System

Abstract

A system is provided.SOLUTION: This system includes a means for inputting diary data and image data, a means for analyzing the inputted diary data and image data, a means for generating an individual childcare plan on the basis of an analysis result, a means for incorporating the generated childcare plan into a childcare schedule, and a means for notifying a protector of a home support method.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] Currently, there is a strong demand for childcare that nurtures the individuality of each child, but it is difficult for childcare workers who are in charge of many children to accurately grasp each child's individuality and interests and create childcare plans based on that. This task places a heavy burden on childcare workers, and as a result, it is possible that children's potential will not be maximized. Furthermore, parents often do not know how to provide support at home, which tends to result in insufficient cooperation between school and home. Therefore, a system is needed that accurately grasps each child's individuality and interests and efficiently provides childcare plans based on those. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. Specifically, it provides a system including a means for inputting diary data and image data, a means for analyzing the input diary data and image data, a means for generating an individual childcare plan based on the analysis results, a means for incorporating the generated childcare plan into a childcare schedule, and a means for notifying parents of support methods at home. In this system, natural language processing and image recognition technologies are used to analyze the diary data and image data. Furthermore, a generative AI model is used to generate the individual childcare plan based on the analysis results, enabling the provision of a more accurate and efficient childcare plan.

[0006] Below are definitions of important words:

[0007] "Diary data" is digital text data recorded by caregivers about their daily childcare activities and children's behavior, interests, and characteristics.

[0008] "Image data" refers to digitized data of drawings, paintings, and other visual representations created by children.

[0009] "Means of input" is a general term for devices and software that childcare workers use to input diary data and image data into the system.

[0010] "Means of analysis" is a general term for algorithms and technologies used to analyze input diary data and image data and identify children's personalities, interests, and characteristics.

[0011] A "childcare plan" is a specific plan that shows the childcare activities and learning plans that are best suited to each individual child based on the analysis results.

[0012] The "means for generating" refers to a generative AI model and related technologies for automatically creating childcare plans based on the analysis results.

[0013] The "means for incorporating" is a scheduling technique for automatically or semi-automatically reflecting the generated childcare plan in the schedule of the childcare worker.

[0014] "Means of notification" is a general term for communication methods and software used to notify childcare workers and parents of necessary information based on analysis results and childcare plans.

[0015] "Natural language processing" is a technology for analyzing the text of diary data, understanding human language, and extracting appropriate information.

[0016] "Image recognition technology" is a technology for analyzing image data, identifying visual elements, and understanding their meaning.

[0017] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and generates optimal childcare plans based on the analysis results. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

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

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

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

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0033] 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.

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

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system aims to analyze each child's personality and interests using diary data and image data entered primarily by caregivers, and to generate an optimal childcare plan based on that analysis.

[0040] System Overview

[0041] The system has the following main components:

[0042] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[0043] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[0044] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[0045] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[0046] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[0047] Program processing

[0048] 1. Enter your journal and drawing details

[0049] The user (caregiver) records the daily childcare diary and scans the images of the children's drawings, entering the data digitally using a device such as a tablet.

[0050] 2. Sending data to the server

[0051] The device sends the entered log data and image data to a cloud server, where the data is uploaded in real time.

[0052] 3. Data Analysis

[0053] The server uses natural language processing (NLP) and image recognition technologies to analyze the received data. NLP is used to analyze the text data of the diary and extract descriptions of, for example, "playing with blocks." Image recognition is used to analyze the image data of the drawings and identify the frequency of color use and the objects drawn.

[0054] 4. Create a childcare plan

[0055] Based on the analysis results, the server generates the optimal childcare plan for each child. Specifically, the generative AI model uses the analysis results to suggest activities such as "building games using blocks" and "animal-themed learning activities."

[0056] 5. Scheduling and Proposals

[0057] The server then provides suggestions for incorporating the generated childcare plan into the caregiver's daily schedule, which the caregiver can review and modify as necessary.

[0058] 6. Feedback to parents

[0059] The server then notifies the parents of the generated childcare plan and how to support them at home. For example, it could send a notification to the parents' smartphone and provide advice such as, "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well."

[0060] Specific examples

[0061] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[0062] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[0063] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[0064] 3. The device sends this data to the cloud server.

[0065] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[0066] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[0067] 6. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[0068] 7. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[0069] In this way, this system allows caregivers to provide the best possible care plan for each child and strengthen cooperation with parents, thereby maximizing the individuality and interests of each child.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[0073] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[0074] Step 2:

[0075] The terminal transmits the input diary data and image data to the cloud server.

[0076] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[0077] Step 3:

[0078] The server analyzes the received diary data using natural language processing (NLP) technology.

[0079] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[0080] Step 4:

[0081] The server analyzes the received image data using image recognition technology.

[0082] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[0083] Step 5:

[0084] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[0085] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[0086] Step 6:

[0087] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[0088] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[0089] Step 7:

[0090] The user (caregiver) checks the proposed childcare plan, modifies it as necessary, and then approves it.

[0091] Specific operation: The caregiver checks the proposed schedule on the tablet and clicks the approval button.

[0092] Step 8:

[0093] The server notifies the parents of the generated childcare plan and how to support them at home.

[0094] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[0095] Step 9:

[0096] The user (guardian) receives a notification and provides support at home.

[0097] Specific action: Parents check the notification and incorporate play with blocks at home.

[0098] This is the specific processing flow of the system. This process allows caregivers and parents to work together to provide optimal care tailored to the individuality and interests of each child.

[0099] Example 1

[0100] 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."

[0101] Conventional childcare systems have difficulty efficiently generating individual childcare plans based on each child's individuality and interests. Furthermore, they lack a system for providing parents with support at home, which hinders cooperation between childcare workers and parents. There is a need to improve this situation by providing childcare plans that maximize children's individuality and interests, and to strengthen cooperation between childcare workers and parents.

[0102] 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.

[0103] In this invention, the server includes a means for inputting diary data and image data, a means for transmitting the input diary data and image data to the cloud server, a means for analyzing the input diary data and image data, a means for generating an individual childcare plan based on the analysis results, a means for incorporating the generated childcare plan into a childcare schedule, and a means for notifying parents of support methods at home. This makes it possible to efficiently provide an optimal childcare plan for each child and strengthen cooperation between caregivers and parents.

[0104] "Diary data" is textual information recorded by caregivers about children's daily activities, behavior, interests, etc.

[0105] "Image data" is visual information stored in digital format, such as drawings and crafts created by children.

[0106] A "cloud server" is a server for storing, processing, and managing data remotely via the Internet.

[0107] "Natural language processing" is a technology for analyzing human language and understanding its meaning and structure.

[0108] "Image recognition technology" is a technology for analyzing image data and understanding or classifying its content.

[0109] A "generative AI model" is an artificial intelligence model trained to automatically generate appropriate childcare plans from input data.

[0110] A "childcare schedule" is a schedule for planning daily childcare activities according to time and order.

[0111] A "guardian" is a person responsible for the protection and upbringing of a child.

[0112] "Support methods" are specific measures and advice to support children at home.

[0113] MODE FOR CARRYING OUT THE INVENTION

[0114] The present invention relates to a system for analyzing diary data and image data provided by caregivers and proposing individual childcare plans. The system includes an input unit, a data analysis unit, a generation unit, a scheduling unit, and a notification unit. The following describes an embodiment of the present invention.

[0115] Input Method

[0116] The user (caregiver) records the daily childcare diary using a device such as a tablet or smartphone. When a child draws a picture, the image is scanned into the device using a scanning app and saved as digital data. The childcare diary is recorded by entering text into a dedicated app, such as "Haruto was engrossed in playing with blocks for a long time today." The image data of the drawing is automatically saved in a dedicated folder.

[0117] Data transmission method

[0118] The device sends the entered diary data and image data to the cloud server. The transmission process is done in real time, and all data is encrypted using TLS (Transport Layer Security). The diary data is sent in text format, and the image data is sent in JPEG or PNG format.

[0119] Data Analysis Methods

[0120] The server analyzes the diary data and image data received on the cloud. It applies natural language processing (NLP) technology to the text of the diary data to extract keywords, such as "playing with blocks." It also applies image recognition technology to the image data to identify objects and color patterns drawn in the drawing. These analyses use the Tensorflow® NLP model and OpenCV's image recognition algorithm.

[0121] How to generate childcare plans

[0122] The server uses a generative AI model (e.g., GPT-3 (registered trademark) model) based on the analysis results to generate an optimal childcare plan for each child. Specifically, the server inputs the following prompt sentence into the generative AI model, which then outputs the childcare plan:

[0123] "According to the daycare diary, Haruto was engrossed in playing with blocks for long periods of time. Based on this information, please generate the optimal daycare plan for Haruto."

[0124] "From the image analysis of his drawing, we know that Haruto likes animals. Use this information to create suggestions for animal-themed learning activities."

[0125] The generated childcare plan is saved in a data format such as JSON and handed over to the next process.

[0126] Scheduling Methods

[0127] The server then proposes incorporating the generated childcare plan into the childcare worker's daily schedule. Using an optimization algorithm, the server appropriately places the new plan within the existing schedule. The proposal is then sent to the childcare worker's device, where the childcare worker can review the details in the dedicated app and make any necessary corrections or approvals.

[0128] Notification means

[0129] The server then notifies the parents of the generated childcare plan and how to support them at home by sending a push notification to their smartphone, providing specific advice such as:

[0130] "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well."

[0131] Notifications will also be displayed within the parent-only app, allowing parents to view past childcare plans and feedback.

[0132] As described above, by implementing the present invention, caregivers can efficiently provide optimal care plans for individual children and strengthen cooperation with parents.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Program processing steps

[0135] Step 1: User Data Entry

[0136] Users (caregivers) enter daily childcare diary entries using a tablet or smartphone. When a child draws a picture, the image is captured using a scanning app and saved in digital format.

[0137] Input: Childcare diary text data, drawing image data

[0138] Output: Digital logbook data and image data

[0139] Specific operations: The daycare diary involves entering text such as "Haruto was engrossed in playing with blocks for a long time today" into a dedicated app, taking a picture of the drawing with the smartphone camera, and importing it using the scanning function.

[0140] Step 2: Send data to the server

[0141] The terminal transmits the input diary data and image data to the cloud server in real time.

[0142] Input: Journal data and image data stored on the device

[0143] Output: Data stored on the cloud server

[0144] Specific operation: Data is encrypted and transmitted using TLS (Transport Layer Security). Diary data is transmitted in text format, and image data is transmitted in JPEG or PNG format.

[0145] Step 3: Data analysis

[0146] The server analyzes the diary data and image data received on the cloud. The diary text data is analyzed using natural language processing (NLP) technology, and the image data is analyzed using image recognition technology.

[0147] Input: Journal data and image data stored on the cloud server

[0148] Output: Analysis results (keyword extraction, object identification)

[0149] Specific operation: The diary data is analyzed using an NLP model (e.g., TensorFlow) to extract keywords such as "playing with blocks," and the image data is analyzed using OpenCV to identify drawn objects and color patterns.

[0150] Step 4: Generate a childcare plan

[0151] The server generates a childcare plan using a generative AI model (e.g., a GPT-3 model) based on the analysis results.

[0152] Input: Analysis results (keyword extraction results from text data and object identification results from image data)

[0153] Output: Individual childcare plan (e.g., JSON format)

[0154] Specific operation: The following prompt is input into the generative AI model: "According to the daycare diary, Haruto spent a lot of time engrossed in playing with blocks. Based on this information, please generate the optimal daycare plan for Haruto." The generated daycare plan is saved in JSON format.

[0155] Step 5: Scheduling and Proposal

[0156] The server makes a proposal to incorporate the generated childcare plan into the childcare provider's daily schedule.

[0157] Input: Generated childcare plan, existing schedule

[0158] Output: Optimized schedule proposal

[0159] How it works: Applying an optimization algorithm to add new plans to an existing schedule. The proposed plan is sent to the caregiver's device, who can then review the details in the dedicated app and make any necessary changes or approvals.

[0160] Step 6: Feedback to parents

[0161] The server notifies the parents of the generated childcare plan and how to support them at home.

[0162] Input: Generated childcare plan, support methods at home

[0163] Output: Notification to parents (push notification, display of dedicated app)

[0164] Specific operation: A push notification is sent to the parent's smartphone, providing specific advice such as, "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well." The notification is also displayed in the parent-only app, where past childcare plans and feedback can also be viewed.

[0165] (Application example 1)

[0166] 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."

[0167] Currently, school supply stores lack the means to recommend the most suitable school supplies based on each child's personality and interests. Furthermore, parents are not provided with adequate individual suggestions or support, which makes it difficult for them to make effective choices. This results in the problem of not maximizing children's motivation and interest in learning.

[0168] 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.

[0169] In this invention, the server includes a means for inputting diary data and image data, a means for analyzing the input diary data and image data, a means for generating an individual childcare plan and school supplies based on the analysis results, a means for informing parents of support methods at home, and a means for displaying the suggested school supplies. This makes it possible to suggest optimal school supplies based on the personality and interests of each child, thereby strengthening support for parents.

[0170] "Diary data" is text data recorded by caregivers or store staff about daily activities, children's behavior, and interests.

[0171] "Image data" refers to children's drawings and other visual content stored in digital format.

[0172] "Analysis means" refers to the technology or method for analyzing the input diary data and image data and extracting useful information and trends based on the analysis.

[0173] "Generation means" refers to technology or a system for generating individual childcare plans and school supply proposals based on the analyzed results.

[0174] A "childcare schedule" is a planned timetable that allows caregivers to manage a child's daily activities and carry them out in the appropriate order.

[0175] "Means of notification" refers to the techniques and methods used to communicate important information and support methods to parents and other relevant parties.

[0176] "School supplies" is a general term for tools and teaching materials used by children to study.

[0177] "Display means" refers to devices or techniques for visually presenting the proposed school supplies or plans.

[0178] "Natural Language Processing (NLP)" is the technology that enables computers to understand, interpret, and generate human language.

[0179] "Image recognition technology" is a technology in which a computer analyzes the content of an image and recognizes specific features and patterns.

[0180] A "generative AI model" is an artificial intelligence system that automatically generates content based on large amounts of data.

[0181] The present invention relates to a system that analyzes input data and proposes individual childcare plans and school supplies. This system aims to provide optimal childcare plans and school supplies by having a childcare worker or store clerk input diary data and image data, and analyzing the data.

[0182] Overall system configuration

[0183] The system has the following main components:

[0184] 1. Journal and image data input method

[0185] - A device such as a smartphone or tablet for childcare workers or store staff to input diary data and image data.

[0186] 2. Data analysis methods

[0187] - Natural language processing (NLP) and image recognition technologies for analyzing input data, specifically using NLP libraries such as Google® BERT and SpaCy, and image recognition libraries such as OpenCV and TensorFlow.

[0188] 3. Childcare plan and school supply generator

[0189] - Generative AI models (e.g., GPT-4 (registered trademark)) that generate personalized childcare plans and school supplies based on analytical results.

[0190] 4. Means of notification

[0191] - APIs and communication methods to inform parents about support methods at home and suggested school supplies.

[0192] 5. Display means

[0193] - An interface for displaying suggested school supplies and childcare plans to store staff and parents.

[0194] Data entry examples

[0195] The user (store clerk) inputs the diary data and image data from a smartphone. For example, a picture drawn by a child can be scanned using the smartphone camera.

[0196] For example, if a child is interested in dinosaurs, the store clerk might enter "Today's child drew a dinosaur" into the journal data and then scan the dinosaur drawing. The following is an example of a prompt that might be entered:

[0197] "Today's children drew pictures of dinosaurs."

[0198] Data analysis

[0199] The server receives the diary data and image data and analyzes the data using natural language processing and image recognition technology. Specifically, NLP technology is used to analyze the text data in the diary and extract the interests and activities described. Image recognition technology is used to analyze the content of the drawings and identify the objects drawn.

[0200] Plan and generate school supplies

[0201] Based on the analyzed data, the server uses a generative AI model to propose optimal childcare plans and school supplies, such as "building games with blocks" and "dinosaur-related learning materials."

[0202] Scheduling and Notifications

[0203] The generated childcare plan and school supply suggestions are sent to the user from the server. Store staff and parents can check the suggestions on their smartphones or tablets. For example, parents may be notified of support methods such as, "Your child is interested in dinosaurs, so it would be good to use dinosaur-related learning materials."

[0204] Overall implementation example

[0205] As a concrete example, the following steps can be considered:

[0206] 1. The store clerk uses a smartphone to enter the child's diary data and a picture of a dinosaur.

[0207] 2. The entered data is sent to the cloud server.

[0208] 3. The server analyzes the data and generates school supplies and childcare plans based on the child's interests and personality.

[0209] 4. The server displays the generated suggestions on the smartphone and notifies the parents.

[0210] 5. The store staff and parents review the suggestions and put them into action.

[0211] In this way, the system of the present invention can suggest the most suitable school supplies based on the child's personality and interests, strengthen cooperation with parents, and improve the child's learning experience.

[0212] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0213] Step 1:

[0214] The user enters diary data and image data on a smartphone. The user opens the diary app and scans the child's drawing, which inputs text and image data. The input data is temporarily saved on the device.

[0215] Input: Journal text data, scanned drawing images

[0216] Output: Temporarily saved journal data and image data

[0217] Step 2:

[0218] The device sends the entered diary data and image data to the cloud server, and the data is uploaded to the cloud server using an HTTP request.

[0219] Input: Temporarily saved journal data and image data

[0220] Output: Diary data and image data sent to the cloud server

[0221] Step 3:

[0222] The server analyzes the received diary data and image data. It uses natural language processing (NLP) to extract important keywords and interests from the diary text data, and uses image recognition technology to analyze the content of the drawings.

[0223] Input: Diary data and image data stored on the cloud server

[0224] Output: Analyzed diary data and image data features

[0225] Step 4:

[0226] The server generates personalized childcare plans and school supplies based on the analyzed data using a generative AI model (e.g., GPT-4). The generative AI model receives the analysis results as prompts and generates optimal suggestions.

[0227] Input: Analyzed diary data and image data features

[0228] Output: Generated childcare plans and school supply suggestions

[0229] Step 5:

[0230] The server incorporates the generated childcare plan and school supply suggestions into the childcare provider's daily schedule and notifies the user of the contents. The childcare provider can then review the proposed plan and make any necessary adjustments.

[0231] Input: Generated childcare plans and school supply suggestions

[0232] Output: Childcare plan and school supply suggestions notified to the user, and childcare schedule

[0233] Step 6:

[0234] The server notifies the parents of the generated childcare plan and school supply suggestions and suggests ways to support the child at home. The parents can receive the suggestions and put them into practice.

[0235] Input: Child care plans and school supply suggestions to inform parents

[0236] Output: Parents are notified of childcare plans, school supply suggestions, and support methods

[0237] 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.

[0238] The present invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system primarily uses diary data and image data entered by caregivers to analyze each child's personality and interests, and then generates an optimal childcare plan based on that data. Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing it to evaluate the emotional compatibility of the childcare plan and dynamically adjust the plan.

[0239] System Overview

[0240] The system has the following main components:

[0241] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[0242] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[0243] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[0244] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[0245] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[0246] Emotion Engine: A technology that recognizes the user's emotions, evaluates the emotional compatibility of the childcare plan, and dynamically adjusts the childcare plan.

