified information back to the server.

[1249] Server: The resent modified data is saved back into the database.

[1250] Enter: Revised Child Care Plan.

[1251] Output: The modified data resaved to the database.

[1252] Step 9:

[1253] Database updates and audit support

[1254] Server: Stores the final childcare plan and growth records in a database and provides them to the parent portal and audit system as needed.

[1255] Input: Final childcare plan, growth record.

[1256] Output: Final data provided to parent portal and audit systems.

[1257] (Application example 1)

[1258] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1259] In childcare operations and factory operation management, large amounts of data entry and management work are required, which has led to problems with reduced work efficiency. In addition, data analysis takes time, making it difficult to create appropriate childcare and maintenance plans. In particular, creating plans and records manually takes time and effort, and is prone to human error.

[1260] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1261] In this invention, the server

[1262] 1. A means for users to input observations of children's behavior and the atmosphere of the class using a device;

[1263] 2. A means for transmitting the input data to the server and for the server to store it in a database;

[1264] 3. A means for the server to analyze the stored data and generate an optimal childcare plan by referring to the childcare teaching guidelines;

[1265] 4. A means for transmitting the generated childcare plan to the user's terminal so that the user can check and modify it;

[1266] 5. A means for a user to input the observed operating status and maintenance notes of factory equipment using a terminal;

[1267] 6. A means for the server to analyze the operational status data based on the input information and generate a preventive maintenance plan;

[1268] 7. A means for transmitting the generated preventive maintenance plan to a user's terminal so that the user can check and modify it;

[1269] This automates the large amount of data entry and analysis work involved in childcare operations and factory management, making it possible to quickly and efficiently generate optimal plans and maintenance plans.

[1270] "User terminals" refer to machines, such as computers, tablets, and smartphones, used by childcare workers and factory workers.

[1271] "Means for inputting observed behavior of children and classroom atmosphere" refers to an interface or software that allows childcare workers to input children's behavior and classroom conditions into a terminal.

[1272] "Means for transmitting data to a server and having the server store it in a database" refers to the process and device for transmitting information entered from a terminal to a server via a network, and for the server to record it in a database.

[1273] The "means of analyzing data and generating a childcare plan by referring to the childcare curriculum guidelines" refers to the process and program in which the server analyzes the information in the database using machine learning algorithms and generative AI models, and automatically creates an educational plan based on childcare standards.

[1274] "Means for sending the generated childcare plan to the user's terminal for confirmation and modification" refers to software or an interface that sends the childcare plan created by the server back to the terminal and allows the childcare worker to confirm and edit its contents.

[1275] The "means for inputting the operating status and maintenance notes of observed factory equipment" refers to an interface or software that allows factory workers to input information about the operating status and maintenance inspection of equipment into a terminal.

[1276] The "means for analyzing operational status data and generating preventive maintenance plans" refers to a process and program that analyzes operational data collected by a server, predicts future maintenance needs using machine learning algorithms, and automatically generates specific work plans.

[1277] "Means for sending the generated preventive maintenance plan to the user's terminal for confirmation and modification" refers to software or an interface that sends the maintenance plan created by the server to the terminal and allows factory workers to confirm and edit its contents.

[1278] This invention is a system that reduces the user's workload and efficiently generates childcare plans and maintenance plans. This system consists of a series of processes in which the user inputs observation data using a terminal, the server receives and analyzes the data, generates an optimal plan, and sends it back to the terminal.

[1279] Specifically, nursery teachers use their devices to input information about the children's behavior and the atmosphere in the classroom. For example, they input information such as, "During free play today, A-kun and B-chan were enjoying a game of dominoes." This information is sent to the server using the HTTPS protocol. The server then stores the information in a database. The stored information also includes a timestamp and the user ID that was entered.

[1280] Next, the server retrieves the stored information from the database and analyzes it using a generative AI model. For example, by referencing past behavioral data and classroom situation data, it may determine that "Mr. A is interested in cooperating with other children." The generative AI model then references childcare guidelines and automatically generates an optimal childcare plan based on the analysis results. For example, it creates a childcare plan such as, "Next week, plan an activity for Mr. A that incorporates cooperative play."

