System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting basic information of a child by a parent; generative artificial intelligence means for generating individual child-care advice based on a character or preference of the child; means for storing the generated child-care advice; means for acquiring the child-care advice by the parent; means for collecting feedback from the parent; and means for improving the generative artificial intelligence means based on the collected feedback.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] When raising children, parents often seek appropriate child-rearing advice based on the personality and preferences of each child. Conventional child-rearing information providers only provide general information, making it difficult to receive advice tailored to each child. Furthermore, they lack a mechanism for improving services based on feedback from parents. This can increase parents' anxiety and burden regarding child-rearing, potentially resulting in a decline in the quality of child-rearing. The present invention aims to solve these problems and support parents in raising their children with greater confidence. [Means for solving the problem]

[0005] The present invention solves various parenting problems faced by parents through a system including the following means: a means for parents to input basic information about their child; a generative artificial intelligence means for generating individualized parenting advice based on the child's personality and preferences; a means for saving the generated parenting advice; a means for parents to obtain the parenting advice; a means for collecting feedback from parents; and a means for improving the generative artificial intelligence means based on the collected feedback. This allows parents to receive advice that is optimal for each individual child, thereby reducing parental anxiety and burden and improving the quality of parenting.

[0006] "Parents" are those who raise children, generally referring to fathers and mothers.

[0007] "Children" are beings who are raised by their parents and include a wide range of ages from infants to adolescents.

[0008] "Basic information" refers to basic data about the child, such as age, gender, favorite foods, and special skills.

[0009] "Generative AI" is an AI technology that automatically generates sentences and recommended content based on input data.

[0010] "Parenting advice" refers to specific parenting instructions or recommendations based on a child's personality and preferences.

[0011] "Storage" refers to the act of retaining the generated parenting advice and collected feedback as data.

[0012] "Feedback" refers to parents' opinions and evaluations of the parenting advice they were given.

[0013] A "system" refers to a combination of multiple means or devices configured to achieve a specific function. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The system of the present invention aims to allow parents to input basic information about their children, generate personalized parenting advice based on the child's personality and preferences, and continuously improve the system based on feedback. The system consists of a server, terminals, and users.

[0036] Enter your child's basic information

[0037] User

[0038] Parents enter basic information about their children, such as their age, gender, favorite foods, and special skills, through a mobile or web app.

[0039] Submission of Children's Information

[0040] Terminal

[0041] The entered child information is converted into JSON format and sent to the server. The data entered by the parent on the device is first converted into JSON format and sent to the server via an HTTP request.

[0042] Receiving and storing children's information

[0043] server

[0044] The received child information is saved in the database. The server connects to the database and saves the received child's basic information in each field appropriately.

[0045] Generating parenting advice

[0046] server

[0047] Generative AI (e.g., GPT-2 model) is used to generate parenting advice based on information about the child. Information about the child (such as age, gender, favorite foods, and special skills) is fed into the AI ​​model to generate individually customized advice.

[0048] Saving Parenting Advice

[0049] server

[0050] Save the generated parenting advice in a database. Save the generated text in a specific field in the database for future reference.

[0051] Obtaining and viewing parenting advice

[0052] User

[0053] The parent retrieves and displays the parenting advice. The parent again uses the device to send a request to retrieve the advice from the server.

[0054] Terminal

[0055] The fetched parenting advice is displayed in a user interface in a format that is easy for parents to understand.

[0056] Enter and submit feedback

[0057] User

[0058] Parents can input and submit feedback on the child-rearing advice. Parents who receive the advice can also input feedback on the content, such as an evaluation and areas for improvement.

[0059] Terminal

[0060] The feedback data is converted to JSON format and sent to the server. The input feedback is converted back to JSON format so that it can be securely sent to the server.

[0061] Receiving and storing feedback

[0062] server

[0063] The feedback received is stored in a database to help improve the generative artificial intelligence model. The feedback is stored in a database and later used as material for retraining and improving the AI ​​model.

[0064] Specific examples

[0065] Example 1

[0066] A parent enters information about a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing). This information is sent by the device to a server and stored in a database.

[0067] The server uses a generative artificial intelligence model based on the stored information to generate individualized parenting advice, such as, "She likes drawing, so if she cries, draw a picture with her to calm her down."

[0068] Parents receive this advice through their devices, and if they are satisfied with the displayed information, they can enter feedback such as "It was very helpful." This feedback is then sent back to the server and stored in a database.

[0069] The server analyzes the collected feedback and uses it to improve the performance of the generative artificial intelligence model.

[0070] As described above, the system of the present invention is an embodiment that can provide individual child-rearing advice specific to parents and children and can achieve continuous improvement.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The user enters basic information about the child (age, gender, favorite food, special skills, etc.) through the mobile app or web app, enters the information in the input form provided in the user interface, and clicks the submit button.

[0074] Step 2:

[0075] The device converts the entered child information into JSON format and sends it to the server. Specifically, it sends the JSON data as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[0076] Step 3:

[0077] The server processes the incoming POST request, parses the JSON data, connects to the database, saves the parsed information to the "children" table in the database, and returns the newly created child's ID as a response.

[0078] Step 4:

[0079] The server generates parenting advice using generative AI based on the child's basic information stored in the database. The generative AI receives input such as the child's age, gender, favorite foods, and special skills, and generates appropriate parenting advice text.

[0080] Step 5:

[0081] The server stores the generated parenting advice in the "children" table of the database, specifically by associating the advice text with the corresponding child's ID.

[0082] Step 6:

[0083] The user sends a request to the server through the application to get parenting advice for a child. This request is sent using the GET method.

[0084] Step 7:

[0085] The device displays the parenting advice received from the server on the user interface. Specifically, the device extracts the parenting advice from the JSON data returned from the server and displays it in a format that is easy for parents to understand.

[0086] Step 8:

[0087] The user inputs feedback on the parenting advice, which is then converted back to JSON format by the device.

[0088] Step 9:

[0089] The device sends the feedback data to the server by using an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[0090] Step 10:

[0091] The server stores the received feedback in a database, storing the feedback along with the child's ID in a "feedback" table in the database.

[0092] Step 11:

[0093] The server uses the collected feedback to improve the generative artificial intelligence model. This improvement process includes analyzing the feedback data and retraining the model as needed. The feedback is then incorporated into the next advice generation process.

[0094] Example 1

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

[0096] Today's parents often find it difficult to find individualized parenting methods that suit each child's personality and preferences. Furthermore, existing parenting advice is often general and may not be applicable to each family's specific situation. Therefore, there is a need for a system that allows parents to quickly and effectively obtain appropriate parenting advice.

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

[0098] In this invention, the server includes: means for a parent to input basic information about their child using a mobile app or web app; means for the device to convert the input information about the child into JSON format and send it to the server; means for the server to save the received information about the child in a database; means for the server to generate personalized parenting advice based on the information about the child using generative artificial intelligence; means for saving the generated parenting advice in a database; means for the parent to obtain the parenting advice; means for the device to display the fetched parenting advice; means for the parent to input and send feedback on the generated parenting advice; means for the device to convert the feedback data into JSON format and send it to the server; and means for the server to save the received feedback in a database to help improve the model of the generative artificial intelligence. This enables parents to quickly obtain appropriate parenting advice tailored to the individual characteristics of their child.