[0247] Program processing

[0248] 1. Enter your journal and drawing details

[0249] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[0250] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[0251] 2. Sending data to the server

[0252] The terminal transmits the input diary data and image data to the cloud server.

[0253] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[0254] 3. Data Analysis

[0255] The server analyzes the received diary data using natural language processing (NLP) technology.

[0256] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[0257] 4. Image Analysis

[0258] The server analyzes the received image data using image recognition technology.

[0259] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[0260] 5. Create a childcare plan

[0261] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[0262] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[0263] 6. Emotional compatibility assessment

[0264] The server evaluates the emotions of the caregiver and the child based on the care plan generated using the emotion engine and daily feedback.

[0265] Specific operation: For example, if a caregiver writes in their journal after implementing a childcare plan, "Haruto seemed to be having a lot of fun," the emotion engine analyzes this feedback and rates the plan's emotional compatibility highly.

[0266] 7. Dynamic adjustment of childcare plans

[0267] The server dynamically adjusts the childcare plan based on the analysis results using an emotion engine.

[0268] Specific action: Update the childcare plan to avoid elements that are deemed incompatible and incorporate new compatible elements.

[0269] 8. Scheduling and Proposals

[0270] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[0271] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[0272] 9. Feedback to Parents

[0273] The server notifies the parents of the generated childcare plan and how to support them at home.

[0274] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[0275] 10. Support at home

[0276] The user (guardian) receives a notification and provides support at home.

[0277] Specific action: Parents check the notification and incorporate play with blocks at home.

[0278] Specific examples

[0279] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[0280] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[0281] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[0282] 3. The device sends this data to the cloud server.

[0283] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[0284] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[0285] 6. The server uses the emotion engine to evaluate the feedback from Haruto and the caregivers and evaluate the emotional suitability of the care plan. For example, it reflects the feedback that "Haruto seemed to be having a lot of fun."

[0286] 7. The server dynamically adjusts the childcare plan as needed, removing elements that are deemed incompatible and updating the plan to include new compatible elements.

[0287] 8. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[0288] 9. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[0289] 10. The user (parent) checks the notification and starts playing with blocks at home.

[0290] In this way, this system allows caregivers to provide optimal childcare plans for each child and strengthen cooperation with parents, maximizing the individuality and interests of each child. Furthermore, the use of an emotion engine makes it possible to provide childcare plans that take into account the emotional aspects of both the child and the caregiver.

[0291] The processing flow will be explained below.

[0292] Step 1:

[0293] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[0294] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[0295] Step 2:

[0296] The terminal transmits the input diary data and image data to the cloud server.

[0297] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[0298] Step 3:

[0299] The server analyzes the received diary data using natural language processing (NLP) technology.

[0300] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[0301] Step 4:

[0302] The server analyzes the received image data using image recognition technology.

[0303] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[0304] Step 5:

[0305] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[0306] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[0307] Step 6:

[0308] The server evaluates the emotions of the caregiver and the child based on the care plan generated using the emotion engine and daily feedback.

[0309] Specific operation: For example, if a caregiver writes in their journal after implementing a childcare plan, "Haruto seemed to be having a lot of fun," the emotion engine analyzes this feedback and rates the plan's emotional compatibility highly.

[0310] Step 7:

[0311] The server dynamically adjusts the childcare plan based on the analysis results using an emotion engine.

[0312] Specific action: Update the childcare plan to avoid elements that are deemed incompatible and incorporate new compatible elements.

[0313] Step 8:

[0314] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[0315] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[0316] Step 9:

[0317] The user (caregiver) checks the proposed childcare plan, modifies it as necessary, and then approves it.

[0318] Specific operation: The caregiver checks the proposed schedule on the tablet and clicks the approval button.

[0319] Step 10:

[0320] The server notifies the parents of the generated childcare plan and how to support them at home.

[0321] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[0322] Step 11:

[0323] The user (guardian) receives a notification and provides support at home.

[0324] Specific action: Parents check the notification and incorporate play with blocks at home.

[0325] The above is the specific processing flow of the system. This process allows caregivers and parents to work together to provide optimal care tailored to the child's personality and interests. In addition, the introduction of an emotion engine makes it possible to provide care plans that take into account the emotional aspects of the child and caregivers.

[0326] Example 2

[0327] 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."

[0328] Conventional childcare plan creation systems are limited to providing childcare plans using diary data and image data, making it difficult to generate plans that fully reflect each child's personality and interests. Furthermore, because the plans do not take into account the emotional aspects of the child or caregiver, they can sometimes be ineffective after implementation. There was a need to provide a system that could solve these problems, provide optimal childcare plans based on each child's personality and interests, and dynamically adjust them by evaluating emotional compatibility.

[0329] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0330] In this invention, the server includes means for inputting diary data and image data, means for analyzing the input diary data and image data, means for generating an individual childcare plan based on the analysis results, means for incorporating the generated childcare plan into a childcare schedule, means for informing the guardian of support methods at home, means for evaluating the user's emotions and evaluating the emotional compatibility of the childcare plan, and means for dynamically adjusting the childcare plan. This makes it possible to provide an optimal childcare plan that corresponds to the child's personality and interests, and further to evaluate the emotional compatibility of the childcare plan and dynamically adjust it based on feedback after implementation.

[0331] "Diary data" is text data in which caregivers record their daily childcare activities and the behavior of children.

[0332] "Image data" refers to image files scanned or photographed when a child draws or makes crafts.

[0333] "Natural language processing technology" is a technology that analyzes text data and extracts specific keywords and phrases.

[0334] "Image recognition technology" is a technology that analyzes image data and recognizes the objects and patterns depicted in it.

[0335] A "generative AI model" is an artificial intelligence model that automatically generates the optimal childcare plan for each individual child based on the analysis results.

[0336] The "emotion engine" is a technology that evaluates the emotional feedback of users and children and evaluates the emotional suitability of childcare plans based on that feedback.

[0337] A "childcare schedule" is a schedule that lists the daily childcare activities and timetables that childcare workers carry out.

[0338] "Means for notifying parents" refers to communication technologies used to inform parents of the created childcare plan and how to provide support at home.

[0339] "Dynamic adjustment" refers to the process of changing and updating childcare plans in real time based on user feedback and emotional assessment.

[0340] A "server" is a computer system that performs processes such as data analysis, childcare plan generation, emotion evaluation, and dynamic adjustment within a cloud environment.

[0341] The present invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system primarily uses diary data and image data entered by caregivers to analyze each child's personality and interests, and then generates an optimal childcare plan based on that data. Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing it to evaluate the emotional compatibility of the childcare plan and dynamically adjust the plan.

[0342] System Overview

[0343] The system has the following main components:

[0344] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[0345] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[0346] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[0347] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[0348] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[0349] Emotion Engine: A technology that recognizes the user's emotions, evaluates the emotional compatibility of the childcare plan, and dynamically adjusts the childcare plan.

[0350] Hardware and software used

[0351] Hardware: Tablets, smartphones, and cloud servers used by childcare workers.

[0352] Software: Google Cloud Natural Language API and Amazon Comprehend are used for natural language processing, Google Cloud Vision API and Amazon Rekognition are used for image recognition, and generative AI models (e.g., GPT-4 from OpenAI (registered trademark)) are used to generate childcare plans.

[0353] Specific examples

[0354] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[0355] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[0356] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[0357] 3. The device sends this data to the cloud server.

[0358] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[0359] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[0360] 6. The server uses the emotion engine to evaluate the feedback from Haruto and the caregivers and evaluate the emotional suitability of the care plan. For example, it reflects the feedback that "Haruto seemed to be having a lot of fun."

[0361] 7. The server dynamically adjusts the childcare plan as needed, removing elements that are deemed incompatible and updating the plan to include new compatible elements.

[0362] 8. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[0363] 9. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[0364] 10. The user (parent) checks the notification and starts playing with blocks at home.

[0365] Prompt Sentence Examples

[0366] "What activities have you been interested in lately, Haruto?"

[0367] "Generate the optimal childcare plan based on the following text and image data. Text: 'Haruto has been obsessed with playing with blocks lately.' Image: Pictures of animals."

[0368] "Dynamic adjustments to care plans should be made taking into account caregiver feedback and emotional fit."

[0369] By implementing the present invention, it is possible to provide an optimal childcare plan tailored to the individual characteristics and interests of each child, and to dynamically adjust the plan to take into account emotional compatibility, thereby improving the quality of childcare and enabling smooth communication between caregivers and parents.

[0370] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0371] Step 1:

[0372] The user (caregiver) uses a device such as a tablet to enter daily childcare diaries, and when a child draws a picture, the image is scanned and the data is entered.

[0373] Specific actions: The caregiver opens the app on the tablet and writes, "Today, Child A was absorbed in playing with blocks for a long time." The caregiver also scans the animal drawing that Child A has made with the tablet's camera and saves it as image data.

[0374] Input: Journal text data, scanned image data

[0375] Output: Journal text data and image data temporarily saved on the device

[0376] Step 2:

[0377] The terminal transmits the input diary data and image data to a cloud server.

[0378] Specific operation: The tablet uploads input data to a cloud server in real time via Wi-Fi.

[0379] Input: Temporarily saved journal text data and image data

[0380] Output: Journal text data and image data uploaded to the cloud server

[0381] Step 3:

[0382] The server analyzes the received diary data using natural language processing (NLP) technology.

[0383] Specific operation: The NLP algorithm extracts keywords such as "playing with blocks" and "interest" from the text "Today, Child A was engrossed in playing with blocks for a long time" and identifies the child's focus of interest.

[0384] Input: Journal text data

[0385] Output: Keywords extracted from text data and their analysis results

[0386] Step 4:

[0387] The server analyzes the received image data using image recognition technology.

[0388] How it works: Image recognition algorithms analyze scanned images to identify objects like animals and playground equipment, as well as the frequency and patterns of color usage.

[0389] Input: Scanned image data

[0390] Output: Object information extracted from image data and its analysis results

[0391] Step 5:

[0392] The server uses a generative AI model to generate an individualized childcare plan based on the analysis results.

[0393] Specific operation: Based on the results of NLP and image recognition analysis, the generative AI model proposes childcare activities suitable for Child A. It generates childcare plans such as "building games using blocks" and "animal-themed learning activities."

[0394] Input: Analysis results using NLP and image recognition technology

[0395] Output: Generated individual childcare plan

[0396] Step 6:

[0397] The server evaluates the generated childcare plan and the user's feedback using the emotion engine.

[0398] Specific operation: After implementing a childcare plan, when a caregiver gives feedback such as "Child A seemed to enjoy it very much," the emotion engine analyzes this feedback and evaluates the emotional compatibility of the plan.

[0399] Input: Generated childcare plan, feedback from caregivers

[0400] Output: Emotional compatibility evaluation result

[0401] Step 7:

[0402] The server dynamically adjusts the childcare plan based on the emotion engine's evaluation.

[0403] Specific behavior: The system removes elements that are deemed incompatible and updates the plan to include new, compatible elements. For example, activities that children dislike are removed.

[0404] Input: Emotional compatibility assessment results, existing childcare plan

[0405] Output: Dynamically adjusted childcare plan

[0406] Step 8:

[0407] The server proposes incorporating the generated childcare plan into the schedule of the childcare provider.

[0408] Specific operation: The server references the childcare worker's daily schedule and generates a plan that incorporates the proposed childcare activities at the optimal time. The childcare worker then reviews and approves the proposal.

[0409] Input: Dynamically adjusted childcare plans, childcare worker schedules

[0410] Output: A childcare plan that is integrated into the childcare worker's schedule

[0411] Step 9:

[0412] The device notifies the parent of the generated childcare plan and how to provide support at home.

[0413] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Child A is interested in playing with blocks, so it would be a good idea to play with them at home."

[0414] Input: Childcare plan information, parental contact information

[0415] Output: Parental notification message

[0416] Step 10:

[0417] The user (parent) receives a notification and provides support at home.

[0418] Action: Parents check the notification and incorporate block play into their children's home activities, ensuring the quality of childcare is maintained in the home environment.

[0419] Input: Parental notification message

[0420] Output: Home implementation report (optional)

[0421] (Application example 2)

[0422] 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."

[0423] With conventional childcare plan generation systems, it was difficult to effectively generate specific childcare plans that reflected a child's personality and interests, and there was a problem of insufficient support at home due to a lack of cooperation between caregivers and parents. The purpose of this invention is to solve these problems, generate an optimal childcare plan based on a child's interests and characteristics, and realize closer cooperation between caregivers and parents.

[0424] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0425] In this invention, the server includes means for inputting diary data and image data, means for analyzing the input diary data and image data, means for generating an individual childcare plan based on the analysis results, means for incorporating the generated childcare plan into a childcare schedule, means for notifying parents of support methods at home, means for analyzing text data of the childcare diary using natural language processing technology and performing keyword extraction and sentiment analysis, means for analyzing pictures drawn by the child using image processing technology and identifying recognized objects, means for generating a childcare plan based on the analysis results using a generative AI model, and means for notifying childcare workers and parents of the generated childcare plan and sentiment analysis results. This makes it possible to generate a childcare plan that is optimal for the child's interests and personality, strengthens cooperation between childcare workers and parents, and enables effective support at home.

[0426] "Diary data" is text data in which caregivers record children's daily activities and conditions.

[0427] "Image data" refers to digital images obtained by scanning or photographing drawings or artwork created by children.

[0428] "Natural language processing technology" is a technology for analyzing text data and extracting meaning and emotion.

[0429] "Image processing technology" is a technology for analyzing digital images to recognize objects and features.

[0430] A "generative AI model" is an artificial intelligence model that generates new data and suggestions based on input data.

[0431] A "childcare plan" is a specific childcare activity plan proposed based on a child's personality and interests.

[0432] A "childcare schedule" is a plan that incorporates a childcare plan into specific times and dates.

[0433] "Childcare workers" are professionals involved in children's daily lives and education.

[0434] "Guardians" are family members who are responsible for raising and educating children.

[0435] "Sentiment analysis" is a technique for extracting and evaluating emotions from text data and feedback.

[0436] A "recognized object" is an object or shape that has been identified within a digital image using image processing techniques.

[0437] This invention relates to a system that automates the creation and management of childcare plans. This system inputs and analyzes diary data and image data, and creates individual childcare plans based on the analysis results. It also has the function of incorporating the childcare plans into childcare schedules and informing parents of support methods at home.

[0438] Hardware and software used

[0439] This system uses the following hardware and software.

[0440] Hardware:

[0441] Smartphones and tablets: Devices that allow users (caregivers) to enter daily childcare diaries and scan drawings by children.

[0442] Server: A central processing unit for performing the processes of data analysis and childcare plan generation.

[0443] software:

[0444] Python: Program implementation language.

[0445] OpenCV: A library for image processing.

[0446] TextBlob: A natural language processing library for analyzing text data.

[0447] Transformers: Libraries for using generative AI and sentiment analysis models.

[0448] Data processing and calculation

[0449] 1. Data Entry:

[0450] Caregivers enter daily childcare diaries into text on smartphones or tablets, and scan pictures drawn by children and save them as image data.

[0451] 2. Data transmission:

[0452] The terminal transmits the input diary data and image data to the server.

[0453] 3. Data Analysis:

[0454] The server uses TextBlob to perform text analysis of the diary data, extracting keywords and carrying out sentiment analysis.

[0455] The image data is analyzed using OpenCV to identify the recognized objects in the child's drawing.

[0456] 4. Childcare plan generation:

[0457] The server uses a generative AI model (Transformers library) based on the analysis results to generate an individual childcare plan.

[0458] 5. Notice:

[0459] The generated childcare plan and the results of the sentiment analysis are then sent to caregivers and parents via a smartphone app or email.

[0460] Specific examples

[0461] As a concrete example of this system, consider the following scenario: A caregiver records on a tablet that "Today, my child was engrossed in playing with blocks for a long time," and then scans and saves a drawing of an animal the child drew. This data is sent to a server in real time via Wi-Fi. The server analyzes the text and images using TextBlob and OpenCV. Based on the extracted keywords (e.g., "playing with blocks") and recognized objects (e.g., "animals"), a generative AI model generates a childcare plan such as: "Next time, we will use building games with blocks as teaching materials and incorporate animal-themed learning activities." The generated plan and the results of the sentiment analysis are then sent to the caregiver's smartphone, and home activities based on the child's interests are also suggested to parents.

[0462] Prompt Sentence Examples

[0463] Your child is interested in building blocks and animals. Create a suitable childcare plan.

[0464] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0465] Step 1:

[0466] Data Entry

[0467] Users (caregivers) enter their daily childcare diary into text on their smartphones or tablets, and scan pictures drawn by children and save them as image data.

[0468] Input: Text data from the daycare diary, recording that "Today the children were absorbed in playing with blocks for a long time," and image data of drawings made by the children.

[0469] Specific actions: The caregiver uses the tablet app to enter the childcare diary into the text field and then scans it using the camera function.

[0470] Step 2:

[0471] Data transmission

[0472] The terminal transmits the input diary data and image data to the server.

[0473] Input: Text data and image data.

[0474] Data processing: Converts input data into a format that can be sent to the cloud server.

[0475] Output: Text and image data uploaded to the cloud server.

[0476] Specific operation: The tablet uploads data to a cloud server in real time via Wi-Fi.

[0477] Step 3:

[0478] Data analysis

[0479] The server uses TextBlob to perform text analysis of the diary data, extracting keywords and carrying out sentiment analysis.

[0480] Input: Text data uploaded to the cloud server.

[0481] Data Operations: Use TextBlob for natural language processing to parse keywords and sentiment from text.

[0482] Output: A list of keywords and sentiment scores as the analysis results.

[0483] Specific operation: Using the TextBlob library, extract keywords such as "playing with blocks" and calculate sentiment values ​​such as positive or negative.

[0484] Step 4:

[0485] Image analysis

[0486] The server uses OpenCV to analyze the image data and identify recognizable objects in the child's drawing.

[0487] Input: Image data stored on a cloud server.

[0488] Data Computation: Image analysis is performed using OpenCV to recognize objects in the image.

[0489] Output: A list of recognized objects.

[0490] Specific operation: Uses the OpenCV library to identify objects such as "animals" from scanned images.

[0491] Step 5:

[0492] Childcare plan generation

[0493] The server uses a generative AI model based on the analysis results to generate an individual childcare plan.

[0494] Input: Text analysis results (keyword list and emotion rating), image analysis results (list of recognized objects).

[0495] Data calculation: Using a generative AI model, an optimal childcare plan is generated based on the input results.

[0496] Output: The generated childcare plan.

[0497] Specific behavior: Using a generative AI model from the Transformers library, it generates a care plan based on the prompt, "My child is interested in playing with blocks and animals. Please generate an appropriate care plan."

[0498] Step 6:

[0499] notification

[0500] The server notifies the generated childcare plan and the emotion analysis results to the childcare provider and the parent.

[0501] Input: Generated childcare plan and sentiment analysis results.

[0502] Data processing: Create notification messages and send them to smartphone apps or emails.

[0503] Output: Notification message sent to caregivers and parents.

[0504] Specific operation: The server generates a notification message, sends an app notification to the childcare worker's smartphone, and also sends an email to the parents informing them of how to provide support at home.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] [Second embodiment]

[0509] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0510] 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.

[0511] 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).

[0512] 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.

[0513] 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.

[0514] 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).

[0515] 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.

[0516] 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.

[0517] 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.

[0518] 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.