[1281] The generated childcare plan is sent from the server to the terminal, where the user can review the document and make any necessary corrections. For example, a correction could be to "increase free play time and incorporate cooperative activities." This corrected information is sent back to the server and re-stored in the database. This re-stored information is used for audits and reports to parents. It can also be provided to parent portals and auditing systems.

[1282] Meanwhile, factory workers use their terminals to input the observed operating status of factory equipment and maintenance notes. For example, they might input "Robot_1 was operating normally today." This information is also sent to the server using the HTTPS protocol. The server stores the information in a database and analyzes the operating status data to generate a preventive maintenance plan. For example, the operating status data can be used to predict future maintenance needs, generating a plan such as "Robot_1 requires maintenance next week." This plan is then sent from the server to the terminal, where the factory worker can review it and make any necessary adjustments.

[1283] Specific hardware used includes PCs, tablets, smartphones, etc. Software used includes Python, Flask, Pandas, Scikit-learn, Requests, etc. By combining these, users can efficiently input data, and the generative AI model can automatically generate, revise, and manage optimal plans based on that data.

[1284] As a concrete example, a user inputs into a device, "Today during free playtime, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The generative AI model analyzes this information and concludes that "A-kun is interested in cooperating with other children." The childcare plan then includes the following: "Next week, we will increase group cooperative play as an activity for A-kun." This plan is then sent from the server to the device, where the user can review it and make any necessary adjustments.

[1285] Example prompt sentence:

[1286] "Robot_1 was running fine today. What should next week's preventive maintenance include?"

[1287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1288] Step 1:

[1289] Users use their devices to input observations of children's behavior, the atmosphere in the classroom, or the operating status of factory equipment and maintenance notes. For example, consider the case where a nursery teacher inputs, "During free play today, A-kun and B-chan were enjoying a game of dominoes." A timestamp and user ID are automatically assigned to the input data.

[1290] Step 2:

[1291] The device sends the input data to the server using the HTTPS protocol. This communication ensures security and protects the integrity of the data. The input data consists of information such as the observation, a timestamp, and a user ID.

[1292] Step 3:

[1293] The server saves the received data to the database. This save operation converts the data format (e.g., from JSON to table format) and stores it in the database in the appropriate format. The saved data is used in the subsequent analysis process.

[1294] Step 4:

[1295] The server retrieves the stored information from the database and analyzes it using a generative AI model. Specifically, it combines past behavioral data and classroom situation data to create the next childcare plan while referring to the childcare curriculum. This analysis involves data frame manipulation using Pandas and machine learning models using Scikit-learn.

[1296] Step 5:

[1297] The server's AI model automatically generates an optimal childcare plan based on the analysis results. For example, it creates a childcare plan such as, "For A-kun, plan an activity that incorporates cooperative play next week." This generation process references childcare instruction standards and past data, and automatically generates the plan using a Python script.

[1298] Step 6:

[1299] The server sends the generated care plan or maintenance plan to the device. The user can review the plan and make any necessary modifications. This operation includes editing functions on a front-end interface (e.g., a web application or a mobile app).

[1300] Step 7:

[1301] The user sends the revised information back to the server. The device sends the revised data to the server using the HTTPS protocol. This data transmission causes the revised plan to be saved in the database again.

[1302] Step 8:

[1303] The server stores the final childcare plan and maintenance plan in a database and provides them to the parent portal and audit system when required. This process involves retrieving the output from the database in an appropriate format and providing it through a specific user interface or API.

[1304] The above steps allow for an efficient execution of a series of processes, from data entry to analysis, plan generation, feedback resubmission, and final data storage and provision.

[1305] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1306] This invention relates to a system that reduces the amount of paperwork that childcare workers do in their daily work and improves the quality of childcare. This system has the function of allowing users to input information about the children's behavior and the atmosphere of the class using a terminal, sending the data to a server, saving it, and analyzing it, and automatically generating optimal childcare plans and growth records. In addition, by combining it with an emotion engine that recognizes the user's emotions, it is possible to create more personalized childcare plans.

[1307] Program processing

[1308] 1. Information input and emotion recognition

[1309] Device: The user logs in to the device and accesses a screen where they can enter information about their child's behavior and the atmosphere in the class. For example, they might enter, "Today, A-kun was playing with blocks during free play."