[0099] "Parents" are those who enter basic information about their children, receive parenting advice, and provide feedback.

[0100] A "mobile or web app" is a software application that runs on a smartphone or computer and allows parents to enter basic information about their children.

[0101] "Terminal" refers to a device or equipment that converts the information entered by the parent into JSON format and sends it to the server.

[0102] "JSON format" is a lightweight data description format widely used for data exchange, and is an abbreviation for JavaScript Object Notation.

[0103] An "HTTP request" is a form of communication protocol used by a client to send data to a server.

[0104] "Server" refers to a computer system that receives and stores data sent by users and generates child-rearing advice using generative artificial intelligence.

[0105] The "database" is a data management system for systematically storing basic information about children, generated parenting advice, feedback, and so on.

[0106] "Generative AI" refers to AI techniques that generate new information or suggestions based on data, and examples include the GPT-2 model.

[0107] "Parenting advice" refers to specific parenting advice and suggestions generated by generative artificial intelligence based on basic information about a child.

[0108] "Feedback" refers to opinions such as evaluations and improvements that users provide regarding the generated parenting advice.

[0109] "Fetch" refers to the operation of retrieving and obtaining data or information from a server.

[0110] "User interface" refers to the screen and operation method displayed on the terminal, and is the interface through which parents input information and check advice.

[0111] The system of the present invention consists of three main elements: a server, a terminal, and a user. The system as a whole is designed to smoothly carry out a series of processes: parents input basic information about their children, receive parenting advice generated based on that information, and provide feedback.

[0112] Enter your child's basic information

[0113] User

[0114] Parents use a mobile or web app to enter basic information about their child, such as their age, gender, favorite foods, and special skills. Specifically, parents fill out the required information in the application form and press the submit button. This information is stored on the device in JSON format.

[0115] Submission of Children's Information

[0116] Terminal

[0117] The device converts the child information entered by the user into JSON format and sends it to the server via an HTTP request. Specifically, the device makes a POST request to the backend endpoint (e.g., http: / / example.com / api / childinfo).

[0118] Receiving and storing children's information

[0119] server

[0120] The server receives the JSON data sent via the HTTP request, validates it, and then saves it to a database. The server connects to a database (e.g. MySQL) and stores the information in tables in the database, with the appropriate fields.

[0121] Generating parenting advice

[0122] server

[0123] The server retrieves basic information about the child from the database and supplies this to a generative artificial intelligence (e.g., GPT-2 model) to generate individualized parenting advice. Specifically, the server calls the AI ​​model's API and supplies a prompt to generate specific advice. An example of a prompt is, "Please tell me some parenting advice for a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing)."

[0124] Saving Parenting Advice

[0125] server

[0126] The server saves the generated parenting advice back into the database, and the generated text data is stored in a field of an appropriate table so that the user can refer to it later.

[0127] Obtaining and viewing parenting advice

[0128] User

[0129] The parent then uses the device to send a request for parenting advice and view the advice. The request is initiated by pressing the "Get Advice" button in the app.

[0130] Terminal

[0131] The device displays the parenting advice received from the server on a user interface. The device also receives the advice text returned from the server and displays it in a text view within the app.

[0132] Enter and submit feedback

[0133] User

[0134] Parents can provide feedback on the displayed parenting advice within the app by entering their ratings and comments in the app's feedback form and pressing the submit button.

[0135] Terminal

[0136] The device converts the feedback data into JSON format and sends it to the server via an HTTP request. The device also sends the feedback data as a POST request to the server's feedback endpoint.

[0137] Receiving and storing feedback

[0138] server

[0139] The server receives the feedback, validates it, and then stores it in a database. The stored feedback is used to improve the generative artificial intelligence model and is used as material for retraining and improving the AI ​​model later.

[0140] Through the above process, the system is able to provide individualized parenting advice tailored to parents and children, and to continuously improve its accuracy.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] User

[0144] Parents use a mobile or web app to enter basic information about their child (age, gender, favorite foods, special skills, etc.). Specifically, they fill in the required information in the input form within the app and press the submit button. The input data is in the form of text, options, numbers, etc., and as the parent enters the information, individual basic information about the child is accumulated. The input is received from the application front end, converted into JSON format, and prepared for sending to the next process.

[0145] Input: Child's basic information (age, gender, favorite food, special skills, etc.)

[0146] Output: Child basic information in JSON format

[0147] Step 2:

[0148] Terminal

[0149] The device converts the child information entered by the user into JSON format and sends this data to the server via an HTTP request. Specifically, the data in the input form is converted into structured data (JSON format) by a program such as JavaScript. The device then makes a POST request to the backend endpoint (e.g., http: / / example.com / api / childinfo).

[0150] Input: Child basic information in JSON format

[0151] Output: HTTP POST request

[0152] Step 3:

[0153] server

[0154] The server receives the JSON data sent via the HTTP request. This data is validated and saved to the database. Specifically, the server parses the received JSON data, checks for invalid data, and stores it in the appropriate table in the database (e.g. MySQL). Validation includes checking for required fields and validating the data format.

[0155] Input: HTTP POST request (child's basic information in JSON format)

[0156] Output: Basic information about the child stored in the database

[0157] Step 4:

[0158] server

[0159] The server retrieves basic information about the child from the database and supplies it to a generative artificial intelligence (e.g., GPT-2 model) to generate individualized parenting advice. Specifically, the server uses the child's basic information retrieved from the database as part of a prompt sentence and calls the AI ​​model's API to generate advice. An example prompt sentence is, "Please tell me some parenting advice for a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing)."

[0160] Input: Basic information about the child retrieved from the database

[0161] Output: Generated parenting advice

[0162] Step 5:

[0163] server

[0164] The server stores the generated child-rearing advice in the database again, specifically, stores the generated text data in a field of an appropriate table so that the user can refer to it later.

[0165] Input: Generated parenting advice

[0166] Output: Parenting advice stored in a database

[0167] Step 6:

[0168] User

[0169] The parent again uses the device to send a request for parenting advice by pressing the "Get Advice" button in the app. The user's request is sent to the server via the device and processed there.

[0170] Input: Parenting advice request

[0171] Output: None

[0172] Terminal

[0173] The device displays the parenting advice received from the server in a user interface. Specifically, it retrieves the advice text returned from the server and displays it in a text view within the app.

[0174] Input: Parenting advice from the server

[0175] Output: Advice displayed in the user interface

[0176] Step 7:

[0177] User

[0178] Parents can provide feedback on the displayed parenting advice within the app by entering their ratings and comments in the app's feedback form and pressing the submit button.

[0179] Input: Feedback on parenting advice

[0180] Output: Feedback data in JSON format

[0181] Terminal

[0182] The terminal converts the feedback data into JSON format and sends it to the server via an HTTP request. Specifically, the terminal converts the feedback data back into JSON format and sends a POST request to the feedback endpoint.