[0519] 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.

[0520] 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."

[0521] This invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system aims to analyze each child's personality and interests using diary data and image data entered primarily by caregivers, and to generate an optimal childcare plan based on that analysis.

[0522] System Overview

[0523] The system has the following main components:

[0524] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[0525] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[0526] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[0527] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[0528] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[0529] Program processing

[0530] 1. Enter your journal and drawing details

[0531] The user (caregiver) records the daily childcare diary and scans the images of the children's drawings, entering the data digitally using a device such as a tablet.

[0532] 2. Sending data to the server

[0533] The device sends the entered log data and image data to a cloud server, where the data is uploaded in real time.

[0534] 3. Data Analysis

[0535] The server uses natural language processing (NLP) and image recognition technologies to analyze the received data. NLP is used to analyze the text data of the diary and extract descriptions of, for example, "playing with blocks." Image recognition is used to analyze the image data of the drawings and identify the frequency of color use and the objects drawn.

[0536] 4. Create a childcare plan

[0537] Based on the analysis results, the server generates the optimal childcare plan for each child. Specifically, the generative AI model uses the analysis results to suggest activities such as "building games using blocks" and "animal-themed learning activities."

[0538] 5. Scheduling and Proposals

[0539] The server then provides suggestions for incorporating the generated childcare plan into the caregiver's daily schedule, which the caregiver can review and modify as necessary.

[0540] 6. Feedback to parents

[0541] The server then notifies the parents of the generated childcare plan and how to support them at home. For example, it could send a notification to the parents' smartphone and provide advice such as, "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well."

[0542] Specific examples

[0543] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[0544] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[0545] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[0546] 3. The device sends this data to the cloud server.

[0547] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[0548] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[0549] 6. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[0550] 7. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[0551] In this way, this system allows caregivers to provide the best possible care plan for each child and strengthen cooperation with parents, thereby maximizing the individuality and interests of each child.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[0555] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[0556] Step 2:

[0557] The terminal transmits the input diary data and image data to the cloud server.

[0558] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[0559] Step 3:

[0560] The server analyzes the received diary data using natural language processing (NLP) technology.

[0561] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[0562] Step 4:

[0563] The server analyzes the received image data using image recognition technology.

[0564] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[0565] Step 5:

[0566] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[0567] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[0568] Step 6:

[0569] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[0570] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[0571] Step 7:

[0572] The user (caregiver) checks the proposed childcare plan, modifies it as necessary, and then approves it.

[0573] Specific operation: The caregiver checks the proposed schedule on the tablet and clicks the approval button.

[0574] Step 8:

[0575] The server notifies the parents of the generated childcare plan and how to support them at home.

[0576] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[0577] Step 9:

[0578] The user (guardian) receives a notification and provides support at home.

[0579] Specific action: Parents check the notification and incorporate play with blocks at home.

[0580] This is the specific processing flow of the system. This process allows caregivers and parents to work together to provide optimal care tailored to the individuality and interests of each child.

[0581] Example 1

[0582] 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."

[0583] Conventional childcare systems have difficulty efficiently generating individual childcare plans based on each child's individuality and interests. Furthermore, they lack a system for providing parents with support at home, which hinders cooperation between childcare workers and parents. There is a need to improve this situation by providing childcare plans that maximize children's individuality and interests, and to strengthen cooperation between childcare workers and parents.

[0584] 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.

[0585] In this invention, the server includes a means for inputting diary data and image data, a means for transmitting the input diary data and image data to the cloud server, a means for analyzing the input diary data and image data, a means for generating an individual childcare plan based on the analysis results, a means for incorporating the generated childcare plan into a childcare schedule, and a means for notifying parents of support methods at home. This makes it possible to efficiently provide an optimal childcare plan for each child and strengthen cooperation between caregivers and parents.

[0586] "Diary data" is textual information recorded by caregivers about children's daily activities, behavior, interests, etc.

[0587] "Image data" is visual information stored in digital format, such as drawings and crafts created by children.

[0588] A "cloud server" is a server for storing, processing, and managing data remotely via the Internet.

[0589] "Natural language processing" is a technology for analyzing human language and understanding its meaning and structure.

[0590] "Image recognition technology" is a technology for analyzing image data and understanding or classifying its content.

[0591] A "generative AI model" is an artificial intelligence model trained to automatically generate appropriate childcare plans from input data.

[0592] A "childcare schedule" is a schedule for planning daily childcare activities according to time and order.

[0593] A "guardian" is a person responsible for the protection and upbringing of a child.

[0594] "Support methods" are specific measures and advice to support children at home.

[0595] MODE FOR CARRYING OUT THE INVENTION

[0596] The present invention relates to a system for analyzing diary data and image data provided by caregivers and proposing individual childcare plans. The system includes an input unit, a data analysis unit, a generation unit, a scheduling unit, and a notification unit. The following describes an embodiment of the present invention.

[0597] Input Method

[0598] The user (caregiver) records the daily childcare diary using a device such as a tablet or smartphone. When a child draws a picture, the image is scanned into the device using a scanning app and saved as digital data. The childcare diary is recorded by entering text into a dedicated app, such as "Haruto was engrossed in playing with blocks for a long time today." The image data of the drawing is automatically saved in a dedicated folder.

[0599] Data transmission method

[0600] The device sends the entered diary data and image data to the cloud server. The transmission process is done in real time, and all data is encrypted using TLS (Transport Layer Security). The diary data is sent in text format, and the image data is sent in JPEG or PNG format.

[0601] Data Analysis Methods

[0602] The server analyzes the diary data and image data received on the cloud. It applies natural language processing (NLP) technology to the text in the diary data to extract keywords, such as "playing with blocks." It also applies image recognition technology to the image data to identify objects and color patterns drawn in the drawings. These analyses use TensorFlow's NLP model and OpenCV's image recognition algorithm.

[0603] How to generate childcare plans

[0604] The server uses a generative AI model (e.g., GPT-3 model) based on the analysis results to generate the optimal childcare plan for each child. Specifically, the server inputs the following prompt sentence into the generative AI model, which then outputs the childcare plan:

[0605] "According to the daycare diary, Haruto was engrossed in playing with blocks for long periods of time. Based on this information, please generate the optimal daycare plan for Haruto."

[0606] "From the image analysis of his drawing, we know that Haruto likes animals. Use this information to create suggestions for animal-themed learning activities."

[0607] The generated childcare plan is saved in a data format such as JSON and handed over to the next process.

[0608] Scheduling Methods

[0609] The server then proposes incorporating the generated childcare plan into the childcare worker's daily schedule. Using an optimization algorithm, the server appropriately places the new plan within the existing schedule. The proposal is then sent to the childcare worker's device, where the childcare worker can review the details in the dedicated app and make any necessary corrections or approvals.

[0610] Notification means

[0611] The server then notifies the parents of the generated childcare plan and how to support them at home by sending a push notification to their smartphone, providing specific advice such as:

[0612] "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well."

[0613] Notifications will also be displayed within the parent-only app, allowing parents to view past childcare plans and feedback.

[0614] As described above, by implementing the present invention, caregivers can efficiently provide optimal care plans for individual children and strengthen cooperation with parents.

[0615] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0616] Program processing steps

[0617] Step 1: User Data Entry

[0618] Users (caregivers) enter daily childcare diary entries using a tablet or smartphone. When a child draws a picture, the image is captured using a scanning app and saved in digital format.

[0619] Input: Childcare diary text data, drawing image data

[0620] Output: Digital logbook data and image data

[0621] Specific operations: The daycare diary involves entering text such as "Haruto was engrossed in playing with blocks for a long time today" into a dedicated app, taking a picture of the drawing with the smartphone camera, and importing it using the scanning function.

[0622] Step 2: Send data to the server

[0623] The terminal transmits the input diary data and image data to the cloud server in real time.

[0624] Input: Journal data and image data stored on the device

[0625] Output: Data stored on the cloud server

[0626] Specific operation: Data is encrypted and transmitted using TLS (Transport Layer Security). Diary data is transmitted in text format, and image data is transmitted in JPEG or PNG format.

[0627] Step 3: Data analysis

[0628] The server analyzes the diary data and image data received on the cloud. The diary text data is analyzed using natural language processing (NLP) technology, and the image data is analyzed using image recognition technology.

[0629] Input: Journal data and image data stored on the cloud server

[0630] Output: Analysis results (keyword extraction, object identification)

[0631] Specific operation: The diary data is analyzed using an NLP model (e.g., TensorFlow) to extract keywords such as "playing with blocks," and the image data is analyzed using OpenCV to identify drawn objects and color patterns.

[0632] Step 4: Generate a childcare plan

[0633] The server generates a childcare plan using a generative AI model (e.g., a GPT-3 model) based on the analysis results.

[0634] Input: Analysis results (keyword extraction results from text data and object identification results from image data)

[0635] Output: Individual childcare plan (e.g., JSON format)

[0636] Specific operation: The following prompt is input into the generative AI model: "According to the daycare diary, Haruto spent a lot of time engrossed in playing with blocks. Based on this information, please generate the optimal daycare plan for Haruto." The generated daycare plan is saved in JSON format.

[0637] Step 5: Scheduling and Proposal

[0638] The server makes a proposal to incorporate the generated childcare plan into the childcare provider's daily schedule.

[0639] Input: Generated childcare plan, existing schedule

[0640] Output: Optimized schedule proposal

[0641] How it works: Applying an optimization algorithm to add new plans to an existing schedule. The proposed plan is sent to the caregiver's device, who can then review the details in the dedicated app and make any necessary changes or approvals.

[0642] Step 6: Feedback to parents

[0643] The server notifies the parents of the generated childcare plan and how to support them at home.

[0644] Input: Generated childcare plan, support methods at home

[0645] Output: Notification to parents (push notification, display of dedicated app)

[0646] Specific operation: A push notification is sent to the parent's smartphone, providing specific advice such as, "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well." The notification is also displayed in the parent-only app, where past childcare plans and feedback can also be viewed.

[0647] (Application example 1)

[0648] 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."

[0649] Currently, school supply stores lack the means to recommend the most suitable school supplies based on each child's personality and interests. Furthermore, parents are not provided with adequate individual suggestions or support, which makes it difficult for them to make effective choices. This results in the problem of not maximizing children's motivation and interest in learning.

[0650] 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.

[0651] In this invention, the server includes a means for inputting diary data and image data, a means for analyzing the input diary data and image data, a means for generating an individual childcare plan and school supplies based on the analysis results, a means for informing parents of support methods at home, and a means for displaying the suggested school supplies. This makes it possible to suggest optimal school supplies based on the personality and interests of each child, thereby strengthening support for parents.

[0652] "Diary data" is text data recorded by caregivers or store staff about daily activities, children's behavior, and interests.

[0653] "Image data" refers to children's drawings and other visual content stored in digital format.

[0654] "Analysis means" refers to the technology or method for analyzing the input diary data and image data and extracting useful information and trends based on the analysis.

[0655] "Generation means" refers to technology or a system for generating individual childcare plans and school supply proposals based on the analyzed results.

[0656] A "childcare schedule" is a planned timetable that allows caregivers to manage a child's daily activities and carry them out in the appropriate order.

[0657] "Means of notification" refers to the techniques and methods used to communicate important information and support methods to parents and other relevant parties.

[0658] "School supplies" is a general term for tools and teaching materials used by children to study.

[0659] "Display means" refers to devices or techniques for visually presenting the proposed school supplies or plans.

[0660] "Natural Language Processing (NLP)" is the technology that enables computers to understand, interpret, and generate human language.

[0661] "Image recognition technology" is a technology in which a computer analyzes the content of an image and recognizes specific features and patterns.

[0662] A "generative AI model" is an artificial intelligence system that automatically generates content based on large amounts of data.

[0663] The present invention relates to a system that analyzes input data and proposes individual childcare plans and school supplies. This system aims to provide optimal childcare plans and school supplies by having a childcare worker or store clerk input diary data and image data, and analyzing the data.

[0664] Overall system configuration

[0665] The system has the following main components:

[0666] 1. Journal and image data input method

[0667] - A device such as a smartphone or tablet for childcare workers or store staff to input diary data and image data.

[0668] 2. Data analysis methods

[0669] - Natural language processing (NLP) and image recognition technologies are used to analyze the input data. Specifically, NLP libraries such as Google BERT and SpaCy, and image recognition libraries such as OpenCV and TensorFlow are used.

[0670] 3. Childcare plan and school supply generator

[0671] - Generative AI models (e.g., GPT-4) that generate personalized childcare plans or school supplies based on analytical results.

[0672] 4. Means of notification

[0673] - APIs and communication methods to inform parents about support methods at home and suggested school supplies.

[0674] 5. Display means

[0675] - An interface for displaying suggested school supplies and childcare plans to store staff and parents.

[0676] Data entry examples

[0677] The user (store clerk) inputs the diary data and image data from a smartphone. For example, a picture drawn by a child can be scanned using the smartphone camera.

[0678] For example, if a child is interested in dinosaurs, the store clerk might enter "Today's child drew a dinosaur" into the journal data and then scan the dinosaur drawing. The following is an example of a prompt that might be entered:

[0679] "Today's children drew pictures of dinosaurs."

[0680] Data analysis

[0681] The server receives the diary data and image data and analyzes the data using natural language processing and image recognition technology. Specifically, NLP technology is used to analyze the text data in the diary and extract the interests and activities described. Image recognition technology is used to analyze the content of the drawings and identify the objects drawn.

[0682] Plan and generate school supplies

[0683] Based on the analyzed data, the server uses a generative AI model to propose optimal childcare plans and school supplies, such as "building games with blocks" and "dinosaur-related learning materials."

[0684] Scheduling and Notifications

[0685] The generated childcare plan and school supply suggestions are sent to the user from the server. Store staff and parents can check the suggestions on their smartphones or tablets. For example, parents may be notified of support methods such as, "Your child is interested in dinosaurs, so it would be good to use dinosaur-related learning materials."

[0686] Overall implementation example

[0687] As a concrete example, the following steps can be considered:

[0688] 1. The store clerk uses a smartphone to enter the child's diary data and a picture of a dinosaur.

[0689] 2. The entered data is sent to the cloud server.

[0690] 3. The server analyzes the data and generates school supplies and childcare plans based on the child's interests and personality.

[0691] 4. The server displays the generated suggestions on the smartphone and notifies the parents.

[0692] 5. The store staff and parents review the suggestions and put them into action.

[0693] In this way, the system of the present invention can suggest the most suitable school supplies based on the child's personality and interests, strengthen cooperation with parents, and improve the child's learning experience.

[0694] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0695] Step 1:

[0696] The user enters diary data and image data on a smartphone. The user opens the diary app and scans the child's drawing, which inputs text and image data. The input data is temporarily saved on the device.

[0697] Input: Journal text data, scanned drawing images

[0698] Output: Temporarily saved journal data and image data

[0699] Step 2:

[0700] The device sends the entered diary data and image data to the cloud server, and the data is uploaded to the cloud server using an HTTP request.

[0701] Input: Temporarily saved journal data and image data

[0702] Output: Diary data and image data sent to the cloud server

[0703] Step 3:

[0704] The server analyzes the received diary data and image data. It uses natural language processing (NLP) to extract important keywords and interests from the diary text data, and uses image recognition technology to analyze the content of the drawings.

[0705] Input: Diary data and image data stored on the cloud server

[0706] Output: Analyzed diary data and image data features

[0707] Step 4:

[0708] The server generates personalized childcare plans and school supplies based on the analyzed data using a generative AI model (e.g., GPT-4). The generative AI model receives the analysis results as prompts and generates optimal suggestions.

[0709] Input: Analyzed diary data and image data features

[0710] Output: Generated childcare plans and school supply suggestions

[0711] Step 5:

[0712] The server incorporates the generated childcare plan and school supply suggestions into the childcare provider's daily schedule and notifies the user of the contents. The childcare provider can then review the proposed plan and make any necessary adjustments.

[0713] Input: Generated childcare plans and school supply suggestions

[0714] Output: Childcare plan and school supply suggestions notified to the user, and childcare schedule

[0715] Step 6:

[0716] The server notifies the parents of the generated childcare plan and school supply suggestions and suggests ways to support the child at home. The parents can receive the suggestions and put them into practice.

[0717] Input: Child care plans and school supply suggestions to inform parents

[0718] Output: Parents are notified of childcare plans, school supply suggestions, and support methods

[0719] 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.

[0720] The present invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system primarily uses diary data and image data entered by caregivers to analyze each child's personality and interests, and then generates an optimal childcare plan based on that data. Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing it to evaluate the emotional compatibility of the childcare plan and dynamically adjust the plan.

[0721] System Overview

[0722] The system has the following main components:

[0723] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[0724] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[0725] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[0726] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[0727] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[0728] Emotion Engine: A technology that recognizes the user's emotions, evaluates the emotional compatibility of the childcare plan, and dynamically adjusts the childcare plan.

[0729] Program processing

[0730] 1. Enter your journal and drawing details

[0731] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[0732] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[0733] 2. Sending data to the server

[0734] The terminal transmits the input diary data and image data to the cloud server.

[0735] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[0736] 3. Data Analysis

[0737] The server analyzes the received diary data using natural language processing (NLP) technology.

[0738] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[0739] 4. Image Analysis

[0740] The server analyzes the received image data using image recognition technology.

[0741] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[0742] 5. Create a childcare plan

[0743] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[0744] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[0745] 6. Emotional compatibility assessment

[0746] The server evaluates the emotions of the caregiver and the child based on the care plan generated using the emotion engine and daily feedback.

[0747] Specific operation: For example, if a caregiver writes in their journal after implementing a childcare plan, "Haruto seemed to be having a lot of fun," the emotion engine analyzes this feedback and rates the plan's emotional compatibility highly.

[0748] 7. Dynamic adjustment of childcare plans

[0749] The server dynamically adjusts the childcare plan based on the analysis results using an emotion engine.

[0750] Specific action: Update the childcare plan to avoid elements that are deemed incompatible and incorporate new compatible elements.

[0751] 8. Scheduling and Proposals

[0752] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[0753] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[0754] 9. Feedback to Parents

[0755] The server notifies the parents of the generated childcare plan and how to support them at home.

[0756] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[0757] 10. Support at home

[0758] The user (guardian) receives a notification and provides support at home.

[0759] Specific action: Parents check the notification and incorporate play with blocks at home.

[0760] Specific examples

[0761] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[0762] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[0763] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[0764] 3. The device sends this data to the cloud server.

[0765] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[0766] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[0767] 6. The server uses the emotion engine to evaluate the feedback from Haruto and the caregivers and evaluate the emotional suitability of the care plan. For example, it reflects the feedback that "Haruto seemed to be having a lot of fun."

[0768] 7. The server dynamically adjusts the childcare plan as needed, removing elements that are deemed incompatible and updating the plan to include new compatible elements.

[0769] 8. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[0770] 9. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[0771] 10. The user (parent) checks the notification and starts playing with blocks at home.

[0772] In this way, this system allows caregivers to provide optimal childcare plans for each child and strengthen cooperation with parents, maximizing the individuality and interests of each child. Furthermore, the use of an emotion engine makes it possible to provide childcare plans that take into account the emotional aspects of both the child and the caregiver.

[0773] The processing flow will be explained below.

[0774] Step 1:

[0775] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[0776] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[0777] Step 2:

[0778] The terminal transmits the input diary data and image data to the cloud server.