[1310] Emotion engine: Analyzes the user's emotions when they input. For example, recognize the user's stress or satisfaction level from voice input or text analysis.

[1311] 2. Data transmission and storage

[1312] Terminal: The input information and sentiment analysis results are sent to the server using the HTTPS protocol.

[1313] Server: The received information is stored in a database, including the timestamp, the entered user ID, and the sentiment analysis results.

[1314] 3. Data analysis and automatic generation of childcare plans

[1315] Server: Retrieves stored information from the database and analyzes it with a generative AI model.

[1316] Generative AI model: Refers to childcare guidelines and automatically generates an optimal childcare plan based on the analysis results. The analysis results of the emotion engine are also taken into account to adjust the contents of the plan. For example, if the user is feeling stressed, the activities will be adjusted to reduce those emotions.

[1317] 4. Send and review the generated documents

[1318] Server: Sends the generated childcare plan and growth record to the terminal.

[1319] Terminal: The user checks this and makes corrections as necessary. For example, they may make corrections such as "increase free play time and incorporate cooperative activities."

[1320] 5. Feedback and Resave

[1321] Terminal: The user sends the modified information back to the server.

[1322] Server: The corrected information is restored to the database. This restored information is used for audits and parental reporting.

[1323] 6. Database updates and audit support

[1324] Server: The final childcare plan and growth record are saved in a database. If necessary, the server provides them to the parent portal or audit system. For example, "A-kun's growth record" is displayed on the parent portal so that parents can check it.

[1325] Example

[1326] As a concrete example, suppose a nursery teacher inputs into their device, "During free play today, A-kun enjoyed playing dominoes with B-chan." This information is sent to the server and stored in a database. The emotion engine analyzes the emotion (e.g., satisfaction or stress) felt by the user when they entered the information and adds it to the data. The generative AI model analyzes the information and, referring to the childcare guidelines, determines that "A-kun is interested in cooperating with other children." As a result, the nursery school plan includes the following: "Next week, we will increase group cooperative play as an activity for A-kun." This plan is then sent from the server to the nursery school teacher's device, who reviews it and makes any necessary revisions.

[1327] In this way, this system can significantly reduce the workload of childcare workers and provide high-quality childcare plans. In addition, by taking the user's emotions into consideration, it can realize more personalized plans and provide a better environment for both childcare workers and children.

[1328] The processing flow will be explained below.

[1329] Step 1:

[1330] User input and emotion recognition

[1331] The user (childcare worker) logs in to the device and inputs the behavior of the children they observed and the atmosphere of the class. The emotion engine also analyzes the user's voice and input speed to recognize the user's emotions. For example, if a user inputs "Mr. A was playing with blocks during free play today," the emotion engine simultaneously detects the teacher's level of satisfaction.

[1332] Step 2:

[1333] Sending data and adding emotion data

[1334] The device sends the input information and recognized emotional data, including the stress level and satisfaction level of the childcare worker, to the server using the HTTPS protocol.

[1335] Step 3:

[1336] Data storage

[1337] The server stores the received information in a database. The stored data includes a timestamp, the entered user ID, and the results of sentiment analysis. For example, it might be stored as "2023-10-05 14:30:00, A-kun was playing with blocks during free play, and the childcare worker was very satisfied."

[1338] Step 4:

[1339] Data Acquisition

[1340] The server retrieves the stored information from the database, including past behavioral data and classroom situation data to use the necessary information for analysis, including emotional data.

[1341] Step 5:

[1342] Data analysis

[1343] The server analyzes the data using a generative AI model. Data from the emotion engine is also incorporated into the analysis process. The result is that "Kid A is interested in cooperating with other children," and the conclusion is that "the nursery teachers are highly satisfied with the cooperative play activities."

[1344] Step 6:

[1345] Generate childcare plans

[1346] The generative AI model automatically generates an optimal childcare plan based on the analysis results and childcare teaching guidelines. The content of the plan is further adjusted based on emotional data. For example, it might state, "Next week, we will plan an activity for A-kun that incorporates cooperative play. We will also specifically incorporate activities that will give childcare workers a high level of satisfaction."