[0183] Input: Feedback data in JSON format

[0184] Output: HTTP POST request

[0185] Step 8:

[0186] server

[0187] The server receives the feedback, validates it, and then stores it in a database. The stored feedback is used to improve the generative artificial intelligence model. Specifically, the feedback data is analyzed and used to retrain the AI ​​model and adjust its parameters.

[0188] Input: HTTP POST request (feedback data in JSON format)

[0189] Output: Feedback data stored in a database

[0190] (Application example 1)

[0191] 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."

[0192] Previous childcare advice systems only provided a means for parents to receive specific childcare advice for their children, and did not take into consideration customer service advice for physical stores. This made it difficult for store staff to provide optimal customer service, and as a result, customer satisfaction could not be expected to improve. There was also a lack of a system for using collected feedback to improve the quality of advice.

[0193] 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.

[0194] In this invention, the server includes: a means for parents to input basic information about their children; a generative artificial intelligence (AI) system that generates personalized childcare advice based on the child's personality and preferences; a means for saving the generated childcare advice; a means for parents to obtain the childcare advice; a means for collecting feedback from parents; a means for improving the generative AI system based on the collected feedback; a means for inputting basic information about store customers; a means for generating personalized customer service advice based on the customer's past purchase history and interests; a means for providing the generated customer service advice to store staff; and a means for collecting feedback from staff and using it to improve the generative AI system. This not only enables personalized childcare advice to be provided to parents, but also enables store staff to provide personalized customer service advice to customers, which is expected to result in improved customer satisfaction. Furthermore, the feedback can be used to continuously improve the quality of advice.

[0195] "Parents" are guardians responsible for raising their children.

[0196] A "child" is a young person who has not yet reached adulthood.

[0197] "Basic information" refers to information about an individual, such as age, gender, favorite foods, and special skills.

[0198] "Generative AI" is an AI system that generates individually optimized information and advice based on input data.

[0199] "Childcare advice" refers to specific guidance and advice regarding child rearing.

[0200] "Feedback" refers to the user's reactions and opinions regarding the advice provided.

[0201] A "server" is a computing device that processes and stores data.

[0202] A "terminal" is a device through which a user inputs data and receives results.

[0203] A "database" is a system for storing and managing information in an organized manner.

[0204] "Customer" means a person who uses a product or service.

[0205] A "store" is a place that offers goods and services.

[0206] "Customer service advice" is advice for providing effective product suggestions and services to customers.

[0207] "Staff" refers to employees who serve customers and perform administrative duties at the store.

[0208] "Purchase history" is a record of products and services that a customer has purchased in the past.

[0209] An "interest" is a field or thing in which an individual has a particular interest.

[0210] A "prompt sentence" is an instruction sentence input to a generative artificial intelligence.

[0211] A system for implementing this invention includes a means for parents to input basic information about their child, a generative artificial intelligence means for generating personalized child-rearing advice based on the child's personality and preferences, a means for saving the generated child-rearing advice, a means for parents to obtain the child-rearing advice, a means for collecting feedback from parents, a means for improving the generative artificial intelligence means based on the collected feedback, a means for inputting basic information about store customers, a means for generating personalized customer service advice based on the customer's past purchase history and interests, a means for providing the generated customer service advice to store staff, and a means for collecting feedback from staff and using it to improve the generative artificial intelligence means.

[0212] The system is built with a server-side backend using Python and Django, and uses OpenAI's GPT-3 model. The server stores the received data in a database (e.g., PostgreSQL) and generates personalized advice using generative artificial intelligence. The server also collects user feedback and stores it in the database.

[0213] The user (parent or store staff) accesses the system using a device (such as a smartphone or tablet) and enters the necessary information. The device converts the entered information into JSON format and sends it to the server via an HTTP request. The server stores the received information in a database, generates advice using generative artificial intelligence, and then sends the advice back to the device for display. The user acts based on the displayed advice and then sends feedback to the server via the device.

[0214] As a concrete example, a parent enters information about a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing). This information is sent by the device to a server and stored in a database. Based on the stored information, the server uses a generative artificial intelligence model to generate individual parenting advice. For example, specific advice such as "He likes drawing, so if he cries, draw a picture with him to calm him down" is generated.

[0215] Another example of application in a physical store is when a staff member inputs basic customer information (for example, a 30-year-old male who has previously purchased cameras and related accessories, and whose interest category is cameras and photography). An example of a configured prompt might be, "The customer's age is 30, their gender is male, their past purchase history is cameras and related accessories, and their interest category is cameras and photography. Please provide the best customer service advice for this customer." The server uses an AI model based on this prompt to generate advice such as, "Introduce this customer to the latest camera models and carefully explain photography techniques and related products." The staff member serves the customer based on this advice, and also collects feedback to reflect in future visits.

[0216] As described above, this system can be applied not only to providing childcare advice but also to customer service advice in physical stores, which is expected to increase customer satisfaction and continuously improve the quality of service.

[0217] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0218] Step 1:

[0219] The user (parent or store staff) uses a terminal to enter basic information. For example, the user uses a smartphone to enter information such as the age, gender, favorite foods, and past purchase history of the child or customer into an input form displayed on the application. This information becomes the input data.

[0220] Step 2:

[0221] The terminal converts the basic information entered into JSON format. A program running on the terminal serializes the data entered into the form into JSON data in key-value format. This JSON data is output.

[0222] Step 3:

[0223] The device sends basic information in JSON format to the server via an HTTP request. The device sends a POST request to the specified endpoint via an internet connection. The JSON data is the input, and the request reaching the server is the output.

[0224] Step 4:

[0225] The server saves the received JSON data to the database. The server parses the contents of the received request and stores the data in the appropriate fields using the API provided by the database. Saving to the database is the output.

[0226] Step 5:

[0227] The server provides prompts to a generative artificial intelligence model (e.g., GPT-3) based on the information stored in the database, generating personalized advice. The server reads the stored data, generates appropriate prompts, and sends the prompts to the AI ​​model. The generated advice is the output.

[0228] Step 6:

[0229] The server saves the generated advice back to the database. The server retrieves the generated text data and stores it in the corresponding field of the database. This saving to the database is the output.

[0230] Step 7:

[0231] The user uses a terminal to send a request to the server to obtain advice. The user sends an HTTP GET request to the server by, for example, pressing a button on an application. The request is the input, and the advice is the output.

[0232] Step 8:

[0233] The server receives a request from the device, fetches the generated advice from the database, and returns the content to the device in JSON format. The server performs a database query, obtains the generated advice, and returns it as a response in JSON format. This response is the output.

[0234] Step 9:

[0235] The user checks the advice displayed on the device and enters feedback as necessary. The user acts based on the displayed advice and enters the results and opinions in the application's input form. The input feedback is input data.

[0236] Step 10:

[0237] The device converts the feedback data into JSON format and sends it back to the server via an HTTP request. The device serializes the feedback into JSON data and sends a POST request to the specified endpoint. The request is the output.

