System

Abstract

A system is provided.SOLUTION: A childcare assistant system comprising: means for receiving a question about childcare from a user via communication means; means for analyzing the question using a natural-language processing technique and extracting a keyword and a category; means for retrieving relevant information from a database based on the extracted keyword or category; means for generating an answer based on the relevant information using a generative AI model; and means for providing the generated answer to the user via the communication means.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] Conventional childcare information services have provided mothers with scattered information, making it difficult for them to easily obtain reliable information. As a result, mothers' anxiety about childcare has increased, and they have been unable to efficiently gather information or seek advice. The present invention aims to solve these conventional problems and provide a system that can provide reliable and easy-to-use childcare information and reduce mothers' anxiety about childcare. [Means for solving the problem]

[0005] The present invention solves the problem by providing a childcare assistant system that includes: means for accepting childcare-related questions from a user via a communication means; means for analyzing the questions and extracting keywords and categories using natural language processing technology; means for searching a database for related information based on the extracted keywords or categories; means for generating answers based on the related information using a generative AI model; and means for providing the generated answers to the user via the communication means. Further advantages can be achieved by including means for accepting information for initial setup from the user, means for storing the information for initial setup in a data cloud and generating a user profile, means for receiving feedback from the user regarding the generated answers, and means for improving the accuracy of the system based on the feedback.

[0006] The "childcare assistance system" is a system that provides information about childcare to mothers and others and supports childcare.

[0007] "Communication means" refers to means for sending and receiving data via the Internet, mobile communication networks, etc.

[0008] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0009] "Keywords" refer to important words or phrases contained in a user's question, and serve as the basis for search and analysis.

[0010] The "category" indicates the field or theme of the question classified based on the keyword.

[0011] A "database" is a system of information collection in which information related to the keywords and categories is organized and stored.

[0012] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate appropriate answers to user questions.

[0013] A "user profile" is a data set containing basic information about a user and their children.

[0014] "Feedback" refers to a user's evaluation or opinion of the answers or services provided.

[0015] A "data cloud" refers to a cloud computing environment for storing and managing data over the Internet.

[0016] An "answer" refers to information or advice generated in response to a user's question. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a childcare assistance system that allows mothers to easily obtain reliable information about childcare. This system accepts questions about childcare from users via communication means and provides prompt and appropriate answers to those questions. Specific embodiments of this system are described below.

[0039] Basic system configuration

[0040] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the Childcare Assistant System account as a friend on a messaging app such as LINE using their device.

[0041] User registration and initial settings

[0042] User:

[0043] First, users add the official account of the childcare assistant system as a friend through a messaging app such as LINE, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[0044] Device:

[0045] The terminal is responsible for transmitting the information entered by the user to the server.

[0046] server:

[0047] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the terminal.

[0048] Question reception and analysis

[0049] User:

[0050] Users can enter specific questions or concerns about childcare into LINE chat.

[0051] Device:

[0052] The entered text data is immediately sent to the server.

[0053] server:

[0054] The server analyzes the question using natural language processing technology and extracts important keywords and categories. For example, if a question is entered about "baby food for a 6-month-old baby," keywords such as "6 months," "baby," and "baby food" will be extracted.

[0055] Information Acquisition and Answer Generation

[0056] server:

[0057] The server searches for relevant information from a database based on the extracted keywords, retrieving relevant information from existing knowledge bases of childcare experts and the latest childcare trends. It then uses a generative AI model (e.g., GPT-3 (registered trademark) 5) to generate appropriate answers based on the collected information.

[0058] For example, an answer might be generated: "For a 6-month-old baby, it's a good idea to start with grain-based porridge, followed by vegetable purees. At this age, start with small amounts to allow for flavor and texture exploration."

[0059] Providing answers

[0060] Device:

[0061] The generated answer is sent from the server to the device and displayed on the LINE chat.

[0062] User:

[0063] The user can check the answers provided in the chat and use them to help with child-rearing.

[0064] Personalization and Consistency

[0065] server:

[0066] Based on the information provided by the user and the accumulated life log, the server generates personalized advice according to the child's growth and provides it periodically via LINE chat. For example, it can send specific advice such as, "Based on the recent changes in your baby's weight, consider when it's time to move on to the next step."

[0067] Feedback and Improvements

[0068] User:

[0069] Users can provide feedback on the answers provided via LINE chat to help improve the system.

[0070] Device:

[0071] Send feedback to the server.

[0072] server:

[0073] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of answers.

[0074] In this way, the childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, it can provide long-term childcare support by continuously providing personalized support to users.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] User:

[0078] Add the official account of the Childcare Assistant System as a friend on the LINE app. Start a chat and enter your information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[0079] Step 2:

[0080] Device:

[0081] The information entered by the user is sent to the server.

[0082] Step 3:

[0083] server:

[0084] The received information is stored in the data cloud, a user profile is generated, and a registration completion message is generated and sent to the terminal.

[0085] Step 4:

[0086] Device:

[0087] A registration completion message from the server will be displayed on the LINE chat.

[0088] Step 5:

[0089] User:

[0090] Enter specific questions or concerns about childcare into LINE chat.

[0091] Step 6:

[0092] Device:

[0093] The input text data is sent to the server.

[0094] Step 7:

[0095] server:

[0096] The system receives the question text and uses natural language processing technology to analyze it, understand the intent of the question, and extract important keywords and categories.

[0097] Step 8:

[0098] server:

[0099] Based on the extracted keywords and categories, relevant information is searched for in databases and expert knowledge bases.

[0100] Step 9:

[0101] server:

[0102] Based on relevant information, a generative AI model is used to generate the optimal answer.

[0103] Step 10:

[0104] server:

[0105] The generated response is sent to the device.

[0106] Step 11:

[0107] Device:

[0108] The response sent from the server is displayed in the LINE chat.

[0109] Step 12:

[0110] User:

[0111] Check the answers provided on LINE chat and get the information you need.

[0112] Step 13:

[0113] User:

[0114] Enter your feedback on the provided answer (whether it was helpful, additional questions, etc.) in LINE chat.

[0115] Step 14:

[0116] Device:

[0117] Send user feedback to the server.

[0118] Step 15:

[0119] server:

[0120] The system receives feedback and stores it as data to improve the answers and accuracy of the system, and generates new answers to new questions as needed.

[0121] Step 16:

[0122] server:

[0123] The system analyzes the user's life log and personal data to generate personalized advice tailored to the child's development.

[0124] Step 17:

[0125] server:

[0126] Relevant childcare information and advice will be provided on a regular basis via LINE chat, providing ongoing support to users in their childcare.

[0127] Step 18:

[0128] User:

[0129] Review the personalized information and advice provided to help you with your parenting.

[0130] Example 1

[0131] 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."

[0132] Conventional childcare assistance systems have limited answers to users' questions about childcare, making it difficult to provide personalized advice that takes into account each user's individual circumstances. Furthermore, the accuracy of the system has not been sufficiently improved using user feedback, so there is a need for an improved user experience. This will reduce anxiety about childcare and provide more reliable information.

[0133] 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.

[0134] In this invention, the server includes: means for accepting childcare-related questions from users via communication means; means for analyzing the questions and extracting keywords and categories using natural language processing technology; means for searching for related information from an information storage device based on the extracted keywords or categories; means for generating answers based on the related information using a generative AI model; and means for providing the generated answers to users via the communication means. This enables prompt and accurate answers to users' questions and reliable childcare information. Furthermore, the system includes means for accepting information for initial setup, means for storing the information for initial setup in a data cloud to generate a user profile, and means for periodically providing personalized advice using the generated answers, thereby enabling detailed support tailored to individual situations. Furthermore, the system includes means for accepting user feedback on the generated answers and means for storing the feedback in a data cloud to improve the system's accuracy and the quality of the answers, enabling continuous system improvement.

[0135] "Communication means" refers to a communication path for transmitting input data from a user to a server and for providing output data from the server to the user.

[0136] "User" refers to a person who uses the childcare assistant system to ask questions about childcare and receive answers.

[0137] "Natural language processing technology" refers to technology that analyzes text data entered by a user and understands their meaning and intent.

[0138] "Keywords" are important words extracted from the content of a user's question, and are used to search for related information when generating an answer.

[0139] "Category" refers to a classification criterion for classifying questions and information, and is used to improve the efficiency of searching for related information.

[0140] "Information storage device" refers to a recording medium or database that stores related information and makes it searchable when needed.

[0141] A "generative AI model" refers to a program equipped with artificial intelligence technology that automatically generates sentences based on data.

[0142] A "user profile" is individual account information generated based on information provided by a user, and is used to provide personalized services to each individual user.

[0143] "Personalized advice" refers to advice that is optimized for an individual user based on the user profile and accumulated data.

[0144] "Feedback" refers to a user's input of evaluations and opinions regarding the results of using the system and the answers provided.

[0145] This invention relates to a childcare assistance system, and its purpose is to enable mothers and guardians to easily obtain reliable information about childcare. This system accepts questions about childcare from users via communication means and provides prompt and appropriate answers to those questions. Specific embodiments for carrying out the present invention are described below.

[0146] Basic system configuration

[0147] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend in a messaging app (e.g., messaging application) using their device.

[0148] User registration and initial settings

[0149] User:

[0150] Users add the official account of the childcare assistant system as a friend through a messaging app, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[0151] Device:

[0152] The terminal is responsible for transmitting the information entered by the user to the server.

[0153] server:

[0154] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the user's device.

[0155] Examples:

[0156] After adding a friend, the user enters the name "Yamada Taro" and the child's date of birth "April 1, 2022" into a form and sends it to the server.

[0157] Example prompt: "Enter my information. My name is Taro Yamada, and my child's birth date is April 1, 2022."

[0158] Question reception and analysis

[0159] User:

[0160] Users type specific questions or concerns about childcare into the messaging app chat, for example, "Can you tell me about solid food for my 6-month-old baby?"

[0161] Device:

[0162] The entered text data is immediately sent to the server.

[0163] server:

[0164] The server uses natural language processing technology (e.g., Natural Language Toolkit) to analyze the question and extract important keywords and categories. For example, if a question about "baby food for a 6-month-old baby" is entered, keywords such as "6 months," "baby," and "baby food" will be extracted.

[0165] Example prompt:

[0166] "Please tell me about baby food for a 6-month-old baby."

[0167] Information Acquisition and Answer Generation

[0168] server:

[0169] The server searches for relevant information from an information storage device based on the extracted keywords. It retrieves relevant information from a knowledge base of childcare experts and the latest childcare trends. It then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer based on the collected information. For example, it might generate an answer like, "For a 6-month-old baby, it's a good idea to start with grain-based porridge, followed by vegetable puree. At this age, start with a small amount to allow for enjoyment of the taste and texture."

[0170] Examples:

[0171] The server searches for information about "6 months," "baby," and "baby food," and the generative AI model generates the appropriate answer.

[0172] Example prompt: "For 6-month-old babies, it's a good idea to try grain-based porridge first, followed by vegetable purees."

[0173] Providing answers

[0174] server:

[0175] The generated answer is sent from the server to the user's terminal.

[0176] Device:

[0177] The device will display the received response in the chat on the messaging app.

[0178] User:

[0179] The user can check the answers provided in the chat and use them to help with child-rearing.

[0180] Examples:

[0181] The user's device displays the message, "For 6-month-old babies, try grain-based porridge first, followed by vegetable puree."

[0182] Personalization and Consistency

[0183] server:

[0184] The server generates personalized advice based on the information provided by the user and the accumulated life log, and delivers it periodically via the messaging app according to the child's growth. For example, it can provide specific advice such as, "Based on the recent changes in your baby's weight, consider when it's time to move on to the next step."

[0185] Examples:

[0186] Send specific advice based on your child's development.

[0187] Sample prompt: "Based on your baby's recent weight changes, consider when it's time to move forward with the next step."

[0188] Feedback and Improvements

[0189] User:

[0190] Users can provide feedback on the answers provided in the messaging app's chat to help improve the system.

[0191] Device:

[0192] The entered feedback is sent to the server.

[0193] server:

[0194] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of answers.

[0195] Examples:

[0196] The user enters their satisfaction with the answer, and the feedback is sent to and stored on the server.

[0197] Sample prompt: "I'm very pleased with the answer you provided. Thank you very much."

[0198] The above is a detailed description of a specific embodiment of the present invention. This enables users to receive prompt and appropriate answers to their questions and obtain reliable childcare information. Furthermore, personalized support tailored to individual situations and system improvements based on user feedback are realized.

[0199] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0200] System program processing flow

[0201] ---

[0202] Step 1: User registration and initial setup

[0203] Step 1-1: User accesses the system

[0204] User:

[0205] The user opens a messaging app on a device such as a smartphone and adds the official account of the childcare assistant system as a friend. This action allows the user to start accessing the system.

[0206] input:

[0207] The act of adding an official account as a friend through a messaging app.

[0208] output:

[0209] You will be able to access the official account of the childcare assistance system.

[0210] Specific behavior:

[0211] Tap the "Add Friends" button within the messaging app, then search for and add the official account.

[0212] ---

[0213] Step 1-2: Fill in the initial setup form

[0214] User:

[0215] After adding a friend, start chatting and enter your information (e.g., name, contact information, children's birth dates, gender) according to the initial setup questionnaire sent by the system.

[0216] Device:

[0217] The terminal transmits the information entered by the user to the server in real time.

[0218] input:

[0219] Enter the user's personal information and basic information about childcare.

[0220] output:

[0221] User information sent to the server.

[0222] Specific behavior:

[0223] The action of a user filling out a form in a chat window and pressing the "Send" button.

[0224] ---

[0225] Step 1-3: The server generates a user profile

[0226] server:

[0227] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the user's device.

[0228] input:

[0229] User information sent from the device.

[0230] output:

[0231] User profile and registration completion message stored in the data cloud.

[0232] Specific behavior:

[0233] The received data is stored in an appropriate location in the data cloud, a profile creation algorithm is run to generate a user profile, and a message such as "Registration complete" is sent to the user's device.

[0234] ---

[0235] Step 2: Question collection and analysis

[0236] Step 2-1: User enters a question

[0237] User:

[0238] Type specific questions or concerns about childcare into a messaging app chat, such as "Can you tell me about solid food for my 6-month-old baby?"

[0239] input:

[0240] Text with specific childcare questions.

[0241] output:

[0242] The question text sent to the server through a messaging app.

[0243] Specific behavior:

[0244] The action of a user typing a question into a chat window and pressing the "send" button.

[0245] ---

[0246] Step 2-2: Send the question text

[0247] Device:

[0248] The entered question text data is immediately sent to the server.

[0249] input:

[0250] The question text entered by the user into the messaging app.

[0251] output:

[0252] The question text sent to the server.

[0253] Specific behavior:

[0254] The text data received by the device is immediately sent to the specified API endpoint on the server.

[0255] ---

[0256] Step 2-3: Question Analysis

[0257] server:

[0258] The server uses natural language processing technology to analyze the question and extract important keywords and categories. If a question is entered about "baby food for a 6-month-old baby," keywords such as "6 months," "baby," and "baby food" will be extracted.

[0259] input:

[0260] The question text sent from the device.

[0261] output:

[0262] Extracted keyword and category information.

[0263] Specific behavior:

[0264] The server performs text analysis using natural language processing libraries (e.g., Natural Language Toolkit) to identify and extract important keywords and categories.

[0265] Example prompt:

[0266] "Please tell me about baby food for a 6-month-old baby."

[0267] ---

[0268] Step 3: Information Acquisition and Answer Generation

[0269] Step 3-1: Search for related information

[0270] server:

[0271] The server searches for relevant information from the information storage device based on the extracted keywords, and obtains relevant information from the knowledge base of childcare experts and the latest childcare trends.

[0272] input:

[0273] Extracted keyword and category information.

[0274] output:

[0275] Related information found.

[0276] Specific behavior:

[0277] Perform database queries to find and retrieve relevant information that matches your keywords.

[0278] ---

[0279] Step 3-2: Generate an answer

[0280] server:

[0281] Using this information, a generative AI model (e.g., GPT-3.5) is used to generate an appropriate answer, such as "For a 6-month-old baby, try grain-based porridge first, followed by vegetable puree."

[0282] input:

[0283] Related information found.

[0284] output:

[0285] Answer text obtained through a generative AI model.

[0286] Specific behavior:

[0287] Relevant information is provided as input to a generative AI model, which then generates and outputs natural answers.

[0288] Example prompt:

[0289] "For six-month-old babies, it's a good idea to start with cereal-based porridge, followed by vegetable purees."

[0290] ---

[0291] Step 4: Provide your answers

[0292] Step 4-1: Submit your response

[0293] server:

[0294] The generated answer is sent to the user's terminal.

[0295] input:

[0296] Answer text generated by a generative AI model.

[0297] output:

[0298] The answer text sent to the user's terminal.

[0299] Specific behavior:

[0300] The server sends the generated response to the user's device via the messaging app's API.

[0301] ---

[0302] Step 4-2: View the answer

[0303] Device:

[0304] The device will display the received response in the chat on the messaging app.

[0305] input:

[0306] The response text sent by the server.

[0307] output:

[0308] The reply text that will be displayed to the user in the chat screen.

[0309] Specific behavior:

[0310] The messaging app displays the received response in the chat window, allowing the user to easily check the response.

[0311] ---

[0312] Step 5: Personalization and Retention

[0313] Step 5-1: Providing regular advice

[0314] server:

[0315] Based on the information provided by the user and the accumulated life logs, the server generates personalized advice tailored to the child's development and provides it periodically via the messaging app.

[0316] input:

[0317] User information and lifelog data.

[0318] output:

[0319] Personalized advice.

[0320] Specific behavior:

[0321] The server periodically analyzes the user's profile and latest life log data, uses a generative AI model to generate the next piece of advice needed, and sends it via the messaging app.

[0322] Example prompt:

[0323] "Based on your baby's recent weight changes, consider when it's time to move on to the next step."

[0324] ---

[0325] Step 6: Feedback and Improvement

[0326] Step 6-1: Enter your feedback

[0327] User:

[0328] Users can provide feedback on the answers provided in the messaging app's chat to help improve the system.

[0329] input:

[0330] User feedback text.

[0331] output:

[0332] Feedback text sent to the server through the messaging app.

[0333] Specific behavior:

[0334] The action of a user typing feedback into a chat window and pressing the "Send" button.

[0335] ---

[0336] Step 6-2: Send feedback

[0337] Device:

[0338] The entered feedback is sent to the server.

[0339] input:

[0340] Feedback entered by users into messaging apps.

[0341] output:

[0342] The feedback text sent to the server.

[0343] Specific behavior:

[0344] The feedback data received by the device is immediately sent to the specified API endpoint on the server.

[0345] ---

[0346] Step 6-3: Accumulating and utilizing feedback

[0347] server:

[0348] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of its answers.

[0349] input:

[0350] Feedback sent from the device.

[0351] output:

[0352] Feedback data and updated analytical models stored in the data cloud.

[0353] Specific behavior:

[0354] The server stores the received feedback in a data cloud and improves the system by updating the analytical model.

[0355] Example prompt:

[0356] "I am very satisfied with the answer provided. Thank you very much."

[0357] The above is a specific flow of program processing for the system of the present invention, which allows users to quickly and appropriately obtain highly reliable childcare information, and the system is continually improved.

[0358] (Application example 1)

[0359] 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."

[0360] Parents raising children often have a wide range of questions and anxieties about childcare, but there are few ways to quickly obtain reliable information when they are out and about, especially while shopping, and the time it takes to obtain information is a problem. Furthermore, if there were a way to obtain answers through voice input or visually, it would be much more convenient for parents raising children.

[0361] 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.

[0362] In this invention, the server includes means for recognizing a user's voice using a voice input device, means for analyzing the question using natural language processing technology and extracting keywords and categories, and means for displaying the generated answer on a visual output device, thereby enabling users to quickly obtain reliable information about childcare even when they are out and about.

[0363] "Communication means" refers to means for sending and receiving data via the Internet or the like.

[0364] "Natural language processing technology" is a technology for analyzing text and voice input from users, understanding their meaning, and extracting keywords and categories.

[0365] "Keyword and category extraction means" refers to a means for automatically identifying important words and themes from user questions using natural language processing techniques.

[0366] A "database" is a structured collection of information that stores related information systematically and can be retrieved as needed.

[0367] A "generative AI model" is an artificial intelligence model used to generate optimal answers to user questions based on collected information.

[0368] A "voice input device" is a device that receives a user's voice as input and converts the content into text.

[0369] A "visual output device" is a device for visually presenting information to a user through smart glasses or the like.

[0370] "User profile" refers to data that compiles personal information and childcare information provided by the user.

[0371] "Feedback" refers to opinions or evaluations given by users regarding answers or services provided.

[0372] MODE FOR CARRYING OUT THE INVENTION

[0373] 1. System Overview

[0374] This invention is a childcare assistant system that allows parents to easily ask questions about childcare through voice input and quickly obtain reliable information. This system is realized by combining a wide range of technologies, including communication methods, natural language processing technology, generative AI models, voice input devices, visual output devices, and data clouds.

[0375] 2. Hardware and software used

[0376] Smart glasses: devices that input voice and provide information visually (e.g., Google® Glass®, NReal).

[0377] Smartphone: A device for installing applications and working with smart glasses.

[0378] Server: A central control unit that processes data and generates and provides answers to users (e.g., AWS®, Google Cloud).

[0379] Natural language processing engine: Software for analyzing user questions (e.g., Google Cloud Natural Language API).

[0380] Generative AI model: An AI model for generating optimal answers to questions (e.g., OpenAI® GPT-3.5).

[0381] 3. Data processing and calculation

[0382] Voice input

[0383] Users can voice-input questions about childcare through the smart glasses, and this voice data is sent to a server via a smartphone.

[0384] Voice Recognition

[0385] The server receives the voice data and converts it into text using AWS Transcribe, which allows the voice-input questions to be processed as text information.

[0386] Natural Language Processing

[0387] The converted text data is then parsed using the Google Cloud Natural Language API to extract keywords and categories from the question, helping to accurately understand the question and prepare the search for the appropriate information.

[0388] Keyword search

[0389] Based on the extracted keywords and categories, relevant information is searched for in the database, which includes expert knowledge bases and the latest childcare trends.

[0390] Answer generation

[0391] Based on the searched information, the best answer is automatically generated using a generative AI model (OpenAI GPT-3.5) to provide information that meets the user's needs.

[0392] Visual and audio feedback

[0393] The generated answer is visually displayed on the smart glasses' display and also provided to the user audibly, using gTTS (Google Text-to-Speech) to convert the text data into speech and play it through the smart glasses.