[0779] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[0780] Step 3:

[0781] The server analyzes the received diary data using natural language processing (NLP) technology.

[0782] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[0783] Step 4:

[0784] The server analyzes the received image data using image recognition technology.

[0785] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[0786] Step 5:

[0787] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[0788] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[0789] Step 6:

[0790] The server evaluates the emotions of the caregiver and the child based on the care plan generated using the emotion engine and daily feedback.

[0791] Specific operation: For example, if a caregiver writes in their journal after implementing a childcare plan, "Haruto seemed to be having a lot of fun," the emotion engine analyzes this feedback and rates the plan's emotional compatibility highly.

[0792] Step 7:

[0793] The server dynamically adjusts the childcare plan based on the analysis results using an emotion engine.

[0794] Specific action: Update the childcare plan to avoid elements that are deemed incompatible and incorporate new compatible elements.

[0795] Step 8:

[0796] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[0797] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[0798] Step 9:

[0799] The user (caregiver) checks the proposed childcare plan, modifies it as necessary, and then approves it.

[0800] Specific operation: The caregiver checks the proposed schedule on the tablet and clicks the approval button.

[0801] Step 10:

[0802] The server notifies the parents of the generated childcare plan and how to support them at home.

[0803] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[0804] Step 11:

[0805] The user (guardian) receives a notification and provides support at home.

[0806] Specific action: Parents check the notification and incorporate play with blocks at home.

[0807] The above is the specific processing flow of the system. This process allows caregivers and parents to work together to provide optimal care tailored to the child's personality and interests. In addition, the introduction of an emotion engine makes it possible to provide care plans that take into account the emotional aspects of the child and caregivers.

[0808] Example 2

[0809] 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."

[0810] Conventional childcare plan creation systems are limited to providing childcare plans using diary data and image data, making it difficult to generate plans that fully reflect each child's personality and interests. Furthermore, because the plans do not take into account the emotional aspects of the child or caregiver, they can sometimes be ineffective after implementation. There was a need for a system that could solve these problems, provide optimal childcare plans based on each child's personality and interests, and dynamically adjust them by evaluating emotional compatibility.

[0811] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0812] In this invention, the server includes means for inputting diary data and image data, means for analyzing the input diary data and image data, means for generating an individual childcare plan based on the analysis results, means for incorporating the generated childcare plan into a childcare schedule, means for informing the guardian of support methods at home, means for evaluating the user's emotions and evaluating the emotional compatibility of the childcare plan, and means for dynamically adjusting the childcare plan. This makes it possible to provide an optimal childcare plan that corresponds to the child's personality and interests, and further to evaluate the emotional compatibility of the childcare plan and dynamically adjust it based on feedback after implementation.

[0813] "Diary data" is text data in which caregivers record their daily childcare activities and the behavior of children.

[0814] "Image data" refers to image files scanned or photographed when a child draws or makes crafts.

[0815] "Natural language processing technology" is a technology that analyzes text data and extracts specific keywords and phrases.

[0816] "Image recognition technology" is a technology that analyzes image data and recognizes the objects and patterns depicted in it.

[0817] A "generative AI model" is an artificial intelligence model that automatically generates the optimal childcare plan for each individual child based on the analysis results.

[0818] The "emotion engine" is a technology that evaluates the emotional feedback of users and children and evaluates the emotional suitability of childcare plans based on that feedback.

[0819] A "childcare schedule" is a schedule that lists the daily childcare activities and timetables that childcare workers carry out.

[0820] "Means for notifying parents" refers to communication technologies used to inform parents of the created childcare plan and how to provide support at home.

[0821] "Dynamic adjustment" refers to the process of changing and updating childcare plans in real time based on user feedback and emotional assessment.

[0822] A "server" is a computer system that performs processes such as data analysis, childcare plan generation, emotion evaluation, and dynamic adjustment within a cloud environment.

[0823] The present invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system primarily uses diary data and image data entered by caregivers to analyze each child's personality and interests, and then generates an optimal childcare plan based on that data. Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing it to evaluate the emotional compatibility of the childcare plan and dynamically adjust the plan.

[0824] System Overview

[0825] The system has the following main components:

[0826] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[0827] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[0828] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[0829] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[0830] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[0831] Emotion Engine: A technology that recognizes the user's emotions, evaluates the emotional compatibility of the childcare plan, and dynamically adjusts the childcare plan.

[0832] Hardware and software used

[0833] Hardware: Tablets, smartphones, and cloud servers used by childcare workers.

[0834] Software: Google Cloud Natural Language API and Amazon Comprehend are used for natural language processing, Google Cloud Vision API and Amazon Rekognition for image recognition, and generative AI models (e.g., OpenAI's GPT-4) are used to generate childcare plans.

[0835] Specific examples

[0836] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[0837] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[0838] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[0839] 3. The device sends this data to the cloud server.

[0840] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[0841] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[0842] 6. The server uses the emotion engine to evaluate the feedback from Haruto and the caregivers and evaluate the emotional suitability of the care plan. For example, it reflects the feedback that "Haruto seemed to be having a lot of fun."

[0843] 7. The server dynamically adjusts the childcare plan as needed, removing elements that are deemed incompatible and updating the plan to include new compatible elements.

[0844] 8. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[0845] 9. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[0846] 10. The user (parent) checks the notification and starts playing with blocks at home.

[0847] Prompt Sentence Examples

[0848] "What activities have you been interested in lately, Haruto?"

[0849] "Generate the optimal childcare plan based on the following text and image data. Text: 'Haruto has been obsessed with playing with blocks lately.' Image: Pictures of animals."

[0850] "Dynamic adjustments to care plans should be made taking into account caregiver feedback and emotional fit."

[0851] By implementing the present invention, it is possible to provide an optimal childcare plan tailored to the individual characteristics and interests of each child, and to dynamically adjust the plan to take into account emotional compatibility, thereby improving the quality of childcare and enabling smooth communication between caregivers and parents.

[0852] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0853] Step 1:

[0854] The user (caregiver) uses a device such as a tablet to enter daily childcare diaries, and when a child draws a picture, the image is scanned and the data is entered.

[0855] Specific actions: The caregiver opens the app on the tablet and writes, "Today, Child A was absorbed in playing with blocks for a long time." The caregiver also scans the animal drawing that Child A has made with the tablet's camera and saves it as image data.

[0856] Input: Journal text data, scanned image data

[0857] Output: Journal text data and image data temporarily saved on the device

[0858] Step 2:

[0859] The terminal transmits the input diary data and image data to a cloud server.

[0860] Specific operation: The tablet uploads input data to a cloud server in real time via Wi-Fi.

[0861] Input: Temporarily saved journal text data and image data

[0862] Output: Journal text data and image data uploaded to the cloud server

[0863] Step 3:

[0864] The server analyzes the received diary data using natural language processing (NLP) technology.

[0865] Specific operation: The NLP algorithm extracts keywords such as "playing with blocks" and "interest" from the text "Today, Child A was engrossed in playing with blocks for a long time" and identifies the child's focus of interest.

[0866] Input: Journal text data

[0867] Output: Keywords extracted from text data and their analysis results

[0868] Step 4:

[0869] The server analyzes the received image data using image recognition technology.

[0870] How it works: Image recognition algorithms analyze scanned images to identify objects like animals and playground equipment, as well as the frequency and patterns of color usage.

[0871] Input: Scanned image data

[0872] Output: Object information extracted from image data and its analysis results

[0873] Step 5:

[0874] The server uses a generative AI model to generate an individualized childcare plan based on the analysis results.

[0875] Specific operation: Based on the results of NLP and image recognition analysis, the generative AI model proposes childcare activities suitable for Child A. It generates childcare plans such as "building games using blocks" and "animal-themed learning activities."

[0876] Input: Analysis results using NLP and image recognition technology

[0877] Output: Generated individual childcare plan

[0878] Step 6:

[0879] The server evaluates the generated childcare plan and the user's feedback using the emotion engine.

[0880] Specific operation: After implementing a childcare plan, when a caregiver gives feedback such as "Child A seemed to enjoy it very much," the emotion engine analyzes this feedback and evaluates the emotional compatibility of the plan.

[0881] Input: Generated childcare plan, feedback from caregivers

[0882] Output: Emotional compatibility evaluation result

[0883] Step 7:

[0884] The server dynamically adjusts the childcare plan based on the emotion engine's evaluation.

[0885] Specific behavior: The system removes elements that are deemed incompatible and updates the plan to include new, compatible elements. For example, activities that children dislike are removed.

[0886] Input: Emotional compatibility assessment results, existing childcare plan

[0887] Output: Dynamically adjusted childcare plan

[0888] Step 8:

[0889] The server proposes incorporating the generated childcare plan into the schedule of the childcare provider.

[0890] Specific operation: The server references the childcare worker's daily schedule and generates a plan that incorporates the proposed childcare activities at the optimal time. The childcare worker then reviews and approves the proposal.

[0891] Input: Dynamically adjusted childcare plans, childcare worker schedules

[0892] Output: A childcare plan that is integrated into the childcare worker's schedule

[0893] Step 9:

[0894] The device notifies the parent of the generated childcare plan and how to provide support at home.

[0895] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Child A is interested in playing with blocks, so it would be a good idea to play with them at home."

[0896] Input: Childcare plan information, parental contact information

[0897] Output: Parental notification message

[0898] Step 10:

[0899] The user (parent) receives a notification and provides support at home.

[0900] Action: Parents check the notification and incorporate block play into their children's home activities, ensuring the quality of childcare is maintained in the home environment.

[0901] Input: Parental notification message

[0902] Output: Home implementation report (optional)

[0903] (Application example 2)

[0904] 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."

[0905] With conventional childcare plan generation systems, it was difficult to effectively generate specific childcare plans that reflected a child's personality and interests, and there was a problem of insufficient support at home due to a lack of cooperation between caregivers and parents. The purpose of this invention is to solve these problems, generate an optimal childcare plan based on a child's interests and characteristics, and realize closer cooperation between caregivers and parents.

[0906] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0907] In this invention, the server includes means for inputting diary data and image data, means for analyzing the input diary data and image data, means for generating an individual childcare plan based on the analysis results, means for incorporating the generated childcare plan into a childcare schedule, means for notifying parents of support methods at home, means for analyzing text data of the childcare diary using natural language processing technology and performing keyword extraction and sentiment analysis, means for analyzing pictures drawn by the child using image processing technology and identifying recognized objects, means for generating a childcare plan based on the analysis results using a generative AI model, and means for notifying childcare workers and parents of the generated childcare plan and sentiment analysis results. This makes it possible to generate a childcare plan that is optimal for the child's interests and personality, strengthens cooperation between childcare workers and parents, and enables effective support at home.

[0908] "Diary data" is text data in which caregivers record children's daily activities and conditions.

[0909] "Image data" refers to digital images obtained by scanning or photographing drawings or artwork created by children.

[0910] "Natural language processing technology" is a technology for analyzing text data and extracting meaning and emotion.

[0911] "Image processing technology" is a technology for analyzing digital images to recognize objects and features.

[0912] A "generative AI model" is an artificial intelligence model that generates new data and suggestions based on input data.

[0913] A "childcare plan" is a specific childcare activity plan proposed based on a child's personality and interests.

[0914] A "childcare schedule" is a plan that incorporates a childcare plan into specific times and dates.

[0915] "Childcare workers" are professionals involved in children's daily lives and education.

[0916] "Guardians" are family members who are responsible for raising and educating children.

[0917] "Sentiment analysis" is a technique for extracting and evaluating emotions from text data and feedback.

[0918] A "recognized object" is an object or shape that has been identified within a digital image using image processing techniques.

[0919] This invention relates to a system that automates the creation and management of childcare plans. This system inputs and analyzes diary data and image data, and creates individual childcare plans based on the analysis results. It also has the function of incorporating the childcare plans into childcare schedules and informing parents of support methods at home.

[0920] Hardware and software used

[0921] This system uses the following hardware and software.

[0922] Hardware:

[0923] Smartphones and tablets: Devices that allow users (caregivers) to enter daily childcare diaries and scan drawings by children.

[0924] Server: A central processing unit for performing the processes of data analysis and childcare plan generation.

[0925] software:

[0926] Python: Program implementation language.

[0927] OpenCV: A library for image processing.

[0928] TextBlob: A natural language processing library for analyzing text data.

[0929] Transformers: Libraries for using generative AI and sentiment analysis models.

[0930] Data processing and calculation

[0931] 1. Data Entry:

[0932] Caregivers enter daily childcare diaries into text on smartphones or tablets, and scan pictures drawn by children and save them as image data.

[0933] 2. Data transmission:

[0934] The terminal transmits the input diary data and image data to the server.

[0935] 3. Data Analysis:

[0936] The server uses TextBlob to perform text analysis of the diary data, extracting keywords and carrying out sentiment analysis.

[0937] The image data is analyzed using OpenCV to identify the recognized objects in the child's drawing.

[0938] 4. Childcare plan generation:

[0939] The server uses a generative AI model (Transformers library) based on the analysis results to generate an individual childcare plan.

[0940] 5. Notice:

[0941] The generated childcare plan and the results of the sentiment analysis are then sent to caregivers and parents via a smartphone app or email.

[0942] Specific examples

[0943] As a concrete example of this system, consider the following scenario: A caregiver records on a tablet that "Today, my child was engrossed in playing with blocks for a long time," and then scans and saves a drawing of an animal the child drew. This data is sent to a server in real time via Wi-Fi. The server analyzes the text and images using TextBlob and OpenCV. Based on the extracted keywords (e.g., "playing with blocks") and recognized objects (e.g., "animals"), a generative AI model generates a childcare plan such as: "Next time, we will use building games with blocks as teaching materials and incorporate animal-themed learning activities." The generated plan and the results of the sentiment analysis are then sent to the caregiver's smartphone, and home activities based on the child's interests are also suggested to parents.

[0944] Prompt Sentence Examples

[0945] Your child is interested in building blocks and animals. Create a suitable childcare plan.

[0946] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0947] Step 1:

[0948] Data Entry

[0949] Users (caregivers) enter their daily childcare diary into text on their smartphones or tablets, and scan pictures drawn by children and save them as image data.

[0950] Input: Text data from the daycare diary, recording that "Today the children were absorbed in playing with blocks for a long time," and image data of drawings made by the children.

[0951] Specific actions: The caregiver uses the tablet app to enter the childcare diary into the text field and then scans it using the camera function.

[0952] Step 2:

[0953] Data transmission

[0954] The terminal transmits the input diary data and image data to the server.

[0955] Input: Text data and image data.

[0956] Data processing: Converts input data into a format that can be sent to the cloud server.

[0957] Output: Text and image data uploaded to the cloud server.

[0958] Specific operation: The tablet uploads data to a cloud server in real time via Wi-Fi.

[0959] Step 3:

[0960] Data analysis

[0961] The server uses TextBlob to perform text analysis of the diary data, extracting keywords and carrying out sentiment analysis.

[0962] Input: Text data uploaded to the cloud server.

[0963] Data Operations: Use TextBlob for natural language processing to parse keywords and sentiment from text.

[0964] Output: A list of keywords and sentiment scores as the analysis results.

[0965] Specific operation: Using the TextBlob library, extract keywords such as "playing with blocks" and calculate sentiment values ​​such as positive or negative.

[0966] Step 4:

[0967] Image analysis

[0968] The server uses OpenCV to analyze the image data and identify recognizable objects in the child's drawing.

[0969] Input: Image data stored on a cloud server.

[0970] Data Computation: Image analysis is performed using OpenCV to recognize objects in the image.

[0971] Output: A list of recognized objects.

[0972] Specific operation: Uses the OpenCV library to identify objects such as "animals" from scanned images.

[0973] Step 5:

[0974] Childcare plan generation

[0975] The server uses a generative AI model based on the analysis results to generate an individual childcare plan.

[0976] Input: Text analysis results (keyword list and emotion rating), image analysis results (list of recognized objects).

[0977] Data calculation: Using a generative AI model, an optimal childcare plan is generated based on the input results.

[0978] Output: The generated childcare plan.

[0979] Specific behavior: Using a generative AI model from the Transformers library, it generates a care plan based on the prompt, "My child is interested in playing with blocks and animals. Please generate an appropriate care plan."

[0980] Step 6:

[0981] notification

[0982] The server notifies the generated childcare plan and the emotion analysis results to the childcare provider and the parent.

[0983] Input: Generated childcare plan and sentiment analysis results.

[0984] Data processing: Create notification messages and send them to smartphone apps or emails.

[0985] Output: Notification message sent to caregivers and parents.

[0986] Specific operation: The server generates a notification message, sends an app notification to the childcare worker's smartphone, and also sends an email to the parents informing them of how to provide support at home.

[0987] 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.

[0988] 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.

[0989] 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.

[0990] [Third embodiment]

[0991] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0992] 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.

[0993] 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).

[0994] 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.

[0995] 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.

[0996] 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).

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] 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.

[1002] 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."

[1003] This invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system aims to analyze each child's personality and interests using diary data and image data entered primarily by caregivers, and to generate an optimal childcare plan based on that analysis.

[1004] System Overview

[1005] The system has the following main components:

[1006] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[1007] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[1008] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[1009] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[1010] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[1011] Program processing

[1012] 1. Enter your journal and drawing details

[1013] The user (caregiver) records the daily childcare diary and scans the images of the children's drawings, entering the data digitally using a device such as a tablet.

[1014] 2. Sending data to the server

[1015] The device sends the entered log data and image data to a cloud server, where the data is uploaded in real time.

[1016] 3. Data Analysis

[1017] The server uses natural language processing (NLP) and image recognition technologies to analyze the received data. NLP is used to analyze the text data of the diary and extract descriptions of, for example, "playing with blocks." Image recognition is used to analyze the image data of the drawings and identify the frequency of color use and the objects drawn.

[1018] 4. Create a childcare plan

[1019] Based on the analysis results, the server generates the optimal childcare plan for each child. Specifically, the generative AI model uses the analysis results to suggest activities such as "building games using blocks" and "animal-themed learning activities."

[1020] 5. Scheduling and Proposals

[1021] The server then provides suggestions for incorporating the generated childcare plan into the caregiver's daily schedule, which the caregiver can review and modify as necessary.

[1022] 6. Feedback to parents

[1023] The server then notifies the parents of the generated childcare plan and how to support them at home. For example, it could send a notification to the parents' smartphone and provide advice such as, "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well."

[1024] Specific examples

[1025] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[1026] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[1027] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[1028] 3. The device sends this data to the cloud server.

[1029] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[1030] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[1031] 6. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[1032] 7. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[1033] In this way, this system allows caregivers to provide the best possible care plan for each child and strengthen cooperation with parents, thereby maximizing the individuality and interests of each child.

[1034] The processing flow will be explained below.

[1035] Step 1:

[1036] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[1037] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[1038] Step 2:

[1039] The terminal transmits the input diary data and image data to the cloud server.

[1040] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[1041] Step 3:

[1042] The server analyzes the received diary data using natural language processing (NLP) technology.

[1043] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[1044] Step 4:

[1045] The server analyzes the received image data using image recognition technology.

[1046] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[1047] Step 5:

[1048] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[1049] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[1050] Step 6:

[1051] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[1052] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[1053] Step 7:

[1054] The user (caregiver) checks the proposed childcare plan, modifies it as necessary, and then approves it.

[1055] Specific operation: The caregiver checks the proposed schedule on the tablet and clicks the approval button.