[0238] Step 11:

[0239] The server stores the received feedback in a database to help improve the generative artificial intelligence model. The server parses the feedback data, stores it in a database, and then uses it to retrain the model and improve its performance. The database storage and model improvement are the outputs.

[0240] 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.

[0241] The system of the present invention allows parents to input basic information about their children, generates personalized parenting advice based on the child's personality and preferences, and further combines it with an emotion engine that recognizes the user's emotions, aiming to continuously improve the system based on feedback. The system is composed of a server, terminals, and users.

[0242] Enter your child's basic information

[0243] User

[0244] Parents enter their child's basic information (age, gender, favorite food, special skills, etc.) through a mobile or web app. For example, they enter the information of a 4-year-old boy (favorite food: apples, special skill: drawing).

[0245] Submission of Children's Information

[0246] Terminal

[0247] The entered child information is converted to JSON format and sent to the server. Specifically, the JSON data is sent as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[0248] Receiving and storing children's information

[0249] server

[0250] It processes the incoming POST request, parses the JSON data, connects to the database, saves the parsed information in the "children" table, and returns the ID of the newly created child as a response.

[0251] Emotion recognition

[0252] Terminal

[0253] When a parent operates the application, the emotion engine is activated and recognizes emotions by analyzing the user's voice input and facial expressions. The recognized emotion data is sent to the server.

[0254] Generating parenting advice

[0255] server

[0256] Based on the child's information and emotion data from the emotion engine, generative artificial intelligence (e.g., GPT-2 model) is used to generate parenting advice. For example, if a parent is feeling stressed, advice suggesting specific relaxation methods is generated.

[0257] Saving Parenting Advice

[0258] server

[0259] The generated parenting advice is saved in the "children" table of the database, specifically by saving the advice text in association with the corresponding child's ID.

[0260] Obtaining and viewing parenting advice

[0261] User

[0262] Parents obtain and display parenting advice. Advice generated by a request from the server is sent to the device.

[0263] Terminal

[0264] The device displays the parenting advice it has acquired on the user interface in a format that is easy for parents to understand.

[0265] Enter and submit feedback

[0266] User

[0267] Parents can input their feedback on the parenting advice, for example, by entering an evaluation such as "This advice was very helpful."

[0268] Terminal

[0269] Convert the feedback data into JSON format and send it to the server. Use an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[0270] Receiving and storing feedback

[0271] server

[0272] Save the received feedback in the "feedback" table of the database. Save the feedback content along with the child's ID in the "feedback" table of the database.

[0273] Model Improvements

[0274] server

[0275] The collected feedback is used to improve the generative artificial intelligence model. This improvement process involves analyzing the feedback data and retraining the model as needed. The feedback is then incorporated into the next advice generation process.

[0276] As a specific example, if a user inputs the information of a 4-year-old boy and facial expression recognition determines that he is feeling stressed, the generative AI will suggest a way to relax by "enjoying drawing together." If the parent who received this advice evaluates it as effective and sends it back to the server, the server will use that feedback to improve the AI ​​model and increase the accuracy of future advice.

[0277] As described above, the system of the present invention is an embodiment that provides individualized parenting advice specific to parents and children, and further recognizes the user's emotions and provides appropriate advice according to the context.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] Users enter basic information about their children (age, gender, favorite foods, special skills, etc.) through a mobile or web app. For example, a parent enters information about a 4-year-old boy.

[0281] Step 2:

[0282] The device converts the entered child information into JSON format and sends it to the server. Specifically, it sends JSON data containing the child's basic information as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[0283] Step 3:

[0284] The server processes the received POST request, parses the JSON data, and saves the parsed data (age, gender, favorite food, special skills, etc.) in the "children" table in the database. Once saved, it returns the created child's ID as a response.

[0285] Step 4:

[0286] When a parent operates an application on the device, the emotion engine is activated. The emotion engine recognizes the user's voice input and facial expressions and analyzes their emotional state. For example, an emotion recognition algorithm can analyze the parent's face and tone of voice to determine if the user is feeling stressed.

[0287] Step 5:

[0288] The device converts the recognized emotion data into JSON format and sends it to the server. It uses an HTTP POST request to send the emotion data to a specific endpoint on the server (e.g., / submit_emotion).

[0289] Step 6:

[0290] The server stores the received emotion data in a database, in the "emotions" table of the database, in association with the child's ID.

[0291] Step 7:

[0292] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. For example, specific parenting advice is generated using the child's age, gender, favorite foods, special skills, and the parent's emotional state as input. If the parent is feeling stressed, advice including relaxation methods is generated.

[0293] Step 8:

[0294] The server stores the generated parenting advice in a database table called "children," associating the advice text with the corresponding child's ID.

[0295] Step 9:

[0296] A user submits a request to get parenting advice through the application, for example, a parent clicks a button and selects "Get Advice."

[0297] Step 10:

[0298] The device sends a GET request to the server to get parenting advice for a specific child. This is sent as a request to a specific endpoint on the server (e.g., / get_advice / {child_id}).

[0299] Step 11:

[0300] The server processes the received GET request, retrieves parenting advice associated with the child's ID from the database, and returns this advice in a JSON format as a response.

[0301] Step 12:

[0302] The device displays the parenting advice received from the server on the user interface in a format that is easy for parents to understand.

[0303] Step 13:

[0304] The user inputs feedback on the parenting advice, such as whether the advice was helpful or not, and what needs to be improved.

[0305] Step 14:

[0306] The device converts the feedback data into JSON format and sends it to the server. It uses an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[0307] Step 15:

[0308] The server stores the received feedback in a database, storing the feedback along with the child's ID in a "feedback" table in the database.

[0309] Step 16:

[0310] The server uses the collected feedback to improve the generative AI model. This process includes analyzing the feedback data, retraining the model, and evaluating its performance. The improved model is used to generate advice in future years.

[0311] Example 2

[0312] 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."

[0313] Conventional parenting advice systems struggle to provide advice that fully takes into account the individual personality and preferences of children and the emotional state of parents. They also lack a mechanism for effectively utilizing parental feedback to improve the system. This reduces the effectiveness of parenting advice and leads to insufficient support for parents.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0315] In this invention, the server includes means for a parent to input basic information about their child, means for transmitting the input information about the child to the server, means for receiving and storing the transmitted information, means for recognizing a user's emotions, generative artificial intelligence means for generating individualized parenting advice based on the child's personality and preferences, means for storing the generated parenting advice, means for providing the generated parenting advice to the parent, means for the parent to obtain the parenting advice, means for collecting feedback from the parent, and means for improving the generative artificial intelligence means based on the collected feedback. This makes it possible to provide useful parenting advice based on individual child information and the parent's emotional state, and to continuously improve the system by utilizing feedback from the parent.

[0316] "Means for parents to enter basic information about their children" refers to an interface that allows parents to enter information such as their children's age, gender, favorite foods, and special skills using a mobile or web app.

[0317] "Means for sending the entered child information to the server" refers to the function of converting the data entered by the user into JSON format and sending it to the specified endpoint on the server using an HTTP POST request.