[0394] 4. Examples of concrete examples and prompts

[0395] Specific examples

[0396] For example, consider a case where a user is in a store selling childcare products and has a question about "how to choose a baby bottle." They ask through their smart glasses, "How do I choose a baby bottle?" This question is sent to the server, converted into text, and analyzed using natural language processing technology. Relevant information is then searched for in the database, and the generative AI model generates the answer: "They come in glass and plastic. Glass bottles are heavy but durable, while plastic bottles are light and easy to carry." This information is finally displayed on the smart glasses' display and provided via voice.

[0397] Prompt Sentence Examples

[0398] How do I choose a baby bottle?

[0399] "There are glass and plastic baby bottles. I'd like to know the advantages and disadvantages of each."

[0400] As described above, the present invention enables users to receive real-time childcare support and provides users with quick and reliable information even when they are away from home.

[0401] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0402] Step 1:

[0403] The user wears the smart glasses and asks questions about childcare via a voice input device.

[0404] Input: User's voice data

[0405] Action: The user says, "How do I choose a baby bottle?"

[0406] Step 2:

[0407] The terminal receives the voice data and transmits it to a server via the Internet.

[0408] Input: User's voice data

[0409] Data processing: The audio data is sent to the AWS Transcribe service to prepare it for conversion into text.

[0410] Output: Audio data received by the server

[0411] Step 3:

[0412] The server uses AWS Transcribe to convert the audio data into text data.

[0413] Input: Audio data received by the server

[0414] Data processing: Using voice recognition technology, audio is converted into text.

[0415] Output: Converted text data

[0416] Step 4:

[0417] The server uses a natural language processing engine (Google Cloud Natural Language API) to analyze the text data and extract keywords and categories.

[0418] Input: Converted text data

[0419] Data calculation: Extract the keywords "baby bottle" and "how to choose" from the text.

[0420] Output: Extracted keywords and categories

[0421] Step 5:

[0422] The server searches for relevant information based on keywords and categories extracted from the database.

[0423] Input: Extracted keywords and categories

[0424] Data retrieval: Searching for relevant information in a database.

[0425] Output: Retrieved childcare information

[0426] Step 6:

[0427] The server uses a generative AI model (OpenAI GPT-3.5) to generate appropriate answers based on relevant information.

[0428] Input: Searched childcare information

[0429] Data computation: A generative AI model processes the information and generates a specific answer, such as "There are glass and plastic baby bottles."

[0430] Output: Generated answer text

[0431] Step 7:

[0432] The server uses gTTS (Google Text-to-Speech) to convert the generated response text into audio data.

[0433] Input: Generated answer text

[0434] Data processing: Convert text into an audio file.

[0435] Output: Generated answer audio data

[0436] Step 8:

[0437] The server transmits the generated answer text and voice data to the terminal.

[0438] Input: Generated answer text and audio data

[0439] Operation: Sending data from the server to the device

[0440] Output: Terminal received data

[0441] Step 9:

[0442] The terminal displays the received answer text on the visual output device of the smart glasses and plays the audio data.

[0443] Input: Terminal received data

[0444] How it works: The smart glasses display will say "There are glass and plastic baby bottles" and simultaneously play back the answer aloud.

[0445] Output: The user gets a visual and audio response.

[0446] Through the above processing steps, the present invention serves as a childcare assistant system that quickly and effectively solves questions about childcare through voice input.

[0447] 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.

[0448] This invention is a childcare assistant system that allows mothers to easily obtain reliable information about childcare. The system incorporates communication methods, natural language processing technology, generative AI models, and an emotion engine. Because the system includes user emotion analysis, it not only improves the quality of childcare but also supports mothers' emotional care.

[0449] Basic system configuration

[0450] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend on a messaging app such as LINE using their device.

[0451] User registration and initial settings

[0452] User:

[0453] First, users add the official account of the childcare assistant system as a friend through a messaging app such as LINE, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[0454] Device:

[0455] The terminal is responsible for transmitting the information entered by the user to the server.

[0456] server:

[0457] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the terminal.

[0458] Question reception and analysis

[0459] User:

[0460] Users can enter specific questions or concerns about childcare into LINE chat.

[0461] Device:

[0462] The entered text data is immediately sent to the server.

[0463] server:

[0464] The server analyzes the question using natural language processing techniques to extract important keywords and categories.

[0465] Information Acquisition and Answer Generation

[0466] server:

[0467] The system searches for relevant information from a database based on the extracted keywords. It then uses a generative AI model (e.g., GPT-3.5) to generate appropriate answers based on the collected information. It also uses an emotion engine to analyze the user's emotions and adjust the content and tone of the answer based on those emotions. For example, if the user is "anxious," it adds encouraging words to ease their anxiety.

[0468] Providing answers

[0469] Device:

[0470] The generated answer is sent from the server to the device and displayed on the LINE chat.

[0471] User:

[0472] The user can check the answers provided in the chat and use them to help with child-rearing.

[0473] Personalization and Consistency

[0474] server:

[0475] The server analyzes the user's life log and personal data to generate personalized advice based on the child's development. It also performs continuous emotion analysis using an emotion engine to provide appropriate support based on the user's emotions.

[0476] Feedback and Improvements

[0477] User:

[0478] Users can provide feedback on the answers provided via LINE chat to help improve the system.

[0479] Device:

[0480] Send feedback to the server.

[0481] server:

[0482] The server receives feedback and stores it as data to improve the answers and accuracy of the system, and also generates new answers to new questions as needed.

[0483] In this way, this childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, the system also provides psychological support by analyzing the user's emotions and providing support accordingly. This makes it possible to provide long-term, continuous childcare support.

[0484] The processing flow will be explained below.

[0485] Step 1:

[0486] User:

[0487] Add the official account of the Childcare Assistant System as a friend on the LINE app. Start a chat and enter your basic information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[0488] Step 2:

[0489] Device:

[0490] The information entered by the user is sent to the server.

[0491] Step 3:

[0492] server:

[0493] The received information is stored in a data cloud and a user profile is generated. After the profile is generated, a registration completion message is generated and sent to the terminal.

[0494] Step 4:

[0495] Device:

[0496] A registration completion message from the server will be displayed on the LINE chat.

[0497] Step 5:

[0498] User:

[0499] Enter specific questions or concerns about childcare in the LINE chat. For example, "What kind of baby food does a 6-month-old baby need?"

[0500] Step 6:

[0501] Device:

[0502] The input text data is sent to the server.

[0503] Step 7:

[0504] server:

[0505] The received question text is analyzed using natural language processing technology to extract important keywords and categories, such as "6 months," "baby," and "baby food."

[0506] Step 8:

[0507] server:

[0508] The system searches for relevant information from a database based on the extracted keywords and categories, while simultaneously analyzing emotions (e.g., anxiety, doubt, joy) from the user's text using an emotion engine.

[0509] Step 9:

[0510] server:

[0511] Based on the results of the emotion engine, a generative AI model is used to generate an appropriate response. For example, if the user expresses anxiety, the system generates a response in a reassuring tone, such as, "First of all, don't worry. Porridge is suitable for a 6-month-old baby."

[0512] Step 10:

[0513] server:

[0514] The generated response is sent to the device.

[0515] Step 11:

[0516] Device:

[0517] The response sent from the server is displayed in the LINE chat.

[0518] Step 12:

[0519] User:

[0520] Check the answers provided via LINE chat and use them to help with childcare. For example, make porridge based on the advice on baby food.

[0521] Step 13:

[0522] User:

[0523] Enter your feedback on the provided answer (whether it was helpful, additional questions, etc.) in LINE chat.

[0524] Step 14:

[0525] Device:

[0526] Send user feedback to the server.

[0527] Step 15:

[0528] server:

[0529] The system receives feedback and stores it as data to improve the answers and accuracy of the system, and generates new answers to new questions as needed.

[0530] Step 16:

[0531] server:

[0532] The system analyzes the user's life log and personal data to generate personalized advice based on the child's growth. For example, it generates specific advice such as, "Based on recent weight changes, it's time to move on to the next step."

[0533] Step 17:

[0534] server:

[0535] Relevant childcare information and advice will be provided on a regular basis via LINE chat, providing ongoing support to users in their childcare.

[0536] Step 18:

[0537] User:

[0538] Review the personalized information and advice provided to help you with your parenting.

[0539] Example 2

[0540] 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."

[0541] Conventional childcare assistance systems are not always able to respond quickly to user questions and are sometimes unable to provide reliable information. Furthermore, they lack emotional support for mothers, resulting in insufficient psychological support. This creates challenges for improving the quality of childcare and for providing adequate psychological care for mothers.

[0542] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a question about child-rearing from a user via communication means, means for analyzing the question and extracting keywords and categories using natural language processing technology, means for searching a database for related information based on the extracted keywords or categories, means for generating an answer based on the related information using a generative AI model, means for analyzing the user's emotions using emotion analysis technology and adjusting the content of the answer, and means for providing the generated answer to the user via the communication means. This makes it possible to quickly and appropriately respond to a question from a user, provide reliable child-rearing information, and analyze the user's emotions to provide appropriate support.

[0543] "Communication means" refers to the infrastructure for sending and receiving data from users, including the Internet, Wi-Fi, mobile communication networks, etc.

[0544] "Natural language processing technology" refers to the technology of analyzing, understanding, and generating natural language used by humans using a computer, and includes morphological analysis, keyword extraction, and sentence classification.

[0545] A "generative AI model" refers to an artificial intelligence model that generates natural language based on large amounts of data, and includes, for example, GPT-3.5.

[0546] "Emotion analysis technology" refers to technology that identifies a user's emotions from text data and adjusts responses based on those emotions, and includes emotion analysis engines and tone analyzers.

[0547] A "data cloud" refers to a distributed storage system for storing and managing data over the Internet, and includes cloud storage services.

[0548] A "user profile" refers to a collection of information constructed based on data such as a user's personal information and behavioral history, and includes name, contact information, childcare history, etc.

[0549] "Feedback" refers to the evaluations and opinions that users give about the answers and services provided, and serves as data that can be used to improve the system.

[0550] This invention relates to a childcare assistant system that provides users with tools to easily and quickly resolve questions about childcare. The system includes a communication means, natural language processing technology, a generative AI model, emotion analysis technology, a data cloud, and a user profile management function.

[0551] Basic system configuration

[0552] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend in a messaging app on their smartphone.

[0553] User registration and initial settings

[0554] User:

[0555] First, users add the official account of the childcare assistant system as a friend in a messaging app and start chatting, then follow the system's instructions to enter information such as the child's name, contact details, date of birth, and gender into the initial setup questionnaire.

[0556] Device:

[0557] The terminal generates a data packet for transmitting the information entered by the user to the server.

[0558] server:

[0559] The server analyzes the received information, stores it in the data cloud, creates a user profile, and sends a registration completion message to the terminal.

[0560] Question reception and analysis

[0561] User:

[0562] Users type parenting questions into the messaging app.

[0563] Device:

[0564] The entered text data is sent to a server via the Internet.

[0565] server:

[0566] The server analyzes the text of the received question using natural language processing technology, such as SpaCy or NLTK, to extract important keywords.

[0567] Information Acquisition and Answer Generation

[0568] server:

[0569] The server uses the extracted keywords to search for relevant information from a database, then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer, and furthermore, uses sentiment analysis technology (e.g., IBM Watson® Tone Analyzer) to analyze the user's emotions and adjust the tone of the answer.

[0570] Examples of prompts:

[0571] If a user submits a question like, "My baby won't stop crying in the middle of the night, what should I do?"

[0572] In that case the prompt would look like this:

[0573] User: "My baby won't stop crying in the middle of the night. What should I do?" Gently explain why babies cry at night and what you can do about it. Also, add some reassurance, as the mother may be worried.

[0574] Providing answers

[0575] server:

[0576] The generated response is sent to the device.

[0577] Device:

[0578] The response received from the server is displayed on the messaging app.

[0579] User:

[0580] The user can check the provided answers and use them to help with childcare.

[0581] Personalization and Consistency

[0582] server:

[0583] The server analyzes the user's life log and personal data to generate personalized advice tailored to the child's development, and also performs continuous emotion analysis using emotion analysis technology.

[0584] Feedback and Improvements

[0585] User:

[0586] Users provide feedback on the answers provided in the messaging app.

[0587] Device:

[0588] Converts data into packets for sending feedback to the server.

[0589] server:

[0590] The server receives the feedback, stores it as data to improve the system, and generates new answers as needed and sends them back to the user.

[0591] In this way, this childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, the system also provides psychological support by analyzing the user's emotions and providing support accordingly. This makes it possible to provide long-term, continuous childcare support.

[0592] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0593] Step 1: User registration and initial setup

[0594] User: The user adds the official account of the childcare assistant system as a friend on a messaging app such as LINE and starts chatting. The system then sends the user an initial setup questionnaire, in which the user enters personal information such as their name, contact details, and the child's date of birth and gender.

[0595] Input: Initial setup information (e.g. name, contact information, child's date of birth, gender)

[0596] Output: The data the user entered into the form

[0597] Terminal: The terminal generates a data packet for transmitting the information input by the user to the server. The generated data packet is transmitted to the server via a communication means.

[0598] Input: Data entered by the user

[0599] Output: Data packet sent to the server

[0600] Server: The server analyzes the received information, stores it in the data cloud, creates a user profile, and sends a registration completion message to the device.

[0601] Input: Data packets sent from the device

[0602] Output: User information stored in the data cloud, registration completion message

[0603] Step 2: Question collection and analysis

[0604] User: A user types a parenting question into a messaging app.

[0605] Input: User question text (e.g., "My baby won't stop crying in the middle of the night. What should I do?")

[0606] Output: Submitted question text data

[0607] Terminal: The entered question text data is sent to the server via the Internet.

[0608] Input: Question text data submitted by the user

[0609] Output: Text data sent to the server

[0610] Server: The server uses natural language processing technology to analyze the text of the received question and extract important keywords. For example, SpaCy or NLTK can be used to extract keywords such as "baby," "midnight," and "won't stop crying."

[0611] Input: Question text data sent from the terminal

[0612] Output: Extracted keywords (e.g., "baby," "midnight," "can't stop crying")

[0613] Step 3: Information Acquisition and Answer Generation

[0614] server:

[0615] The server uses the extracted keywords to search for relevant information in a database. For example, it runs a database query based on the keywords "baby," "midnight," and "won't stop crying." It then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer. For example, it sends the following prompt to the generative AI:

[0616] User: "My baby won't stop crying in the middle of the night. What should I do?" Gently explain why babies cry at night and what you can do about it. Also, add some reassurance, as the mother may be worried.

[0617] Using emotion analysis technology, it analyzes the user's emotions and adds reassuring phrases such as, "Don't worry. It's normal for babies to cry in the middle of the night."

[0618] Input: Extracted keywords, prompt sentence for generative AI model

[0619] Output: Generated answer text, final answer text with tone adjusted by sentiment analysis

[0620] Step 4: Provide your answers

[0621] Server: Sends the generated answer to the device.

[0622] Input: Answer text generated by the generative AI model, sentiment analysis results

[0623] Output: Message from the server to the terminal

[0624] On your device: The response received from the server is displayed on the messaging app.

[0625] Input: Answer text received from the server

[0626] Output: The response displayed in the user's messaging app

[0627] User: The user checks the displayed answers and uses them to help with parenting.

[0628] Input: Displayed answer text

[0629] Output: Childcare information actually obtained by the user

[0630] Step 5: Personalization and Retention

[0631] Server: The server continuously analyzes the user's life log and personal data to generate personalized advice, such as advice related to specific events or appointment reminders. It also continuously analyzes the user's emotions using emotion analysis technology and takes the results into account to respond appropriately.

[0632] Input: User's life log, personal data, past emotion analysis results

[0633] Output: personalized advice, continuous emotional response

[0634] Step 6: Feedback and Improvement

[0635] User: The user provides feedback on the provided answer in the messaging app.

[0636] Input: Feedback text

[0637] Output: Feedback data sent to the server

[0638] Terminal: Packetizes data to send feedback to the server.

[0639] Input: Feedback text

[0640] Output: Data packet sent to the server

[0641] Server: The server receives the feedback, stores it as data to improve the system, and generates new answers if necessary and sends them back to the user.

[0642] Input: User feedback data

[0643] Output: Improved system accuracy, newly generated answers

[0644] (Application example 2)

[0645] 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."

[0646] In modern child-rearing, mothers need to obtain parenting advice from a variety of sources, but there is a lack of a reliable, centralized source of information. Furthermore, when mothers experience stress or anxiety associated with child-rearing, there is a lack of support to help them cope with these emotions. Furthermore, there is a demand for personalized information tailored to the circumstances of each parent and child, but this is difficult to achieve with conventional systems.

[0647] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting a question about child-rearing from a user via communication means, means for analyzing the question and extracting keywords and categories using natural language processing technology, means for searching a database for related information based on the extracted keywords or categories, means for generating an answer based on the related information using a generative AI model, means for analyzing the user's emotions using emotion analysis technology, means for adjusting the generated answer based on the results of the emotion analysis, and means for providing the adjusted answer to the user via the communication means. This makes it possible to easily obtain reliable information about child-rearing and reduce child-rearing stress by providing care that is tailored to the user's emotional state.

[0648] The "childcare assistant system" is a system that allows mothers to easily obtain reliable information about childcare, and is composed of a combination of communication methods, natural language processing technology, generative AI models, emotion analysis technology, and more.

[0649] "Communication means" refers to technology for receiving questions and information from users via the Internet or the like, and for sending generated answers to users.

[0650] "Natural language processing technology" is a technology for analyzing questions from users and extracting keywords and categories from the sentences.

[0651] "Keywords and categories" are important words and concepts extracted from users' questions using natural language processing techniques.

[0652] A "database" is an information source that stores information related to childcare, and is used to search for related information based on extracted keywords and categories.

[0653] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on extracted relevant information.

[0654] "Emotion analysis technology" is a technology that analyzes emotions from users' questions and feedback and adjusts the content of responses based on those emotions.

[0655] A "user profile" refers to individual information about a user that is generated based on information input by the user for initial settings.

[0656] "Adjusted answers" are answers generated by a generative AI model whose content and tone are changed according to the user's emotions using emotion analysis technology.

[0657] "Feedback" refers to ratings and comments that users make on answers provided.

[0658] This invention relates to a childcare assistant system that allows mothers to easily obtain reliable information about childcare. This system is composed of a combination of communication means, natural language processing technology, generative AI models, and emotion analysis technology.

[0659] Basic Building Blocks

[0660] System Overview:

[0661] This system consists of a user's device (e.g., a smartphone), a communication method (e.g., the Internet), and a server. Users can access the system via a messaging app.

[0662] User registration and initial settings

[0663] User:

[0664] Users add the official account of the childcare assistant system as a friend through a messaging app and start chatting. They then enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[0665] Device:

[0666] The terminal transmits the information entered by the user to the server.

[0667] server:

[0668] The server stores the received information in a data cloud, generates a user profile, and generates a registration completion message based on the generated user profile and sends it to the terminal.

[0669] Question reception and analysis

[0670] User:

[0671] Users type specific parenting questions into the messaging app.

[0672] Device:

[0673] The input text data is sent to the server.

[0674] server:

[0675] The server analyzes the question using natural language processing techniques to extract important keywords and categories.

[0676] Information Acquisition and Answer Generation

[0677] server:

[0678] Based on the extracted keywords, relevant information is searched for in the database, and a generative AI model (e.g., OpenAI GPT-3.5) is used to generate appropriate answers based on the collected information.

[0679] Sentiment analysis and response adjustment

[0680] server:

[0681] Using emotion analysis technology, it analyzes the user's emotions and adjusts the content and tone of the response based on those emotions.

[0682] Providing answers

[0683] Device:

[0684] The generated answer is sent from the server to the device and displayed on the messaging app.

[0685] User:

[0686] The user can check the provided answers and use them to help with childcare.

[0687] Personalization and Consistency

[0688] server:

[0689] The system analyzes the user's life log and personal data to generate personalized advice tailored to the child's development. It also uses emotion analysis technology to continuously analyze emotions and provide appropriate support based on the user's emotions.

[0690] Feedback and Improvements

[0691] User:

[0692] Help improve the system by providing feedback on the answers provided in the messaging app.

[0693] Device:

[0694] Send feedback to the server.

[0695] server:

[0696] The server receives the feedback and stores it as data to improve the answers and accuracy of the system.

[0697] Specific examples

[0698] Example prompt sentence:

[0699] User Question: "My baby cries a lot at night and I don't know what to do."

[0700] Prompt for generative AI model: "You are an intelligent and caring assistant. Answer this question related to parenting: My baby often cries at night, and I don't know what to do."

[0701] Examples of sentiment analysis and tailored responses:

[0702] If a user's question is judged to be "anxious" using emotion analysis technology, an encouraging response such as "You're a great mom!" is added to the generated answer to provide the user with an encouraging response.

[0703] In this way, the childcare assistance system responds quickly and appropriately to user questions, provides reliable information, and also provides emotional support, thereby reducing mothers' anxieties about childcare and providing long-term, continuous support for childcare.

[0704] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0705] Step 1:

[0706] Users add the official account of the childcare assistant system as a friend through a messaging app and start chatting. Users enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire. This becomes the input data.

[0707] Input: User information (name, contact information, children's date of birth, gender, etc.)

[0708] Output: A user profile containing information for initial setup.

[0709] What happens: A user enters information into a messaging app, and the device sends that information to a server.

[0710] Step 2:

[0711] The server stores the received initial setting information in a data cloud and generates a user profile. The server generates a registration completion message based on this information and sends it to the terminal.

[0712] Input: Initial setting information

[0713] Output: User profile, registration completion message

[0714] Specific operations: The server stores the received information in the data cloud, creates a user profile, and then generates and sends a registration completion message to the device.

[0715] Step 3:

[0716] A user types a specific parenting question into a messaging app, which becomes a query for further processing.

[0717] Input: Questions about childcare (text data)

[0718] Output: Question data

[0719] Specific operation: The user enters a question, and the device sends it to the server.

[0720] Step 4:

[0721] The server receives the query data and analyzes it using natural language processing techniques, which extracts important keywords and categories.

[0722] Input: User question data

[0723] Output: Keywords and Categories

[0724] Specific operation: The server performs text analysis using natural language processing techniques (e.g., Python's NLTK or spaCy) to extract keywords and categories.

[0725] Step 5:

[0726] The server searches the database for related information based on the extracted keywords.