[1056] Step 8:

[1057] The server notifies the parents of the generated childcare plan and how to support them at home.

[1058] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[1059] Step 9:

[1060] The user (guardian) receives a notification and provides support at home.

[1061] Specific action: Parents check the notification and incorporate play with blocks at home.

[1062] This is the specific processing flow of the system. This process allows caregivers and parents to work together to provide optimal care tailored to the individuality and interests of each child.

[1063] Example 1

[1064] 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."

[1065] Conventional childcare systems have difficulty efficiently generating individual childcare plans based on each child's individuality and interests. Furthermore, they lack a system for providing parents with support at home, which hinders cooperation between childcare workers and parents. There is a need to improve this situation by providing childcare plans that maximize children's individuality and interests, and to strengthen cooperation between childcare workers and parents.

[1066] 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.

[1067] In this invention, the server includes a means for inputting diary data and image data, a means for transmitting the input diary data and image data to the cloud server, a means for analyzing the input diary data and image data, a means for generating an individual childcare plan based on the analysis results, a means for incorporating the generated childcare plan into a childcare schedule, and a means for notifying parents of support methods at home. This makes it possible to efficiently provide an optimal childcare plan for each child and strengthen cooperation between caregivers and parents.

[1068] "Diary data" is textual information recorded by caregivers about children's daily activities, behavior, interests, etc.

[1069] "Image data" is visual information stored in digital format, such as drawings and crafts created by children.

[1070] A "cloud server" is a server for storing, processing, and managing data remotely via the Internet.

[1071] "Natural language processing" is a technology for analyzing human language and understanding its meaning and structure.

[1072] "Image recognition technology" is a technology for analyzing image data and understanding or classifying its content.

[1073] A "generative AI model" is an artificial intelligence model trained to automatically generate appropriate childcare plans from input data.

[1074] A "childcare schedule" is a schedule for planning daily childcare activities according to time and order.

[1075] A "guardian" is a person responsible for the protection and upbringing of a child.

[1076] "Support methods" are specific measures and advice to support children at home.

[1077] MODE FOR CARRYING OUT THE INVENTION

[1078] The present invention relates to a system for analyzing diary data and image data provided by caregivers and proposing individual childcare plans. The system includes an input unit, a data analysis unit, a generation unit, a scheduling unit, and a notification unit. The following describes an embodiment of the present invention.

[1079] Input Method

[1080] The user (caregiver) records the daily childcare diary using a device such as a tablet or smartphone. When a child draws a picture, the image is scanned into the device using a scanning app and saved as digital data. The childcare diary is recorded by entering text into a dedicated app, such as "Haruto was engrossed in playing with blocks for a long time today." The image data of the drawing is automatically saved in a dedicated folder.

[1081] Data transmission method

[1082] The device sends the entered diary data and image data to the cloud server. The transmission process is done in real time, and all data is encrypted using TLS (Transport Layer Security). The diary data is sent in text format, and the image data is sent in JPEG or PNG format.

[1083] Data Analysis Methods

[1084] The server analyzes the diary data and image data received on the cloud. It applies natural language processing (NLP) technology to the text in the diary data to extract keywords, such as "playing with blocks." It also applies image recognition technology to the image data to identify objects and color patterns drawn in the drawings. These analyses use TensorFlow's NLP model and OpenCV's image recognition algorithm.

[1085] How to generate childcare plans

[1086] The server uses a generative AI model (e.g., GPT-3 model) based on the analysis results to generate the optimal childcare plan for each child. Specifically, the server inputs the following prompt sentence into the generative AI model, which then outputs the childcare plan:

[1087] "According to the daycare diary, Haruto was engrossed in playing with blocks for long periods of time. Based on this information, please generate the optimal daycare plan for Haruto."

[1088] "From the image analysis of his drawing, we know that Haruto likes animals. Use this information to create suggestions for animal-themed learning activities."

[1089] The generated childcare plan is saved in a data format such as JSON and handed over to the next process.

[1090] Scheduling Methods

[1091] The server then proposes incorporating the generated childcare plan into the childcare worker's daily schedule. Using an optimization algorithm, the server appropriately places the new plan within the existing schedule. The proposal is then sent to the childcare worker's device, where the childcare worker can review the details in the dedicated app and make any necessary corrections or approvals.

[1092] Notification means

[1093] The server then notifies the parents of the generated childcare plan and how to support them at home by sending a push notification to their smartphone, providing specific advice such as:

[1094] "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well."

[1095] Notifications will also be displayed within the parent-only app, allowing parents to view past childcare plans and feedback.

[1096] As described above, by implementing the present invention, caregivers can efficiently provide optimal care plans for individual children and strengthen cooperation with parents.

[1097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1098] Program processing steps

[1099] Step 1: User Data Entry

[1100] Users (caregivers) enter daily childcare diary entries using a tablet or smartphone. When a child draws a picture, the image is captured using a scanning app and saved in digital format.

[1101] Input: Childcare diary text data, drawing image data

[1102] Output: Digital logbook data and image data

[1103] Specific operations: The daycare diary involves entering text such as "Haruto was engrossed in playing with blocks for a long time today" into a dedicated app, taking a picture of the drawing with the smartphone camera, and importing it using the scanning function.

[1104] Step 2: Send data to the server

[1105] The terminal transmits the input diary data and image data to the cloud server in real time.

[1106] Input: Journal data and image data stored on the device

[1107] Output: Data stored on the cloud server

[1108] Specific operation: Data is encrypted and transmitted using TLS (Transport Layer Security). Diary data is transmitted in text format, and image data is transmitted in JPEG or PNG format.

[1109] Step 3: Data analysis

[1110] The server analyzes the diary data and image data received on the cloud. The diary text data is analyzed using natural language processing (NLP) technology, and the image data is analyzed using image recognition technology.

[1111] Input: Journal data and image data stored on the cloud server

[1112] Output: Analysis results (keyword extraction, object identification)

[1113] Specific operation: The diary data is analyzed using an NLP model (e.g., TensorFlow) to extract keywords such as "playing with blocks," and the image data is analyzed using OpenCV to identify drawn objects and color patterns.

[1114] Step 4: Generate a childcare plan

[1115] The server generates a childcare plan using a generative AI model (e.g., a GPT-3 model) based on the analysis results.

[1116] Input: Analysis results (keyword extraction results from text data and object identification results from image data)

[1117] Output: Individual childcare plan (e.g., JSON format)

[1118] Specific operation: The following prompt is input into the generative AI model: "According to the daycare diary, Haruto spent a lot of time engrossed in playing with blocks. Based on this information, please generate the optimal daycare plan for Haruto." The generated daycare plan is saved in JSON format.

[1119] Step 5: Scheduling and Proposal

[1120] The server makes a proposal to incorporate the generated childcare plan into the childcare provider's daily schedule.

[1121] Input: Generated childcare plan, existing schedule

[1122] Output: Optimized schedule proposal

[1123] How it works: Applying an optimization algorithm to add new plans to an existing schedule. The proposed plan is sent to the caregiver's device, who can then review the details in the dedicated app and make any necessary changes or approvals.

[1124] Step 6: Feedback to parents

[1125] The server notifies the parents of the generated childcare plan and how to support them at home.

[1126] Input: Generated childcare plan, support methods at home

[1127] Output: Notification to parents (push notification, display of dedicated app)

[1128] Specific operation: A push notification is sent to the parent's smartphone, providing specific advice such as, "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well." The notification is also displayed in the parent-only app, where past childcare plans and feedback can also be viewed.

[1129] (Application example 1)

[1130] 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."

[1131] Currently, school supply stores lack the means to recommend the most suitable school supplies based on each child's personality and interests. Furthermore, parents are not provided with adequate individual suggestions or support, which makes it difficult for them to make effective choices. This results in the problem of not maximizing children's motivation and interest in learning.

[1132] 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.

[1133] In this invention, the server includes a means for inputting diary data and image data, a means for analyzing the input diary data and image data, a means for generating an individual childcare plan and school supplies based on the analysis results, a means for informing parents of support methods at home, and a means for displaying the suggested school supplies. This makes it possible to suggest optimal school supplies based on the personality and interests of each child, thereby strengthening support for parents.

[1134] "Diary data" is text data recorded by caregivers or store staff about daily activities, children's behavior, and interests.

[1135] "Image data" refers to children's drawings and other visual content stored in digital format.

[1136] "Analysis means" refers to the technology or method for analyzing the input diary data and image data and extracting useful information and trends based on the analysis.

[1137] "Generation means" refers to technology or a system for generating individual childcare plans and school supply proposals based on the analyzed results.

[1138] A "childcare schedule" is a planned timetable that allows caregivers to manage a child's daily activities and carry them out in the appropriate order.

[1139] "Means of notification" refers to the techniques and methods used to communicate important information and support methods to parents and other relevant parties.

[1140] "School supplies" is a general term for tools and teaching materials used by children to study.

[1141] "Display means" refers to devices or techniques for visually presenting the proposed school supplies or plans.

[1142] "Natural Language Processing (NLP)" is the technology that enables computers to understand, interpret, and generate human language.

[1143] "Image recognition technology" is a technology in which a computer analyzes the content of an image and recognizes specific features and patterns.

[1144] A "generative AI model" is an artificial intelligence system that automatically generates content based on large amounts of data.

[1145] The present invention relates to a system that analyzes input data and proposes individual childcare plans and school supplies. This system aims to provide optimal childcare plans and school supplies by having a childcare worker or store clerk input diary data and image data, and analyzing the data.

[1146] Overall system configuration

[1147] The system has the following main components:

[1148] 1. Journal and image data input method

[1149] - A device such as a smartphone or tablet for childcare workers or store staff to input diary data and image data.

[1150] 2. Data analysis methods

[1151] - Natural language processing (NLP) and image recognition technologies are used to analyze the input data. Specifically, NLP libraries such as Google BERT and SpaCy, and image recognition libraries such as OpenCV and TensorFlow are used.

[1152] 3. Childcare plan and school supply generator

[1153] - Generative AI models (e.g., GPT-4) that generate personalized childcare plans or school supplies based on analytical results.

[1154] 4. Means of notification

[1155] - APIs and communication methods to inform parents about support methods at home and suggested school supplies.

[1156] 5. Display means

[1157] - An interface for displaying suggested school supplies and childcare plans to store staff and parents.

[1158] Data entry examples

[1159] The user (store clerk) inputs the diary data and image data from a smartphone. For example, a picture drawn by a child can be scanned using the smartphone camera.

[1160] For example, if a child is interested in dinosaurs, the store clerk might enter "Today's child drew a dinosaur" into the journal data and then scan the dinosaur drawing. The following is an example of a prompt that might be entered:

[1161] "Today's children drew pictures of dinosaurs."

[1162] Data analysis

[1163] The server receives the diary data and image data and analyzes the data using natural language processing and image recognition technology. Specifically, NLP technology is used to analyze the text data in the diary and extract the interests and activities described. Image recognition technology is used to analyze the content of the drawings and identify the objects drawn.

[1164] Plan and generate school supplies

[1165] Based on the analyzed data, the server uses a generative AI model to propose optimal childcare plans and school supplies, such as "building games with blocks" and "dinosaur-related learning materials."

[1166] Scheduling and Notifications

[1167] The generated childcare plan and school supply suggestions are sent to the user from the server. Store staff and parents can check the suggestions on their smartphones or tablets. For example, parents may be notified of support methods such as, "Your child is interested in dinosaurs, so it would be good to use dinosaur-related learning materials."

[1168] Overall implementation example

[1169] As a concrete example, the following steps can be considered:

[1170] 1. The store clerk uses a smartphone to enter the child's diary data and a picture of a dinosaur.

[1171] 2. The entered data is sent to the cloud server.

[1172] 3. The server analyzes the data and generates school supplies and childcare plans based on the child's interests and personality.

[1173] 4. The server displays the generated suggestions on the smartphone and notifies the parents.

[1174] 5. The store staff and parents review the suggestions and put them into action.

[1175] In this way, the system of the present invention can suggest the most suitable school supplies based on the child's personality and interests, strengthen cooperation with parents, and improve the child's learning experience.

[1176] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1177] Step 1:

[1178] The user enters diary data and image data on a smartphone. The user opens the diary app and scans the child's drawing, which inputs text and image data. The input data is temporarily saved on the device.

[1179] Input: Journal text data, scanned drawing images

[1180] Output: Temporarily saved journal data and image data

[1181] Step 2:

[1182] The device sends the entered diary data and image data to the cloud server, and the data is uploaded to the cloud server using an HTTP request.

[1183] Input: Temporarily saved journal data and image data

[1184] Output: Diary data and image data sent to the cloud server

[1185] Step 3:

[1186] The server analyzes the received diary data and image data. It uses natural language processing (NLP) to extract important keywords and interests from the diary text data, and uses image recognition technology to analyze the content of the drawings.

[1187] Input: Diary data and image data stored on the cloud server

[1188] Output: Analyzed diary data and image data features

[1189] Step 4:

[1190] The server generates personalized childcare plans and school supplies based on the analyzed data using a generative AI model (e.g., GPT-4). The generative AI model receives the analysis results as prompts and generates optimal suggestions.

[1191] Input: Analyzed diary data and image data features

[1192] Output: Generated childcare plans and school supply suggestions

[1193] Step 5:

[1194] The server incorporates the generated childcare plan and school supply suggestions into the childcare provider's daily schedule and notifies the user of the contents. The childcare provider can then review the proposed plan and make any necessary adjustments.

[1195] Input: Generated childcare plans and school supply suggestions

[1196] Output: Childcare plan and school supply suggestions notified to the user, and childcare schedule

[1197] Step 6:

[1198] The server notifies the parents of the generated childcare plan and school supply suggestions and suggests ways to support the child at home. The parents can receive the suggestions and put them into practice.

[1199] Input: Child care plans and school supply suggestions to inform parents

[1200] Output: Parents are notified of childcare plans, school supply suggestions, and support methods

[1201] 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.

[1202] The present invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system primarily uses diary data and image data entered by caregivers to analyze each child's personality and interests, and then generates an optimal childcare plan based on that data. Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing it to evaluate the emotional compatibility of the childcare plan and dynamically adjust the plan.

[1203] System Overview

[1204] The system has the following main components:

[1205] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[1206] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[1207] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[1208] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[1209] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[1210] Emotion Engine: A technology that recognizes the user's emotions, evaluates the emotional compatibility of the childcare plan, and dynamically adjusts the childcare plan.

[1211] Program processing

[1212] 1. Enter your journal and drawing details

[1213] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[1214] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[1215] 2. Sending data to the server

[1216] The terminal transmits the input diary data and image data to the cloud server.

[1217] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[1218] 3. Data Analysis

[1219] The server analyzes the received diary data using natural language processing (NLP) technology.

[1220] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "block play" and "interests," and identify the child's focus of interest.

[1221] 4. Image Analysis

[1222] The server analyzes the received image data using image recognition technology.

[1223] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[1224] 5. Create a childcare plan

[1225] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[1226] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[1227] 6. Emotional compatibility assessment

[1228] The server evaluates the emotions of the caregiver and the child based on the care plan generated using the emotion engine and daily feedback.

[1229] Specific operation: For example, if a caregiver writes in their journal after implementing a childcare plan, "Haruto seemed to be having a lot of fun," the emotion engine analyzes this feedback and rates the plan's emotional compatibility highly.

[1230] 7. Dynamic adjustment of childcare plans

[1231] The server dynamically adjusts the childcare plan based on the analysis results using an emotion engine.

[1232] Specific action: Update the childcare plan to avoid elements that are deemed incompatible and incorporate new compatible elements.

[1233] 8. Scheduling and Proposals

[1234] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[1235] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[1236] 9. Feedback to Parents

[1237] The server notifies the parents of the generated childcare plan and how to support them at home.

[1238] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[1239] 10. Support at home

[1240] The user (guardian) receives a notification and provides support at home.

[1241] Specific action: Parents check the notification and incorporate play with blocks at home.

[1242] Specific examples

[1243] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[1244] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[1245] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[1246] 3. The device sends this data to the cloud server.

[1247] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[1248] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[1249] 6. The server uses the emotion engine to evaluate the feedback from Haruto and the caregivers and evaluate the emotional suitability of the care plan. For example, it reflects the feedback that "Haruto seemed to be having a lot of fun."

[1250] 7. The server dynamically adjusts the childcare plan as needed, removing elements that are deemed incompatible and updating the plan to include new compatible elements.

[1251] 8. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[1252] 9. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[1253] 10. The user (parent) checks the notification and starts playing with blocks at home.

[1254] In this way, this system allows caregivers to provide optimal childcare plans for each child and strengthen cooperation with parents, maximizing the individuality and interests of each child. Furthermore, the use of an emotion engine makes it possible to provide childcare plans that take into account the emotional aspects of both the child and the caregiver.

[1255] The processing flow will be explained below.

[1256] Step 1:

[1257] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[1258] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[1259] Step 2:

[1260] The terminal transmits the input diary data and image data to the cloud server.

[1261] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[1262] Step 3:

[1263] The server analyzes the received diary data using natural language processing (NLP) technology.

[1264] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[1265] Step 4:

[1266] The server analyzes the received image data using image recognition technology.

[1267] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[1268] Step 5:

[1269] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[1270] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[1271] Step 6:

[1272] The server evaluates the emotions of the caregiver and the child based on the care plan generated using the emotion engine and daily feedback.

[1273] Specific operation: For example, if a caregiver writes in their journal after implementing a childcare plan, "Haruto seemed to be having a lot of fun," the emotion engine analyzes this feedback and rates the plan's emotional compatibility highly.

[1274] Step 7:

[1275] The server dynamically adjusts the childcare plan based on the analysis results using an emotion engine.

[1276] Specific action: Update the childcare plan to avoid elements that are deemed incompatible and incorporate new compatible elements.

[1277] Step 8:

[1278] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[1279] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[1280] Step 9:

[1281] The user (caregiver) checks the proposed childcare plan, modifies it as necessary, and then approves it.

[1282] Specific operation: The caregiver checks the proposed schedule on the tablet and clicks the approval button.

[1283] Step 10:

[1284] The server notifies the parents of the generated childcare plan and how to support them at home.

[1285] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[1286] Step 11:

[1287] The user (guardian) receives a notification and provides support at home.

[1288] Specific action: Parents check the notification and incorporate play with blocks at home.

[1289] The above is the specific processing flow of the system. This process allows caregivers and parents to work together to provide optimal care tailored to the child's personality and interests. In addition, the introduction of an emotion engine makes it possible to provide care plans that take into account the emotional aspects of the child and caregivers.

[1290] Example 2

[1291] 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."

[1292] Conventional childcare plan creation systems are limited to providing childcare plans using diary data and image data, making it difficult to generate plans that fully reflect each child's personality and interests. Furthermore, because the plans do not take into account the emotional aspects of the child or caregiver, they can sometimes be ineffective after implementation. There was a need to provide a system that could solve these problems, provide optimal childcare plans based on each child's personality and interests, and dynamically adjust them by evaluating emotional compatibility.

[1293] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1294] In this invention, the server includes means for inputting diary data and image data, means for analyzing the input diary data and image data, means for generating an individual childcare plan based on the analysis results, means for incorporating the generated childcare plan into a childcare schedule, means for informing the guardian of support methods at home, means for evaluating the user's emotions and evaluating the emotional compatibility of the childcare plan, and means for dynamically adjusting the childcare plan. This makes it possible to provide an optimal childcare plan that corresponds to the child's personality and interests, and further to evaluate the emotional compatibility of the childcare plan and dynamically adjust it based on feedback after implementation.