[1347] Step 7:

[1348] Sending generated documents

[1349] The server sends the generated childcare plan to the terminal, where the user can check it.

[1350] Step 8:

[1351] User verification and correction

[1352] The user can check the generated childcare plan on the device and make any necessary changes, such as "increase free play time and incorporate cooperative activities."

[1353] Step 9:

[1354] Sending correction data

[1355] The terminal sends the corrected information back to the server, which receives the corrected data and updates the database.

[1356] Step 10:

[1357] Save the final data

[1358] The server stores the final revised childcare plan and progress records in a database, which is used for audits and reporting to parents.

[1359] Step 11:

[1360] Audit and Reporting

[1361] The server then provides the final data stored in the database to a parent portal or audit system as needed, allowing parents to check, for example, "A-kun's growth record."

[1362] Through this series of steps, the system reduces the workload of childcare workers and provides more personalized childcare plans using the emotion engine, thereby improving the quality of childcare and childcare worker satisfaction.

[1363] Example 2

[1364] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1365] To improve the quality of childcare, it is necessary to provide a system that allows childcare workers to create childcare plans and growth records effectively and efficiently without being overwhelmed by the large amount of paperwork in their daily work. There is also a challenge in creating more personalized childcare plans by taking into account the emotional state of childcare workers.

[1366] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes emotion recognition means for analyzing input data and recognizing the user's emotion, means for transmitting the input data and the recognized emotion data to the server and storing them in a database, and generation AI model means for analyzing the stored data and generating an optimal childcare plan by referring to the childcare teaching guidelines. This makes it possible to recognize and analyze emotions from data input by childcare workers and automatically generate a more personalized childcare plan efficiently based on the results.

[1367] "Users" refer to childcare workers and educators who operate the devices to input information about the children's behavior and the atmosphere in the class.

[1368] "Terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[1369] "Emotion recognition" refers to the process of analyzing data entered by a user, as well as the voice and facial expressions used when entering data, to identify the user's emotional state.

[1370] "Database" refers to a data storage system where the server stores user input data and analysis results.

[1371] "Server" refers to a computer system that receives, stores, and analyzes input data and transmits the generated childcare plan to the user.

[1372] "Generative AI model" refers to an artificial intelligence model that automatically generates optimal childcare plans based on childcare guidelines, stored data, and emotion analysis results.

[1373] "Childcare plan" refers to a document that details the education and activities of children.

[1374] A "growth record" refers to a document that records in detail the development and growth process of children.

[1375] A "parent portal" refers to a web system that allows parents to view their children's growth records and childcare plans online.

[1376] "Audit system" refers to a system for auditing the contents of childcare plans and growth records.

[1377] This invention relates to a system that reduces the amount of paperwork that childcare workers do in their daily work and improves the quality of childcare. This system has the function of allowing users to input information about the children's behavior and the atmosphere in the class using a terminal, sending the data to a server, saving it, and analyzing it, and automatically generating optimal childcare plans and growth records. Furthermore, by combining it with an emotion recognition engine that recognizes the user's emotions, it is possible to create more personalized childcare plans.

[1378] The specific hardware of this system includes the devices used by users (e.g., computers, smartphones, tablets, etc.) and the server that sends, receives, stores, and analyzes data. The software includes an emotion recognition engine, a generative AI model, and a database management system.

[1379] First, the user logs in to the device and launches a dedicated application. The user then accesses a form within the application and enters text about the child's condition and the atmosphere in the class. For example, the user might enter, "During free play today, A-kun enjoyed playing dominoes with B-chan."

[1380] Next, the emotion recognition engine analyzes the user's emotions from their voice and facial expressions. This is done using algorithms that analyze voice input and text. For example, it analyzes keywords such as "happy" to identify a feeling of satisfaction.

[1381] The device then sends the input information and the emotion analysis results to the server using the HTTPS protocol. The server then stores the received data in a database. The stored information includes a timestamp, user ID, input content, and emotion analysis results.

[1382] The server periodically crawls the stored data and sends the information that needs to be analyzed to the generative AI model.