[0727] Input: Keywords and Categories

[0728] Output: Related information

[0729] What happens: The server queries the database to retrieve relevant childcare information.

[0730] Step 6:

[0731] The server uses a generative AI model (e.g., OpenAI GPT-3.5) to generate an answer based on the collected information. This answer is generated in text format and is used in further steps.

[0732] Input: Related Information

[0733] Output: The generated answer

[0734] Specific operation: The server calls the generative AI model, generates a prompt sentence based on relevant information, and inputs it into the model to generate an answer.

[0735] Step 7:

[0736] The server uses emotion analysis technology to analyze the user's emotions and adjusts the generated answers based on those emotions. For example, if the user is analyzed as "anxious," the server adds encouraging words to the answer.

[0737] Input: Generated answers, user sentiment analysis results

[0738] Output: Adjusted answer

[0739] What it does: The server uses emotion analysis technology (e.g., TextBlob) to analyze the user's emotional state and adjusts the generated answer accordingly.

[0740] Step 8:

[0741] The server then sends the adjusted response to the user's device via a communication means, and the user receives the response via a messaging app.

[0742] Input: Adjusted answer

[0743] Output: The answer displayed on the user's terminal

[0744] Specific operation: The server sends the tailored response to the user's device via the messaging app, where it is displayed on the device.

[0745] Step 9:

[0746] Users enter feedback on the answers they provide, which is used to improve the system.

[0747] Input: User feedback

[0748] Output: Feedback data

[0749] Specific operation: The user enters feedback, which the device sends to the server.

[0750] Step 10:

[0751] The server receives the feedback and analyzes it to improve the accuracy of the system, and if necessary, to improve the accuracy of the answers and the system as a whole.

[0752] Input: Feedback data

[0753] Output: Improved system

[0754] What it does: The server stores your feedback in a database and analyzes it to help improve, optimizing the system to provide better answers and responses next time.

[0755] In this way, the childcare assistant system responds quickly and appropriately to the user's questions and concerns, providing both reliable information and emotional care.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] [Second embodiment]

[0760] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0761] 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.

[0762] 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).

[0763] 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.

[0764] 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.

[0765] 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).

[0766] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] In the smart glasses 214, 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.

[0771] 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."

[0772] The present invention is a childcare assistance system that allows mothers to easily obtain reliable information about childcare. This system accepts questions about childcare from users via communication means and provides prompt and appropriate answers to those questions. Specific embodiments of this system are described below.

[0773] Basic system configuration

[0774] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the Childcare Assistant System account as a friend on a messaging app such as LINE using their device.

[0775] User registration and initial settings

[0776] User:

[0777] First, users add the official account of the childcare assistant system as a friend through a messaging app such as LINE, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[0778] Device:

[0779] The terminal is responsible for transmitting the information entered by the user to the server.

[0780] server:

[0781] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the terminal.

[0782] Question reception and analysis

[0783] User:

[0784] Users can enter specific questions or concerns about childcare into LINE chat.

[0785] Device:

[0786] The entered text data is immediately sent to the server.

[0787] server:

[0788] The server analyzes the question using natural language processing technology and extracts important keywords and categories. For example, if a question is entered about "baby food for a 6-month-old baby," keywords such as "6 months," "baby," and "baby food" will be extracted.

[0789] Information Acquisition and Answer Generation

[0790] server:

[0791] The server searches for relevant information from a database based on the extracted keywords, retrieving relevant information from existing knowledge bases of childcare experts and the latest childcare trends, and then uses a generative AI model (e.g., GPT-3.5) to generate appropriate answers based on the collected information.

[0792] For example, an answer might be generated: "For a 6-month-old baby, it's a good idea to start with grain-based porridge, followed by vegetable purees. At this age, start with small amounts to allow for flavor and texture exploration."

[0793] Providing answers

[0794] Device:

[0795] The generated answer is sent from the server to the device and displayed on the LINE chat.

[0796] User:

[0797] The user can check the answers provided in the chat and use them to help with child-rearing.

[0798] Personalization and Consistency

[0799] server:

[0800] Based on the information provided by the user and the accumulated life log, the server generates personalized advice according to the child's growth and provides it periodically via LINE chat. For example, it can send specific advice such as, "Based on the recent changes in your baby's weight, consider when it's time to move on to the next step."

[0801] Feedback and Improvements

[0802] User:

[0803] Users can provide feedback on the answers provided via LINE chat to help improve the system.

[0804] Device:

[0805] Send feedback to the server.

[0806] server:

[0807] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of answers.

[0808] In this way, the childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, it can provide long-term childcare support by continuously providing personalized support to users.

[0809] The processing flow will be explained below.

[0810] Step 1:

[0811] User:

[0812] Add the official account of the Childcare Assistant System as a friend on the LINE app. Start a chat and enter your information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[0813] Step 2:

[0814] Device:

[0815] The information entered by the user is sent to the server.

[0816] Step 3:

[0817] server:

[0818] The received information is stored in the data cloud, a user profile is generated, and a registration completion message is generated and sent to the terminal.

[0819] Step 4:

[0820] Device:

[0821] A registration completion message from the server will be displayed on the LINE chat.

[0822] Step 5:

[0823] User:

[0824] Enter specific questions or concerns about childcare into LINE chat.

[0825] Step 6:

[0826] Device:

[0827] The input text data is sent to the server.

[0828] Step 7:

[0829] server:

[0830] The system receives the question text and uses natural language processing technology to analyze it, understand the intent of the question, and extract important keywords and categories.

[0831] Step 8:

[0832] server:

[0833] Based on the extracted keywords and categories, relevant information is searched for in databases and expert knowledge bases.

[0834] Step 9:

[0835] server:

[0836] Based on relevant information, a generative AI model is used to generate the optimal answer.

[0837] Step 10:

[0838] server:

[0839] The generated response is sent to the device.

[0840] Step 11:

[0841] Device:

[0842] The response sent from the server is displayed in the LINE chat.

[0843] Step 12:

[0844] User:

[0845] Check the answers provided on LINE chat and get the information you need.

[0846] Step 13:

[0847] User:

[0848] Enter your feedback on the provided answer (whether it was helpful, additional questions, etc.) in LINE chat.

[0849] Step 14:

[0850] Device:

[0851] Send user feedback to the server.

[0852] Step 15:

[0853] server:

[0854] The system receives feedback and stores it as data to improve the answers and accuracy of the system, and generates new answers to new questions as needed.

[0855] Step 16:

[0856] server:

[0857] The system analyzes the user's life log and personal data to generate personalized advice tailored to the child's development.

[0858] Step 17:

[0859] server:

[0860] Relevant childcare information and advice will be provided on a regular basis via LINE chat, providing ongoing support to users in their childcare.

[0861] Step 18:

[0862] User:

[0863] Review the personalized information and advice provided to help you with your parenting.

[0864] Example 1

[0865] 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."

[0866] Conventional childcare assistance systems have limited answers to users' questions about childcare, making it difficult to provide personalized advice that takes into account each user's individual circumstances. Furthermore, the accuracy of the system has not been sufficiently improved using user feedback, so there is a need for an improved user experience. This will reduce anxiety about childcare and provide more reliable information.

[0867] 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.

[0868] In this invention, the server includes: means for accepting childcare-related questions from users via communication means; means for analyzing the questions and extracting keywords and categories using natural language processing technology; means for searching for related information from an information storage device based on the extracted keywords or categories; means for generating answers based on the related information using a generative AI model; and means for providing the generated answers to users via the communication means. This enables prompt and accurate answers to users' questions and reliable childcare information. Furthermore, the system includes means for accepting information for initial setup, means for storing the information for initial setup in a data cloud to generate a user profile, and means for periodically providing personalized advice using the generated answers, thereby enabling detailed support tailored to individual situations. Furthermore, the system includes means for accepting user feedback on the generated answers and means for storing the feedback in a data cloud to improve the system's accuracy and the quality of the answers, enabling continuous system improvement.

[0869] "Communication means" refers to a communication path for transmitting input data from a user to a server and for providing output data from the server to the user.

[0870] "User" refers to a person who uses the childcare assistant system to ask questions about childcare and receive answers.

[0871] "Natural language processing technology" refers to technology that analyzes text data entered by a user and understands their meaning and intent.

[0872] "Keywords" are important words extracted from the content of a user's question, and are used to search for related information when generating an answer.

[0873] "Category" refers to a classification criterion for classifying questions and information, and is used to improve the efficiency of searching for related information.

[0874] "Information storage device" refers to a recording medium or database that stores related information and makes it searchable when needed.

[0875] A "generative AI model" refers to a program equipped with artificial intelligence technology that automatically generates sentences based on data.

[0876] A "user profile" is individual account information generated based on information provided by a user, and is used to provide personalized services to each individual user.

[0877] "Personalized advice" refers to advice that is optimized for an individual user based on the user profile and accumulated data.

[0878] "Feedback" refers to a user's input of evaluations and opinions regarding the results of using the system and the answers provided.

[0879] This invention relates to a childcare assistance system, and its purpose is to enable mothers and guardians to easily obtain reliable information about childcare. This system accepts questions about childcare from users via communication means and provides prompt and appropriate answers to those questions. Specific embodiments for carrying out the present invention are described below.

[0880] Basic system configuration

[0881] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend in a messaging app (e.g., messaging application) using their device.

[0882] User registration and initial settings

[0883] User:

[0884] Users add the official account of the childcare assistant system as a friend through a messaging app, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[0885] Device:

[0886] The terminal is responsible for transmitting the information entered by the user to the server.

[0887] server:

[0888] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the user's device.

[0889] Examples:

[0890] After adding a friend, the user enters the name "Yamada Taro" and the child's date of birth "April 1, 2022" into a form and sends it to the server.

[0891] Example prompt: "Enter my information. My name is Taro Yamada, and my child's birth date is April 1, 2022."

[0892] Question reception and analysis

[0893] User:

[0894] Users type specific questions or concerns about childcare into the messaging app chat, for example, "Can you tell me about solid food for my 6-month-old baby?"

[0895] Device:

[0896] The entered text data is immediately sent to the server.

[0897] server:

[0898] The server uses natural language processing technology (e.g., Natural Language Toolkit) to analyze the question and extract important keywords and categories. For example, if a question about "baby food for a 6-month-old baby" is entered, keywords such as "6 months," "baby," and "baby food" will be extracted.

[0899] Example prompt:

[0900] "Please tell me about baby food for a 6-month-old baby."

[0901] Information Acquisition and Answer Generation

[0902] server:

[0903] The server searches for relevant information from an information storage device based on the extracted keywords. It retrieves relevant information from a knowledge base of childcare experts and the latest childcare trends. It then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer based on the collected information. For example, it might generate an answer like, "For a 6-month-old baby, it's a good idea to start with grain-based porridge, followed by vegetable puree. At this age, start with a small amount to allow for enjoyment of the taste and texture."

[0904] Examples:

[0905] The server searches for information about "6 months," "baby," and "baby food," and the generative AI model generates the appropriate answer.

[0906] Example prompt: "For 6-month-old babies, it's a good idea to try grain-based porridge first, followed by vegetable purees."

[0907] Providing answers

[0908] server:

[0909] The generated answer is sent from the server to the user's terminal.

[0910] Device:

[0911] The device will display the received response in the chat on the messaging app.

[0912] User:

[0913] The user can check the answers provided in the chat and use them to help with child-rearing.

[0914] Examples:

[0915] The user's device displays the message, "For 6-month-old babies, try grain-based porridge first, followed by vegetable puree."

[0916] Personalization and Consistency

[0917] server:

[0918] The server generates personalized advice based on the information provided by the user and the accumulated life log, and delivers it periodically via the messaging app according to the child's growth. For example, it can provide specific advice such as, "Based on the recent changes in your baby's weight, consider when it's time to move on to the next step."

[0919] Examples:

[0920] Send specific advice based on your child's development.

[0921] Sample prompt: "Based on your baby's recent weight changes, consider when it's time to move forward with the next step."

[0922] Feedback and Improvements

[0923] User:

[0924] Users can provide feedback on the answers provided in the messaging app's chat to help improve the system.

[0925] Device:

[0926] The entered feedback is sent to the server.

[0927] server:

[0928] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of answers.

[0929] Examples:

[0930] The user enters their satisfaction with the answer, and the feedback is sent to and stored on the server.

[0931] Sample prompt: "I'm very pleased with the answer you provided. Thank you very much."

[0932] The above is a detailed description of a specific embodiment of the present invention. This enables users to receive prompt and appropriate answers to their questions and obtain reliable childcare information. Furthermore, personalized support tailored to individual situations and system improvements based on user feedback are realized.

[0933] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0934] System program processing flow

[0935] ---

[0936] Step 1: User registration and initial setup

[0937] Step 1-1: User accesses the system

[0938] User:

[0939] The user opens a messaging app on a device such as a smartphone and adds the official account of the childcare assistant system as a friend. This action allows the user to start accessing the system.

[0940] input:

[0941] The act of adding an official account as a friend through a messaging app.

[0942] output:

[0943] You will be able to access the official account of the childcare assistance system.

[0944] Specific behavior:

[0945] Tap the "Add Friends" button within the messaging app, then search for and add the official account.

[0946] ---

[0947] Step 1-2: Fill in the initial setup form

[0948] User:

[0949] After adding a friend, start chatting and enter your information (e.g., name, contact information, children's birth dates, gender) according to the initial setup questionnaire sent by the system.

[0950] Device:

[0951] The terminal transmits the information entered by the user to the server in real time.

[0952] input:

[0953] Enter the user's personal information and basic information about childcare.

[0954] output:

[0955] User information sent to the server.

[0956] Specific behavior:

[0957] The action of a user filling out a form in a chat window and pressing the "Send" button.

[0958] ---

[0959] Step 1-3: The server generates a user profile

[0960] server:

[0961] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the user's device.

[0962] input:

[0963] User information sent from the device.

[0964] output:

[0965] User profile and registration completion message stored in the data cloud.

[0966] Specific behavior:

[0967] The received data is stored in an appropriate location in the data cloud, a profile creation algorithm is run to generate a user profile, and a message such as "Registration complete" is sent to the user's device.

[0968] ---

[0969] Step 2: Question collection and analysis

[0970] Step 2-1: User enters a question

[0971] User:

[0972] Type specific questions or concerns about childcare into a messaging app chat, such as "Can you tell me about solid food for my 6-month-old baby?"

[0973] input:

[0974] Text with specific childcare questions.

[0975] output:

[0976] The question text sent to the server through a messaging app.

[0977] Specific behavior:

[0978] The action of a user typing a question into a chat window and pressing the "send" button.

[0979] ---

[0980] Step 2-2: Send the question text

[0981] Device:

[0982] The entered question text data is immediately sent to the server.

[0983] input:

[0984] The question text entered by the user into the messaging app.

[0985] output:

[0986] The question text sent to the server.

[0987] Specific behavior:

[0988] The text data received by the device is immediately sent to the specified API endpoint on the server.

[0989] ---

[0990] Step 2-3: Question Analysis

[0991] server:

[0992] The server uses natural language processing technology to analyze the question and extract important keywords and categories. If a question is entered about "baby food for a 6-month-old baby," keywords such as "6 months," "baby," and "baby food" will be extracted.

[0993] input:

[0994] The question text sent from the device.

[0995] output:

[0996] Extracted keyword and category information.

[0997] Specific behavior:

[0998] The server performs text analysis using natural language processing libraries (e.g., Natural Language Toolkit) to identify and extract important keywords and categories.

[0999] Example prompt:

[1000] "Please tell me about baby food for a 6-month-old baby."

[1001] ---

[1002] Step 3: Information Acquisition and Answer Generation

[1003] Step 3-1: Search for related information

[1004] server:

[1005] The server searches for relevant information from the information storage device based on the extracted keywords, and obtains relevant information from the knowledge base of childcare experts and the latest childcare trends.

[1006] input:

[1007] Extracted keyword and category information.

[1008] output:

[1009] Related information found.

[1010] Specific behavior:

[1011] Perform database queries to find and retrieve relevant information that matches your keywords.

[1012] ---

[1013] Step 3-2: Generate an answer

[1014] server:

[1015] Using this information, a generative AI model (e.g., GPT-3.5) is used to generate an appropriate answer, such as "For a 6-month-old baby, try grain-based porridge first, followed by vegetable puree."

[1016] input:

[1017] Related information found.

[1018] output:

[1019] Answer text obtained through a generative AI model.

[1020] Specific behavior:

[1021] Relevant information is provided as input to a generative AI model, which then generates and outputs natural answers.

[1022] Example prompt:

[1023] "For six-month-old babies, it's a good idea to start with cereal-based porridge, followed by vegetable purees."

[1024] ---

[1025] Step 4: Provide your answers

[1026] Step 4-1: Submit your response

[1027] server:

[1028] The generated answer is sent to the user's terminal.

[1029] input:

[1030] Answer text generated by a generative AI model.

[1031] output:

[1032] The answer text sent to the user's terminal.

[1033] Specific behavior:

[1034] The server sends the generated response to the user's device via the messaging app's API.

[1035] ---

[1036] Step 4-2: View the answer

[1037] Device:

[1038] The device will display the received response in the chat on the messaging app.

[1039] input:

[1040] The response text sent by the server.

[1041] output:

[1042] The reply text that will be displayed to the user in the chat screen.

[1043] Specific behavior:

[1044] The messaging app displays the received response in the chat window, allowing the user to easily check the response.

[1045] ---

[1046] Step 5: Personalization and Retention

[1047] Step 5-1: Providing regular advice

[1048] server:

[1049] Based on the information provided by the user and the accumulated life logs, the server generates personalized advice tailored to the child's development and provides it periodically via the messaging app.

[1050] input:

[1051] User information and lifelog data.

[1052] output:

[1053] Personalized advice.

[1054] Specific behavior:

[1055] The server periodically analyzes the user's profile and latest life log data, uses a generative AI model to generate the next piece of advice needed, and sends it via the messaging app.

[1056] Example prompt:

[1057] "Based on your baby's recent weight changes, consider when it's time to move on to the next step."

[1058] ---

[1059] Step 6: Feedback and Improvement

[1060] Step 6-1: Enter your feedback

[1061] User:

[1062] Users can provide feedback on the answers provided in the messaging app's chat to help improve the system.

[1063] input:

[1064] User feedback text.

[1065] output:

[1066] Feedback text sent to the server through the messaging app.

[1067] Specific behavior:

[1068] The action of a user typing feedback into a chat window and pressing the "Send" button.

[1069] ---

[1070] Step 6-2: Send feedback

[1071] Device:

[1072] The entered feedback is sent to the server.

[1073] input:

[1074] Feedback entered by users into messaging apps.

[1075] output:

[1076] The feedback text sent to the server.

[1077] Specific behavior:

[1078] The feedback data received by the device is immediately sent to the specified API endpoint on the server.

[1079] ---

[1080] Step 6-3: Accumulating and utilizing feedback

[1081] server:

[1082] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of its answers.

[1083] input:

[1084] Feedback sent from the device.

[1085] output:

[1086] Feedback data and updated analytical models stored in the data cloud.

[1087] Specific behavior:

[1088] The server stores the received feedback in a data cloud and improves the system by updating the analytical model.

[1089] Example prompt:

[1090] "I am very satisfied with the answer provided. Thank you very much."

[1091] The above is a specific flow of program processing for the system of the present invention, which allows users to quickly and appropriately obtain highly reliable childcare information, and the system is continually improved.

[1092] (Application example 1)

[1093] 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."

[1094] Parents raising children often have a wide range of questions and anxieties about childcare, but there are few ways to quickly obtain reliable information when they are out and about, especially while shopping, and the time it takes to obtain information is a problem. Furthermore, if there were a way to obtain answers through voice input or visually, it would be much more convenient for parents raising children.

[1095] 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.

[1096] In this invention, the server includes means for recognizing a user's voice using a voice input device, means for analyzing the question using natural language processing technology and extracting keywords and categories, and means for displaying the generated answer on a visual output device, thereby enabling users to quickly obtain reliable information about childcare even when they are out and about.

[1097] "Communication means" refers to means for sending and receiving data via the Internet or the like.

[1098] "Natural language processing technology" is a technology for analyzing text and voice input from users, understanding their meaning, and extracting keywords and categories.

[1099] "Keyword and category extraction means" refers to a means for automatically identifying important words and themes from user questions using natural language processing techniques.

[1100] A "database" is a structured collection of information that stores related information systematically and can be retrieved as needed.

[1101] A "generative AI model" is an artificial intelligence model used to generate optimal answers to user questions based on collected information.

[1102] A "voice input device" is a device that receives a user's voice as input and converts the content into text.

[1103] A "visual output device" is a device for visually presenting information to a user through smart glasses or the like.

[1104] "User profile" refers to data that compiles personal information and childcare information provided by the user.

[1105] "Feedback" refers to opinions or evaluations given by users regarding answers or services provided.

[1106] MODE FOR CARRYING OUT THE INVENTION

[1107] 1. System Overview

[1108] This invention is a childcare assistant system that allows parents to easily ask questions about childcare through voice input and quickly obtain reliable information. This system is realized by combining a wide range of technologies, including communication methods, natural language processing technology, generative AI models, voice input devices, visual output devices, and data clouds.

[1109] 2. Hardware and software used

[1110] Smart glasses: devices that take voice input and provide visual information (e.g., Google Glass, NReal).

[1111] Smartphone: A device for installing applications and working with smart glasses.

[1112] Server: A central control unit that processes data and generates and provides answers to users (e.g., AWS, Google Cloud).

[1113] Natural language processing engine: Software for analyzing user questions (e.g., Google Cloud Natural Language API).

[1114] Generative AI model: An AI model that generates optimal answers to questions (e.g., OpenAI GPT-3.5).

[1115] 3. Data processing and calculation

[1116] Voice input

[1117] Users can voice-input questions about childcare through the smart glasses, and this voice data is sent to a server via a smartphone.

[1118] Voice Recognition

[1119] The server receives the voice data and converts it into text using AWS Transcribe, which allows the voice-input questions to be processed as text information.

[1120] Natural Language Processing

[1121] The converted text data is then parsed using the Google Cloud Natural Language API to extract keywords and categories from the question, helping to accurately understand the question and prepare the search for the appropriate information.

[1122] Keyword search

[1123] Based on the extracted keywords and categories, relevant information is searched for in the database, which includes expert knowledge bases and the latest childcare trends.

[1124] Answer generation

[1125] Based on the searched information, the best answer is automatically generated using a generative AI model (OpenAI GPT-3.5) to provide information that meets the user's needs.