[0318] "Means of receiving and storing the transmitted information" refers to the server's ability to process the received POST request, parse the JSON data, connect to a database, and store the information in the appropriate tables.

[0319] "Means for recognizing user emotions" refers to the function in which the emotion engine built into the application analyzes the user's voice input and facial expressions, and generates emotional data as a result.

[0320] "Generative artificial intelligence means for generating individualized childcare advice based on a child's personality and preferences" refers to a function that generates childcare advice using a generative artificial intelligence model (e.g., GPT-2) based on the child's basic information and the user's emotional data.

[0321] "Means for saving the generated parenting advice" refers to a function for saving the generated parenting advice in an appropriate table in a database and managing it in association with the ID of the corresponding child.

[0322] "Means for providing generated parenting advice to parents" refers to a function in which, when a parent requests parenting advice from an application, the server obtains the relevant advice, sends it to the terminal, and displays it on the user interface.

[0323] "Means for parents to obtain parenting advice" refers to the interface through which parents request and obtain up-to-date parenting advice via functionality within the application.

[0324] "Means for collecting feedback from parents" refers to a function that allows parents to enter ratings and comments on parenting advice within the application, convert them into JSON format, and send them to the server.

[0325] "Means for improving the generative artificial intelligence means based on the collected feedback" refers to the function of the server analyzing the collected feedback data, retraining the generative artificial intelligence model as necessary, and improving the advice generation process from the next time onwards.

[0326] MODE FOR CARRYING OUT THE INVENTION

[0327] The system of the present invention allows parents to input basic information about their children, generates personalized parenting advice based on the child's personality and preferences, and further combines it with an emotion engine that recognizes the user's emotions, aiming to continuously improve the system based on feedback. The system is composed of a server, terminals, and users.

[0328] Enter your child's basic information

[0329] User

[0330] Parents use a mobile or web app to enter basic information about their child, including the child's age, gender, favorite food, and special skills. For example, a parent might enter information such as "A 4-year-old boy whose favorite food is apples and whose special skill is drawing."

[0331] Submission of Children's Information

[0332] Terminal

[0333] The device converts the entered child information into JSON format and sends it to the server using an HTTP POST request. The destination endpoint is, for example, " / submit_info."

[0334] Receiving and storing children's information

[0335] server

[0336] The server processes the incoming POST request, parses the JSON data, and extracts the information that is stored in the "children" table in the database. Once saved, the newly created child's ID is returned in the response.

[0337] Emotion recognition

[0338] Terminal

[0339] The emotion engine built into the device is activated and analyzes the parent's voice input and facial expressions. The emotion engine recognizes the user's emotional state and generates emotion data. This data is sent to the server. For example, it may recognize that the parent is feeling stressed.

[0340] Generating parenting advice

[0341] server

[0342] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. The generated advice is a specific relaxation method, such as "How to relax by enjoying drawing together." This advice is generated in text format.

[0343] Saving Parenting Advice

[0344] server

[0345] The generated parenting advice is stored in the database in a "children" table, where the advice is associated with the child's ID.

[0346] Obtaining and viewing parenting advice

[0347] User

[0348] A parent sends a request to the server to get parenting advice through the application, for example by clicking a "Get the latest parenting advice" button.

[0349] Terminal

[0350] The device displays the parenting advice it receives from the server on its user interface. The displayed content is presented in a format that is easy for parents to understand. For example, the screen might say, "How to relax by enjoying drawing together."

[0351] Enter and submit feedback

[0352] User

[0353] Parents can enter their feedback on the parenting advice into a feedback form within the application, such as "This advice was very helpful."

[0354] Terminal

[0355] The feedback data is converted to JSON format and sent to the server using an HTTP POST request to an endpoint such as " / submit_feedback".

[0356] Receiving and storing feedback

[0357] server

[0358] The server processes the received feedback data and stores it in a database table called "feedback," associating the child's ID with the feedback content.

[0359] Model Improvements

[0360] server

[0361] The server analyzes the collected feedback data and retrains the generative AI model as needed, improving the advice generation process from the next time onwards. This improvement process allows the system to provide more appropriate advice that reflects parental feedback.

[0362] Examples of concrete examples and prompts

[0363] Examples of specific prompts include:

[0364] "Child information: 4-year-old boy, favorite food: apples, special skill: drawing. Parent's emotion: stress. Based on this information, please suggest specific parenting advice for the parents."

[0365] In this way, the system of the present invention is an embodiment that provides individualized parenting advice specific to parents and children, and further recognizes the user's emotions to provide appropriate advice according to the context.

[0366] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0367] Step 1:

[0368] Enter your child's basic information

[0369] User: Parents use a mobile or web app to enter basic information about their child, including the child's age, gender, favorite food, and special skills. Specifically, a parent might enter information such as "4 years old, male, favorite food: apples, special skill: drawing" into a form.

[0370] Input: Child's basic information (age, gender, favorite food, special skills)

[0371] Output: Entered child information

[0372] Step 2:

[0373] Submission of Children's Information

[0374] Terminal: Converts the entered child information into JSON format. Sends the generated JSON data as an HTTP POST request to the specified endpoint on the server. Specifically, the endpoint " / submit_info" is used.

[0375] Input: Basic information about the child entered by the user

[0376] Data processing: Convert to JSON format

[0377] Output: Child information in JSON format, sent as an HTTP POST request

[0378] Step 3:

[0379] Receiving and storing children's information

[0380] Server: Processes the incoming POST request, parses the JSON data, extracts the information, stores it in the "children" table in the database, and returns the newly created child's ID as a response.

[0381] Input: Child information in JSON format

[0382] Data operations: Parse JSON data and extract information

[0383] Output: Extracted information saved in database, newly generated child ID

[0384] Step 4:

[0385] Emotion recognition

[0386] Device: The emotion engine runs and analyzes the parent's voice input and facial expressions to recognize emotions. The recognized emotion data is sent to the server.

[0387] Input: Parent's voice input and facial expression data

[0388] Data Computation: Analysis and Emotional Data Generation by Emotion Engine

[0389] Output: Emotion data, sent to server

[0390] Step 5:

[0391] Generating parenting advice

[0392] Server: Generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. Specifically, it generates advice such as "How to relax by enjoying drawing together."

[0393] Input: Child's basic information, emotional data

[0394] Data Computation: Generating Parenting Advice Using Generative Artificial Intelligence Models

[0395] Output: Generated parenting advice

[0396] Step 6:

[0397] Saving Parenting Advice

[0398] Server: The generated parenting advice is saved in the "children" table of the database, associated with the ID of the child to whom the advice applies.

[0399] Input: Generated parenting advice

[0400] Data processing: linking advice to child ID

[0401] Output: Parenting advice stored in a database

[0402] Step 7:

[0403] Obtaining and viewing parenting advice

[0404] User: A parent sends a request to the server to get parenting advice through the application by clicking the "Get the latest parenting advice" button.