[1383] "The nursery teacher input that A-kun enjoyed playing dominoes with B-chan during free play today. Based on this, please use the emotion recognition engine to analyze the teacher's level of satisfaction and generate an optimal childcare plan based on the childcare teaching guidelines."

[1384] The generative AI model analyzes the prompt sentence and automatically generates an optimal childcare plan by referring to the childcare curriculum guidelines. For example, it might state, "Next week, increase cooperative group play as an activity for A-kun."

[1385] The generated childcare plan is sent from the server to the user's device, where the user can review it and make any necessary changes. Any changes made by the user are sent back to the server and re-saved in the database. This re-saved information is used for audits and reporting to parents.

[1386] Finally, the server stores the final childcare plan and growth records in a database and provides them to a parent portal and audit system, allowing parents to view their child's growth records through a web portal.

[1387] In this way, this system can significantly reduce the workload of childcare workers and provide high-quality childcare plans. In addition, by taking the user's emotions into consideration, it can realize more personalized plans and provide a better environment for both childcare workers and children.

[1388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1389] Step 1:

[1390] The user logs in to the device and launches the dedicated application. The user accesses the form within the application and enters, for example, "During free play time today, Mr. A enjoyed playing a game of dominoes with Ms. B." This form is made up of text boxes, allowing the user to enter any text they like. Once they have finished entering the information, they click the submit button.

[1391] Input: Text data about children's behavior and classroom atmosphere

[1392] Output: Input data after the submit button is pressed

[1393] Step 2:

[1394] The device receives the input text data and uses an emotion recognition engine to analyze the user's emotions. This analysis includes voice input and facial expression recognition, and can also identify emotional states from text data. For example, it can extract positive and negative keywords and quantify the user's stress level and satisfaction.

[1395] Input: Text data entered by the user

[1396] Output: Emotion analysis results (quantified stress level and satisfaction)

[1397] Step 3:

[1398] The device sends the text data and sentiment analysis results to the server using the HTTPS protocol, where the data is encrypted and securely transmitted, including a timestamp and user ID.

[1399] Input: Text data and sentiment analysis results

[1400] Output: Data sent to the server

[1401] Step 4:

[1402] The server stores the received data in a database, which includes the timestamp, user ID, input content, and sentiment analysis results. Specifically, it inserts the data into the database using an SQL query.

[1403] Input: Submitted text data and sentiment analysis results

[1404] Output: Data stored in the database

[1405] Step 5:

[1406] The server periodically crawls the database to retrieve data that needs to be analyzed. This includes the latest input data and emotion analysis results. The server generates a prompt for the generative AI model based on the retrieved data and sends the data. For example, the prompt might read, "The nursery teacher input that A-kun enjoyed playing dominoes with B-chan during free play today. Based on this, please use the emotion recognition engine to analyze the nursery teacher's level of satisfaction and generate an optimal childcare plan based on the childcare guidance guidelines."

[1407] Input: Latest data retrieved from the database

[1408] Output: Generated prompt statement

[1409] Step 6:

[1410] The generative AI model receives prompts, analyzes the data, and automatically generates an optimal childcare plan while referencing childcare guidelines. For example, based on the data, it generates a suggestion such as "Increase group cooperative play as an activity for A-kun next week." The plan is then generated as text data using specialized algorithms.

[1411] Input: prompt statement

[1412] Output: Generated childcare plan text

[1413] Step 7:

[1414] The server then sends the generated childcare plan to the user's device. The data is sent securely using the HTTPS protocol. This includes the PDF file of the document and text data.

[1415] Input: Generated childcare plan text

[1416] Output: Childcare plan sent to the user's device

[1417] Step 8:

[1418] The user can review the childcare plan sent on their device and make any necessary changes. They can use a text editor to make changes such as "increase free play time and incorporate cooperative activities." Once the changes are complete, they can click the resend button.

[1419] Input: Generated childcare plan

[1420] Output: Revised childcare plan

[1421] Step 9:

[1422] The device then sends the revised childcare plan back to the server, again using the HTTPS protocol, so the data is transmitted securely.

[1423] Input: Revised Childcare Plan

[1424] Output: Corrected data sent to the server

[1425] Step 10:

[1426] The server then re-stores the revised childcare plan in the database. Storing both the original and revised data is useful for future audits and verification. Specifically, the server uses SQL queries to update the data.