[1126] Visual and audio feedback

[1127] The generated answer is visually displayed on the smart glasses' display and also provided to the user audibly, using gTTS (Google Text-to-Speech) to convert the text data into speech and play it through the smart glasses.

[1128] 4. Examples of concrete examples and prompts

[1129] Specific examples

[1130] For example, consider a case where a user is in a store selling childcare products and has a question about "how to choose a baby bottle." They ask through their smart glasses, "How do I choose a baby bottle?" This question is sent to the server, converted into text, and analyzed using natural language processing technology. Relevant information is then searched for in the database, and the generative AI model generates the answer: "They come in glass and plastic. Glass bottles are heavy but durable, while plastic bottles are light and easy to carry." This information is finally displayed on the smart glasses' display and provided via voice.

[1131] Prompt Sentence Examples

[1132] How do I choose a baby bottle?

[1133] "There are glass and plastic baby bottles. I'd like to know the advantages and disadvantages of each."

[1134] As described above, the present invention enables users to receive real-time childcare support and provides users with quick and reliable information even when they are away from home.

[1135] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1136] Step 1:

[1137] The user wears the smart glasses and asks questions about childcare via a voice input device.

[1138] Input: User's voice data

[1139] Action: The user says, "How do I choose a baby bottle?"

[1140] Step 2:

[1141] The terminal receives the voice data and transmits it to a server via the Internet.

[1142] Input: User's voice data

[1143] Data processing: The audio data is sent to the AWS Transcribe service to prepare it for conversion into text.

[1144] Output: Audio data received by the server

[1145] Step 3:

[1146] The server uses AWS Transcribe to convert the audio data into text data.

[1147] Input: Audio data received by the server

[1148] Data processing: Using voice recognition technology, audio is converted into text.

[1149] Output: Converted text data

[1150] Step 4:

[1151] The server uses a natural language processing engine (Google Cloud Natural Language API) to analyze the text data and extract keywords and categories.

[1152] Input: Converted text data

[1153] Data calculation: Extract the keywords "baby bottle" and "how to choose" from the text.

[1154] Output: Extracted keywords and categories

[1155] Step 5:

[1156] The server searches for relevant information based on keywords and categories extracted from the database.

[1157] Input: Extracted keywords and categories

[1158] Data retrieval: Searching for relevant information in a database.

[1159] Output: Retrieved childcare information

[1160] Step 6:

[1161] The server uses a generative AI model (OpenAI GPT-3.5) to generate appropriate answers based on relevant information.

[1162] Input: Searched childcare information

[1163] Data computation: A generative AI model processes the information and generates a specific answer, such as "There are glass and plastic baby bottles."

[1164] Output: Generated answer text

[1165] Step 7:

[1166] The server uses gTTS (Google Text-to-Speech) to convert the generated response text into audio data.

[1167] Input: Generated answer text

[1168] Data processing: Convert text into an audio file.

[1169] Output: Generated answer audio data

[1170] Step 8:

[1171] The server transmits the generated answer text and voice data to the terminal.

[1172] Input: Generated answer text and audio data

[1173] Operation: Sending data from the server to the device

[1174] Output: Terminal received data

[1175] Step 9:

[1176] The terminal displays the received answer text on the visual output device of the smart glasses and plays the audio data.

[1177] Input: Terminal received data

[1178] How it works: The smart glasses display will say "There are glass and plastic baby bottles" and simultaneously play back the answer aloud.

[1179] Output: The user gets a visual and audio response.

[1180] Through the above processing steps, the present invention serves as a childcare assistant system that quickly and effectively solves questions about childcare through voice input.

[1181] 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.

[1182] This invention is a childcare assistant system that allows mothers to easily obtain reliable information about childcare. The system incorporates communication methods, natural language processing technology, generative AI models, and an emotion engine. Because the system includes user emotion analysis, it not only improves the quality of childcare but also supports mothers' emotional care.

[1183] Basic system configuration

[1184] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend on a messaging app such as LINE using their device.

[1185] User registration and initial settings

[1186] User:

[1187] First, users add the official account of the childcare assistant system as a friend through a messaging app such as LINE, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[1188] Device:

[1189] The terminal is responsible for transmitting the information entered by the user to the server.

[1190] server:

[1191] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the terminal.

[1192] Question reception and analysis

[1193] User:

[1194] Users can enter specific questions or concerns about childcare into LINE chat.

[1195] Device:

[1196] The entered text data is immediately sent to the server.

[1197] server:

[1198] The server analyzes the question using natural language processing techniques to extract important keywords and categories.

[1199] Information Acquisition and Answer Generation

[1200] server:

[1201] The system searches for relevant information from a database based on the extracted keywords. It then uses a generative AI model (e.g., GPT-3.5) to generate appropriate answers based on the collected information. It also uses an emotion engine to analyze the user's emotions and adjust the content and tone of the answer based on those emotions. For example, if the user is "anxious," it adds encouraging words to ease their anxiety.

[1202] Providing answers

[1203] Device:

[1204] The generated answer is sent from the server to the device and displayed on the LINE chat.

[1205] User:

[1206] The user can check the answers provided in the chat and use them to help with child-rearing.

[1207] Personalization and Consistency

[1208] server:

[1209] The server analyzes the user's life log and personal data to generate personalized advice based on the child's development. It also performs continuous emotion analysis using an emotion engine to provide appropriate support based on the user's emotions.

[1210] Feedback and Improvements

[1211] User:

[1212] Users can provide feedback on the answers provided via LINE chat to help improve the system.

[1213] Device:

[1214] Send feedback to the server.

[1215] server:

[1216] The server receives feedback and stores it as data to improve the answers and accuracy of the system, and also generates new answers to new questions as needed.

[1217] In this way, this childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, the system also provides psychological support by analyzing the user's emotions and providing support accordingly. This makes it possible to provide long-term, continuous childcare support.

[1218] The processing flow will be explained below.

[1219] Step 1:

[1220] User:

[1221] Add the official account of the Childcare Assistant System as a friend on the LINE app. Start a chat and enter your basic information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[1222] Step 2:

[1223] Device:

[1224] The information entered by the user is sent to the server.

[1225] Step 3:

[1226] server:

[1227] The received information is stored in a data cloud and a user profile is generated. After the profile is generated, a registration completion message is generated and sent to the terminal.

[1228] Step 4:

[1229] Device:

[1230] A registration completion message from the server will be displayed on the LINE chat.

[1231] Step 5:

[1232] User:

[1233] Enter specific questions or concerns about childcare in the LINE chat. For example, "What kind of baby food does a 6-month-old baby need?"

[1234] Step 6:

[1235] Device:

[1236] The input text data is sent to the server.

[1237] Step 7:

[1238] server:

[1239] The received question text is analyzed using natural language processing technology to extract important keywords and categories, such as "6 months," "baby," and "baby food."

[1240] Step 8:

[1241] server:

[1242] The system searches for relevant information from a database based on the extracted keywords and categories, while simultaneously analyzing emotions (e.g., anxiety, doubt, joy) from the user's text using an emotion engine.

[1243] Step 9:

[1244] server:

[1245] Based on the results of the emotion engine, a generative AI model is used to generate an appropriate response. For example, if the user expresses anxiety, the system generates a response in a reassuring tone, such as, "First of all, don't worry. Porridge is suitable for a 6-month-old baby."

[1246] Step 10:

[1247] server:

[1248] The generated response is sent to the device.

[1249] Step 11:

[1250] Device:

[1251] The response sent from the server is displayed in the LINE chat.

[1252] Step 12:

[1253] User:

[1254] Check the answers provided via LINE chat and use them to help with childcare. For example, make porridge based on the advice on baby food.

[1255] Step 13:

[1256] User:

[1257] Enter your feedback on the provided answer (whether it was helpful, additional questions, etc.) in LINE chat.

[1258] Step 14:

[1259] Device:

[1260] Send user feedback to the server.

[1261] Step 15:

[1262] server:

[1263] The system receives feedback and stores it as data to improve the answers and accuracy of the system, and generates new answers to new questions as needed.

[1264] Step 16:

[1265] server:

[1266] The system analyzes the user's life log and personal data to generate personalized advice based on the child's growth. For example, it generates specific advice such as, "Based on recent weight changes, it's time to move on to the next step."

[1267] Step 17:

[1268] server:

[1269] Relevant childcare information and advice will be provided on a regular basis via LINE chat, providing ongoing support to users in their childcare.

[1270] Step 18:

[1271] User:

[1272] Review the personalized information and advice provided to help you with your parenting.

[1273] Example 2

[1274] 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."

[1275] Conventional childcare assistance systems are not always able to respond quickly to user questions and are sometimes unable to provide reliable information. Furthermore, they lack emotional support for mothers, resulting in insufficient psychological support. This creates challenges for improving the quality of childcare and for providing adequate psychological care for mothers.

[1276] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a question about child-rearing from a user via communication means, means for analyzing the question and extracting keywords and categories using natural language processing technology, means for searching a database for related information based on the extracted keywords or categories, means for generating an answer based on the related information using a generative AI model, means for analyzing the user's emotions using emotion analysis technology and adjusting the content of the answer, and means for providing the generated answer to the user via the communication means. This makes it possible to quickly and appropriately respond to a question from a user, provide reliable child-rearing information, and analyze the user's emotions to provide appropriate support.

[1277] "Communication means" refers to the infrastructure for sending and receiving data from users, including the Internet, Wi-Fi, mobile communication networks, etc.

[1278] "Natural language processing technology" refers to the technology of analyzing, understanding, and generating natural language used by humans using a computer, and includes morphological analysis, keyword extraction, and sentence classification.

[1279] A "generative AI model" refers to an artificial intelligence model that generates natural language based on large amounts of data, and includes, for example, GPT-3.5.

[1280] "Emotion analysis technology" refers to technology that identifies a user's emotions from text data and adjusts responses based on those emotions, and includes emotion analysis engines and tone analyzers.

[1281] A "data cloud" refers to a distributed storage system for storing and managing data over the Internet, and includes cloud storage services.

[1282] A "user profile" refers to a collection of information constructed based on data such as a user's personal information and behavioral history, and includes name, contact information, childcare history, etc.

[1283] "Feedback" refers to the evaluations and opinions that users give about the answers and services provided, and serves as data that can be used to improve the system.

[1284] This invention relates to a childcare assistant system that provides users with tools to easily and quickly resolve questions about childcare. The system includes a communication means, natural language processing technology, a generative AI model, emotion analysis technology, a data cloud, and a user profile management function.

[1285] Basic system configuration

[1286] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend in a messaging app on their smartphone.

[1287] User registration and initial settings

[1288] User:

[1289] First, users add the official account of the childcare assistant system as a friend in a messaging app and start chatting, then follow the system's instructions to enter information such as the child's name, contact details, date of birth, and gender into the initial setup questionnaire.

[1290] Device:

[1291] The terminal generates a data packet for transmitting the information entered by the user to the server.

[1292] server:

[1293] The server analyzes the received information, stores it in the data cloud, creates a user profile, and sends a registration completion message to the terminal.

[1294] Question reception and analysis

[1295] User:

[1296] Users type parenting questions into the messaging app.

[1297] Device:

[1298] The entered text data is sent to a server via the Internet.

[1299] server:

[1300] The server analyzes the text of the received question using natural language processing technology, such as SpaCy or NLTK, to extract important keywords.

[1301] Information Acquisition and Answer Generation

[1302] server:

[1303] The server uses the extracted keywords to search for relevant information from a database, then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer, and furthermore, uses sentiment analysis technology (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions and adjust the tone of the answer.

[1304] Examples of prompts:

[1305] If a user submits a question like, "My baby won't stop crying in the middle of the night, what should I do?"

[1306] In that case the prompt would look like this:

[1307] User: "My baby won't stop crying in the middle of the night. What should I do?" Gently explain why babies cry at night and what you can do about it. Also, add some reassurance, as the mother may be worried.

[1308] Providing answers

[1309] server:

[1310] The generated response is sent to the device.

[1311] Device:

[1312] The response received from the server is displayed on the messaging app.

[1313] User:

[1314] The user can check the provided answers and use them to help with childcare.

[1315] Personalization and Consistency

[1316] server:

[1317] The server analyzes the user's life log and personal data to generate personalized advice tailored to the child's development, and also performs continuous emotion analysis using emotion analysis technology.

[1318] Feedback and Improvements

[1319] User:

[1320] Users provide feedback on the answers provided in the messaging app.

[1321] Device:

[1322] Converts data into packets for sending feedback to the server.

[1323] server:

[1324] The server receives the feedback, stores it as data to improve the system, and generates new answers as needed and sends them back to the user.

[1325] In this way, this childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, the system also provides psychological support by analyzing the user's emotions and providing support accordingly. This makes it possible to provide long-term, continuous childcare support.

[1326] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1327] Step 1: User registration and initial setup

[1328] User: The user adds the official account of the childcare assistant system as a friend on a messaging app such as LINE and starts chatting. The system then sends the user an initial setup questionnaire, in which the user enters personal information such as their name, contact details, and the child's date of birth and gender.

[1329] Input: Initial setup information (e.g. name, contact information, child's date of birth, gender)

[1330] Output: The data the user entered into the form

[1331] Terminal: The terminal generates a data packet for transmitting the information input by the user to the server. The generated data packet is transmitted to the server via a communication means.

[1332] Input: Data entered by the user

[1333] Output: Data packet sent to the server

[1334] Server: The server analyzes the received information, stores it in the data cloud, creates a user profile, and sends a registration completion message to the device.

[1335] Input: Data packets sent from the device

[1336] Output: User information stored in the data cloud, registration completion message

[1337] Step 2: Question collection and analysis

[1338] User: A user types a parenting question into a messaging app.

[1339] Input: User question text (e.g., "My baby won't stop crying in the middle of the night. What should I do?")

[1340] Output: Submitted question text data

[1341] Terminal: The entered question text data is sent to the server via the Internet.

[1342] Input: Question text data submitted by the user

[1343] Output: Text data sent to the server

[1344] Server: The server uses natural language processing technology to analyze the text of the received question and extract important keywords. For example, SpaCy or NLTK can be used to extract keywords such as "baby," "midnight," and "won't stop crying."

[1345] Input: Question text data sent from the terminal

[1346] Output: Extracted keywords (e.g., "baby," "midnight," "can't stop crying")

[1347] Step 3: Information Acquisition and Answer Generation

[1348] server:

[1349] The server uses the extracted keywords to search for relevant information in a database. For example, it runs a database query based on the keywords "baby," "midnight," and "won't stop crying." It then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer. For example, it sends the following prompt to the generative AI:

[1350] User: "My baby won't stop crying in the middle of the night. What should I do?" Gently explain why babies cry at night and what you can do about it. Also, add some reassurance, as the mother may be worried.

[1351] Using emotion analysis technology, it analyzes the user's emotions and adds reassuring phrases such as, "Don't worry. It's normal for babies to cry in the middle of the night."

[1352] Input: Extracted keywords, prompt sentence for generative AI model

[1353] Output: Generated answer text, final answer text with tone adjusted by sentiment analysis

[1354] Step 4: Provide your answers

[1355] Server: Sends the generated answer to the device.

[1356] Input: Answer text generated by the generative AI model, sentiment analysis results

[1357] Output: Message from the server to the terminal

[1358] On your device: The response received from the server is displayed on the messaging app.

[1359] Input: Answer text received from the server

[1360] Output: The response displayed in the user's messaging app

[1361] User: The user checks the displayed answers and uses them to help with parenting.

[1362] Input: Displayed answer text

[1363] Output: Childcare information actually obtained by the user

[1364] Step 5: Personalization and Retention

[1365] Server: The server continuously analyzes the user's life log and personal data to generate personalized advice, such as advice related to specific events or appointment reminders. It also continuously analyzes the user's emotions using emotion analysis technology and takes the results into account to respond appropriately.

[1366] Input: User's life log, personal data, past emotion analysis results

[1367] Output: personalized advice, continuous emotional response

[1368] Step 6: Feedback and Improvement

[1369] User: The user provides feedback on the provided answer in the messaging app.

[1370] Input: Feedback text

[1371] Output: Feedback data sent to the server

[1372] Terminal: Packetizes data to send feedback to the server.

[1373] Input: Feedback text

[1374] Output: Data packet sent to the server

[1375] Server: The server receives the feedback, stores it as data to improve the system, and generates new answers if necessary and sends them back to the user.

[1376] Input: User feedback data

[1377] Output: Improved system accuracy, newly generated answers

[1378] (Application example 2)

[1379] 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."

[1380] In modern child-rearing, mothers need to obtain parenting advice from a variety of sources, but there is a lack of a reliable, centralized source of information. Furthermore, when mothers experience stress or anxiety associated with child-rearing, there is a lack of support to help them cope with these emotions. Furthermore, there is a demand for personalized information tailored to the circumstances of each parent and child, but this is difficult to achieve with conventional systems.

[1381] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting a question about child-rearing from a user via communication means, means for analyzing the question and extracting keywords and categories using natural language processing technology, means for searching a database for related information based on the extracted keywords or categories, means for generating an answer based on the related information using a generative AI model, means for analyzing the user's emotions using emotion analysis technology, means for adjusting the generated answer based on the results of the emotion analysis, and means for providing the adjusted answer to the user via the communication means. This makes it possible to easily obtain reliable information about child-rearing and reduce child-rearing stress by providing care that is tailored to the user's emotional state.

[1382] The "childcare assistant system" is a system that allows mothers to easily obtain reliable information about childcare, and is composed of a combination of communication methods, natural language processing technology, generative AI models, emotion analysis technology, and more.

[1383] "Communication means" refers to technology for receiving questions and information from users via the Internet or the like, and for sending generated answers to users.

[1384] "Natural language processing technology" is a technology for analyzing questions from users and extracting keywords and categories from the sentences.

[1385] "Keywords and categories" are important words and concepts extracted from users' questions using natural language processing techniques.

[1386] A "database" is an information source that stores information related to childcare, and is used to search for related information based on extracted keywords and categories.

[1387] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on extracted relevant information.

[1388] "Emotion analysis technology" is a technology that analyzes emotions from users' questions and feedback and adjusts the content of responses based on those emotions.

[1389] A "user profile" refers to individual information about a user that is generated based on information input by the user for initial settings.

[1390] "Adjusted answers" are answers generated by a generative AI model whose content and tone are changed according to the user's emotions using emotion analysis technology.

[1391] "Feedback" refers to ratings and comments that users make on answers provided.

[1392] This invention relates to a childcare assistant system that allows mothers to easily obtain reliable information about childcare. This system is composed of a combination of communication means, natural language processing technology, generative AI models, and emotion analysis technology.

[1393] Basic Building Blocks

[1394] System Overview:

[1395] This system consists of a user's device (e.g., a smartphone), a communication method (e.g., the Internet), and a server. Users can access the system via a messaging app.

[1396] User registration and initial settings

[1397] User:

[1398] Users add the official account of the childcare assistant system as a friend through a messaging app and start chatting. They then enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[1399] Device:

[1400] The terminal transmits the information entered by the user to the server.

[1401] server:

[1402] The server stores the received information in a data cloud, generates a user profile, and generates a registration completion message based on the generated user profile and sends it to the terminal.

[1403] Question reception and analysis

[1404] User:

[1405] Users type specific parenting questions into the messaging app.

[1406] Device:

[1407] The input text data is sent to the server.

[1408] server:

[1409] The server analyzes the question using natural language processing techniques to extract important keywords and categories.

[1410] Information Acquisition and Answer Generation

[1411] server:

[1412] Based on the extracted keywords, relevant information is searched for in the database, and a generative AI model (e.g., OpenAI GPT-3.5) is used to generate appropriate answers based on the collected information.

[1413] Sentiment analysis and response adjustment

[1414] server:

[1415] Using emotion analysis technology, it analyzes the user's emotions and adjusts the content and tone of the response based on those emotions.

[1416] Providing answers

[1417] Device:

[1418] The generated answer is sent from the server to the device and displayed on the messaging app.

[1419] User:

[1420] The user can check the provided answers and use them to help with childcare.

[1421] Personalization and Consistency

[1422] server:

[1423] The system analyzes the user's life log and personal data to generate personalized advice tailored to the child's development. It also uses emotion analysis technology to continuously analyze emotions and provide appropriate support based on the user's emotions.

[1424] Feedback and Improvements

[1425] User:

[1426] Help improve the system by providing feedback on the answers provided in the messaging app.

[1427] Device:

[1428] Send feedback to the server.

[1429] server:

[1430] The server receives the feedback and stores it as data to improve the answers and accuracy of the system.

[1431] Specific examples

[1432] Example prompt sentence:

[1433] User Question: "My baby cries a lot at night and I don't know what to do."

[1434] Prompt for generative AI model: "You are an intelligent and caring assistant. Answer this question related to parenting: My baby often cries at night, and I don't know what to do."

[1435] Examples of sentiment analysis and tailored responses:

[1436] If a user's question is judged to be "anxious" using emotion analysis technology, an encouraging response such as "You're a great mom!" is added to the generated answer to provide the user with an encouraging response.

[1437] In this way, the childcare assistance system responds quickly and appropriately to user questions, provides reliable information, and also provides emotional support, thereby reducing mothers' anxieties about childcare and providing long-term, continuous support for childcare.

[1438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1439] Step 1:

[1440] Users add the official account of the childcare assistant system as a friend through a messaging app and start chatting. Users enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire. This becomes the input data.

[1441] Input: User information (name, contact information, children's date of birth, gender, etc.)

[1442] Output: A user profile containing information for initial setup.

[1443] What happens: A user enters information into a messaging app, and the device sends that information to a server.

[1444] Step 2:

[1445] The server stores the received initial setting information in a data cloud and generates a user profile. The server generates a registration completion message based on this information and sends it to the terminal.

[1446] Input: Initial setting information

[1447] Output: User profile, registration completion message

[1448] Specific operations: The server stores the received information in the data cloud, creates a user profile, and then generates and sends a registration completion message to the device.

[1449] Step 3:

[1450] A user types a specific parenting question into a messaging app, which becomes a query for further processing.

[1451] Input: Questions about childcare (text data)

[1452] Output: Question data

[1453] Specific operation: The user enters a question, and the device sends it to the server.

[1454] Step 4:

[1455] The server receives the query data and analyzes it using natural language processing techniques, which extracts important keywords and categories.

[1456] Input: User question data

[1457] Output: Keywords and Categories

[1458] Specific operation: The server performs text analysis using natural language processing techniques (e.g., Python's NLTK or spaCy) to extract keywords and categories.

[1459] Step 5:

[1460] The server searches the database for related information based on the extracted keywords.