[0405] Input: Get request

[0406] Output: Parenting advice request

[0407] Device: The parenting advice obtained from the server is displayed on the user interface. The screen displays "How to relax by enjoying drawing together."

[0408] Input: Parenting advice from the server

[0409] Output: Parenting advice displayed in a user interface

[0410] Step 8:

[0411] Enter and submit feedback

[0412] User: Parents enter their feedback on the parenting advice into a feedback form within the application, such as "This advice was very helpful."

[0413] Input: Feedback

[0414] Output: Input feedback

[0415] Terminal: Convert the feedback data into JSON format and send it to the server using an HTTP POST request with the endpoint " / submit_feedback".

[0416] Input: Feedback

[0417] Data processing: Convert to JSON format

[0418] Output: Feedback data in JSON format, sent to the server

[0419] Step 9:

[0420] Receiving and storing feedback

[0421] Server: Processes the received feedback data and stores it in the "feedback" table in the database. The child's ID and the feedback content are associated.

[0422] Input: Feedback data in JSON format

[0423] Data calculation: Parsing and storing feedback data

[0424] Output: Feedback stored in a database

[0425] Step 10:

[0426] Model Improvements

[0427] Server: Analyzes the collected feedback data and retrains the generative AI model. This improvement process improves the accuracy of the advice generation process in future.

[0428] Input: Collected feedback data

[0429] Data Computation: Analyzing feedback data and retraining models

[0430] Output: An improved generative artificial intelligence model

[0431] (Application example 2)

[0432] 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."

[0433] Conventional parenting advice systems provide general advice without taking into account the parents' emotional state, making it difficult to provide appropriate advice tailored to the parents' circumstances and emotions. Furthermore, while feedback to improve the accuracy of advice provided by generative AI is common, specific improvements that reflect the parents' evaluations are insufficient. As a result, there is a need for a system that can reduce the stress and burden felt by parents and provide appropriate parenting methods for their children.

[0434] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0435] In this invention, the server includes means for parents to input basic information about their children, generative artificial intelligence means for generating personalized child-rearing advice based on the child's personality and preferences, means for saving the generated child-rearing advice, means for parents to obtain the child-rearing advice, means for collecting feedback from the parents, means for improving the generative artificial intelligence means based on the collected feedback, emotion recognition means for recognizing the emotions of the parents, and means for adjusting the generation of child-rearing advice based on emotion data recognized by the emotion recognition means. This makes it possible to provide child-rearing advice that takes the parent's emotional state into consideration and to continuously improve the accuracy of the advice based on the feedback.

[0436] "Parent" means a guardian who raises a child.

[0437] "Child's basic information" refers to important information such as the child's age, gender, favorite foods, and special skills.

[0438] "Generative artificial intelligence" refers to AI technology that can generate personalized advice and content based on input information.

[0439] "Emotion recognition means" refers to technology or devices that recognize emotions by analyzing the user's voice input and facial expressions.

[0440] "Parenting advice" refers to specific suggestions and advice regarding child rearing.

[0441] "Feedback" refers to the act of a user returning information, including ratings and opinions, regarding the advice provided.

[0442] A "database" refers to a system for structuring, storing, and managing various types of data.

[0443] "Server" refers to a computer system that provides services to clients over a network.

[0444] The system for implementing this invention comprises a server, a terminal, and a user. The specific flow is as follows.

[0445] First, the user (parent) enters basic information about their child, including age, gender, favorite food, and special skills. This allows parents to register their child's personality and preferences in the system. For example, a 4-year-old boy's favorite food is apples and his special skill is drawing can be entered through a mobile or web app.

[0446] Next, the device converts the entered child information into JSON format and sends it to the server. Specifically, it sends the JSON data as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[0447] The server processes the incoming POST request, parses the JSON data, stores the parsed information in the "children" table, and responds with the ID of the newly created child.

[0448] When a parent operates the application, emotion recognition is activated. This technology recognizes the user's emotions by analyzing the user's voice input and facial expressions. For example, if the parent is feeling stressed, the emotional data is recognized and sent to the server.

[0449] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's information and emotion data from the emotion engine. For example, if a parent is feeling stressed, advice will be generated suggesting specific relaxation methods, such as "relaxing by enjoying drawing together."

[0450] The generated parenting advice is then stored in the database in a "children" table, with the advice text associated with the corresponding child's ID.

[0451] The parent obtains parenting advice, and the advice generated by the server is sent to the device upon request. The obtained parenting advice is displayed on the device's user interface. The displayed content is presented in a format that is easy for the parent to understand.

[0452] Furthermore, parents can input their feedback on the parenting advice. For example, they can input a rating such as "This advice was very helpful." The feedback data is converted into JSON format and sent to the server. The JSON feedback data is sent to a specific endpoint (e.g., / submit_feedback) on the server using an HTTP POST request.

[0453] The server stores the received feedback in a database table called "feedback," along with the child's ID.

[0454] Finally, the server improves the generative artificial intelligence model based on the collected feedback. This improvement process involves analyzing the feedback data and retraining the model if necessary, so that the feedback can be incorporated into the next advice generation process.

[0455] An example of a specific prompt is as follows:

[0456] "I would like some suggestions for how a 4-year-old boy can relax through drawing when he is feeling stressed."

[0457] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0458] Step 1:

[0459] Parents enter basic information about their children. Parents (users) enter information such as their children's age, gender, favorite foods, and special skills through a mobile or web app. The entered information is temporarily stored in the device's local database. Input: Basic information about the child. Output: Entered information.

[0460] Step 2:

[0461] The device converts the entered child information into JSON format and sends it to the server. Specifically, the data is processed by organizing the entered information into a JSON object and sending it as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info). Input: Basic information about the child. Output: JSON format data and HTTP request.

[0462] Step 3:

[0463] The server receives the HTTP POST request and parses the JSON data. The parsed information is structured and stored in the server's database. Specifically, it is stored in the "children" table, and the newly generated child's ID is returned to the terminal as a response. Input: Basic information about the child in JSON format. Output: Data stored in the "children" table and the child's ID.

[0464] Step 4:

[0465] The emotion recognition means is activated while the parent (user) is operating the application. The device's camera and microphone are used to analyze the user's voice and facial expressions, and emotional data is acquired. The acquired emotional data is sent to the server in real time. Input: Parent's voice or facial expressions. Output: Emotional data and HTTP request.

[0466] Step 5:

[0467] The server parses the received emotion data and stores it in a database. In addition, it uses generative AI (e.g., GPT-2 model) to generate parenting advice based on the child's basic information and emotion data. Specific prompts are created and input into the generative AI to generate advice. Input: Emotion data and basic information about the child. Output: Generated parenting advice.

[0468] Step 6:

[0469] The server saves the generated parenting advice in the database. It associates the advice with the ID of the child in the "children" table and saves it. Input: The generated parenting advice. Output: The advice saved in the database.

[0470] Step 7:

[0471] A parent (user) sends a request to the server through the application to get parenting advice. The server searches for the corresponding advice and sends it to the device as an HTTP response. Input: Database query request. Output: Parenting advice sent as an HTTP response.