[1427] Input: Correction data sent to the server

[1428] Output: Corrected data stored in the database

[1429] Step 11:

[1430] The server stores the final childcare plan and progress records in a database and provides them when needed. This stored data is used for audits and reporting to parents, and is also provided to the parent portal and audit system.

[1431] Input: Final childcare plan and growth record

[1432] Output: Final data stored in the database

[1433] (Application example 2)

[1434] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1435] In the past, improving the work efficiency of factory robots required a lot of clerical work, placing a heavy burden on operators. It was also difficult to generate optimal work plans and maintenance schedules that took into account the robot's operating status and the operator's emotional state. Given these circumstances, there is a need for an efficient and flexible management system that reduces the burden on operators.

[1436] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input the robot's operation and work environment observed using a terminal; means for transmitting the input data to the server and for the server to store it in a database; means including an emotion recognition engine that recognizes the user's emotion from voice input and text analysis; means for transmitting the user's emotion analysis results together with the data to the server; means for the generative AI model to adjust the optimal work plan taking into account the emotion analysis results; means for transmitting the generated work plan and maintenance schedule to the user's terminal so that the user can check and modify them; and means for transmitting the user's feedback and modifications back to the server and storing them in a database. This reduces the operator's workload and enables the generation of an optimal work plan that takes into account the robot's operation status and the operator's emotions.

[1437] A "terminal" is a device or equipment operated by a user, and is used to input information about the robot's operation and working environment.

[1438] An "emotion recognition engine" is a technology that analyzes a user's emotions through voice input and text analysis and obtains the results.

[1439] A "generative AI model" is an artificial intelligence algorithm used to analyze data and automatically generate optimal work plans and maintenance schedules.

[1440] A "work plan" is a document that describes plans for the operation and maintenance schedule of factory robots, and is created by a generative AI model.

[1441] A "maintenance schedule" is a schedule that shows a plan for regular maintenance, inspection, and repair of factory robots.

[1442] A "database" is a system that systematically stores data received by a server so that it can be analyzed and searched as needed.

[1443] "User" refers to an operator or manager who manages and operates a factory robot, and is a person who uses a terminal to input, check, and modify information.

[1444] "Server" means a computer system that receives, stores, and analyzes data sent by users.

[1445] "Feedback" refers to opinions and corrections provided by users after using the system, and is information that is re-stored in the database.

[1446] The present invention relates to a system for managing factory robots and improving work efficiency. How to implement this system will be described below in detail.

[1447] First, the factory robot operator (user) inputs information about the robot's operating status and work environment using a terminal (e.g., a smartphone or head-mounted display). This terminal is equipped with an emotion recognition engine and can recognize the user's emotions through voice input and text analysis, and obtain analysis results.

[1448] The device then transmits the input information and sentiment analysis results to a server, which stores the received data in a database and later analyzes it using a generative AI model.

[1449] The generative AI model used by the server automatically generates optimal work plans and maintenance schedules based on input data and the results of emotion analysis, and adjusts the plans by taking into account the operating status of factory robots and the emotional state of operators.

[1450] The generated work plans and maintenance schedules are sent from the server to the terminal, where the user can review them and make any necessary corrections. The user's feedback and corrections are then sent back to the server and saved in the database. This process reduces the operator's workload and enables efficient and flexible management.

[1451] As a concrete example, consider the case where an operator inputs into a terminal, "Robot A was operating normally on the assembly line today." This information is sent to the server and stored in a database. The emotion recognition engine analyzes the operator's emotions at the time of input, such as satisfaction, and adds the results to the data. The generative AI model analyzes this data and determines that "the next maintenance will be on schedule and there will be no problems." This plan is then sent from the server to the operator's terminal, where the operator confirms it.

[1452] Here are some example prompts that the generative AI model uses to parse:

[1453] Please analyze the following robot status and generate an optimal maintenance schedule:

[1454] Robot A was running fine in assembly line today.

[1455] This system will enable the generation of optimal work plans that take into account the operating status of factory robots and the emotions of operators, which will likely significantly reduce the workload of operators.