[1461] Input: Keywords and Categories

[1462] Output: Related information

[1463] What happens: The server queries the database to retrieve relevant childcare information.

[1464] Step 6:

[1465] The server uses a generative AI model (e.g., OpenAI GPT-3.5) to generate an answer based on the collected information. This answer is generated in text format and is used in further steps.

[1466] Input: Related Information

[1467] Output: The generated answer

[1468] Specific operation: The server calls the generative AI model, generates a prompt sentence based on relevant information, and inputs it into the model to generate an answer.

[1469] Step 7:

[1470] The server uses emotion analysis technology to analyze the user's emotions and adjusts the generated answers based on those emotions. For example, if the user is analyzed as "anxious," the server adds encouraging words to the answer.

[1471] Input: Generated answers, user sentiment analysis results

[1472] Output: Adjusted answer

[1473] What it does: The server uses emotion analysis technology (e.g., TextBlob) to analyze the user's emotional state and adjusts the generated answer accordingly.

[1474] Step 8:

[1475] The server then sends the adjusted response to the user's device via a communication means, and the user receives the response via a messaging app.

[1476] Input: Adjusted answer

[1477] Output: The answer displayed on the user's terminal

[1478] Specific operation: The server sends the tailored response to the user's device via the messaging app, where it is displayed on the device.

[1479] Step 9:

[1480] Users enter feedback on the answers they provide, which is used to improve the system.

[1481] Input: User feedback

[1482] Output: Feedback data

[1483] Specific operation: The user enters feedback, which the device sends to the server.

[1484] Step 10:

[1485] The server receives the feedback and analyzes it to improve the accuracy of the system, and if necessary, to improve the accuracy of the answers and the system as a whole.

[1486] Input: Feedback data

[1487] Output: Improved system

[1488] What it does: The server stores your feedback in a database and analyzes it to help improve, optimizing the system to provide better answers and responses next time.

[1489] In this way, the childcare assistant system responds quickly and appropriately to the user's questions and concerns, providing both reliable information and emotional care.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] [Third embodiment]

[1494] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1495] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1496] 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).

[1497] 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.

[1498] 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.

[1499] 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).

[1500] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] 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.

[1505] 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."

[1506] The present invention is a childcare assistance system that allows mothers to easily obtain reliable information about childcare. This system accepts questions about childcare from users via communication means and provides prompt and appropriate answers to those questions. Specific embodiments of this system are described below.

[1507] Basic system configuration

[1508] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the Childcare Assistant System account as a friend on a messaging app such as LINE using their device.

[1509] User registration and initial settings

[1510] User:

[1511] First, users add the official account of the childcare assistant system as a friend through a messaging app such as LINE, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[1512] Device:

[1513] The terminal is responsible for transmitting the information entered by the user to the server.

[1514] server:

[1515] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the terminal.

[1516] Question reception and analysis

[1517] User:

[1518] Users can enter specific questions or concerns about childcare into LINE chat.

[1519] Device:

[1520] The entered text data is immediately sent to the server.

[1521] server:

[1522] The server analyzes the question using natural language processing technology and extracts important keywords and categories. For example, if a question is entered about "baby food for a 6-month-old baby," keywords such as "6 months," "baby," and "baby food" will be extracted.

[1523] Information Acquisition and Answer Generation

[1524] server:

[1525] The server searches for relevant information from a database based on the extracted keywords, retrieving relevant information from existing knowledge bases of childcare experts and the latest childcare trends, and then uses a generative AI model (e.g., GPT-3.5) to generate appropriate answers based on the collected information.

[1526] For example, an answer might be generated: "For a 6-month-old baby, it's a good idea to start with grain-based porridge, followed by vegetable purees. At this age, start with small amounts to allow for flavor and texture exploration."

[1527] Providing answers

[1528] Device:

[1529] The generated answer is sent from the server to the device and displayed on the LINE chat.

[1530] User:

[1531] The user can check the answers provided in the chat and use them to help with child-rearing.

[1532] Personalization and Consistency

[1533] server:

[1534] Based on the information provided by the user and the accumulated life log, the server generates personalized advice according to the child's growth and provides it periodically via LINE chat. For example, it can send specific advice such as, "Based on the recent changes in your baby's weight, consider when it's time to move on to the next step."

[1535] Feedback and Improvements

[1536] User:

[1537] Users can provide feedback on the answers provided via LINE chat to help improve the system.

[1538] Device:

[1539] Send feedback to the server.

[1540] server:

[1541] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of answers.

[1542] In this way, the childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, it can provide long-term childcare support by continuously providing personalized support to users.

[1543] The processing flow will be explained below.

[1544] Step 1:

[1545] User:

[1546] Add the official account of the Childcare Assistant System as a friend on the LINE app. Start a chat and enter your information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[1547] Step 2:

[1548] Device:

[1549] The information entered by the user is sent to the server.

[1550] Step 3:

[1551] server:

[1552] The received information is stored in the data cloud, a user profile is generated, and a registration completion message is generated and sent to the terminal.

[1553] Step 4:

[1554] Device:

[1555] A registration completion message from the server will be displayed on the LINE chat.

[1556] Step 5:

[1557] User:

[1558] Enter specific questions or concerns about childcare into LINE chat.

[1559] Step 6:

[1560] Device:

[1561] The input text data is sent to the server.

[1562] Step 7:

[1563] server:

[1564] The system receives the question text and uses natural language processing technology to analyze it, understand the intent of the question, and extract important keywords and categories.

[1565] Step 8:

[1566] server:

[1567] Based on the extracted keywords and categories, relevant information is searched for in databases and expert knowledge bases.

[1568] Step 9:

[1569] server:

[1570] Based on relevant information, a generative AI model is used to generate the optimal answer.

[1571] Step 10:

[1572] server:

[1573] The generated response is sent to the device.

[1574] Step 11:

[1575] Device:

[1576] The response sent from the server is displayed in the LINE chat.

[1577] Step 12:

[1578] User:

[1579] Check the answers provided on LINE chat and get the information you need.

[1580] Step 13:

[1581] User:

[1582] Enter your feedback on the provided answer (whether it was helpful, additional questions, etc.) in LINE chat.

[1583] Step 14:

[1584] Device:

[1585] Send user feedback to the server.

[1586] Step 15:

[1587] server:

[1588] The system receives feedback and stores it as data to improve the answers and accuracy of the system, and generates new answers to new questions as needed.

[1589] Step 16:

[1590] server:

[1591] The system analyzes the user's life log and personal data to generate personalized advice tailored to the child's development.

[1592] Step 17:

[1593] server:

[1594] Relevant childcare information and advice will be provided on a regular basis via LINE chat, providing ongoing support to users in their childcare.

[1595] Step 18:

[1596] User:

[1597] Review the personalized information and advice provided to help you with your parenting.

[1598] Example 1

[1599] 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."

[1600] Conventional childcare assistance systems have limited answers to users' questions about childcare, making it difficult to provide personalized advice that takes into account each user's individual circumstances. Furthermore, the accuracy of the system has not been sufficiently improved using user feedback, so there is a need for an improved user experience. This will reduce anxiety about childcare and provide more reliable information.

[1601] 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.

[1602] In this invention, the server includes: means for accepting childcare-related questions from users via communication means; means for analyzing the questions and extracting keywords and categories using natural language processing technology; means for searching for related information from an information storage device based on the extracted keywords or categories; means for generating answers based on the related information using a generative AI model; and means for providing the generated answers to users via the communication means. This enables prompt and accurate answers to users' questions and reliable childcare information. Furthermore, the system includes means for accepting information for initial setup, means for storing the information for initial setup in a data cloud to generate a user profile, and means for periodically providing personalized advice using the generated answers, thereby enabling detailed support tailored to individual situations. Furthermore, the system includes means for accepting user feedback on the generated answers and means for storing the feedback in a data cloud to improve the system's accuracy and the quality of the answers, enabling continuous system improvement.

[1603] "Communication means" refers to a communication path for transmitting input data from a user to a server and for providing output data from the server to the user.

[1604] "User" refers to a person who uses the childcare assistant system to ask questions about childcare and receive answers.

[1605] "Natural language processing technology" refers to technology that analyzes text data entered by a user and understands their meaning and intent.

[1606] "Keywords" are important words extracted from the content of a user's question, and are used to search for related information when generating an answer.

[1607] "Category" refers to a classification criterion for classifying questions and information, and is used to improve the efficiency of searching for related information.

[1608] "Information storage device" refers to a recording medium or database that stores related information and makes it searchable when needed.

[1609] A "generative AI model" refers to a program equipped with artificial intelligence technology that automatically generates sentences based on data.

[1610] A "user profile" is individual account information generated based on information provided by a user, and is used to provide personalized services to each individual user.

[1611] "Personalized advice" refers to advice that is optimized for an individual user based on the user profile and accumulated data.

[1612] "Feedback" refers to a user's input of evaluations and opinions regarding the results of using the system and the answers provided.

[1613] This invention relates to a childcare assistance system, and its purpose is to enable mothers and guardians to easily obtain reliable information about childcare. This system accepts questions about childcare from users via communication means and provides prompt and appropriate answers to those questions. Specific embodiments for carrying out the present invention are described below.

[1614] Basic system configuration

[1615] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend in a messaging app (e.g., messaging application) using their device.

[1616] User registration and initial settings

[1617] User:

[1618] Users add the official account of the childcare assistant system as a friend through a messaging app, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[1619] Device:

[1620] The terminal is responsible for transmitting the information entered by the user to the server.

[1621] server:

[1622] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the user's device.

[1623] Examples:

[1624] After adding a friend, the user enters the name "Yamada Taro" and the child's date of birth "April 1, 2022" into a form and sends it to the server.

[1625] Example prompt: "Enter my information. My name is Taro Yamada, and my child's birth date is April 1, 2022."

[1626] Question reception and analysis

[1627] User:

[1628] Users type specific questions or concerns about childcare into the messaging app chat, for example, "Can you tell me about solid food for my 6-month-old baby?"

[1629] Device:

[1630] The entered text data is immediately sent to the server.

[1631] server:

[1632] The server uses natural language processing technology (e.g., Natural Language Toolkit) to analyze the question and extract important keywords and categories. For example, if a question about "baby food for a 6-month-old baby" is entered, keywords such as "6 months," "baby," and "baby food" will be extracted.

[1633] Example prompt:

[1634] "Please tell me about baby food for a 6-month-old baby."

[1635] Information Acquisition and Answer Generation

[1636] server:

[1637] The server searches for relevant information from an information storage device based on the extracted keywords. It retrieves relevant information from a knowledge base of childcare experts and the latest childcare trends. It then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer based on the collected information. For example, it might generate an answer like, "For a 6-month-old baby, it's a good idea to start with grain-based porridge, followed by vegetable puree. At this age, start with a small amount to allow for enjoyment of the taste and texture."

[1638] Examples:

[1639] The server searches for information about "6 months," "baby," and "baby food," and the generative AI model generates the appropriate answer.

[1640] Example prompt: "For 6-month-old babies, it's a good idea to try grain-based porridge first, followed by vegetable purees."

[1641] Providing answers

[1642] server:

[1643] The generated answer is sent from the server to the user's terminal.

[1644] Device:

[1645] The device will display the received response in the chat on the messaging app.

[1646] User:

[1647] The user can check the answers provided in the chat and use them to help with child-rearing.

[1648] Examples:

[1649] The user's device displays the message, "For 6-month-old babies, try grain-based porridge first, followed by vegetable puree."

[1650] Personalization and Consistency

[1651] server:

[1652] The server generates personalized advice based on the information provided by the user and the accumulated life log, and delivers it periodically via the messaging app according to the child's growth. For example, it can provide specific advice such as, "Based on the recent changes in your baby's weight, consider when it's time to move on to the next step."

[1653] Examples:

[1654] Send specific advice based on your child's development.

[1655] Sample prompt: "Based on your baby's recent weight changes, consider when it's time to move forward with the next step."

[1656] Feedback and Improvements

[1657] User:

[1658] Users can provide feedback on the answers provided in the messaging app's chat to help improve the system.

[1659] Device:

[1660] The entered feedback is sent to the server.

[1661] server:

[1662] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of answers.

[1663] Examples:

[1664] The user enters their satisfaction with the answer, and the feedback is sent to and stored on the server.

[1665] Sample prompt: "I'm very pleased with the answer you provided. Thank you very much."

[1666] The above is a detailed description of a specific embodiment of the present invention. This enables users to receive prompt and appropriate answers to their questions and obtain reliable childcare information. Furthermore, personalized support tailored to individual situations and system improvements based on user feedback are realized.

[1667] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1668] System program processing flow

[1669] ---

[1670] Step 1: User registration and initial setup

[1671] Step 1-1: User accesses the system

[1672] User:

[1673] The user opens a messaging app on a device such as a smartphone and adds the official account of the childcare assistant system as a friend. This action allows the user to start accessing the system.

[1674] input:

[1675] The act of adding an official account as a friend through a messaging app.

[1676] output:

[1677] You will be able to access the official account of the childcare assistance system.

[1678] Specific behavior:

[1679] Tap the "Add Friends" button within the messaging app, then search for and add the official account.

[1680] ---

[1681] Step 1-2: Fill in the initial setup form

[1682] User:

[1683] After adding a friend, start chatting and enter your information (e.g., name, contact information, children's birth dates, gender) according to the initial setup questionnaire sent by the system.

[1684] Device:

[1685] The terminal transmits the information entered by the user to the server in real time.

[1686] input:

[1687] Enter the user's personal information and basic information about childcare.

[1688] output:

[1689] User information sent to the server.

[1690] Specific behavior:

[1691] The action of a user filling out a form in a chat window and pressing the "Send" button.

[1692] ---

[1693] Step 1-3: The server generates a user profile

[1694] server:

[1695] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the user's device.

[1696] input:

[1697] User information sent from the device.

[1698] output:

[1699] User profile and registration completion message stored in the data cloud.

[1700] Specific behavior:

[1701] The received data is stored in an appropriate location in the data cloud, a profile creation algorithm is run to generate a user profile, and a message such as "Registration complete" is sent to the user's device.

[1702] ---

[1703] Step 2: Question collection and analysis

[1704] Step 2-1: User enters a question

[1705] User:

[1706] Type specific questions or concerns about childcare into a messaging app chat, such as "Can you tell me about solid food for my 6-month-old baby?"

[1707] input:

[1708] Text with specific childcare questions.

[1709] output:

[1710] The question text sent to the server through a messaging app.

[1711] Specific behavior:

[1712] The action of a user typing a question into a chat window and pressing the "send" button.

[1713] ---

[1714] Step 2-2: Send the question text

[1715] Device:

[1716] The entered question text data is immediately sent to the server.

[1717] input:

[1718] The question text entered by the user into the messaging app.

[1719] output:

[1720] The question text sent to the server.

[1721] Specific behavior:

[1722] The text data received by the device is immediately sent to the specified API endpoint on the server.

[1723] ---

[1724] Step 2-3: Question Analysis

[1725] server:

[1726] The server uses natural language processing technology to analyze the question and extract important keywords and categories. If a question is entered about "baby food for a 6-month-old baby," keywords such as "6 months," "baby," and "baby food" will be extracted.

[1727] input:

[1728] The question text sent from the device.

[1729] output:

[1730] Extracted keyword and category information.

[1731] Specific behavior:

[1732] The server performs text analysis using natural language processing libraries (e.g., Natural Language Toolkit) to identify and extract important keywords and categories.

[1733] Example prompt:

[1734] "Please tell me about baby food for a 6-month-old baby."

[1735] ---

[1736] Step 3: Information Acquisition and Answer Generation

[1737] Step 3-1: Search for related information

[1738] server:

[1739] The server searches for relevant information from the information storage device based on the extracted keywords, and obtains relevant information from the knowledge base of childcare experts and the latest childcare trends.

[1740] input:

[1741] Extracted keyword and category information.

[1742] output:

[1743] Related information found.

[1744] Specific behavior:

[1745] Perform database queries to find and retrieve relevant information that matches your keywords.

[1746] ---

[1747] Step 3-2: Generate an answer

[1748] server:

[1749] Using this information, a generative AI model (e.g., GPT-3.5) is used to generate an appropriate answer, such as "For a 6-month-old baby, try grain-based porridge first, followed by vegetable puree."

[1750] input:

[1751] Related information found.

[1752] output:

[1753] Answer text obtained through a generative AI model.

[1754] Specific behavior:

[1755] Relevant information is provided as input to a generative AI model, which then generates and outputs natural answers.

[1756] Example prompt:

[1757] "For six-month-old babies, it's a good idea to start with cereal-based porridge, followed by vegetable purees."

[1758] ---

[1759] Step 4: Provide your answers

[1760] Step 4-1: Submit your response

[1761] server:

[1762] The generated answer is sent to the user's terminal.

[1763] input:

[1764] Answer text generated by a generative AI model.

[1765] output:

[1766] The answer text sent to the user's terminal.

[1767] Specific behavior:

[1768] The server sends the generated response to the user's device via the messaging app's API.

[1769] ---

[1770] Step 4-2: View the answer

[1771] Device:

[1772] The device will display the received response in the chat on the messaging app.

[1773] input:

[1774] The response text sent by the server.

[1775] output:

[1776] The reply text that will be displayed to the user in the chat screen.

[1777] Specific behavior:

[1778] The messaging app displays the received response in the chat window, allowing the user to easily check the response.

[1779] ---

[1780] Step 5: Personalization and Retention

[1781] Step 5-1: Providing regular advice

[1782] server:

[1783] Based on the information provided by the user and the accumulated life logs, the server generates personalized advice tailored to the child's development and provides it periodically via the messaging app.

[1784] input:

[1785] User information and lifelog data.

[1786] output:

[1787] Personalized advice.

[1788] Specific behavior:

[1789] The server periodically analyzes the user's profile and latest life log data, uses a generative AI model to generate the next piece of advice needed, and sends it via the messaging app.

[1790] Example prompt:

[1791] "Based on your baby's recent weight changes, consider when it's time to move on to the next step."

[1792] ---

[1793] Step 6: Feedback and Improvement

[1794] Step 6-1: Enter your feedback

[1795] User:

[1796] Users can provide feedback on the answers provided in the messaging app's chat to help improve the system.

[1797] input:

[1798] User feedback text.

[1799] output:

[1800] Feedback text sent to the server through the messaging app.

[1801] Specific behavior:

[1802] The action of a user typing feedback into a chat window and pressing the "Send" button.

[1803] ---

[1804] Step 6-2: Send feedback

[1805] Device:

[1806] The entered feedback is sent to the server.

[1807] input:

[1808] Feedback entered by users into messaging apps.

[1809] output:

[1810] The feedback text sent to the server.

[1811] Specific behavior:

[1812] The feedback data received by the device is immediately sent to the specified API endpoint on the server.

[1813] ---

[1814] Step 6-3: Accumulating and utilizing feedback

[1815] server:

[1816] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of its answers.

[1817] input:

[1818] Feedback sent from the device.

[1819] output:

[1820] Feedback data and updated analytical models stored in the data cloud.

[1821] Specific behavior:

[1822] The server stores the received feedback in a data cloud and improves the system by updating the analytical model.

[1823] Example prompt:

[1824] "I am very satisfied with the answer provided. Thank you very much."

[1825] The above is a specific flow of program processing for the system of the present invention, which allows users to quickly and appropriately obtain highly reliable childcare information, and the system is continually improved.

[1826] (Application example 1)

[1827] 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."

[1828] Parents raising children often have a wide range of questions and anxieties about childcare, but there are few ways to quickly obtain reliable information when they are out and about, especially while shopping, and the time it takes to obtain information is a problem. Furthermore, if there were a way to obtain answers through voice input or visually, it would be much more convenient for parents raising children.

[1829] 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.

[1830] In this invention, the server includes means for recognizing a user's voice using a voice input device, means for analyzing the question using natural language processing technology and extracting keywords and categories, and means for displaying the generated answer on a visual output device, thereby enabling users to quickly obtain reliable information about childcare even when they are out and about.

[1831] "Communication means" refers to means for sending and receiving data via the Internet or the like.

[1832] "Natural language processing technology" is a technology for analyzing text and voice input from users, understanding their meaning, and extracting keywords and categories.

[1833] "Keyword and category extraction means" refers to a means for automatically identifying important words and themes from user questions using natural language processing techniques.

[1834] A "database" is a structured collection of information that stores related information systematically and can be retrieved as needed.

[1835] A "generative AI model" is an artificial intelligence model used to generate optimal answers to user questions based on collected information.

[1836] A "voice input device" is a device that receives a user's voice as input and converts the content into text.

[1837] A "visual output device" is a device for visually presenting information to a user through smart glasses or the like.

[1838] "User profile" refers to data that compiles personal information and childcare information provided by the user.

[1839] "Feedback" refers to opinions or evaluations given by users regarding answers or services provided.

[1840] MODE FOR CARRYING OUT THE INVENTION

[1841] 1. System Overview

[1842] This invention is a childcare assistant system that allows parents to easily ask questions about childcare through voice input and quickly obtain reliable information. This system is realized by combining a wide range of technologies, including communication methods, natural language processing technology, generative AI models, voice input devices, visual output devices, and data clouds.

[1843] 2. Hardware and software used

[1844] Smart glasses: devices that take voice input and provide visual information (e.g., Google Glass, NReal).

[1845] Smartphone: A device for installing applications and working with smart glasses.

[1846] Server: A central control unit that processes data and generates and provides answers to users (e.g., AWS, Google Cloud).

[1847] Natural language processing engine: Software for analyzing user questions (e.g., Google Cloud Natural Language API).

[1848] Generative AI model: An AI model that generates optimal answers to questions (e.g., OpenAI GPT-3.5).

[1849] 3. Data processing and calculation

[1850] Voice input

[1851] Users can voice-input questions about childcare through the smart glasses, and this voice data is sent to a server via a smartphone.

[1852] Voice Recognition

[1853] The server receives the voice data and converts it into text using AWS Transcribe, which allows the voice-input questions to be processed as text information.

[1854] Natural Language Processing

[1855] The converted text data is then parsed using the Google Cloud Natural Language API to extract keywords and categories from the question, helping to accurately understand the question and prepare the search for the appropriate information.

[1856] Keyword search

[1857] Based on the extracted keywords and categories, relevant information is searched for in the database, which includes expert knowledge bases and the latest childcare trends.

[1858] Answer generation

[1859] Based on the searched information, the best answer is automatically generated using a generative AI model (OpenAI GPT-3.5) to provide information that meets the user's needs.