[1295] "Diary data" is text data in which caregivers record their daily childcare activities and the behavior of children.

[1296] "Image data" refers to image files scanned or photographed when a child draws or makes crafts.

[1297] "Natural language processing technology" is a technology that analyzes text data and extracts specific keywords and phrases.

[1298] "Image recognition technology" is a technology that analyzes image data and recognizes the objects and patterns depicted in it.

[1299] A "generative AI model" is an artificial intelligence model that automatically generates the optimal childcare plan for each individual child based on the analysis results.

[1300] The "emotion engine" is a technology that evaluates the emotional feedback of users and children and evaluates the emotional suitability of childcare plans based on that feedback.

[1301] A "childcare schedule" is a schedule that lists the daily childcare activities and timetables that childcare workers carry out.

[1302] "Means for notifying parents" refers to communication technologies used to inform parents of the created childcare plan and how to provide support at home.

[1303] "Dynamic adjustment" refers to the process of changing and updating childcare plans in real time based on user feedback and emotional assessment.

[1304] A "server" is a computer system that performs processes such as data analysis, childcare plan generation, emotion evaluation, and dynamic adjustment within a cloud environment.

[1305] The present invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system primarily uses diary data and image data entered by caregivers to analyze each child's personality and interests, and then generates an optimal childcare plan based on that data. Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing it to evaluate the emotional compatibility of the childcare plan and dynamically adjust the plan.

[1306] System Overview

[1307] The system has the following main components:

[1308] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[1309] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[1310] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[1311] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[1312] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[1313] Emotion Engine: A technology that recognizes the user's emotions, evaluates the emotional compatibility of the childcare plan, and dynamically adjusts the childcare plan.

[1314] Hardware and software used

[1315] Hardware: Tablets, smartphones, and cloud servers used by childcare workers.

[1316] Software: Google Cloud Natural Language API and Amazon Comprehend are used for natural language processing, Google Cloud Vision API and Amazon Rekognition for image recognition, and generative AI models (e.g., OpenAI's GPT-4) are used to generate childcare plans.

[1317] Specific examples

[1318] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[1319] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[1320] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[1321] 3. The device sends this data to the cloud server.

[1322] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[1323] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[1324] 6. The server uses the emotion engine to evaluate the feedback from Haruto and the caregivers and evaluate the emotional suitability of the care plan. For example, it reflects the feedback that "Haruto seemed to be having a lot of fun."

[1325] 7. The server dynamically adjusts the childcare plan as needed, removing elements that are deemed incompatible and updating the plan to include new compatible elements.

[1326] 8. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[1327] 9. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[1328] 10. The user (parent) checks the notification and starts playing with blocks at home.

[1329] Prompt Sentence Examples

[1330] "What activities have you been interested in lately, Haruto?"

[1331] "Generate the optimal childcare plan based on the following text and image data. Text: 'Haruto has been obsessed with playing with blocks lately.' Image: Pictures of animals."

[1332] "Dynamic adjustments to care plans should be made taking into account caregiver feedback and emotional fit."

[1333] By implementing the present invention, it is possible to provide an optimal childcare plan tailored to the individual characteristics and interests of each child, and to dynamically adjust the plan to take into account emotional compatibility, thereby improving the quality of childcare and enabling smooth communication between caregivers and parents.

[1334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1335] Step 1:

[1336] The user (caregiver) uses a device such as a tablet to enter daily childcare diaries, and when a child draws a picture, the image is scanned and the data is entered.

[1337] Specific actions: The caregiver opens the app on the tablet and writes, "Today, Child A was absorbed in playing with blocks for a long time." The caregiver also scans the animal drawing that Child A has made with the tablet's camera and saves it as image data.

[1338] Input: Journal text data, scanned image data

[1339] Output: Journal text data and image data temporarily saved on the device

[1340] Step 2:

[1341] The terminal transmits the input diary data and image data to a cloud server.

[1342] Specific operation: The tablet uploads input data to a cloud server in real time via Wi-Fi.

[1343] Input: Temporarily saved journal text data and image data

[1344] Output: Journal text data and image data uploaded to the cloud server

[1345] Step 3:

[1346] The server analyzes the received diary data using natural language processing (NLP) technology.

[1347] Specific operation: The NLP algorithm extracts keywords such as "playing with blocks" and "interest" from the text "Today, Child A was engrossed in playing with blocks for a long time" and identifies the child's focus of interest.

[1348] Input: Journal text data

[1349] Output: Keywords extracted from text data and their analysis results

[1350] Step 4:

[1351] The server analyzes the received image data using image recognition technology.

[1352] How it works: Image recognition algorithms analyze scanned images to identify objects like animals and playground equipment, as well as the frequency and patterns of color usage.

[1353] Input: Scanned image data

[1354] Output: Object information extracted from image data and its analysis results

[1355] Step 5:

[1356] The server uses a generative AI model to generate an individualized childcare plan based on the analysis results.

[1357] Specific operation: Based on the results of NLP and image recognition analysis, the generative AI model proposes childcare activities suitable for Child A. It generates childcare plans such as "building games using blocks" and "animal-themed learning activities."

[1358] Input: Analysis results using NLP and image recognition technology

[1359] Output: Generated individual childcare plan

[1360] Step 6:

[1361] The server evaluates the generated childcare plan and the user's feedback using the emotion engine.

[1362] Specific operation: After implementing a childcare plan, when a caregiver gives feedback such as "Child A seemed to enjoy it very much," the emotion engine analyzes this feedback and evaluates the emotional compatibility of the plan.

[1363] Input: Generated childcare plan, feedback from caregivers

[1364] Output: Emotional compatibility evaluation result

[1365] Step 7:

[1366] The server dynamically adjusts the childcare plan based on the emotion engine's evaluation.

[1367] Specific behavior: The system removes elements that are deemed incompatible and updates the plan to include new, compatible elements. For example, activities that children dislike are removed.

[1368] Input: Emotional compatibility assessment results, existing childcare plan

[1369] Output: Dynamically adjusted childcare plan

[1370] Step 8:

[1371] The server proposes incorporating the generated childcare plan into the schedule of the childcare provider.

[1372] Specific operation: The server references the childcare worker's daily schedule and generates a plan that incorporates the proposed childcare activities at the optimal time. The childcare worker then reviews and approves the proposal.

[1373] Input: Dynamically adjusted childcare plans, childcare worker schedules

[1374] Output: A childcare plan that is integrated into the childcare worker's schedule

[1375] Step 9:

[1376] The device notifies the parent of the generated childcare plan and how to provide support at home.

[1377] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Child A is interested in playing with blocks, so it would be a good idea to play with them at home."

[1378] Input: Childcare plan information, parental contact information

[1379] Output: Parental notification message

[1380] Step 10:

[1381] The user (parent) receives a notification and provides support at home.

[1382] Action: Parents check the notification and incorporate block play into their children's home activities, ensuring the quality of childcare is maintained in the home environment.

[1383] Input: Parental notification message

[1384] Output: Home implementation report (optional)

[1385] (Application example 2)

[1386] 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."

[1387] With conventional childcare plan generation systems, it was difficult to effectively generate specific childcare plans that reflected a child's personality and interests, and there was a problem of insufficient support at home due to a lack of cooperation between caregivers and parents. The purpose of this invention is to solve these problems, generate an optimal childcare plan based on a child's interests and characteristics, and realize closer cooperation between caregivers and parents.

[1388] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1389] In this invention, the server includes means for inputting diary data and image data, means for analyzing the input diary data and image data, means for generating an individual childcare plan based on the analysis results, means for incorporating the generated childcare plan into a childcare schedule, means for notifying parents of support methods at home, means for analyzing text data of the childcare diary using natural language processing technology and performing keyword extraction and sentiment analysis, means for analyzing pictures drawn by the child using image processing technology and identifying recognized objects, means for generating a childcare plan based on the analysis results using a generative AI model, and means for notifying childcare workers and parents of the generated childcare plan and sentiment analysis results. This makes it possible to generate a childcare plan that is optimal for the child's interests and personality, strengthens cooperation between childcare workers and parents, and enables effective support at home.

[1390] "Diary data" is text data in which caregivers record children's daily activities and conditions.

[1391] "Image data" refers to digital images obtained by scanning or photographing drawings or artwork created by children.

[1392] "Natural language processing technology" is a technology for analyzing text data and extracting meaning and emotion.

[1393] "Image processing technology" is a technology for analyzing digital images to recognize objects and features.

[1394] A "generative AI model" is an artificial intelligence model that generates new data and suggestions based on input data.

[1395] A "childcare plan" is a specific childcare activity plan proposed based on a child's personality and interests.

[1396] A "childcare schedule" is a plan that incorporates a childcare plan into specific times and dates.

[1397] "Childcare workers" are professionals involved in children's daily lives and education.

[1398] "Guardians" are family members who are responsible for raising and educating children.

[1399] "Sentiment analysis" is a technique for extracting and evaluating emotions from text data and feedback.

[1400] A "recognized object" is an object or shape that has been identified within a digital image using image processing techniques.

[1401] This invention relates to a system that automates the creation and management of childcare plans. This system inputs and analyzes diary data and image data, and creates individual childcare plans based on the analysis results. It also has the function of incorporating the childcare plans into childcare schedules and informing parents of support methods at home.

[1402] Hardware and software used

[1403] This system uses the following hardware and software.

[1404] Hardware:

[1405] Smartphones and tablets: Devices that allow users (caregivers) to enter daily childcare diaries and scan drawings by children.

[1406] Server: A central processing unit for performing the processes of data analysis and childcare plan generation.

[1407] software:

[1408] Python: Program implementation language.

[1409] OpenCV: A library for image processing.

[1410] TextBlob: A natural language processing library for analyzing text data.

[1411] Transformers: Libraries for using generative AI and sentiment analysis models.

[1412] Data processing and calculation

[1413] 1. Data Entry:

[1414] Caregivers enter daily childcare diaries into text on smartphones or tablets, and scan pictures drawn by children and save them as image data.

[1415] 2. Data transmission:

[1416] The terminal transmits the input diary data and image data to the server.

[1417] 3. Data Analysis:

[1418] The server uses TextBlob to perform text analysis of the diary data, extracting keywords and carrying out sentiment analysis.

[1419] The image data is analyzed using OpenCV to identify the recognized objects in the child's drawing.

[1420] 4. Childcare plan generation:

[1421] The server uses a generative AI model (Transformers library) based on the analysis results to generate an individual childcare plan.

[1422] 5. Notice:

[1423] The generated childcare plan and the results of the sentiment analysis are then sent to caregivers and parents via a smartphone app or email.

[1424] Specific examples

[1425] As a concrete example of this system, consider the following scenario: A caregiver records on a tablet that "Today, my child was engrossed in playing with blocks for a long time," and then scans and saves a drawing of an animal the child drew. This data is sent to a server in real time via Wi-Fi. The server analyzes the text and images using TextBlob and OpenCV. Based on the extracted keywords (e.g., "playing with blocks") and recognized objects (e.g., "animals"), a generative AI model generates a childcare plan such as: "Next time, we will use building games with blocks as teaching materials and incorporate animal-themed learning activities." The generated plan and the results of the sentiment analysis are then sent to the caregiver's smartphone, and home activities based on the child's interests are also suggested to parents.

[1426] Prompt Sentence Examples

[1427] Your child is interested in building blocks and animals. Create a suitable childcare plan.

[1428] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1429] Step 1:

[1430] Data Entry

[1431] Users (caregivers) enter their daily childcare diary into text on their smartphones or tablets, and scan pictures drawn by children and save them as image data.

[1432] Input: Text data from the daycare diary, recording that "Today the children were absorbed in playing with blocks for a long time," and image data of drawings made by the children.

[1433] Specific actions: The caregiver uses the tablet app to enter the childcare diary into the text field and then scans it using the camera function.

[1434] Step 2:

[1435] Data transmission

[1436] The terminal transmits the input diary data and image data to the server.

[1437] Input: Text data and image data.

[1438] Data processing: Converts input data into a format that can be sent to the cloud server.

[1439] Output: Text and image data uploaded to the cloud server.

[1440] Specific operation: The tablet uploads data to a cloud server in real time via Wi-Fi.

[1441] Step 3:

[1442] Data analysis

[1443] The server uses TextBlob to perform text analysis of the diary data, extracting keywords and carrying out sentiment analysis.

[1444] Input: Text data uploaded to the cloud server.

[1445] Data Operations: Use TextBlob for natural language processing to parse keywords and sentiment from text.

[1446] Output: A list of keywords and sentiment scores as the analysis results.

[1447] Specific operation: Using the TextBlob library, extract keywords such as "playing with blocks" and calculate sentiment values ​​such as positive or negative.

[1448] Step 4:

[1449] Image analysis

[1450] The server uses OpenCV to analyze the image data and identify recognizable objects in the child's drawing.

[1451] Input: Image data stored on a cloud server.

[1452] Data Computation: Image analysis is performed using OpenCV to recognize objects in the image.

[1453] Output: A list of recognized objects.

[1454] Specific operation: Uses the OpenCV library to identify objects such as "animals" from scanned images.

[1455] Step 5:

[1456] Childcare plan generation

[1457] The server uses a generative AI model based on the analysis results to generate an individual childcare plan.

[1458] Input: Text analysis results (keyword list and emotion rating), image analysis results (list of recognized objects).

[1459] Data calculation: Using a generative AI model, an optimal childcare plan is generated based on the input results.

[1460] Output: The generated childcare plan.

[1461] Specific behavior: Using a generative AI model from the Transformers library, it generates a care plan based on the prompt, "My child is interested in playing with blocks and animals. Please generate an appropriate care plan."

[1462] Step 6:

[1463] notification

[1464] The server notifies the generated childcare plan and the emotion analysis results to the childcare provider and the parent.

[1465] Input: Generated childcare plan and sentiment analysis results.

[1466] Data processing: Create notification messages and send them to smartphone apps or emails.

[1467] Output: Notification message sent to caregivers and parents.

[1468] Specific operation: The server generates a notification message, sends an app notification to the childcare worker's smartphone, and also sends an email to the parents informing them of how to provide support at home.

[1469] 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.

[1470] 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.

[1471] 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.

[1472] [Fourth embodiment]

[1473] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1474] 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.

[1475] 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).

[1476] 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.

[1477] 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.

[1478] 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).

[1479] 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.

[1480] 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.

[1481] 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.

[1482] 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.

[1483] 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.

[1484] 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.

[1485] 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."

[1486] This invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system aims to analyze each child's personality and interests using diary data and image data entered primarily by caregivers, and to generate an optimal childcare plan based on that analysis.

[1487] System Overview

[1488] The system has the following main components:

[1489] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[1490] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[1491] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[1492] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[1493] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[1494] Program processing

[1495] 1. Enter your journal and drawing details

[1496] The user (caregiver) records the daily childcare diary and scans the images of the children's drawings, entering the data digitally using a device such as a tablet.

[1497] 2. Sending data to the server

[1498] The device sends the entered log data and image data to a cloud server, where the data is uploaded in real time.

[1499] 3. Data Analysis

[1500] The server uses natural language processing (NLP) and image recognition technologies to analyze the received data. NLP is used to analyze the text data of the diary and extract descriptions of, for example, "playing with blocks." Image recognition is used to analyze the image data of the drawings and identify the frequency of color use and the objects drawn.

[1501] 4. Create a childcare plan

[1502] Based on the analysis results, the server generates the optimal childcare plan for each child. Specifically, the generative AI model uses the analysis results to suggest activities such as "building games using blocks" and "animal-themed learning activities."

[1503] 5. Scheduling and Proposals

[1504] The server then provides suggestions for incorporating the generated childcare plan into the caregiver's daily schedule, which the caregiver can review and modify as necessary.

[1505] 6. Feedback to parents

[1506] The server then notifies the parents of the generated childcare plan and how to support them at home. For example, it could send a notification to the parents' smartphone and provide advice such as, "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well."

[1507] Specific examples

[1508] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[1509] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[1510] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[1511] 3. The device sends this data to the cloud server.

[1512] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[1513] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[1514] 6. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[1515] 7. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[1516] In this way, this system allows caregivers to provide the best possible care plan for each child and strengthen cooperation with parents, thereby maximizing the individuality and interests of each child.

[1517] The processing flow will be explained below.

[1518] Step 1:

[1519] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[1520] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[1521] Step 2:

[1522] The terminal transmits the input diary data and image data to the cloud server.

[1523] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[1524] Step 3:

[1525] The server analyzes the received diary data using natural language processing (NLP) technology.

[1526] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[1527] Step 4:

[1528] The server analyzes the received image data using image recognition technology.

[1529] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[1530] Step 5:

[1531] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[1532] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[1533] Step 6:

[1534] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[1535] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[1536] Step 7:

[1537] The user (caregiver) checks the proposed childcare plan, modifies it as necessary, and then approves it.

[1538] Specific operation: The caregiver checks the proposed schedule on the tablet and clicks the approval button.

[1539] Step 8:

[1540] The server notifies the parents of the generated childcare plan and how to support them at home.

[1541] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[1542] Step 9:

[1543] The user (guardian) receives a notification and provides support at home.

[1544] Specific action: Parents check the notification and incorporate play with blocks at home.

[1545] This is the specific processing flow of the system. This process allows caregivers and parents to work together to provide optimal care tailored to the individuality and interests of each child.

[1546] Example 1

[1547] 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."

[1548] Conventional childcare systems have difficulty efficiently generating individual childcare plans based on each child's individuality and interests. Furthermore, they lack a system for providing parents with support at home, which hinders cooperation between childcare workers and parents. There is a need to improve this situation by providing childcare plans that maximize children's individuality and interests, and to strengthen cooperation between childcare workers and parents.

[1549] 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.

[1550] In this invention, the server includes a means for inputting diary data and image data, a means for transmitting the input diary data and image data to the cloud server, a means for analyzing the input diary data and image data, a means for generating an individual childcare plan based on the analysis results, a means for incorporating the generated childcare plan into a childcare schedule, and a means for notifying parents of support methods at home. This makes it possible to efficiently provide an optimal childcare plan for each child and strengthen cooperation between caregivers and parents.

[1551] "Diary data" is textual information recorded by caregivers about children's daily activities, behavior, interests, etc.

[1552] "Image data" is visual information stored in digital format, such as drawings and crafts created by children.

[1553] A "cloud server" is a server for storing, processing, and managing data remotely via the Internet.

[1554] "Natural language processing" is a technology for analyzing human language and understanding its meaning and structure.

[1555] "Image recognition technology" is a technology for analyzing image data and understanding or classifying its content.

[1556] A "generative AI model" is an artificial intelligence model trained to automatically generate appropriate childcare plans from input data.

[1557] A "childcare schedule" is a schedule for planning daily childcare activities according to time and order.

[1558] A "guardian" is a person responsible for the protection and upbringing of a child.

[1559] "Support methods" are specific measures and advice to support children at home.

[1560] MODE FOR CARRYING OUT THE INVENTION

[1561] The present invention relates to a system for analyzing diary data and image data provided by caregivers and proposing individual childcare plans. The system includes an input unit, a data analysis unit, a generation unit, a scheduling unit, and a notification unit. The following describes an embodiment of the present invention.