[0472] Step 8:

[0473] The device displays the received parenting advice on a user interface, allowing parents to view the information in an easy-to-understand format. Input: Advice received as an HTTP response. Output: Displayed parenting advice.

[0474] Step 9:

[0475] Parents (users) input feedback on the parenting advice provided. Feedback ratings and comments are entered through the application. The entered feedback is temporarily saved on the device. Input: Feedback content. Output: Entered feedback.

[0476] Step 10:

[0477] The device converts the feedback data into JSON format and sends it to the server. It then sends it again as an HTTP POST request to a specific endpoint on the server (e.g., / submit_feedback). Input: Feedback content. Output: JSON format data and HTTP request.

[0478] Step 11:

[0479] The server parses the received feedback and saves it in the "feedback" table of the database. It associates the child's ID with the feedback content and saves it. Input: Feedback data in JSON format. Output: Data saved in the "feedback" table.

[0480] Step 12:

[0481] The server improves the generative AI model based on the collected feedback. Specifically, it analyzes the feedback data and retrains the model as necessary. This allows the feedback to be utilized in the next advice generation process. Input: Feedback data. Output: Improved generative AI model.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] [Second embodiment]

[0486] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0497] 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."

[0498] The system of the present invention aims to allow parents to input basic information about their children, generate personalized parenting advice based on the child's personality and preferences, and continuously improve the system based on feedback. The system consists of a server, terminals, and users.

[0499] Enter your child's basic information

[0500] User

[0501] Parents enter basic information about their children, such as their age, gender, favorite foods, and special skills, through a mobile or web app.

[0502] Submission of Children's Information

[0503] Terminal

[0504] The entered child information is converted into JSON format and sent to the server. The data entered by the parent on the device is first converted into JSON format and sent to the server via an HTTP request.

[0505] Receiving and storing children's information

[0506] server

[0507] The received child information is saved in the database. The server connects to the database and saves the received child's basic information in each field appropriately.

[0508] Generating parenting advice

[0509] server

[0510] Generative AI (e.g., GPT-2 model) is used to generate parenting advice based on information about the child. Information about the child (such as age, gender, favorite foods, and special skills) is fed into the AI ​​model to generate individually customized advice.

[0511] Saving Parenting Advice

[0512] server

[0513] Save the generated parenting advice in a database. Save the generated text in a specific field in the database for future reference.

[0514] Obtaining and viewing parenting advice

[0515] User

[0516] The parent retrieves and displays the parenting advice. The parent again uses the device to send a request to retrieve the advice from the server.

[0517] Terminal

[0518] The fetched parenting advice is displayed in a user interface in a format that is easy for parents to understand.

[0519] Enter and submit feedback

[0520] User

[0521] Parents can input and submit feedback on the child-rearing advice. Parents who receive the advice can also input feedback on the content, such as an evaluation and areas for improvement.

[0522] Terminal

[0523] The feedback data is converted to JSON format and sent to the server. The input feedback is converted back to JSON format so that it can be securely sent to the server.

[0524] Receiving and storing feedback

[0525] server

[0526] The feedback received is stored in a database to help improve the generative artificial intelligence model. The feedback is stored in a database and later used as material for retraining and improving the AI ​​model.

[0527] Specific examples

[0528] Example 1

[0529] A parent enters information about a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing). This information is sent by the device to a server and stored in a database.

[0530] The server uses a generative artificial intelligence model based on the stored information to generate individualized parenting advice, such as, "She likes drawing, so if she cries, draw a picture with her to calm her down."

[0531] Parents receive this advice through their devices, and if they are satisfied with the displayed information, they can enter feedback such as "It was very helpful." This feedback is then sent back to the server and stored in a database.

[0532] The server analyzes the collected feedback and uses it to improve the performance of the generative artificial intelligence model.

[0533] As described above, the system of the present invention is an embodiment that can provide individual child-rearing advice specific to parents and children and can achieve continuous improvement.

[0534] The processing flow will be explained below.

[0535] Step 1:

[0536] The user enters basic information about the child (age, gender, favorite food, special skills, etc.) through the mobile app or web app, enters the information in the input form provided in the user interface, and clicks the submit button.

[0537] Step 2:

[0538] The device converts the entered child information into JSON format and sends it to the server. Specifically, it sends the JSON data as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[0539] Step 3:

[0540] The server processes the incoming POST request, parses the JSON data, connects to the database, saves the parsed information to the "children" table in the database, and returns the newly created child's ID as a response.

[0541] Step 4:

[0542] The server generates parenting advice using generative AI based on the child's basic information stored in the database. The generative AI receives input such as the child's age, gender, favorite foods, and special skills, and generates appropriate parenting advice text.

[0543] Step 5:

[0544] The server stores the generated parenting advice in the "children" table of the database, specifically by associating the advice text with the corresponding child's ID.

[0545] Step 6:

[0546] The user sends a request to the server through the application to get parenting advice for a child. This request is sent using the GET method.

[0547] Step 7:

[0548] The device displays the parenting advice received from the server on the user interface. Specifically, the device extracts the parenting advice from the JSON data returned from the server and displays it in a format that is easy for parents to understand.

[0549] Step 8:

[0550] The user inputs feedback on the parenting advice, which is then converted back to JSON format by the device.

[0551] Step 9:

[0552] The device sends the feedback data to the server by using an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[0553] Step 10:

[0554] The server stores the received feedback in a database, storing the feedback along with the child's ID in a "feedback" table in the database.

[0555] Step 11:

[0556] The server uses the collected feedback to improve the generative artificial intelligence model. This improvement process includes analyzing the feedback data and retraining the model as needed. The feedback is then incorporated into the next advice generation process.

[0557] Example 1

[0558] 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."

[0559] Today's parents often find it difficult to find individualized parenting methods that suit each child's personality and preferences. Furthermore, existing parenting advice is often general and may not be applicable to each family's specific situation. Therefore, there is a need for a system that allows parents to quickly and effectively obtain appropriate parenting advice.

[0560] 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.

[0561] In this invention, the server includes: means for a parent to input basic information about their child using a mobile app or web app; means for the device to convert the input information about the child into JSON format and send it to the server; means for the server to save the received information about the child in a database; means for the server to generate personalized parenting advice based on the information about the child using generative artificial intelligence; means for saving the generated parenting advice in a database; means for the parent to obtain the parenting advice; means for the device to display the fetched parenting advice; means for the parent to input and send feedback on the generated parenting advice; means for the device to convert the feedback data into JSON format and send it to the server; and means for the server to save the received feedback in a database to help improve the model of the generative artificial intelligence. This enables parents to quickly obtain appropriate parenting advice tailored to the individual characteristics of their child.

[0562] "Parents" are those who enter basic information about their children, receive parenting advice, and provide feedback.

[0563] A "mobile or web app" is a software application that runs on a smartphone or computer and allows parents to enter basic information about their children.

[0564] "Terminal" refers to a device or equipment that converts the information entered by the parent into JSON format and sends it to the server.

[0565] "JSON format" is a lightweight data description format widely used for data exchange, and is an abbreviation for JavaScript Object Notation.