[1860] Visual and audio feedback

[1861] The generated answer is visually displayed on the smart glasses' display and also provided to the user audibly, using gTTS (Google Text-to-Speech) to convert the text data into speech and play it through the smart glasses.

[1862] 4. Examples of concrete examples and prompts

[1863] Specific examples

[1864] For example, consider a case where a user is in a store selling childcare products and has a question about "how to choose a baby bottle." They ask through their smart glasses, "How do I choose a baby bottle?" This question is sent to the server, converted into text, and analyzed using natural language processing technology. Relevant information is then searched for in the database, and the generative AI model generates the answer: "They come in glass and plastic. Glass bottles are heavy but durable, while plastic bottles are light and easy to carry." This information is finally displayed on the smart glasses' display and provided via voice.

[1865] Prompt Sentence Examples

[1866] How do I choose a baby bottle?

[1867] "There are glass and plastic baby bottles. I'd like to know the advantages and disadvantages of each."

[1868] As described above, the present invention enables users to receive real-time childcare support and provides users with quick and reliable information even when they are away from home.

[1869] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1870] Step 1:

[1871] The user wears the smart glasses and asks questions about childcare via a voice input device.

[1872] Input: User's voice data

[1873] Action: The user says, "How do I choose a baby bottle?"

[1874] Step 2:

[1875] The terminal receives the voice data and transmits it to a server via the Internet.

[1876] Input: User's voice data

[1877] Data processing: The audio data is sent to the AWS Transcribe service to prepare it for conversion into text.

[1878] Output: Audio data received by the server

[1879] Step 3:

[1880] The server uses AWS Transcribe to convert the audio data into text data.

[1881] Input: Audio data received by the server

[1882] Data processing: Using voice recognition technology, audio is converted into text.

[1883] Output: Converted text data

[1884] Step 4:

[1885] The server uses a natural language processing engine (Google Cloud Natural Language API) to analyze the text data and extract keywords and categories.

[1886] Input: Converted text data

[1887] Data calculation: Extract the keywords "baby bottle" and "how to choose" from the text.

[1888] Output: Extracted keywords and categories

[1889] Step 5:

[1890] The server searches for relevant information based on keywords and categories extracted from the database.

[1891] Input: Extracted keywords and categories

[1892] Data retrieval: Searching for relevant information in a database.

[1893] Output: Retrieved childcare information

[1894] Step 6:

[1895] The server uses a generative AI model (OpenAI GPT-3.5) to generate appropriate answers based on relevant information.

[1896] Input: Searched childcare information

[1897] Data computation: A generative AI model processes the information and generates a specific answer, such as "There are glass and plastic baby bottles."

[1898] Output: Generated answer text

[1899] Step 7:

[1900] The server uses gTTS (Google Text-to-Speech) to convert the generated response text into audio data.

[1901] Input: Generated answer text

[1902] Data processing: Convert text into an audio file.

[1903] Output: Generated answer audio data

[1904] Step 8:

[1905] The server transmits the generated answer text and voice data to the terminal.

[1906] Input: Generated answer text and audio data

[1907] Operation: Sending data from the server to the device

[1908] Output: Terminal received data

[1909] Step 9:

[1910] The terminal displays the received answer text on the visual output device of the smart glasses and plays the audio data.

[1911] Input: Terminal received data

[1912] How it works: The smart glasses display will say "There are glass and plastic baby bottles" and simultaneously play back the answer aloud.

[1913] Output: The user gets a visual and audio response.

[1914] Through the above processing steps, the present invention serves as a childcare assistant system that quickly and effectively solves questions about childcare through voice input.

[1915] 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.

[1916] This invention is a childcare assistant system that allows mothers to easily obtain reliable information about childcare. The system incorporates communication methods, natural language processing technology, generative AI models, and an emotion engine. Because the system includes user emotion analysis, it not only improves the quality of childcare but also supports mothers' emotional care.

[1917] Basic system configuration

[1918] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend on a messaging app such as LINE using their device.

[1919] User registration and initial settings

[1920] User:

[1921] First, users add the official account of the childcare assistant system as a friend through a messaging app such as LINE, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[1922] Device:

[1923] The terminal is responsible for transmitting the information entered by the user to the server.

[1924] server:

[1925] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the terminal.

[1926] Question reception and analysis

[1927] User:

[1928] Users can enter specific questions or concerns about childcare into LINE chat.

[1929] Device:

[1930] The entered text data is immediately sent to the server.

[1931] server:

[1932] The server analyzes the question using natural language processing techniques to extract important keywords and categories.

[1933] Information Acquisition and Answer Generation

[1934] server:

[1935] The system searches for relevant information from a database based on the extracted keywords. It then uses a generative AI model (e.g., GPT-3.5) to generate appropriate answers based on the collected information. It also uses an emotion engine to analyze the user's emotions and adjust the content and tone of the answer based on those emotions. For example, if the user is "anxious," it adds encouraging words to ease their anxiety.

[1936] Providing answers

[1937] Device:

[1938] The generated answer is sent from the server to the device and displayed on the LINE chat.

[1939] User:

[1940] The user can check the answers provided in the chat and use them to help with child-rearing.

[1941] Personalization and Consistency

[1942] server:

[1943] The server analyzes the user's life log and personal data to generate personalized advice based on the child's development. It also performs continuous emotion analysis using an emotion engine to provide appropriate support based on the user's emotions.

[1944] Feedback and Improvements

[1945] User:

[1946] Users can provide feedback on the answers provided via LINE chat to help improve the system.

[1947] Device:

[1948] Send feedback to the server.

[1949] server:

[1950] The server receives feedback and stores it as data to improve the answers and accuracy of the system, and also generates new answers to new questions as needed.

[1951] In this way, this childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, the system also provides psychological support by analyzing the user's emotions and providing support accordingly. This makes it possible to provide long-term, continuous childcare support.

[1952] The processing flow will be explained below.

[1953] Step 1:

[1954] User:

[1955] Add the official account of the Childcare Assistant System as a friend on the LINE app. Start a chat and enter your basic information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[1956] Step 2:

[1957] Device:

[1958] The information entered by the user is sent to the server.

[1959] Step 3:

[1960] server:

[1961] The received information is stored in a data cloud and a user profile is generated. After the profile is generated, a registration completion message is generated and sent to the terminal.

[1962] Step 4:

[1963] Device:

[1964] A registration completion message from the server will be displayed on the LINE chat.

[1965] Step 5:

[1966] User:

[1967] Enter specific questions or concerns about childcare in the LINE chat. For example, "What kind of baby food does a 6-month-old baby need?"

[1968] Step 6:

[1969] Device:

[1970] The input text data is sent to the server.

[1971] Step 7:

[1972] server:

[1973] The received question text is analyzed using natural language processing technology to extract important keywords and categories, such as "6 months," "baby," and "baby food."

[1974] Step 8:

[1975] server:

[1976] The system searches for relevant information from a database based on the extracted keywords and categories, while simultaneously analyzing emotions (e.g., anxiety, doubt, joy) from the user's text using an emotion engine.

[1977] Step 9:

[1978] server:

[1979] Based on the results of the emotion engine, a generative AI model is used to generate an appropriate response. For example, if the user expresses anxiety, the system generates a response in a reassuring tone, such as, "First of all, don't worry. Porridge is suitable for a 6-month-old baby."

[1980] Step 10:

[1981] server:

[1982] The generated response is sent to the device.

[1983] Step 11:

[1984] Device:

[1985] The response sent from the server is displayed in the LINE chat.

[1986] Step 12:

[1987] User:

[1988] Check the answers provided via LINE chat and use them to help with childcare. For example, make porridge based on the advice on baby food.

[1989] Step 13:

[1990] User:

[1991] Enter your feedback on the provided answer (whether it was helpful, additional questions, etc.) in LINE chat.

[1992] Step 14:

[1993] Device:

[1994] Send user feedback to the server.

[1995] Step 15:

[1996] server:

[1997] The system receives feedback and stores it as data to improve the answers and accuracy of the system, and generates new answers to new questions as needed.

[1998] Step 16:

[1999] server:

[2000] The system analyzes the user's life log and personal data to generate personalized advice based on the child's growth. For example, it generates specific advice such as, "Based on recent weight changes, it's time to move on to the next step."

[2001] Step 17:

[2002] server:

[2003] Relevant childcare information and advice will be provided on a regular basis via LINE chat, providing ongoing support to users in their childcare.

[2004] Step 18:

[2005] User:

[2006] Review the personalized information and advice provided to help you with your parenting.

[2007] Example 2

[2008] 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."

[2009] Conventional childcare assistance systems are not always able to respond quickly to user questions and are sometimes unable to provide reliable information. Furthermore, they lack emotional support for mothers, resulting in insufficient psychological support. This creates challenges for improving the quality of childcare and for providing adequate psychological care for mothers.

[2010] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a question about child-rearing from a user via communication means, means for analyzing the question and extracting keywords and categories using natural language processing technology, means for searching a database for related information based on the extracted keywords or categories, means for generating an answer based on the related information using a generative AI model, means for analyzing the user's emotions using emotion analysis technology and adjusting the content of the answer, and means for providing the generated answer to the user via the communication means. This makes it possible to quickly and appropriately respond to a question from a user, provide reliable child-rearing information, and analyze the user's emotions to provide appropriate support.

[2011] "Communication means" refers to the infrastructure for sending and receiving data from users, including the Internet, Wi-Fi, mobile communication networks, etc.

[2012] "Natural language processing technology" refers to the technology of analyzing, understanding, and generating natural language used by humans using a computer, and includes morphological analysis, keyword extraction, and sentence classification.

[2013] A "generative AI model" refers to an artificial intelligence model that generates natural language based on large amounts of data, and includes, for example, GPT-3.5.

[2014] "Emotion analysis technology" refers to technology that identifies a user's emotions from text data and adjusts responses based on those emotions, and includes emotion analysis engines and tone analyzers.

[2015] A "data cloud" refers to a distributed storage system for storing and managing data over the Internet, and includes cloud storage services.

[2016] A "user profile" refers to a collection of information constructed based on data such as a user's personal information and behavioral history, and includes name, contact information, childcare history, etc.

[2017] "Feedback" refers to the evaluations and opinions that users give about the answers and services provided, and serves as data that can be used to improve the system.

[2018] This invention relates to a childcare assistant system that provides users with tools to easily and quickly resolve questions about childcare. The system includes a communication means, natural language processing technology, a generative AI model, emotion analysis technology, a data cloud, and a user profile management function.

[2019] Basic system configuration

[2020] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend in a messaging app on their smartphone.

[2021] User registration and initial settings

[2022] User:

[2023] First, users add the official account of the childcare assistant system as a friend in a messaging app and start chatting, then follow the system's instructions to enter information such as the child's name, contact details, date of birth, and gender into the initial setup questionnaire.

[2024] Device:

[2025] The terminal generates a data packet for transmitting the information entered by the user to the server.

[2026] server:

[2027] The server analyzes the received information, stores it in the data cloud, creates a user profile, and sends a registration completion message to the terminal.

[2028] Question reception and analysis

[2029] User:

[2030] Users type parenting questions into the messaging app.

[2031] Device:

[2032] The entered text data is sent to a server via the Internet.

[2033] server:

[2034] The server analyzes the text of the received question using natural language processing technology, such as SpaCy or NLTK, to extract important keywords.

[2035] Information Acquisition and Answer Generation

[2036] server:

[2037] The server uses the extracted keywords to search for relevant information from a database, then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer, and furthermore, uses sentiment analysis technology (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions and adjust the tone of the answer.

[2038] Examples of prompts:

[2039] If a user submits a question like, "My baby won't stop crying in the middle of the night, what should I do?"

[2040] In that case the prompt would look like this:

[2041] User: "My baby won't stop crying in the middle of the night. What should I do?" Gently explain why babies cry at night and what you can do about it. Also, add some reassurance, as the mother may be worried.

[2042] Providing answers

[2043] server:

[2044] The generated response is sent to the device.

[2045] Device:

[2046] The response received from the server is displayed on the messaging app.

[2047] User:

[2048] The user can check the provided answers and use them to help with childcare.

[2049] Personalization and Consistency

[2050] server:

[2051] The server analyzes the user's life log and personal data to generate personalized advice tailored to the child's development, and also performs continuous emotion analysis using emotion analysis technology.

[2052] Feedback and Improvements

[2053] User:

[2054] Users provide feedback on the answers provided in the messaging app.

[2055] Device:

[2056] Converts data into packets for sending feedback to the server.

[2057] server:

[2058] The server receives the feedback, stores it as data to improve the system, and generates new answers as needed and sends them back to the user.

[2059] In this way, this childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, the system also provides psychological support by analyzing the user's emotions and providing support accordingly. This makes it possible to provide long-term, continuous childcare support.

[2060] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2061] Step 1: User registration and initial setup

[2062] User: The user adds the official account of the childcare assistant system as a friend on a messaging app such as LINE and starts chatting. The system then sends the user an initial setup questionnaire, in which the user enters personal information such as their name, contact details, and the child's date of birth and gender.

[2063] Input: Initial setup information (e.g. name, contact information, child's date of birth, gender)

[2064] Output: The data the user entered into the form

[2065] Terminal: The terminal generates a data packet for transmitting the information input by the user to the server. The generated data packet is transmitted to the server via a communication means.

[2066] Input: Data entered by the user

[2067] Output: Data packet sent to the server

[2068] Server: The server analyzes the received information, stores it in the data cloud, creates a user profile, and sends a registration completion message to the device.

[2069] Input: Data packets sent from the device

[2070] Output: User information stored in the data cloud, registration completion message

[2071] Step 2: Question collection and analysis

[2072] User: A user types a parenting question into a messaging app.

[2073] Input: User question text (e.g., "My baby won't stop crying in the middle of the night. What should I do?")

[2074] Output: Submitted question text data

[2075] Terminal: The entered question text data is sent to the server via the Internet.

[2076] Input: Question text data submitted by the user

[2077] Output: Text data sent to the server

[2078] Server: The server uses natural language processing technology to analyze the text of the received question and extract important keywords. For example, SpaCy or NLTK can be used to extract keywords such as "baby," "midnight," and "won't stop crying."

[2079] Input: Question text data sent from the terminal

[2080] Output: Extracted keywords (e.g., "baby," "midnight," "can't stop crying")

[2081] Step 3: Information Acquisition and Answer Generation

[2082] server:

[2083] The server uses the extracted keywords to search for relevant information in a database. For example, it runs a database query based on the keywords "baby," "midnight," and "won't stop crying." It then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer. For example, it sends the following prompt to the generative AI:

[2084] User: "My baby won't stop crying in the middle of the night. What should I do?" Gently explain why babies cry at night and what you can do about it. Also, add some reassurance, as the mother may be worried.

[2085] Using emotion analysis technology, it analyzes the user's emotions and adds reassuring phrases such as, "Don't worry. It's normal for babies to cry in the middle of the night."

[2086] Input: Extracted keywords, prompt sentence for generative AI model

[2087] Output: Generated answer text, final answer text with tone adjusted by sentiment analysis

[2088] Step 4: Provide your answers

[2089] Server: Sends the generated answer to the device.

[2090] Input: Answer text generated by the generative AI model, sentiment analysis results

[2091] Output: Message from the server to the terminal

[2092] On your device: The response received from the server is displayed on the messaging app.

[2093] Input: Answer text received from the server

[2094] Output: The response displayed in the user's messaging app

[2095] User: The user checks the displayed answers and uses them to help with parenting.

[2096] Input: Displayed answer text

[2097] Output: Childcare information actually obtained by the user

[2098] Step 5: Personalization and Retention

[2099] Server: The server continuously analyzes the user's life log and personal data to generate personalized advice, such as advice related to specific events or appointment reminders. It also continuously analyzes the user's emotions using emotion analysis technology and takes the results into account to respond appropriately.

[2100] Input: User's life log, personal data, past emotion analysis results

[2101] Output: personalized advice, continuous emotional response

[2102] Step 6: Feedback and Improvement

[2103] User: The user provides feedback on the provided answer in the messaging app.

[2104] Input: Feedback text

[2105] Output: Feedback data sent to the server

[2106] Terminal: Packetizes data to send feedback to the server.

[2107] Input: Feedback text

[2108] Output: Data packet sent to the server

[2109] Server: The server receives the feedback, stores it as data to improve the system, and generates new answers if necessary and sends them back to the user.

[2110] Input: User feedback data

[2111] Output: Improved system accuracy, newly generated answers

[2112] (Application example 2)

[2113] 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."

[2114] In modern child-rearing, mothers need to obtain parenting advice from a variety of sources, but there is a lack of a reliable, centralized source of information. Furthermore, when mothers experience stress or anxiety associated with child-rearing, there is a lack of support to help them cope with these emotions. Furthermore, there is a demand for personalized information tailored to the circumstances of each parent and child, but this is difficult to achieve with conventional systems.

[2115] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting a question about child-rearing from a user via communication means, means for analyzing the question and extracting keywords and categories using natural language processing technology, means for searching a database for related information based on the extracted keywords or categories, means for generating an answer based on the related information using a generative AI model, means for analyzing the user's emotions using emotion analysis technology, means for adjusting the generated answer based on the results of the emotion analysis, and means for providing the adjusted answer to the user via the communication means. This makes it possible to easily obtain reliable information about child-rearing and reduce child-rearing stress by providing care that is tailored to the user's emotional state.

[2116] The "childcare assistant system" is a system that allows mothers to easily obtain reliable information about childcare, and is composed of a combination of communication methods, natural language processing technology, generative AI models, emotion analysis technology, and more.

[2117] "Communication means" refers to technology for receiving questions and information from users via the Internet or the like, and for sending generated answers to users.

[2118] "Natural language processing technology" is a technology for analyzing questions from users and extracting keywords and categories from the sentences.

[2119] "Keywords and categories" are important words and concepts extracted from users' questions using natural language processing techniques.

[2120] A "database" is an information source that stores information related to childcare, and is used to search for related information based on extracted keywords and categories.

[2121] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on extracted relevant information.

[2122] "Emotion analysis technology" is a technology that analyzes emotions from users' questions and feedback and adjusts the content of responses based on those emotions.

[2123] A "user profile" refers to individual information about a user that is generated based on information input by the user for initial settings.

[2124] "Adjusted answers" are answers generated by a generative AI model whose content and tone are changed according to the user's emotions using emotion analysis technology.

[2125] "Feedback" refers to ratings and comments that users make on answers provided.

[2126] This invention relates to a childcare assistant system that allows mothers to easily obtain reliable information about childcare. This system is composed of a combination of communication means, natural language processing technology, generative AI models, and emotion analysis technology.

[2127] Basic Building Blocks

[2128] System Overview:

[2129] This system consists of a user's device (e.g., a smartphone), a communication method (e.g., the Internet), and a server. Users can access the system via a messaging app.

[2130] User registration and initial settings

[2131] User:

[2132] Users add the official account of the childcare assistant system as a friend through a messaging app and start chatting. They then enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[2133] Device:

[2134] The terminal transmits the information entered by the user to the server.

[2135] server:

[2136] The server stores the received information in a data cloud, generates a user profile, and generates a registration completion message based on the generated user profile and sends it to the terminal.

[2137] Question reception and analysis

[2138] User:

[2139] Users type specific parenting questions into the messaging app.

[2140] Device:

[2141] The input text data is sent to the server.

[2142] server:

[2143] The server analyzes the question using natural language processing techniques to extract important keywords and categories.

[2144] Information Acquisition and Answer Generation

[2145] server:

[2146] Based on the extracted keywords, relevant information is searched for in the database, and a generative AI model (e.g., OpenAI GPT-3.5) is used to generate appropriate answers based on the collected information.

[2147] Sentiment analysis and response adjustment

[2148] server:

[2149] Using emotion analysis technology, it analyzes the user's emotions and adjusts the content and tone of the response based on those emotions.

[2150] Providing answers

[2151] Device:

[2152] The generated answer is sent from the server to the device and displayed on the messaging app.

[2153] User:

[2154] The user can check the provided answers and use them to help with childcare.

[2155] Personalization and Consistency

[2156] server:

[2157] The system analyzes the user's life log and personal data to generate personalized advice tailored to the child's development. It also uses emotion analysis technology to continuously analyze emotions and provide appropriate support based on the user's emotions.

[2158] Feedback and Improvements

[2159] User:

[2160] Help improve the system by providing feedback on the answers provided in the messaging app.

[2161] Device:

[2162] Send feedback to the server.

[2163] server:

[2164] The server receives the feedback and stores it as data to improve the answers and accuracy of the system.

[2165] Specific examples

[2166] Example prompt sentence:

[2167] User Question: "My baby cries a lot at night and I don't know what to do."

[2168] Prompt for generative AI model: "You are an intelligent and caring assistant. Answer this question related to parenting: My baby often cries at night, and I don't know what to do."

[2169] Examples of sentiment analysis and tailored responses:

[2170] If a user's question is judged to be "anxious" using emotion analysis technology, an encouraging response such as "You're a great mom!" is added to the generated answer to provide the user with an encouraging response.

[2171] In this way, the childcare assistance system responds quickly and appropriately to user questions, provides reliable information, and also provides emotional support, thereby reducing mothers' anxieties about childcare and providing long-term, continuous support for childcare.

[2172] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2173] Step 1:

[2174] Users add the official account of the childcare assistant system as a friend through a messaging app and start chatting. Users enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire. This becomes the input data.

[2175] Input: User information (name, contact information, children's date of birth, gender, etc.)

[2176] Output: A user profile containing information for initial setup.

[2177] What happens: A user enters information into a messaging app, and the device sends that information to a server.

[2178] Step 2:

[2179] The server stores the received initial setting information in a data cloud and generates a user profile. The server generates a registration completion message based on this information and sends it to the terminal.

[2180] Input: Initial setting information

[2181] Output: User profile, registration completion message

[2182] Specific operations: The server stores the received information in the data cloud, creates a user profile, and then generates and sends a registration completion message to the device.

[2183] Step 3:

[2184] A user types a specific parenting question into a messaging app, which becomes a query for further processing.

[2185] Input: Questions about childcare (text data)

[2186] Output: Question data

[2187] Specific operation: The user enters a question, and the device sends it to the server.

[2188] Step 4:

[2189] The server receives the query data and analyzes it using natural language processing techniques, which extracts important keywords and categories.

[2190] Input: User question data

[2191] Output: Keywords and Categories

[2192] Specific operation: The server performs text analysis using natural language processing techniques (e.g., Python's NLTK or spaCy) to extract keywords and categories.

[2193] Step 5:

[2194] The server searches the database for related information based on the extracted keywords.