[1562] Input Method

[1563] The user (caregiver) records the daily childcare diary using a device such as a tablet or smartphone. When a child draws a picture, the image is scanned into the device using a scanning app and saved as digital data. The childcare diary is recorded by entering text into a dedicated app, such as "Haruto was engrossed in playing with blocks for a long time today." The image data of the drawing is automatically saved in a dedicated folder.

[1564] Data transmission method

[1565] The device sends the entered diary data and image data to the cloud server. The transmission process is done in real time, and all data is encrypted using TLS (Transport Layer Security). The diary data is sent in text format, and the image data is sent in JPEG or PNG format.

[1566] Data Analysis Methods

[1567] The server analyzes the diary data and image data received on the cloud. It applies natural language processing (NLP) technology to the text in the diary data to extract keywords, such as "playing with blocks." It also applies image recognition technology to the image data to identify objects and color patterns drawn in the drawings. These analyses use TensorFlow's NLP model and OpenCV's image recognition algorithm.

[1568] How to generate childcare plans

[1569] The server uses a generative AI model (e.g., GPT-3 model) based on the analysis results to generate the optimal childcare plan for each child. Specifically, the server inputs the following prompt sentence into the generative AI model, which then outputs the childcare plan:

[1570] "According to the daycare diary, Haruto was engrossed in playing with blocks for long periods of time. Based on this information, please generate the optimal daycare plan for Haruto."

[1571] "From the image analysis of his drawing, we know that Haruto likes animals. Use this information to create suggestions for animal-themed learning activities."

[1572] The generated childcare plan is saved in a data format such as JSON and handed over to the next process.

[1573] Scheduling Methods

[1574] The server then proposes incorporating the generated childcare plan into the childcare worker's daily schedule. Using an optimization algorithm, the server appropriately places the new plan within the existing schedule. The proposal is then sent to the childcare worker's device, where the childcare worker can review the details in the dedicated app and make any necessary corrections or approvals.

[1575] Notification means

[1576] The server then notifies the parents of the generated childcare plan and how to support them at home by sending a push notification to their smartphone, providing specific advice such as:

[1577] "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well."

[1578] Notifications will also be displayed within the parent-only app, allowing parents to view past childcare plans and feedback.

[1579] As described above, by implementing the present invention, caregivers can efficiently provide optimal care plans for individual children and strengthen cooperation with parents.

[1580] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1581] Program processing steps

[1582] Step 1: User Data Entry

[1583] Users (caregivers) enter daily childcare diary entries using a tablet or smartphone. When a child draws a picture, the image is captured using a scanning app and saved in digital format.

[1584] Input: Childcare diary text data, drawing image data

[1585] Output: Digital logbook data and image data

[1586] Specific operations: The daycare diary involves entering text such as "Haruto was engrossed in playing with blocks for a long time today" into a dedicated app, taking a picture of the drawing with the smartphone camera, and importing it using the scanning function.

[1587] Step 2: Send data to the server

[1588] The terminal transmits the input diary data and image data to the cloud server in real time.

[1589] Input: Journal data and image data stored on the device

[1590] Output: Data stored on the cloud server

[1591] Specific operation: Data is encrypted and transmitted using TLS (Transport Layer Security). Diary data is transmitted in text format, and image data is transmitted in JPEG or PNG format.

[1592] Step 3: Data analysis

[1593] The server analyzes the diary data and image data received on the cloud. The diary text data is analyzed using natural language processing (NLP) technology, and the image data is analyzed using image recognition technology.

[1594] Input: Journal data and image data stored on the cloud server

[1595] Output: Analysis results (keyword extraction, object identification)

[1596] Specific operation: The diary data is analyzed using an NLP model (e.g., TensorFlow) to extract keywords such as "playing with blocks," and the image data is analyzed using OpenCV to identify drawn objects and color patterns.

[1597] Step 4: Generate a childcare plan

[1598] The server generates a childcare plan using a generative AI model (e.g., a GPT-3 model) based on the analysis results.

[1599] Input: Analysis results (keyword extraction results from text data and object identification results from image data)

[1600] Output: Individual childcare plan (e.g., JSON format)

[1601] Specific operation: The following prompt is input into the generative AI model: "According to the daycare diary, Haruto spent a lot of time engrossed in playing with blocks. Based on this information, please generate the optimal daycare plan for Haruto." The generated daycare plan is saved in JSON format.

[1602] Step 5: Scheduling and Proposal

[1603] The server makes a proposal to incorporate the generated childcare plan into the childcare provider's daily schedule.

[1604] Input: Generated childcare plan, existing schedule

[1605] Output: Optimized schedule proposal

[1606] How it works: Applying an optimization algorithm to add new plans to an existing schedule. The proposed plan is sent to the caregiver's device, who can then review the details in the dedicated app and make any necessary changes or approvals.

[1607] Step 6: Feedback to parents

[1608] The server notifies the parents of the generated childcare plan and how to support them at home.

[1609] Input: Generated childcare plan, support methods at home

[1610] Output: Notification to parents (push notification, display of dedicated app)

[1611] Specific operation: A push notification is sent to the parent's smartphone, providing specific advice such as, "Haruto is interested in playing with blocks, so it would be good for him to actively play with blocks at home as well." The notification is also displayed in the parent-only app, where past childcare plans and feedback can also be viewed.

[1612] (Application example 1)

[1613] 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."

[1614] Currently, school supply stores lack the means to recommend the most suitable school supplies based on each child's personality and interests. Furthermore, parents are not provided with adequate individual suggestions or support, which makes it difficult for them to make effective choices. This results in the problem of not maximizing children's motivation and interest in learning.

[1615] 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.

[1616] In this invention, the server includes a means for inputting diary data and image data, a means for analyzing the input diary data and image data, a means for generating an individual childcare plan and school supplies based on the analysis results, a means for informing parents of support methods at home, and a means for displaying the suggested school supplies. This makes it possible to suggest optimal school supplies based on the personality and interests of each child, thereby strengthening support for parents.

[1617] "Diary data" is text data recorded by caregivers or store staff about daily activities, children's behavior, and interests.

[1618] "Image data" refers to children's drawings and other visual content stored in digital format.

[1619] "Analysis means" refers to the technology or method for analyzing the input diary data and image data and extracting useful information and trends based on the analysis.

[1620] "Generation means" refers to technology or a system for generating individual childcare plans and school supply proposals based on the analyzed results.

[1621] A "childcare schedule" is a planned timetable that allows caregivers to manage a child's daily activities and carry them out in the appropriate order.

[1622] "Means of notification" refers to the techniques and methods used to communicate important information and support methods to parents and other relevant parties.

[1623] "School supplies" is a general term for tools and teaching materials used by children to study.

[1624] "Display means" refers to devices or techniques for visually presenting the proposed school supplies or plans.

[1625] "Natural Language Processing (NLP)" is the technology that enables computers to understand, interpret, and generate human language.

[1626] "Image recognition technology" is a technology in which a computer analyzes the content of an image and recognizes specific features and patterns.

[1627] A "generative AI model" is an artificial intelligence system that automatically generates content based on large amounts of data.

[1628] The present invention relates to a system that analyzes input data and proposes individual childcare plans and school supplies. This system aims to provide optimal childcare plans and school supplies by having a childcare worker or store clerk input diary data and image data, and analyzing the data.

[1629] Overall system configuration

[1630] The system has the following main components:

[1631] 1. Journal and image data input method

[1632] - A device such as a smartphone or tablet for childcare workers or store staff to input diary data and image data.

[1633] 2. Data analysis methods

[1634] - Natural language processing (NLP) and image recognition technologies are used to analyze the input data. Specifically, NLP libraries such as Google BERT and SpaCy, and image recognition libraries such as OpenCV and TensorFlow are used.

[1635] 3. Childcare plan and school supply generator

[1636] - Generative AI models (e.g., GPT-4) that generate personalized childcare plans or school supplies based on analytical results.

[1637] 4. Means of notification

[1638] - APIs and communication methods to inform parents about support methods at home and suggested school supplies.

[1639] 5. Display means

[1640] - An interface for displaying suggested school supplies and childcare plans to store staff and parents.

[1641] Data entry examples

[1642] The user (store clerk) inputs the diary data and image data from a smartphone. For example, a picture drawn by a child can be scanned using the smartphone camera.

[1643] For example, if a child is interested in dinosaurs, the store clerk might enter "Today's child drew a dinosaur" into the journal data and then scan the dinosaur drawing. The following is an example of a prompt that might be entered:

[1644] "Today's children drew pictures of dinosaurs."

[1645] Data analysis

[1646] The server receives the diary data and image data and analyzes the data using natural language processing and image recognition technology. Specifically, NLP technology is used to analyze the text data in the diary and extract the interests and activities described. Image recognition technology is used to analyze the content of the drawings and identify the objects drawn.

[1647] Plan and generate school supplies

[1648] Based on the analyzed data, the server uses a generative AI model to propose optimal childcare plans and school supplies, such as "building games with blocks" and "dinosaur-related learning materials."

[1649] Scheduling and Notifications

[1650] The generated childcare plan and school supply suggestions are sent to the user from the server. Store staff and parents can check the suggestions on their smartphones or tablets. For example, parents may be notified of support methods such as, "Your child is interested in dinosaurs, so it would be good to use dinosaur-related learning materials."

[1651] Overall implementation example

[1652] As a concrete example, the following steps can be considered:

[1653] 1. The store clerk uses a smartphone to enter the child's diary data and a picture of a dinosaur.

[1654] 2. The entered data is sent to the cloud server.

[1655] 3. The server analyzes the data and generates school supplies and childcare plans based on the child's interests and personality.

[1656] 4. The server displays the generated suggestions on the smartphone and notifies the parents.

[1657] 5. The store staff and parents review the suggestions and put them into action.

[1658] In this way, the system of the present invention can suggest the most suitable school supplies based on the child's personality and interests, strengthen cooperation with parents, and improve the child's learning experience.

[1659] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1660] Step 1:

[1661] The user enters diary data and image data on a smartphone. The user opens the diary app and scans the child's drawing, which inputs text and image data. The input data is temporarily saved on the device.

[1662] Input: Journal text data, scanned drawing images

[1663] Output: Temporarily saved journal data and image data

[1664] Step 2:

[1665] The device sends the entered diary data and image data to the cloud server, and the data is uploaded to the cloud server using an HTTP request.

[1666] Input: Temporarily saved journal data and image data

[1667] Output: Diary data and image data sent to the cloud server

[1668] Step 3:

[1669] The server analyzes the received diary data and image data. It uses natural language processing (NLP) to extract important keywords and interests from the diary text data, and uses image recognition technology to analyze the content of the drawings.

[1670] Input: Diary data and image data stored on the cloud server

[1671] Output: Analyzed diary data and image data features

[1672] Step 4:

[1673] The server generates personalized childcare plans and school supplies based on the analyzed data using a generative AI model (e.g., GPT-4). The generative AI model receives the analysis results as prompts and generates optimal suggestions.

[1674] Input: Analyzed diary data and image data features

[1675] Output: Generated childcare plans and school supply suggestions

[1676] Step 5:

[1677] The server incorporates the generated childcare plan and school supply suggestions into the childcare provider's daily schedule and notifies the user of the contents. The childcare provider can then review the proposed plan and make any necessary adjustments.

[1678] Input: Generated childcare plans and school supply suggestions

[1679] Output: Childcare plan and school supply suggestions notified to the user, and childcare schedule

[1680] Step 6:

[1681] The server notifies the parents of the generated childcare plan and school supply suggestions and suggests ways to support the child at home. The parents can receive the suggestions and put them into practice.

[1682] Input: Child care plans and school supply suggestions to inform parents

[1683] Output: Parents are notified of childcare plans, school supply suggestions, and support methods

[1684] 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.

[1685] The present invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system primarily uses diary data and image data entered by caregivers to analyze each child's personality and interests, and then generates an optimal childcare plan based on that data. Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing it to evaluate the emotional compatibility of the childcare plan and dynamically adjust the plan.

[1686] System Overview

[1687] The system has the following main components:

[1688] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[1689] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[1690] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[1691] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[1692] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[1693] Emotion Engine: A technology that recognizes the user's emotions, evaluates the emotional compatibility of the childcare plan, and dynamically adjusts the childcare plan.

[1694] Program processing

[1695] 1. Enter your journal and drawing details

[1696] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[1697] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[1698] 2. Sending data to the server

[1699] The terminal transmits the input diary data and image data to the cloud server.

[1700] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[1701] 3. Data Analysis

[1702] The server analyzes the received diary data using natural language processing (NLP) technology.

[1703] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[1704] 4. Image Analysis

[1705] The server analyzes the received image data using image recognition technology.

[1706] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[1707] 5. Create a childcare plan

[1708] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[1709] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[1710] 6. Emotional compatibility assessment

[1711] The server evaluates the emotions of the caregiver and the child based on the care plan generated using the emotion engine and daily feedback.

[1712] Specific operation: For example, if a caregiver writes in their journal after implementing a childcare plan, "Haruto seemed to be having a lot of fun," the emotion engine analyzes this feedback and rates the plan's emotional compatibility highly.

[1713] 7. Dynamic adjustment of childcare plans

[1714] The server dynamically adjusts the childcare plan based on the analysis results using an emotion engine.

[1715] Specific action: Update the childcare plan to avoid elements that are deemed incompatible and incorporate new compatible elements.

[1716] 8. Scheduling and Proposals

[1717] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[1718] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[1719] 9. Feedback to Parents

[1720] The server notifies the parents of the generated childcare plan and how to support them at home.

[1721] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[1722] 10. Support at home

[1723] The user (guardian) receives a notification and provides support at home.

[1724] Specific action: Parents check the notification and incorporate play with blocks at home.

[1725] Specific examples

[1726] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[1727] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[1728] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[1729] 3. The device sends this data to the cloud server.

[1730] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[1731] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[1732] 6. The server uses the emotion engine to evaluate the feedback from Haruto and the caregivers and evaluate the emotional suitability of the care plan. For example, it reflects the feedback that "Haruto seemed to be having a lot of fun."

[1733] 7. The server dynamically adjusts the childcare plan as needed, removing elements that are deemed incompatible and updating the plan to include new compatible elements.

[1734] 8. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[1735] 9. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[1736] 10. The user (parent) checks the notification and starts playing with blocks at home.

[1737] In this way, this system allows caregivers to provide optimal childcare plans for each child and strengthen cooperation with parents, maximizing the individuality and interests of each child. Furthermore, the use of an emotion engine makes it possible to provide childcare plans that take into account the emotional aspects of both the child and the caregiver.

[1738] The processing flow will be explained below.

[1739] Step 1:

[1740] The user (caregiver) enters the daily childcare diary into a device such as a tablet, and when the child draws a picture, the image is scanned and the data is entered.

[1741] Specific actions: The caregiver uses the tablet app to record, "Today, Haruto was absorbed in playing with blocks for a long time." The caregiver also scans and saves the animal drawings that Haruto has made.

[1742] Step 2:

[1743] The terminal transmits the input diary data and image data to the cloud server.

[1744] Specific operation: The tablet uploads data to the server in real time via Wi-Fi.

[1745] Step 3:

[1746] The server analyzes the received diary data using natural language processing (NLP) technology.

[1747] Specific behavior: NLP algorithms analyze the diary text, extract keywords such as "playing with blocks" and "interests," and identify the child's focus of interest.

[1748] Step 4:

[1749] The server analyzes the received image data using image recognition technology.

[1750] What it does: Image recognition algorithms analyze the scanned drawing to identify objects (e.g., animals) and frequency of color use.

[1751] Step 5:

[1752] The server generates the optimal childcare plan for each child based on the analysis of the diary data and image data.

[1753] Specific operation: Based on the analysis results, the generative AI model automatically generates plans that suggest building games using blocks and animal-themed learning activities as "childcare activities suitable for Haruto."

[1754] Step 6:

[1755] The server evaluates the emotions of the caregiver and the child based on the care plan generated using the emotion engine and daily feedback.

[1756] Specific operation: For example, if a caregiver writes in their journal after implementing a childcare plan, "Haruto seemed to be having a lot of fun," the emotion engine analyzes this feedback and rates the plan's emotional compatibility highly.

[1757] Step 7:

[1758] The server dynamically adjusts the childcare plan based on the analysis results using an emotion engine.

[1759] Specific action: Update the childcare plan to avoid elements that are deemed incompatible and incorporate new compatible elements.

[1760] Step 8:

[1761] The server makes a proposal for incorporating the generated childcare plan into the schedule of the childcare provider.

[1762] Specific operation: The server refers to the childcare provider's daily schedule and generates a plan that incorporates the proposed childcare activities into the optimal time slots.

[1763] Step 9:

[1764] The user (caregiver) checks the proposed childcare plan, modifies it as necessary, and then approves it.

[1765] Specific operation: The caregiver checks the proposed schedule on the tablet and clicks the approval button.

[1766] Step 10:

[1767] The server notifies the parents of the generated childcare plan and how to support them at home.

[1768] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Haruto is interested in playing with blocks, so it would be a good idea to play with him at home."

[1769] Step 11:

[1770] The user (guardian) receives a notification and provides support at home.

[1771] Specific action: Parents check the notification and incorporate play with blocks at home.

[1772] The above is the specific processing flow of the system. This process allows caregivers and parents to work together to provide optimal care tailored to the child's personality and interests. In addition, the introduction of an emotion engine makes it possible to provide care plans that take into account the emotional aspects of the child and caregivers.

[1773] Example 2

[1774] 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."

[1775] Conventional childcare plan creation systems are limited to providing childcare plans using diary data and image data, making it difficult to generate plans that fully reflect each child's personality and interests. Furthermore, because the plans do not take into account the emotional aspects of the child or caregiver, they can sometimes be ineffective after implementation. There was a need to provide a system that could solve these problems, provide optimal childcare plans based on each child's personality and interests, and dynamically adjust them by evaluating emotional compatibility.

[1776] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1777] In this invention, the server includes means for inputting diary data and image data, means for analyzing the input diary data and image data, means for generating an individual childcare plan based on the analysis results, means for incorporating the generated childcare plan into a childcare schedule, means for informing the guardian of support methods at home, means for evaluating the user's emotions and evaluating the emotional compatibility of the childcare plan, and means for dynamically adjusting the childcare plan. This makes it possible to provide an optimal childcare plan that corresponds to the child's personality and interests, and further to evaluate the emotional compatibility of the childcare plan and dynamically adjust it based on feedback after implementation.

[1778] "Diary data" is text data in which caregivers record their daily childcare activities and the behavior of children.

[1779] "Image data" refers to image files scanned or photographed when a child draws or makes crafts.

[1780] "Natural language processing technology" is a technology that analyzes text data and extracts specific keywords and phrases.

[1781] "Image recognition technology" is a technology that analyzes image data and recognizes the objects and patterns depicted in it.

[1782] A "generative AI model" is an artificial intelligence model that automatically generates the optimal childcare plan for each individual child based on the analysis results.

[1783] The "emotion engine" is a technology that evaluates the emotional feedback of users and children and evaluates the emotional suitability of childcare plans based on that feedback.

[1784] A "childcare schedule" is a schedule that lists the daily childcare activities and timetables that childcare workers carry out.

[1785] "Means for notifying parents" refers to communication technologies used to inform parents of the created childcare plan and how to provide support at home.

[1786] "Dynamic adjustment" refers to the process of changing and updating childcare plans in real time based on user feedback and emotional assessment.