[1456] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1457] Step 1:

[1458] The user uses a terminal to input information about the robot's operation and working environment. For example, the user might input "Robot A was operating normally on the assembly line today." At this time, the emotion recognition engine operates through voice input or text to recognize the user's emotions. The input data includes the "robot's operation status" and the user's "voice" or "text." The emotion recognition engine analyzes this input data and generates emotion analysis results.

[1459] Step 2:

[1460] The terminal sends the input information and the emotion analysis results to the server. The input data here is the "robot's operating status" and the "emotion analysis results." Secure data transfer is achieved by sending this to the server using the HTTPS protocol. The server then stores the received data in a database.

[1461] Step 3:

[1462] The server retrieves the information stored in the database and analyzes it using the generative AI model. The input data is the data stored in the database. The generative AI model generates optimal work plans and maintenance schedules based on the robot's operating status and the results of user emotion analysis. For example, as a result of the analysis, a work plan may be generated that states, "There will be no problem if the next maintenance is performed according to the regular schedule."

[1463] Step 4:

[1464] The server sends the generated work plan and maintenance schedule to the terminal. Here, the input data is the "work plan" generated by the generative AI model. The server sends this work plan to the terminal so that the user can check it. The terminal displays the received data, and the user can check the contents.

[1465] Step 5:

[1466] The user checks the work plan and maintenance schedule and makes any necessary corrections. For example, they input specific corrections such as "changing the date and time of the next maintenance." The corrected data is sent again from the terminal to the server. At this time, the "corrected work plan" is sent to the server as input data.

[1467] Step 6:

[1468] The server saves the revised information in a database and updates the final work plan and maintenance schedule. The input data is the "revised work plan." This database update maintains an accurate record that can later be used for audits and further analysis.

[1469] Based on the above processing steps, it is possible to generate optimal work plans that take into account the operating status of factory robots and the emotions of operators. This system reduces the workload of operators and enables flexible and efficient factory management.

[1470] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1471] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1472] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1473] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1474] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1475] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1476] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1477] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1478] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1479] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1480] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1481] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1482] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1483] 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.

[1484] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1485] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1486] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1487] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1488] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1489] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1490] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1491] The following is further disclosed regarding the above embodiment.

[1492] (Claim 1)

[1493] A means for the user to input observations of the children's behavior and the atmosphere of the class using a terminal;

[1494] A means for transmitting the input data to a server and storing the data in a database by the server;

[1495] A means for the server to analyze the stored data and generate an optimal childcare plan by referring to the childcare teaching guidelines;

[1496] A means for transmitting the generated childcare plan to a user's terminal so that the user can check and modify it;

[1497] A system including:

[1498] (Claim 2)

[1499] A means for sending the user's feedback and corrections back to the server and storing them in the database;

[1500] The server stores the final childcare plan and growth record in a database and provides them when necessary.

[1501] 10. The system of claim 1, comprising:

[1502] (Claim 3)

[1503] 10. The system of claim 1, further comprising means for providing the generated care plan and growth record to a parent portal or audit system.

[1504] "Example 1"

[1505] (Claim 1)

[1506] A means for the user to input observations of the children's behavior and the atmosphere of the class using a terminal;

[1507] The entered data is sent to the server using the HTTPS protocol, and the server stores it in a database.

[1508] The server analyzes the stored data using a generation AI model, and generates an optimal childcare plan by referring to the childcare guidelines.

[1509] The generated childcare plan and growth record can be sent to the user's device in PDF or Word format, allowing the user to review and edit the plan and growth record.

[1510] A system including:

[1511] (Claim 2)

[1512] a means for transmitting the user's feedback and corrections back to the server, which then stores them in the database;

[1513] The server stores the final childcare plan and growth record in a database and provides them when necessary.

[1514] 10. The system of claim 1, comprising:

[1515] (Claim 3)

[1516] 10. The system of claim 1, further comprising means for providing the generated care plan and growth record to a parent portal or audit system.