[2195] Input: Keywords and Categories

[2196] Output: Related information

[2197] What happens: The server queries the database to retrieve relevant childcare information.

[2198] Step 6:

[2199] The server uses a generative AI model (e.g., OpenAI GPT-3.5) to generate an answer based on the collected information. This answer is generated in text format and is used in further steps.

[2200] Input: Related Information

[2201] Output: The generated answer

[2202] Specific operation: The server calls the generative AI model, generates a prompt sentence based on relevant information, and inputs it into the model to generate an answer.

[2203] Step 7:

[2204] The server uses emotion analysis technology to analyze the user's emotions and adjusts the generated answers based on those emotions. For example, if the user is analyzed as "anxious," the server adds encouraging words to the answer.

[2205] Input: Generated answers, user sentiment analysis results

[2206] Output: Adjusted answer

[2207] What it does: The server uses emotion analysis technology (e.g., TextBlob) to analyze the user's emotional state and adjusts the generated answer accordingly.

[2208] Step 8:

[2209] The server then sends the adjusted response to the user's device via a communication means, and the user receives the response via a messaging app.

[2210] Input: Adjusted answer

[2211] Output: The answer displayed on the user's terminal

[2212] Specific operation: The server sends the tailored response to the user's device via the messaging app, where it is displayed on the device.

[2213] Step 9:

[2214] Users enter feedback on the answers they provide, which is used to improve the system.

[2215] Input: User feedback

[2216] Output: Feedback data

[2217] Specific operation: The user enters feedback, which the device sends to the server.

[2218] Step 10:

[2219] The server receives the feedback and analyzes it to improve the accuracy of the system, and if necessary, to improve the accuracy of the answers and the system as a whole.

[2220] Input: Feedback data

[2221] Output: Improved system

[2222] What it does: The server stores your feedback in a database and analyzes it to help improve, optimizing the system to provide better answers and responses next time.

[2223] In this way, the childcare assistant system responds quickly and appropriately to the user's questions and concerns, providing both reliable information and emotional care.

[2224] 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.

[2225] 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.

[2226] 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.

[2227] [Fourth embodiment]

[2228] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2229] 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.

[2230] 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).

[2231] 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.

[2232] 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.

[2233] 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).

[2234] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2235] 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.

[2236] 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.

[2237] 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.

[2238] 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.

[2239] 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.

[2240] 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."

[2241] The present invention is a childcare assistance system that allows mothers to easily obtain reliable information about childcare. This system accepts questions about childcare from users via communication means and provides prompt and appropriate answers to those questions. Specific embodiments of this system are described below.

[2242] Basic system configuration

[2243] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the Childcare Assistant System account as a friend on a messaging app such as LINE using their device.

[2244] User registration and initial settings

[2245] User:

[2246] First, users add the official account of the childcare assistant system as a friend through a messaging app such as LINE, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[2247] Device:

[2248] The terminal is responsible for transmitting the information entered by the user to the server.

[2249] server:

[2250] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the terminal.

[2251] Question reception and analysis

[2252] User:

[2253] Users can enter specific questions or concerns about childcare into LINE chat.

[2254] Device:

[2255] The entered text data is immediately sent to the server.

[2256] server:

[2257] The server analyzes the question using natural language processing technology and extracts important keywords and categories. For example, if a question is entered about "baby food for a 6-month-old baby," keywords such as "6 months," "baby," and "baby food" will be extracted.

[2258] Information Acquisition and Answer Generation

[2259] server:

[2260] The server searches for relevant information from a database based on the extracted keywords, retrieving relevant information from existing knowledge bases of childcare experts and the latest childcare trends, and then uses a generative AI model (e.g., GPT-3.5) to generate appropriate answers based on the collected information.

[2261] For example, an answer might be generated: "For a 6-month-old baby, it's a good idea to start with grain-based porridge, followed by vegetable purees. At this age, start with small amounts to allow for flavor and texture exploration."

[2262] Providing answers

[2263] Device:

[2264] The generated answer is sent from the server to the device and displayed on the LINE chat.

[2265] User:

[2266] The user can check the answers provided in the chat and use them to help with child-rearing.

[2267] Personalization and Consistency

[2268] server:

[2269] Based on the information provided by the user and the accumulated life log, the server generates personalized advice according to the child's growth and provides it periodically via LINE chat. For example, it can send specific advice such as, "Based on the recent changes in your baby's weight, consider when it's time to move on to the next step."

[2270] Feedback and Improvements

[2271] User:

[2272] Users can provide feedback on the answers provided via LINE chat to help improve the system.

[2273] Device:

[2274] Send feedback to the server.

[2275] server:

[2276] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of answers.

[2277] In this way, the childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, it can provide long-term childcare support by continuously providing personalized support to users.

[2278] The processing flow will be explained below.

[2279] Step 1:

[2280] User:

[2281] Add the official account of the Childcare Assistant System as a friend on the LINE app. Start a chat and enter your information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[2282] Step 2:

[2283] Device:

[2284] The information entered by the user is sent to the server.

[2285] Step 3:

[2286] server:

[2287] The received information is stored in the data cloud, a user profile is generated, and a registration completion message is generated and sent to the terminal.

[2288] Step 4:

[2289] Device:

[2290] A registration completion message from the server will be displayed on the LINE chat.

[2291] Step 5:

[2292] User:

[2293] Enter specific questions or concerns about childcare into LINE chat.

[2294] Step 6:

[2295] Device:

[2296] The input text data is sent to the server.

[2297] Step 7:

[2298] server:

[2299] The system receives the question text and uses natural language processing technology to analyze it, understand the intent of the question, and extract important keywords and categories.

[2300] Step 8:

[2301] server:

[2302] Based on the extracted keywords and categories, relevant information is searched for in databases and expert knowledge bases.

[2303] Step 9:

[2304] server:

[2305] Based on relevant information, a generative AI model is used to generate the optimal answer.

[2306] Step 10:

[2307] server:

[2308] The generated response is sent to the device.

[2309] Step 11:

[2310] Device:

[2311] The response sent from the server is displayed in the LINE chat.

[2312] Step 12:

[2313] User:

[2314] Check the answers provided on LINE chat and get the information you need.

[2315] Step 13:

[2316] User:

[2317] Enter your feedback on the provided answer (whether it was helpful, additional questions, etc.) in LINE chat.

[2318] Step 14:

[2319] Device:

[2320] Send user feedback to the server.

[2321] Step 15:

[2322] server:

[2323] The system receives feedback and stores it as data to improve the answers and accuracy of the system, and generates new answers to new questions as needed.

[2324] Step 16:

[2325] server:

[2326] The system analyzes the user's life log and personal data to generate personalized advice tailored to the child's development.

[2327] Step 17:

[2328] server:

[2329] Relevant childcare information and advice will be provided on a regular basis via LINE chat, providing ongoing support to users in their childcare.

[2330] Step 18:

[2331] User:

[2332] Review the personalized information and advice provided to help you with your parenting.

[2333] Example 1

[2334] 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."

[2335] Conventional childcare assistance systems have limited answers to users' questions about childcare, making it difficult to provide personalized advice that takes into account each user's individual circumstances. Furthermore, the accuracy of the system has not been sufficiently improved using user feedback, so there is a need for an improved user experience. This will reduce anxiety about childcare and provide more reliable information.

[2336] 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.

[2337] In this invention, the server includes: means for accepting childcare-related questions from users via communication means; means for analyzing the questions and extracting keywords and categories using natural language processing technology; means for searching for related information from an information storage device based on the extracted keywords or categories; means for generating answers based on the related information using a generative AI model; and means for providing the generated answers to users via the communication means. This enables prompt and accurate answers to users' questions and reliable childcare information. Furthermore, the system includes means for accepting information for initial setup, means for storing the information for initial setup in a data cloud to generate a user profile, and means for periodically providing personalized advice using the generated answers, thereby enabling detailed support tailored to individual situations. Furthermore, the system includes means for accepting user feedback on the generated answers and means for storing the feedback in a data cloud to improve the system's accuracy and the quality of the answers, enabling continuous system improvement.

[2338] "Communication means" refers to a communication path for transmitting input data from a user to a server and for providing output data from the server to the user.

[2339] "User" refers to a person who uses the childcare assistant system to ask questions about childcare and receive answers.

[2340] "Natural language processing technology" refers to technology that analyzes text data entered by a user and understands their meaning and intent.

[2341] "Keywords" are important words extracted from the content of a user's question, and are used to search for related information when generating an answer.

[2342] "Category" refers to a classification criterion for classifying questions and information, and is used to improve the efficiency of searching for related information.

[2343] "Information storage device" refers to a recording medium or database that stores related information and makes it searchable when needed.

[2344] A "generative AI model" refers to a program equipped with artificial intelligence technology that automatically generates sentences based on data.

[2345] A "user profile" is individual account information generated based on information provided by a user, and is used to provide personalized services to each individual user.

[2346] "Personalized advice" refers to advice that is optimized for an individual user based on the user profile and accumulated data.

[2347] "Feedback" refers to a user's input of evaluations and opinions regarding the results of using the system and the answers provided.

[2348] This invention relates to a childcare assistance system, and its purpose is to enable mothers and guardians to easily obtain reliable information about childcare. This system accepts questions about childcare from users via communication means and provides prompt and appropriate answers to those questions. Specific embodiments for carrying out the present invention are described below.

[2349] Basic system configuration

[2350] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend in a messaging app (e.g., messaging application) using their device.

[2351] User registration and initial settings

[2352] User:

[2353] Users add the official account of the childcare assistant system as a friend through a messaging app, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[2354] Device:

[2355] The terminal is responsible for transmitting the information entered by the user to the server.

[2356] server:

[2357] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the user's device.

[2358] Examples:

[2359] After adding a friend, the user enters the name "Yamada Taro" and the child's date of birth "April 1, 2022" into a form and sends it to the server.

[2360] Example prompt: "Enter my information. My name is Taro Yamada, and my child's birth date is April 1, 2022."

[2361] Question reception and analysis

[2362] User:

[2363] Users type specific questions or concerns about childcare into the messaging app chat, for example, "Can you tell me about solid food for my 6-month-old baby?"

[2364] Device:

[2365] The entered text data is immediately sent to the server.

[2366] server:

[2367] The server uses natural language processing technology (e.g., Natural Language Toolkit) to analyze the question and extract important keywords and categories. For example, if a question about "baby food for a 6-month-old baby" is entered, keywords such as "6 months," "baby," and "baby food" will be extracted.

[2368] Example prompt:

[2369] "Please tell me about baby food for a 6-month-old baby."

[2370] Information Acquisition and Answer Generation

[2371] server:

[2372] The server searches for relevant information from an information storage device based on the extracted keywords. It retrieves relevant information from a knowledge base of childcare experts and the latest childcare trends. It then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer based on the collected information. For example, it might generate an answer like, "For a 6-month-old baby, it's a good idea to start with grain-based porridge, followed by vegetable puree. At this age, start with a small amount to allow for enjoyment of the taste and texture."

[2373] Examples:

[2374] The server searches for information about "6 months," "baby," and "baby food," and the generative AI model generates the appropriate answer.

[2375] Example prompt: "For 6-month-old babies, it's a good idea to try grain-based porridge first, followed by vegetable purees."

[2376] Providing answers

[2377] server:

[2378] The generated answer is sent from the server to the user's terminal.

[2379] Device:

[2380] The device will display the received response in the chat on the messaging app.

[2381] User:

[2382] The user can check the answers provided in the chat and use them to help with child-rearing.

[2383] Examples:

[2384] The user's device displays the message, "For 6-month-old babies, try grain-based porridge first, followed by vegetable puree."

[2385] Personalization and Consistency

[2386] server:

[2387] The server generates personalized advice based on the information provided by the user and the accumulated life log, and delivers it periodically via the messaging app according to the child's growth. For example, it can provide specific advice such as, "Based on the recent changes in your baby's weight, consider when it's time to move on to the next step."

[2388] Examples:

[2389] Send specific advice based on your child's development.

[2390] Sample prompt: "Based on your baby's recent weight changes, consider when it's time to move forward with the next step."

[2391] Feedback and Improvements

[2392] User:

[2393] Users can provide feedback on the answers provided in the messaging app's chat to help improve the system.

[2394] Device:

[2395] The entered feedback is sent to the server.

[2396] server:

[2397] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of answers.

[2398] Examples:

[2399] The user enters their satisfaction with the answer, and the feedback is sent to and stored on the server.

[2400] Sample prompt: "I'm very pleased with the answer you provided. Thank you very much."

[2401] The above is a detailed description of a specific embodiment of the present invention. This enables users to receive prompt and appropriate answers to their questions and obtain reliable childcare information. Furthermore, personalized support tailored to individual situations and system improvements based on user feedback are realized.

[2402] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2403] System program processing flow

[2404] ---

[2405] Step 1: User registration and initial setup

[2406] Step 1-1: User accesses the system

[2407] User:

[2408] The user opens a messaging app on a device such as a smartphone and adds the official account of the childcare assistant system as a friend. This action allows the user to start accessing the system.

[2409] input:

[2410] The act of adding an official account as a friend through a messaging app.

[2411] output:

[2412] You will be able to access the official account of the childcare assistance system.

[2413] Specific behavior:

[2414] Tap the "Add Friends" button within the messaging app, then search for and add the official account.

[2415] ---

[2416] Step 1-2: Fill in the initial setup form

[2417] User:

[2418] After adding a friend, start chatting and enter your information (e.g., name, contact information, children's birth dates, gender) according to the initial setup questionnaire sent by the system.

[2419] Device:

[2420] The terminal transmits the information entered by the user to the server in real time.

[2421] input:

[2422] Enter the user's personal information and basic information about childcare.

[2423] output:

[2424] User information sent to the server.

[2425] Specific behavior:

[2426] The action of a user filling out a form in a chat window and pressing the "Send" button.

[2427] ---

[2428] Step 1-3: The server generates a user profile

[2429] server:

[2430] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the user's device.

[2431] input:

[2432] User information sent from the device.

[2433] output:

[2434] User profile and registration completion message stored in the data cloud.

[2435] Specific behavior:

[2436] The received data is stored in an appropriate location in the data cloud, a profile creation algorithm is run to generate a user profile, and a message such as "Registration complete" is sent to the user's device.

[2437] ---

[2438] Step 2: Question collection and analysis

[2439] Step 2-1: User enters a question

[2440] User:

[2441] Type specific questions or concerns about childcare into a messaging app chat, such as "Can you tell me about solid food for my 6-month-old baby?"

[2442] input:

[2443] Text with specific childcare questions.

[2444] output:

[2445] The question text sent to the server through a messaging app.

[2446] Specific behavior:

[2447] The action of a user typing a question into a chat window and pressing the "send" button.

[2448] ---

[2449] Step 2-2: Send the question text

[2450] Device:

[2451] The entered question text data is immediately sent to the server.

[2452] input:

[2453] The question text entered by the user into the messaging app.

[2454] output:

[2455] The question text sent to the server.

[2456] Specific behavior:

[2457] The text data received by the device is immediately sent to the specified API endpoint on the server.

[2458] ---

[2459] Step 2-3: Question Analysis

[2460] server:

[2461] The server uses natural language processing technology to analyze the question and extract important keywords and categories. If a question is entered about "baby food for a 6-month-old baby," keywords such as "6 months," "baby," and "baby food" will be extracted.

[2462] input:

[2463] The question text sent from the device.

[2464] output:

[2465] Extracted keyword and category information.

[2466] Specific behavior:

[2467] The server performs text analysis using natural language processing libraries (e.g., Natural Language Toolkit) to identify and extract important keywords and categories.

[2468] Example prompt:

[2469] "Please tell me about baby food for a 6-month-old baby."

[2470] ---

[2471] Step 3: Information Acquisition and Answer Generation

[2472] Step 3-1: Search for related information

[2473] server:

[2474] The server searches for relevant information from the information storage device based on the extracted keywords, and obtains relevant information from the knowledge base of childcare experts and the latest childcare trends.

[2475] input:

[2476] Extracted keyword and category information.

[2477] output:

[2478] Related information found.

[2479] Specific behavior:

[2480] Perform database queries to find and retrieve relevant information that matches your keywords.

[2481] ---

[2482] Step 3-2: Generate an answer

[2483] server:

[2484] Using this information, a generative AI model (e.g., GPT-3.5) is used to generate an appropriate answer, such as "For a 6-month-old baby, try grain-based porridge first, followed by vegetable puree."

[2485] input:

[2486] Related information found.

[2487] output:

[2488] Answer text obtained through a generative AI model.

[2489] Specific behavior:

[2490] Relevant information is provided as input to a generative AI model, which then generates and outputs natural answers.

[2491] Example prompt:

[2492] "For six-month-old babies, it's a good idea to start with cereal-based porridge, followed by vegetable purees."

[2493] ---

[2494] Step 4: Provide your answers

[2495] Step 4-1: Submit your response

[2496] server:

[2497] The generated answer is sent to the user's terminal.

[2498] input:

[2499] Answer text generated by a generative AI model.

[2500] output:

[2501] The answer text sent to the user's terminal.

[2502] Specific behavior:

[2503] The server sends the generated response to the user's device via the messaging app's API.

[2504] ---

[2505] Step 4-2: View the answer

[2506] Device:

[2507] The device will display the received response in the chat on the messaging app.

[2508] input:

[2509] The response text sent by the server.

[2510] output:

[2511] The reply text that will be displayed to the user in the chat screen.

[2512] Specific behavior:

[2513] The messaging app displays the received response in the chat window, allowing the user to easily check the response.

[2514] ---

[2515] Step 5: Personalization and Retention

[2516] Step 5-1: Providing regular advice

[2517] server:

[2518] Based on the information provided by the user and the accumulated life logs, the server generates personalized advice tailored to the child's development and provides it periodically via the messaging app.

[2519] input:

[2520] User information and lifelog data.

[2521] output:

[2522] Personalized advice.

[2523] Specific behavior:

[2524] The server periodically analyzes the user's profile and latest life log data, uses a generative AI model to generate the next piece of advice needed, and sends it via the messaging app.

[2525] Example prompt:

[2526] "Based on your baby's recent weight changes, consider when it's time to move on to the next step."

[2527] ---

[2528] Step 6: Feedback and Improvement

[2529] Step 6-1: Enter your feedback

[2530] User:

[2531] Users can provide feedback on the answers provided in the messaging app's chat to help improve the system.

[2532] input:

[2533] User feedback text.

[2534] output:

[2535] Feedback text sent to the server through the messaging app.

[2536] Specific behavior:

[2537] The action of a user typing feedback into a chat window and pressing the "Send" button.

[2538] ---

[2539] Step 6-2: Send feedback

[2540] Device:

[2541] The entered feedback is sent to the server.

[2542] input:

[2543] Feedback entered by users into messaging apps.

[2544] output:

[2545] The feedback text sent to the server.

[2546] Specific behavior:

[2547] The feedback data received by the device is immediately sent to the specified API endpoint on the server.

[2548] ---

[2549] Step 6-3: Accumulating and utilizing feedback

[2550] server:

[2551] The server stores this feedback in a data cloud and uses it to improve the accuracy of the system and the quality of its answers.

[2552] input:

[2553] Feedback sent from the device.

[2554] output:

[2555] Feedback data and updated analytical models stored in the data cloud.

[2556] Specific behavior:

[2557] The server stores the received feedback in a data cloud and improves the system by updating the analytical model.

[2558] Example prompt:

[2559] "I am very satisfied with the answer provided. Thank you very much."

[2560] The above is a specific flow of program processing for the system of the present invention, which allows users to quickly and appropriately obtain highly reliable childcare information, and the system is continually improved.

[2561] (Application example 1)

[2562] 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."

[2563] Parents raising children often have a wide range of questions and anxieties about childcare, but there are few ways to quickly obtain reliable information when they are out and about, especially while shopping, and the time it takes to obtain information is a problem. Furthermore, if there were a way to obtain answers through voice input or visually, it would be much more convenient for parents raising children.

[2564] 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.

[2565] In this invention, the server includes means for recognizing a user's voice using a voice input device, means for analyzing the question using natural language processing technology and extracting keywords and categories, and means for displaying the generated answer on a visual output device, thereby enabling users to quickly obtain reliable information about childcare even when they are out and about.

[2566] "Communication means" refers to means for sending and receiving data via the Internet or the like.

[2567] "Natural language processing technology" is a technology for analyzing text and voice input from users, understanding their meaning, and extracting keywords and categories.

[2568] "Keyword and category extraction means" refers to a means for automatically identifying important words and themes from user questions using natural language processing techniques.

[2569] A "database" is a structured collection of information that stores related information systematically and can be retrieved as needed.

[2570] A "generative AI model" is an artificial intelligence model used to generate optimal answers to user questions based on collected information.

[2571] A "voice input device" is a device that receives a user's voice as input and converts the content into text.

[2572] A "visual output device" is a device for visually presenting information to a user through smart glasses or the like.

[2573] "User profile" refers to data that compiles personal information and childcare information provided by the user.

[2574] "Feedback" refers to opinions or evaluations given by users regarding answers or services provided.

[2575] MODE FOR CARRYING OUT THE INVENTION

[2576] 1. System Overview

[2577] This invention is a childcare assistant system that allows parents to easily ask questions about childcare through voice input and quickly obtain reliable information. This system is realized by combining a wide range of technologies, including communication methods, natural language processing technology, generative AI models, voice input devices, visual output devices, and data clouds.

[2578] 2. Hardware and software used

[2579] Smart glasses: devices that take voice input and provide visual information (e.g., Google Glass, NReal).

[2580] Smartphone: A device for installing applications and working with smart glasses.

[2581] Server: A central control unit that processes data and generates and provides answers to users (e.g., AWS, Google Cloud).

[2582] Natural language processing engine: Software for analyzing user questions (e.g., Google Cloud Natural Language API).

[2583] Generative AI model: An AI model that generates optimal answers to questions (e.g., OpenAI GPT-3.5).

[2584] 3. Data processing and calculation

[2585] Voice input

[2586] Users can voice-input questions about childcare through the smart glasses, and this voice data is sent to a server via a smartphone.

[2587] Voice Recognition

[2588] The server receives the voice data and converts it into text using AWS Transcribe, which allows the voice-input questions to be processed as text information.

[2589] Natural Language Processing

[2590] The converted text data is then parsed using the Google Cloud Natural Language API to extract keywords and categories from the question, helping to accurately understand the question and prepare the search for the appropriate information.

[2591] Keyword search

[2592] Based on the extracted keywords and categories, relevant information is searched for in the database, which includes expert knowledge bases and the latest childcare trends.