[1787] A "server" is a computer system that performs processes such as data analysis, childcare plan generation, emotion evaluation, and dynamic adjustment within a cloud environment.

[1788] The present invention relates to a system that analyzes generated data and proposes individual childcare plans based on that data. The system primarily uses diary data and image data entered by caregivers to analyze each child's personality and interests, and then generates an optimal childcare plan based on that data. Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing it to evaluate the emotional compatibility of the childcare plan and dynamically adjust the plan.

[1789] System Overview

[1790] The system has the following main components:

[1791] Input method: A device or software that allows caregivers to record daily childcare diaries and scan children's drawings to input data.

[1792] Data analysis methods: Natural language processing (NLP) and image recognition technology are used to analyze input data and identify children's personalities and interests.

[1793] Generation method: A generative AI model for creating individual childcare plans based on the analysis results.

[1794] Scheduling tool: A mechanism for incorporating the generated childcare plan into the childcare worker's schedule.

[1795] Notification methods: Communication technology that notifies caregivers and parents of necessary information and shows them how to provide support at home.

[1796] Emotion Engine: A technology that recognizes the user's emotions, evaluates the emotional compatibility of the childcare plan, and dynamically adjusts the childcare plan.

[1797] Hardware and software used

[1798] Hardware: Tablets, smartphones, and cloud servers used by childcare workers.

[1799] Software: Google Cloud Natural Language API and Amazon Comprehend are used for natural language processing, Google Cloud Vision API and Amazon Rekognition for image recognition, and generative AI models (e.g., OpenAI's GPT-4) are used to generate childcare plans.

[1800] Specific examples

[1801] As a concrete example, consider the case where Haruto is interested in playing with blocks.

[1802] 1. The user (caregiver) records on the tablet, "Haruto was engrossed in playing with blocks for a long time today."

[1803] 2. Scan the animal drawings that Haruto has drawn and save them as image data.

[1804] 3. The device sends this data to the cloud server.

[1805] 4. The server uses NLP to analyze the text in the diary and extracts descriptions of "playing with blocks." It also uses image recognition technology to analyze the content of the drawings and identify them as drawings of animals.

[1806] 5. Based on these results, the server generates a childcare plan for Hart, including "building games using blocks" and "animal-themed learning activities."

[1807] 6. The server uses the emotion engine to evaluate the feedback from Haruto and the caregivers and evaluate the emotional suitability of the care plan. For example, it reflects the feedback that "Haruto seemed to be having a lot of fun."

[1808] 7. The server dynamically adjusts the childcare plan as needed, removing elements that are deemed incompatible and updating the plan to include new compatible elements.

[1809] 8. The server proposes to incorporate the generated childcare plan into the childcare provider's schedule. The childcare provider then reviews and approves the proposal.

[1810] 9. The device notifies the parents that Haruto is interested in playing with blocks and suggests that they play with them at home.

[1811] 10. The user (parent) checks the notification and starts playing with blocks at home.

[1812] Prompt Sentence Examples

[1813] "What activities have you been interested in lately, Haruto?"

[1814] "Generate the optimal childcare plan based on the following text and image data. Text: 'Haruto has been obsessed with playing with blocks lately.' Image: Pictures of animals."

[1815] "Dynamic adjustments to care plans should be made taking into account caregiver feedback and emotional fit."

[1816] By implementing the present invention, it is possible to provide an optimal childcare plan tailored to the individual characteristics and interests of each child, and to dynamically adjust the plan to take into account emotional compatibility, thereby improving the quality of childcare and enabling smooth communication between caregivers and parents.

[1817] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1818] Step 1:

[1819] The user (caregiver) uses a device such as a tablet to enter daily childcare diaries, and when a child draws a picture, the image is scanned and the data is entered.

[1820] Specific actions: The caregiver opens the app on the tablet and writes, "Today, Child A was absorbed in playing with blocks for a long time." The caregiver also scans the animal drawing that Child A has made with the tablet's camera and saves it as image data.

[1821] Input: Journal text data, scanned image data

[1822] Output: Journal text data and image data temporarily saved on the device

[1823] Step 2:

[1824] The terminal transmits the input diary data and image data to a cloud server.

[1825] Specific operation: The tablet uploads input data to a cloud server in real time via Wi-Fi.

[1826] Input: Temporarily saved journal text data and image data

[1827] Output: Journal text data and image data uploaded to the cloud server

[1828] Step 3:

[1829] The server analyzes the received diary data using natural language processing (NLP) technology.

[1830] Specific operation: The NLP algorithm extracts keywords such as "playing with blocks" and "interest" from the text "Today, Child A was engrossed in playing with blocks for a long time" and identifies the child's focus of interest.

[1831] Input: Journal text data

[1832] Output: Keywords extracted from text data and their analysis results

[1833] Step 4:

[1834] The server analyzes the received image data using image recognition technology.

[1835] How it works: Image recognition algorithms analyze scanned images to identify objects like animals and playground equipment, as well as the frequency and patterns of color usage.

[1836] Input: Scanned image data

[1837] Output: Object information extracted from image data and its analysis results

[1838] Step 5:

[1839] The server uses a generative AI model to generate an individualized childcare plan based on the analysis results.

[1840] Specific operation: Based on the results of NLP and image recognition analysis, the generative AI model proposes childcare activities suitable for Child A. It generates childcare plans such as "building games using blocks" and "animal-themed learning activities."

[1841] Input: Analysis results using NLP and image recognition technology

[1842] Output: Generated individual childcare plan

[1843] Step 6:

[1844] The server evaluates the generated childcare plan and the user's feedback using the emotion engine.

[1845] Specific operation: After implementing a childcare plan, when a caregiver gives feedback such as "Child A seemed to enjoy it very much," the emotion engine analyzes this feedback and evaluates the emotional compatibility of the plan.

[1846] Input: Generated childcare plan, feedback from caregivers

[1847] Output: Emotional compatibility evaluation result

[1848] Step 7:

[1849] The server dynamically adjusts the childcare plan based on the emotion engine's evaluation.

[1850] Specific behavior: The system removes elements that are deemed incompatible and updates the plan to include new, compatible elements. For example, activities that children dislike are removed.

[1851] Input: Emotional compatibility assessment results, existing childcare plan

[1852] Output: Dynamically adjusted childcare plan

[1853] Step 8:

[1854] The server proposes incorporating the generated childcare plan into the schedule of the childcare provider.

[1855] Specific operation: The server references the childcare worker's daily schedule and generates a plan that incorporates the proposed childcare activities at the optimal time. The childcare worker then reviews and approves the proposal.

[1856] Input: Dynamically adjusted childcare plans, childcare worker schedules

[1857] Output: A childcare plan that is integrated into the childcare worker's schedule

[1858] Step 9:

[1859] The device notifies the parent of the generated childcare plan and how to provide support at home.

[1860] Specific operation: The server sends an app notification or email to the parent's smartphone, notifying them of a message such as, "Child A is interested in playing with blocks, so it would be a good idea to play with them at home."

[1861] Input: Childcare plan information, parental contact information

[1862] Output: Parental notification message

[1863] Step 10:

[1864] The user (parent) receives a notification and provides support at home.

[1865] Action: Parents check the notification and incorporate block play into their children's home activities, ensuring the quality of childcare is maintained in the home environment.

[1866] Input: Parental notification message

[1867] Output: Home implementation report (optional)

[1868] (Application example 2)

[1869] 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."

[1870] With conventional childcare plan generation systems, it was difficult to effectively generate specific childcare plans that reflected a child's personality and interests, and there was a problem of insufficient support at home due to a lack of cooperation between caregivers and parents. The purpose of this invention is to solve these problems, generate an optimal childcare plan based on a child's interests and characteristics, and realize closer cooperation between caregivers and parents.

[1871] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1872] In this invention, the server includes means for inputting diary data and image data, means for analyzing the input diary data and image data, means for generating an individual childcare plan based on the analysis results, means for incorporating the generated childcare plan into a childcare schedule, means for notifying parents of support methods at home, means for analyzing text data of the childcare diary using natural language processing technology and performing keyword extraction and sentiment analysis, means for analyzing pictures drawn by the child using image processing technology and identifying recognized objects, means for generating a childcare plan based on the analysis results using a generative AI model, and means for notifying childcare workers and parents of the generated childcare plan and sentiment analysis results. This makes it possible to generate a childcare plan that is optimal for the child's interests and personality, strengthens cooperation between childcare workers and parents, and enables effective support at home.

[1873] "Diary data" is text data in which caregivers record children's daily activities and conditions.

[1874] "Image data" refers to digital images obtained by scanning or photographing drawings or artwork created by children.

[1875] "Natural language processing technology" is a technology for analyzing text data and extracting meaning and emotion.

[1876] "Image processing technology" is a technology for analyzing digital images to recognize objects and features.

[1877] A "generative AI model" is an artificial intelligence model that generates new data and suggestions based on input data.

[1878] A "childcare plan" is a specific childcare activity plan proposed based on a child's personality and interests.

[1879] A "childcare schedule" is a plan that incorporates a childcare plan into specific times and dates.

[1880] "Childcare workers" are professionals involved in children's daily lives and education.

[1881] "Guardians" are family members who are responsible for raising and educating children.

[1882] "Sentiment analysis" is a technique for extracting and evaluating emotions from text data and feedback.

[1883] A "recognized object" is an object or shape that has been identified within a digital image using image processing techniques.

[1884] This invention relates to a system that automates the creation and management of childcare plans. This system inputs and analyzes diary data and image data, and creates individual childcare plans based on the analysis results. It also has the function of incorporating the childcare plans into childcare schedules and informing parents of support methods at home.

[1885] Hardware and software used

[1886] This system uses the following hardware and software.

[1887] Hardware:

[1888] Smartphones and tablets: Devices that allow users (caregivers) to enter daily childcare diaries and scan drawings by children.

[1889] Server: A central processing unit for performing the processes of data analysis and childcare plan generation.

[1890] software:

[1891] Python: Program implementation language.

[1892] OpenCV: A library for image processing.

[1893] TextBlob: A natural language processing library for analyzing text data.

[1894] Transformers: Libraries for using generative AI and sentiment analysis models.

[1895] Data processing and calculation

[1896] 1. Data Entry:

[1897] Caregivers enter daily childcare diaries into text on smartphones or tablets, and scan pictures drawn by children and save them as image data.

[1898] 2. Data transmission:

[1899] The terminal transmits the input diary data and image data to the server.

[1900] 3. Data Analysis:

[1901] The server uses TextBlob to perform text analysis of the diary data, extracting keywords and carrying out sentiment analysis.

[1902] The image data is analyzed using OpenCV to identify the recognized objects in the child's drawing.

[1903] 4. Childcare plan generation:

[1904] The server uses a generative AI model (Transformers library) based on the analysis results to generate an individual childcare plan.

[1905] 5. Notice:

[1906] The generated childcare plan and the results of the sentiment analysis are then sent to caregivers and parents via a smartphone app or email.

[1907] Specific examples

[1908] As a concrete example of this system, consider the following scenario: A caregiver records on a tablet that "Today, my child was engrossed in playing with blocks for a long time," and then scans and saves a drawing of an animal the child drew. This data is sent to a server in real time via Wi-Fi. The server analyzes the text and images using TextBlob and OpenCV. Based on the extracted keywords (e.g., "playing with blocks") and recognized objects (e.g., "animals"), a generative AI model generates a childcare plan such as: "Next time, we will use building games with blocks as teaching materials and incorporate animal-themed learning activities." The generated plan and the results of the sentiment analysis are then sent to the caregiver's smartphone, and home activities based on the child's interests are also suggested to parents.

[1909] Prompt Sentence Examples

[1910] Your child is interested in building blocks and animals. Create a suitable childcare plan.

[1911] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1912] Step 1:

[1913] Data Entry

[1914] Users (caregivers) enter their daily childcare diary into text on their smartphones or tablets, and scan pictures drawn by children and save them as image data.

[1915] Input: Text data from the daycare diary, recording that "Today the children were absorbed in playing with blocks for a long time," and image data of drawings made by the children.

[1916] Specific actions: The caregiver uses the tablet app to enter the childcare diary into the text field and then scans it using the camera function.

[1917] Step 2:

[1918] Data transmission

[1919] The terminal transmits the input diary data and image data to the server.

[1920] Input: Text data and image data.

[1921] Data processing: Converts input data into a format that can be sent to the cloud server.

[1922] Output: Text and image data uploaded to the cloud server.

[1923] Specific operation: The tablet uploads data to a cloud server in real time via Wi-Fi.

[1924] Step 3:

[1925] Data analysis

[1926] The server uses TextBlob to perform text analysis of the diary data, extracting keywords and carrying out sentiment analysis.

[1927] Input: Text data uploaded to the cloud server.

[1928] Data Operations: Use TextBlob for natural language processing to parse keywords and sentiment from text.

[1929] Output: A list of keywords and sentiment scores as the analysis results.

[1930] Specific operation: Using the TextBlob library, extract keywords such as "playing with blocks" and calculate sentiment values ​​such as positive or negative.

[1931] Step 4:

[1932] Image analysis

[1933] The server uses OpenCV to analyze the image data and identify recognizable objects in the child's drawing.

[1934] Input: Image data stored on a cloud server.

[1935] Data Computation: Image analysis is performed using OpenCV to recognize objects in the image.

[1936] Output: A list of recognized objects.

[1937] Specific operation: Uses the OpenCV library to identify objects such as "animals" from scanned images.

[1938] Step 5:

[1939] Childcare plan generation

[1940] The server uses a generative AI model based on the analysis results to generate an individual childcare plan.

[1941] Input: Text analysis results (keyword list and emotion rating), image analysis results (list of recognized objects).

[1942] Data calculation: Using a generative AI model, an optimal childcare plan is generated based on the input results.

[1943] Output: The generated childcare plan.

[1944] Specific behavior: Using a generative AI model from the Transformers library, it generates a care plan based on the prompt, "My child is interested in playing with blocks and animals. Please generate an appropriate care plan."

[1945] Step 6:

[1946] notification

[1947] The server notifies the generated childcare plan and the emotion analysis results to the childcare provider and the parent.

[1948] Input: Generated childcare plan and sentiment analysis results.

[1949] Data processing: Create notification messages and send them to smartphone apps or emails.

[1950] Output: Notification message sent to caregivers and parents.

[1951] Specific operation: The server generates a notification message, sends an app notification to the childcare worker's smartphone, and also sends an email to the parents informing them of how to provide support at home.

[1952] 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.

[1953] 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.

[1954] 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.

[1955] 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.

[1956] 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.

[1957] 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.

[1958] 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).

[1959] 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.

[1960] 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."

[1961] 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.

[1962] 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).

[1963] 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.

[1964] 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.

[1965] 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.

[1966] 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.

[1967] 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.

[1968] 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.

[1969] 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.

[1970] 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.

[1971] 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.

[1972] 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.

[1973] The following is further disclosed regarding the above embodiment.

[1974] I understand. Below are the draft claims.

[1975] (Claim 1)

[1976] means for inputting diary data and image data;

[1977] A means for analyzing input diary data and image data;

[1978] A means for generating an individual childcare plan based on the analysis results;

[1979] A means for incorporating the generated childcare plan into a childcare schedule;

[1980] a means of informing parents about support methods at home;

[1981] A system including:

[1982] (Claim 2)

[1983] The system of claim 1, wherein natural language processing and image recognition techniques are used to analyze the diary data and image data.

[1984] (Claim 3)

[1985] The system of claim 1, wherein a generative AI model is used to generate an individualized childcare plan based on the analysis results.

[1986] "Example 1"

[1987] (Claim 1)

[1988] means for inputting diary data and image data;

[1989] means for transmitting the input diary data and image data to a cloud server;

[1990] A means for analyzing input diary data and image data;

[1991] A means for generating an individual childcare plan based on the analysis results;

[1992] A means for incorporating the generated childcare plan into a childcare schedule;

[1993] a means of informing parents about support methods at home;

[1994] A system including:

[1995] (Claim 2)

[1996] The system of claim 1, wherein natural language processing and image recognition techniques are used to analyze the diary data and image data.

[1997] (Claim 3)

[1998] The system of claim 1, wherein a generative AI model is used to generate an individualized childcare plan based on the analysis results.

[1999] "Application Example 1"

[2000] (Claim 1)

[2001] means for inputting diary data and image data;

[2002] A means for analyzing input diary data and image data;

[2003] A means for generating an individual childcare plan based on the analysis results;

[2004] A means for incorporating the generated childcare plan into a childcare schedule;

[2005] a means of informing parents about support methods at home;

[2006] A means of suggesting individual school supplies based on the analysis results;

[2007] a means for displaying suggested school supplies;

[2008] A system including:

[2009] (Claim 2)

[2010] The system of claim 1, wherein natural language processing and image recognition techniques are used to analyze the diary data and image data.

[2011] (Claim 3)

[2012] The system of claim 1, wherein a generative AI model is used to suggest individual school supplies based on the analysis results.

[2013] "Example 2: Combining Emotion Engines"

[2014] (Claim 1)

[2015] means for inputting diary data and image data;

[2016] A means for analyzing input diary data and image data;

[2017] A means for generating an individual childcare plan based on the analysis results;

[2018] A means for incorporating the generated childcare plan into a childcare schedule;

[2019] a means of informing parents about support methods at home;

[2020] a means for assessing the user's emotions and assessing the emotional compatibility of the childcare plan;

[2021] A means to dynamically adjust childcare plans;

[2022] A system including:

[2023] (Claim 2)

[2024] 2. The system of claim 1, wherein natural language processing and image recognition techniques are used to analyze the diary data and image data, and an emotion engine is used to evaluate the user's emotional compatibility.

[2025] (Claim 3)

[2026] The system of claim 1, wherein a generative AI model is used to generate an individualized childcare plan based on the analysis results, and prompt sentences are used to dynamically adjust the childcare plan.

[2027] "Application example 2 when combining emotion engines"

[2028] (Claim 1)

[2029] means for inputting diary data and image data;

[2030] A means for analyzing input diary data and image data;

[2031] A means for generating an individual childcare plan based on the analysis results;

[2032] A means for incorporating the generated childcare plan into a childcare schedule;

[2033] a means of informing parents about support methods at home;

[2034] A method for analyzing text data from childcare diaries using natural language processing technology to extract keywords and perform sentiment analysis;

[2035] A means for analyzing a child's drawing using image processing technology and identifying a recognition object;

[2036] A means for generating a childcare plan based on the analysis results using a generative AI model;

[2037] A means for notifying the generated childcare plan and the emotion analysis results to the childcare provider and the guardian;

[2038] A system including:

[2039] (Claim 2)

[2040] The system of claim 1, wherein natural language processing and image recognition techniques are used to analyze the diary data and image data.

[2041] (Claim 3)

[2042] The system of claim 1, wherein a generative AI model is used to generate an individualized childcare plan based on the analysis results. [Explanation of symbols]

[2043] 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. means for inputting diary data and image data; A means for analyzing input diary data and image data; A means for generating an individual childcare plan based on the analysis results; A means for incorporating the generated childcare plan into a childcare schedule; a means of informing parents about support methods at home; A system including:

2. 2. The system according to claim 1, wherein natural language processing and image recognition techniques are used to analyze the diary data and image data.

3. The system of claim 1, wherein a generative AI model is used to generate an individual childcare plan based on the analysis results.

Images