[1517] "Application Example 1"

[1518] (Claim 1)

[1519] A means for the user to input observations of the children's behavior and the atmosphere of the class using a terminal;

[1520] A means for transmitting the input data to a server and storing the data in a database by the server;

[1521] A means for the server to analyze the stored data and generate an optimal childcare plan by referring to the childcare teaching guidelines;

[1522] A means for transmitting the generated childcare plan to a user's terminal so that the user can check and modify it;

[1523] A means for a user to input the observed operating status and maintenance notes of factory equipment using a terminal;

[1524] A means for the server to analyze the operational status data based on the input information and generate a preventive maintenance plan;

[1525] A means for transmitting the generated preventive maintenance plan to a user's terminal so that the user can check and modify it;

[1526] A system including:

[1527] (Claim 2)

[1528] A means for sending the user's feedback and corrections back to the server and storing them in the database;

[1529] The server stores the final childcare plan and growth record in a database and provides them when necessary.

[1530] a means for storing the server-generated preventative maintenance plan in a database and providing it when needed;

[1531] 10. The system of claim 1, comprising:

[1532] (Claim 3)

[1533] a means for providing the server-generated childcare plans and growth records to a parent portal or audit system;

[1534] 10. The system of claim 1, further comprising means for providing the server-generated preventative maintenance plan to a management portal or audit system.

[1535] "Example 2: Combining Emotion Engines"

[1536] (Claim 1)

[1537] A means for the user to input observations of the children's behavior and the atmosphere of the class using a terminal;

[1538] emotion recognition means for analyzing input data and recognizing the emotion of the user;

[1539] a means for transmitting the input data and the recognized emotion data to a server, and for the server to store the data in a database;

[1540] A generation AI model means for analyzing the data stored in the server and generating an optimal childcare plan by referring to the childcare teaching guidelines;

[1541] A means for transmitting the generated childcare plan to a user's terminal so that the user can check and modify it;

[1542] A system including:

[1543] (Claim 2)

[1544] A means for sending the user's feedback and corrections back to the server and storing them in the database;

[1545] The server stores the final childcare plan and growth record in a database and provides them when necessary.

[1546] 10. The system of claim 1, comprising:

[1547] (Claim 3)

[1548] a means for providing the server-generated childcare plans and growth records to a parent portal or audit system;

[1549] A means for sending the childcare plan sent to the user back to the server and storing it as a final childcare plan;

[1550] 10. The system of claim 1, comprising:

[1551] "Application example 2 when combining emotion engines"

[1552] (Claim 1)

[1553] A means for the user to input observations of the children's behavior and the atmosphere of the class using a terminal;

[1554] A means for transmitting the input data to a server and storing the data in a database by the server;

[1555] A means for the server to analyze the stored data and generate an optimal childcare plan by referring to the childcare teaching guidelines;

[1556] A means for transmitting the generated childcare plan to a user's terminal so that the user can check and modify it;

[1557] means including an emotion recognition engine for recognizing a user's emotion from a voice input or a sentence analysis;

[1558] means for transmitting the result of the user's emotion analysis together with the data to a server;

[1559] A means for the AI ​​model to adjust the optimal childcare plan taking into account the results of emotion analysis;

[1560] A system including:

[1561] (Claim 2)

[1562] A means for sending the user's feedback and corrections back to the server and storing them in the database;

[1563] The server stores the final childcare plan and growth record in a database and provides them when necessary.

[1564] 10. The system of claim 1, comprising:

[1565] (Claim 3)

[1566] a means for providing the server-generated childcare plans and growth records to a parent portal or audit system;

[1567] A means of applying plans generated using generative AI models as work plans and maintenance schedules for factory robots;

[1568] 10. The system of claim 1, comprising: [Explanation of symbols]

[1569] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for the user to input observations of the children's behavior and the atmosphere of the class using a terminal; A means for transmitting the input data to a server and storing the data in a database by the server; A means for the server to analyze the stored data and generate an optimal childcare plan by referring to the childcare teaching guidelines; A means for transmitting the generated childcare plan to a user's terminal so that the user can check and modify it; A system including:

2. A means for sending the user's feedback and corrections back to the server and storing them in the database; The server stores the final childcare plan and growth record in a database and provides them when necessary. The system of claim 1 , comprising:

3. 10. The system of claim 1, further comprising means for providing the generated care plan and growth record to a parent portal or audit system.

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