[2593] Answer generation

[2594] Based on the searched information, the best answer is automatically generated using a generative AI model (OpenAI GPT-3.5) to provide information that meets the user's needs.

[2595] Visual and audio feedback

[2596] The generated answer is visually displayed on the smart glasses' display and also provided to the user audibly, using gTTS (Google Text-to-Speech) to convert the text data into speech and play it through the smart glasses.

[2597] 4. Examples of concrete examples and prompts

[2598] Specific examples

[2599] For example, consider a case where a user is in a store selling childcare products and has a question about "how to choose a baby bottle." They ask through their smart glasses, "How do I choose a baby bottle?" This question is sent to the server, converted into text, and analyzed using natural language processing technology. Relevant information is then searched for in the database, and the generative AI model generates the answer: "They come in glass and plastic. Glass bottles are heavy but durable, while plastic bottles are light and easy to carry." This information is finally displayed on the smart glasses' display and provided via voice.

[2600] Prompt Sentence Examples

[2601] How do I choose a baby bottle?

[2602] "There are glass and plastic baby bottles. I'd like to know the advantages and disadvantages of each."

[2603] As described above, the present invention enables users to receive real-time childcare support and provides users with quick and reliable information even when they are away from home.

[2604] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2605] Step 1:

[2606] The user wears the smart glasses and asks questions about childcare via a voice input device.

[2607] Input: User's voice data

[2608] Action: The user says, "How do I choose a baby bottle?"

[2609] Step 2:

[2610] The terminal receives the voice data and transmits it to a server via the Internet.

[2611] Input: User's voice data

[2612] Data processing: The audio data is sent to the AWS Transcribe service to prepare it for conversion into text.

[2613] Output: Audio data received by the server

[2614] Step 3:

[2615] The server uses AWS Transcribe to convert the audio data into text data.

[2616] Input: Audio data received by the server

[2617] Data processing: Using voice recognition technology, audio is converted into text.

[2618] Output: Converted text data

[2619] Step 4:

[2620] The server uses a natural language processing engine (Google Cloud Natural Language API) to analyze the text data and extract keywords and categories.

[2621] Input: Converted text data

[2622] Data calculation: Extract the keywords "baby bottle" and "how to choose" from the text.

[2623] Output: Extracted keywords and categories

[2624] Step 5:

[2625] The server searches for relevant information based on keywords and categories extracted from the database.

[2626] Input: Extracted keywords and categories

[2627] Data retrieval: Searching for relevant information in a database.

[2628] Output: Retrieved childcare information

[2629] Step 6:

[2630] The server uses a generative AI model (OpenAI GPT-3.5) to generate appropriate answers based on relevant information.

[2631] Input: Searched childcare information

[2632] Data computation: A generative AI model processes the information and generates a specific answer, such as "There are glass and plastic baby bottles."

[2633] Output: Generated answer text

[2634] Step 7:

[2635] The server uses gTTS (Google Text-to-Speech) to convert the generated response text into audio data.

[2636] Input: Generated answer text

[2637] Data processing: Convert text into an audio file.

[2638] Output: Generated answer audio data

[2639] Step 8:

[2640] The server transmits the generated answer text and voice data to the terminal.

[2641] Input: Generated answer text and audio data

[2642] Operation: Sending data from the server to the device

[2643] Output: Terminal received data

[2644] Step 9:

[2645] The terminal displays the received answer text on the visual output device of the smart glasses and plays the audio data.

[2646] Input: Terminal received data

[2647] How it works: The smart glasses display will say "There are glass and plastic baby bottles" and simultaneously play back the answer aloud.

[2648] Output: The user gets a visual and audio response.

[2649] Through the above processing steps, the present invention serves as a childcare assistant system that quickly and effectively solves questions about childcare through voice input.

[2650] 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.

[2651] This invention is a childcare assistant system that allows mothers to easily obtain reliable information about childcare. The system incorporates communication methods, natural language processing technology, generative AI models, and an emotion engine. Because the system includes user emotion analysis, it not only improves the quality of childcare but also supports mothers' emotional care.

[2652] Basic system configuration

[2653] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend on a messaging app such as LINE using their device.

[2654] User registration and initial settings

[2655] User:

[2656] First, users add the official account of the childcare assistant system as a friend through a messaging app such as LINE, then start chatting and enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[2657] Device:

[2658] The terminal is responsible for transmitting the information entered by the user to the server.

[2659] server:

[2660] The server stores the received information in a data cloud, creates a user profile, and generates a registration completion message and sends it to the terminal.

[2661] Question reception and analysis

[2662] User:

[2663] Users can enter specific questions or concerns about childcare into LINE chat.

[2664] Device:

[2665] The entered text data is immediately sent to the server.

[2666] server:

[2667] The server analyzes the question using natural language processing techniques to extract important keywords and categories.

[2668] Information Acquisition and Answer Generation

[2669] server:

[2670] The system searches for relevant information from a database based on the extracted keywords. It then uses a generative AI model (e.g., GPT-3.5) to generate appropriate answers based on the collected information. It also uses an emotion engine to analyze the user's emotions and adjust the content and tone of the answer based on those emotions. For example, if the user is "anxious," it adds encouraging words to ease their anxiety.

[2671] Providing answers

[2672] Device:

[2673] The generated answer is sent from the server to the device and displayed on the LINE chat.

[2674] User:

[2675] The user can check the answers provided in the chat and use them to help with child-rearing.

[2676] Personalization and Consistency

[2677] server:

[2678] The server analyzes the user's life log and personal data to generate personalized advice based on the child's development. It also performs continuous emotion analysis using an emotion engine to provide appropriate support based on the user's emotions.

[2679] Feedback and Improvements

[2680] User:

[2681] Users can provide feedback on the answers provided via LINE chat to help improve the system.

[2682] Device:

[2683] Send feedback to the server.

[2684] server:

[2685] The server receives feedback and stores it as data to improve the answers and accuracy of the system, and also generates new answers to new questions as needed.

[2686] In this way, this childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, the system also provides psychological support by analyzing the user's emotions and providing support accordingly. This makes it possible to provide long-term, continuous childcare support.

[2687] The processing flow will be explained below.

[2688] Step 1:

[2689] User:

[2690] Add the official account of the Childcare Assistant System as a friend on the LINE app. Start a chat and enter your basic information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[2691] Step 2:

[2692] Device:

[2693] The information entered by the user is sent to the server.

[2694] Step 3:

[2695] server:

[2696] The received information is stored in a data cloud and a user profile is generated. After the profile is generated, a registration completion message is generated and sent to the terminal.

[2697] Step 4:

[2698] Device:

[2699] A registration completion message from the server will be displayed on the LINE chat.

[2700] Step 5:

[2701] User:

[2702] Enter specific questions or concerns about childcare in the LINE chat. For example, "What kind of baby food does a 6-month-old baby need?"

[2703] Step 6:

[2704] Device:

[2705] The input text data is sent to the server.

[2706] Step 7:

[2707] server:

[2708] The received question text is analyzed using natural language processing technology to extract important keywords and categories, such as "6 months," "baby," and "baby food."

[2709] Step 8:

[2710] server:

[2711] The system searches for relevant information from a database based on the extracted keywords and categories, while simultaneously analyzing emotions (e.g., anxiety, doubt, joy) from the user's text using an emotion engine.

[2712] Step 9:

[2713] server:

[2714] Based on the results of the emotion engine, a generative AI model is used to generate an appropriate response. For example, if the user expresses anxiety, the system generates a response in a reassuring tone, such as, "First of all, don't worry. Porridge is suitable for a 6-month-old baby."

[2715] Step 10:

[2716] server:

[2717] The generated response is sent to the device.

[2718] Step 11:

[2719] Device:

[2720] The response sent from the server is displayed in the LINE chat.

[2721] Step 12:

[2722] User:

[2723] Check the answers provided via LINE chat and use them to help with childcare. For example, make porridge based on the advice on baby food.

[2724] Step 13:

[2725] User:

[2726] Enter your feedback on the provided answer (whether it was helpful, additional questions, etc.) in LINE chat.

[2727] Step 14:

[2728] Device:

[2729] Send user feedback to the server.

[2730] Step 15:

[2731] server:

[2732] The system receives feedback and stores it as data to improve the answers and accuracy of the system, and generates new answers to new questions as needed.

[2733] Step 16:

[2734] server:

[2735] The system analyzes the user's life log and personal data to generate personalized advice based on the child's growth. For example, it generates specific advice such as, "Based on recent weight changes, it's time to move on to the next step."

[2736] Step 17:

[2737] server:

[2738] Relevant childcare information and advice will be provided on a regular basis via LINE chat, providing ongoing support to users in their childcare.

[2739] Step 18:

[2740] User:

[2741] Review the personalized information and advice provided to help you with your parenting.

[2742] Example 2

[2743] 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."

[2744] Conventional childcare assistance systems are not always able to respond quickly to user questions and are sometimes unable to provide reliable information. Furthermore, they lack emotional support for mothers, resulting in insufficient psychological support. This creates challenges for improving the quality of childcare and for providing adequate psychological care for mothers.

[2745] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a question about child-rearing from a user via communication means, means for analyzing the question and extracting keywords and categories using natural language processing technology, means for searching a database for related information based on the extracted keywords or categories, means for generating an answer based on the related information using a generative AI model, means for analyzing the user's emotions using emotion analysis technology and adjusting the content of the answer, and means for providing the generated answer to the user via the communication means. This makes it possible to quickly and appropriately respond to a question from a user, provide reliable child-rearing information, and analyze the user's emotions to provide appropriate support.

[2746] "Communication means" refers to the infrastructure for sending and receiving data from users, including the Internet, Wi-Fi, mobile communication networks, etc.

[2747] "Natural language processing technology" refers to the technology of analyzing, understanding, and generating natural language used by humans using a computer, and includes morphological analysis, keyword extraction, and sentence classification.

[2748] A "generative AI model" refers to an artificial intelligence model that generates natural language based on large amounts of data, and includes, for example, GPT-3.5.

[2749] "Emotion analysis technology" refers to technology that identifies a user's emotions from text data and adjusts responses based on those emotions, and includes emotion analysis engines and tone analyzers.

[2750] A "data cloud" refers to a distributed storage system for storing and managing data over the Internet, and includes cloud storage services.

[2751] A "user profile" refers to a collection of information constructed based on data such as a user's personal information and behavioral history, and includes name, contact information, childcare history, etc.

[2752] "Feedback" refers to the evaluations and opinions that users give about the answers and services provided, and serves as data that can be used to improve the system.

[2753] This invention relates to a childcare assistant system that provides users with tools to easily and quickly resolve questions about childcare. The system includes a communication means, natural language processing technology, a generative AI model, emotion analysis technology, a data cloud, and a user profile management function.

[2754] Basic system configuration

[2755] This system consists of a user's device (e.g., smartphone), a means of communication (e.g., the Internet), and a server. Users access the system by adding the childcare assistant system account as a friend in a messaging app on their smartphone.

[2756] User registration and initial settings

[2757] User:

[2758] First, users add the official account of the childcare assistant system as a friend in a messaging app and start chatting, then follow the system's instructions to enter information such as the child's name, contact details, date of birth, and gender into the initial setup questionnaire.

[2759] Device:

[2760] The terminal generates a data packet for transmitting the information entered by the user to the server.

[2761] server:

[2762] The server analyzes the received information, stores it in the data cloud, creates a user profile, and sends a registration completion message to the terminal.

[2763] Question reception and analysis

[2764] User:

[2765] Users type parenting questions into the messaging app.

[2766] Device:

[2767] The entered text data is sent to a server via the Internet.

[2768] server:

[2769] The server analyzes the text of the received question using natural language processing technology, such as SpaCy or NLTK, to extract important keywords.

[2770] Information Acquisition and Answer Generation

[2771] server:

[2772] The server uses the extracted keywords to search for relevant information from a database, then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer, and furthermore, uses sentiment analysis technology (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions and adjust the tone of the answer.

[2773] Examples of prompts:

[2774] If a user submits a question like, "My baby won't stop crying in the middle of the night, what should I do?"

[2775] In that case the prompt would look like this:

[2776] User: "My baby won't stop crying in the middle of the night. What should I do?" Gently explain why babies cry at night and what you can do about it. Also, add some reassurance, as the mother may be worried.

[2777] Providing answers

[2778] server:

[2779] The generated response is sent to the device.

[2780] Device:

[2781] The response received from the server is displayed on the messaging app.

[2782] User:

[2783] The user can check the provided answers and use them to help with childcare.

[2784] Personalization and Consistency

[2785] server:

[2786] The server analyzes the user's life log and personal data to generate personalized advice tailored to the child's development, and also performs continuous emotion analysis using emotion analysis technology.

[2787] Feedback and Improvements

[2788] User:

[2789] Users provide feedback on the answers provided in the messaging app.

[2790] Device:

[2791] Converts data into packets for sending feedback to the server.

[2792] server:

[2793] The server receives the feedback, stores it as data to improve the system, and generates new answers as needed and sends them back to the user.

[2794] In this way, this childcare assistance system can quickly and appropriately respond to user questions and provide reliable childcare information, thereby reducing mothers' anxiety about childcare. At the same time, the system also provides psychological support by analyzing the user's emotions and providing support accordingly. This makes it possible to provide long-term, continuous childcare support.

[2795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2796] Step 1: User registration and initial setup

[2797] User: The user adds the official account of the childcare assistant system as a friend on a messaging app such as LINE and starts chatting. The system then sends the user an initial setup questionnaire, in which the user enters personal information such as their name, contact details, and the child's date of birth and gender.

[2798] Input: Initial setup information (e.g. name, contact information, child's date of birth, gender)

[2799] Output: The data the user entered into the form

[2800] Terminal: The terminal generates a data packet for transmitting the information input by the user to the server. The generated data packet is transmitted to the server via a communication means.

[2801] Input: Data entered by the user

[2802] Output: Data packet sent to the server

[2803] Server: The server analyzes the received information, stores it in the data cloud, creates a user profile, and sends a registration completion message to the device.

[2804] Input: Data packets sent from the device

[2805] Output: User information stored in the data cloud, registration completion message

[2806] Step 2: Question collection and analysis

[2807] User: A user types a parenting question into a messaging app.

[2808] Input: User question text (e.g., "My baby won't stop crying in the middle of the night. What should I do?")

[2809] Output: Submitted question text data

[2810] Terminal: The entered question text data is sent to the server via the Internet.

[2811] Input: Question text data submitted by the user

[2812] Output: Text data sent to the server

[2813] Server: The server uses natural language processing technology to analyze the text of the received question and extract important keywords. For example, SpaCy or NLTK can be used to extract keywords such as "baby," "midnight," and "won't stop crying."

[2814] Input: Question text data sent from the terminal

[2815] Output: Extracted keywords (e.g., "baby," "midnight," "can't stop crying")

[2816] Step 3: Information Acquisition and Answer Generation

[2817] server:

[2818] The server uses the extracted keywords to search for relevant information in a database. For example, it runs a database query based on the keywords "baby," "midnight," and "won't stop crying." It then uses a generative AI model (e.g., GPT-3.5) to generate an appropriate answer. For example, it sends the following prompt to the generative AI:

[2819] User: "My baby won't stop crying in the middle of the night. What should I do?" Gently explain why babies cry at night and what you can do about it. Also, add some reassurance, as the mother may be worried.

[2820] Using emotion analysis technology, it analyzes the user's emotions and adds reassuring phrases such as, "Don't worry. It's normal for babies to cry in the middle of the night."

[2821] Input: Extracted keywords, prompt sentence for generative AI model

[2822] Output: Generated answer text, final answer text with tone adjusted by sentiment analysis

[2823] Step 4: Provide your answers

[2824] Server: Sends the generated answer to the device.

[2825] Input: Answer text generated by the generative AI model, sentiment analysis results

[2826] Output: Message from the server to the terminal

[2827] On your device: The response received from the server is displayed on the messaging app.

[2828] Input: Answer text received from the server

[2829] Output: The response displayed in the user's messaging app

[2830] User: The user checks the displayed answers and uses them to help with parenting.

[2831] Input: Displayed answer text

[2832] Output: Childcare information actually obtained by the user

[2833] Step 5: Personalization and Retention

[2834] Server: The server continuously analyzes the user's life log and personal data to generate personalized advice, such as advice related to specific events or appointment reminders. It also continuously analyzes the user's emotions using emotion analysis technology and takes the results into account to respond appropriately.

[2835] Input: User's life log, personal data, past emotion analysis results

[2836] Output: personalized advice, continuous emotional response

[2837] Step 6: Feedback and Improvement

[2838] User: The user provides feedback on the provided answer in the messaging app.

[2839] Input: Feedback text

[2840] Output: Feedback data sent to the server

[2841] Terminal: Packetizes data to send feedback to the server.

[2842] Input: Feedback text

[2843] Output: Data packet sent to the server

[2844] Server: The server receives the feedback, stores it as data to improve the system, and generates new answers if necessary and sends them back to the user.

[2845] Input: User feedback data

[2846] Output: Improved system accuracy, newly generated answers

[2847] (Application example 2)

[2848] 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."

[2849] In modern child-rearing, mothers need to obtain parenting advice from a variety of sources, but there is a lack of a reliable, centralized source of information. Furthermore, when mothers experience stress or anxiety associated with child-rearing, there is a lack of support to help them cope with these emotions. Furthermore, there is a demand for personalized information tailored to the circumstances of each parent and child, but this is difficult to achieve with conventional systems.

[2850] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting a question about child-rearing from a user via communication means, means for analyzing the question and extracting keywords and categories using natural language processing technology, means for searching a database for related information based on the extracted keywords or categories, means for generating an answer based on the related information using a generative AI model, means for analyzing the user's emotions using emotion analysis technology, means for adjusting the generated answer based on the results of the emotion analysis, and means for providing the adjusted answer to the user via the communication means. This makes it possible to easily obtain reliable information about child-rearing and reduce child-rearing stress by providing care that is tailored to the user's emotional state.

[2851] The "childcare assistant system" is a system that allows mothers to easily obtain reliable information about childcare, and is composed of a combination of communication methods, natural language processing technology, generative AI models, emotion analysis technology, and more.

[2852] "Communication means" refers to technology for receiving questions and information from users via the Internet or the like, and for sending generated answers to users.

[2853] "Natural language processing technology" is a technology for analyzing questions from users and extracting keywords and categories from the sentences.

[2854] "Keywords and categories" are important words and concepts extracted from users' questions using natural language processing techniques.

[2855] A "database" is an information source that stores information related to childcare, and is used to search for related information based on extracted keywords and categories.

[2856] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on extracted relevant information.

[2857] "Emotion analysis technology" is a technology that analyzes emotions from users' questions and feedback and adjusts the content of responses based on those emotions.

[2858] A "user profile" refers to individual information about a user that is generated based on information input by the user for initial settings.

[2859] "Adjusted answers" are answers generated by a generative AI model whose content and tone are changed according to the user's emotions using emotion analysis technology.

[2860] "Feedback" refers to ratings and comments that users make on answers provided.

[2861] This invention relates to a childcare assistant system that allows mothers to easily obtain reliable information about childcare. This system is composed of a combination of communication means, natural language processing technology, generative AI models, and emotion analysis technology.

[2862] Basic Building Blocks

[2863] System Overview:

[2864] This system consists of a user's device (e.g., a smartphone), a communication method (e.g., the Internet), and a server. Users can access the system via a messaging app.

[2865] User registration and initial settings

[2866] User:

[2867] Users add the official account of the childcare assistant system as a friend through a messaging app and start chatting. They then enter their own information (name, contact details, child's date of birth, gender, etc.) according to the initial setup questionnaire.

[2868] Device:

[2869] The terminal transmits the information entered by the user to the server.

[2870] server:

[2871] The server stores the received information in a data cloud, generates a user profile, and generates a registration completion message based on the generated user profile and sends it to the terminal.

[2872] Question reception and analysis

[2873] User:

[2874] Users type specific parenting questions into the messaging app.

[2875] Device:

[2876] The input text data is sent to the server.

[2877] server:

[2878] The server analyzes the question using natural language processing techniques to extract important keywords and categories.

[2879] Information Acquisition and Answer Generation

[2880] server:

[2881] Based on the extracted keywords, relevant information is searched for in the database, and a generative AI model (e.g., OpenAI GPT-3.5) is used to generate appropriate answers based on the collected information.

[2882] Sentiment analysis and response adjustment

[2883] server:

[2884] Using emotion analysis technology, it analyzes the user's emotions and adjusts the content and tone of the response based on those emotions.

[2885] Providing answers

[2886] Device:

[2887] The generated answer is sent from the server to the device and displayed on the messaging app.

[2888] User:

[2889] The user can check the provided answers and use them to help with childcare.

[2890] Personalization and Consistency

[2891] server:

[2892] The system analyzes the user's life log and personal data to generate personalized advice tailored to the child's development. It also uses emotion analysis technology to continuously analyze emotions and provide appropriate support based on the user's emotions.

[2893] Feedback and Improvements

[2894] User:

[2895] Help improve the system by providing feedback on the answers provided in the messaging app.

[2896] Device:

[2897] Send feedback to the server.

[2898] server:

[2899] The server receives the feedback and stores it as data to improve the answers and accuracy of the system.

[2900] Specific examples

[2901] Example prompt sentence:

[2902] User Question: "My baby cries a lot at night and I don't know what to do."

[2903] Prompt for generative AI model: "You are an intelligent and caring assistant. Answer this question related to parenting: My baby often cries at night, and I don't know what to do."

[2904] Examples of sentiment analysis and tailored responses:

[2905] If a user's question is judged to be "anxious" using emotion analysis technology, an encouraging response such as "You're a great mom!" is added to the generated answer to provide the user with an encouraging response.

[2906] In this way, the childcare assistance system responds quickly and appropriately to user questions, provides reliable information, and also provides emotional support, thereby reducing mothers' anxieties about childcare and providing long-term, continuous support for childcare.

[2907] The flow of the specific processing in the application example 2 w...

Claims

1. A childcare assistance system, a means for receiving questions about childcare from a user via a communication means; means for analyzing the question using natural language processing techniques and extracting keywords and categories; a means for searching for relevant information from a database based on the extracted keywords or categories; A means for generating an answer based on the related information using a generative AI model; means for providing the generated answer to a user via the communication means; A system including:

2. means for receiving information for initial setting from the user; means for storing the information for initial setup in a data cloud and generating a user profile; The system of claim 1 further comprising:

3. means for receiving feedback from a user regarding the generated answer; means for improving the accuracy of the system based on the feedback; The system of claim 1 further comprising:

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