[0566] An "HTTP request" is a form of communication protocol used by a client to send data to a server.

[0567] "Server" refers to a computer system that receives and stores data sent by users and generates child-rearing advice using generative artificial intelligence.

[0568] The "database" is a data management system for systematically storing basic information about children, generated parenting advice, feedback, and so on.

[0569] "Generative AI" refers to AI techniques that generate new information or suggestions based on data, and examples include the GPT-2 model.

[0570] "Parenting advice" refers to specific parenting advice and suggestions generated by generative artificial intelligence based on basic information about a child.

[0571] "Feedback" refers to opinions such as evaluations and improvements that users provide regarding the generated parenting advice.

[0572] "Fetch" refers to the operation of retrieving and obtaining data or information from a server.

[0573] "User interface" refers to the screen and operation method displayed on the terminal, and is the interface through which parents input information and check advice.

[0574] The system of the present invention consists of three main elements: a server, a terminal, and a user. The system as a whole is designed to smoothly carry out a series of processes: parents input basic information about their children, receive parenting advice generated based on that information, and provide feedback.

[0575] Enter your child's basic information

[0576] User

[0577] Parents use a mobile or web app to enter basic information about their child, such as their age, gender, favorite foods, and special skills. Specifically, parents fill out the required information in the application form and press the submit button. This information is stored on the device in JSON format.

[0578] Submission of Children's Information

[0579] Terminal

[0580] The device converts the child information entered by the user into JSON format and sends it to the server via an HTTP request. Specifically, the device makes a POST request to the backend endpoint (e.g., http: / / example.com / api / childinfo).

[0581] Receiving and storing children's information

[0582] server

[0583] The server receives the JSON data sent via the HTTP request, validates it, and then saves it to a database. The server connects to a database (e.g. MySQL) and stores the information in tables in the database, with the appropriate fields.

[0584] Generating parenting advice

[0585] server

[0586] The server retrieves basic information about the child from the database and supplies this to a generative artificial intelligence (e.g., GPT-2 model) to generate individualized parenting advice. Specifically, the server calls the AI ​​model's API and supplies a prompt to generate specific advice. An example of a prompt is, "Please tell me some parenting advice for a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing)."

[0587] Saving Parenting Advice

[0588] server

[0589] The server saves the generated parenting advice back into the database, and the generated text data is stored in a field of an appropriate table so that the user can refer to it later.

[0590] Obtaining and viewing parenting advice

[0591] User

[0592] The parent then uses the device to send a request for parenting advice and view the advice. The request is initiated by pressing the "Get Advice" button in the app.

[0593] Terminal

[0594] The device displays the parenting advice received from the server on a user interface. The device also receives the advice text returned from the server and displays it in a text view within the app.

[0595] Enter and submit feedback

[0596] User

[0597] Parents can provide feedback on the displayed parenting advice within the app by entering their ratings and comments in the app's feedback form and pressing the submit button.

[0598] Terminal

[0599] The device converts the feedback data into JSON format and sends it to the server via an HTTP request. The device also sends the feedback data as a POST request to the server's feedback endpoint.

[0600] Receiving and storing feedback

[0601] server

[0602] The server receives the feedback, validates it, and then stores it in a database. The stored feedback is used to improve the generative artificial intelligence model and is used as material for retraining and improving the AI ​​model later.

[0603] Through the above process, the system is able to provide individualized parenting advice tailored to parents and children, and to continuously improve its accuracy.

[0604] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0605] Step 1:

[0606] User

[0607] Parents use a mobile or web app to enter basic information about their child (age, gender, favorite foods, special skills, etc.). Specifically, they fill in the required information in the input form within the app and press the submit button. The input data is in the form of text, options, numbers, etc., and as the parent enters the information, individual basic information about the child is accumulated. The input is received from the application front end, converted into JSON format, and prepared for sending to the next process.

[0608] Input: Child's basic information (age, gender, favorite food, special skills, etc.)

[0609] Output: Child basic information in JSON format

[0610] Step 2:

[0611] Terminal

[0612] The device converts the child information entered by the user into JSON format and sends this data to the server via an HTTP request. Specifically, the data in the input form is converted into structured data (JSON format) by a program such as JavaScript. The device then makes a POST request to the backend endpoint (e.g., http: / / example.com / api / childinfo).

[0613] Input: Child basic information in JSON format

[0614] Output: HTTP POST request

[0615] Step 3:

[0616] server

[0617] The server receives the JSON data sent via the HTTP request. This data is validated and saved to the database. Specifically, the server parses the received JSON data, checks for invalid data, and stores it in the appropriate table in the database (e.g. MySQL). Validation includes checking for required fields and validating the data format.

[0618] Input: HTTP POST request (child's basic information in JSON format)

[0619] Output: Basic information about the child stored in the database

[0620] Step 4:

[0621] server

[0622] The server retrieves basic information about the child from the database and supplies it to a generative artificial intelligence (e.g., GPT-2 model) to generate individualized parenting advice. Specifically, the server uses the child's basic information retrieved from the database as part of a prompt sentence and calls the AI ​​model's API to generate advice. An example prompt sentence is, "Please tell me some parenting advice for a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing)."

[0623] Input: Basic information about the child retrieved from the database

[0624] Output: Generated parenting advice

[0625] Step 5:

[0626] server

[0627] The server stores the generated child-rearing advice in the database again, specifically, stores the generated text data in a field of an appropriate table so that the user can refer to it later.

[0628] Input: Generated parenting advice

[0629] Output: Parenting advice stored in a database

[0630] Step 6:

[0631] User

[0632] The parent again uses the device to send a request for parenting advice by pressing the "Get Advice" button in the app. The user's request is sent to the server via the device and processed there.

[0633] Input: Parenting advice request

[0634] Output: None

[0635] Terminal

[0636] The device displays the parenting advice received from the server in a user interface. Specifically, it retrieves the advice text returned from the server and displays it in a text view within the app.

[0637] Input: Parenting advice from the server

[0638] Output: Advice displayed in the user interface

[0639] Step 7:

[0640] User

[0641] Parents can provide feedback on the displayed parenting advice within the app by entering their ratings and comments in the app's feedback form and pressing the submit button.

[0642] Input: Feedback on parenting advice

[0643] Output: Feedback data in JSON format

[0644] Terminal

[0645] The terminal converts the feedback data into JSON format and sends it to the server via an HTTP request. Specifically, the terminal converts the feedback data back into JSON format and sends a POST request to the feedback endpoint.

[0646] Input: Feedback data in JSON format

[0647] Output: HTTP POST request

[0648] Step 8:

[0649] server

[0650] The server receives the feedback, validates it, and then stores it in a database. The stored feedback is used to improve the generative artificial intelligence model. Specifically, the feedback data is analyzed and used to retrain the AI ​​model and adjust its parameters.

[0651] Input: HTTP POST request (feedback data in JSON format)

[0652] Output: Feedback data stored in a database

[0653] (Application example 1)

[0654] 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."

[0655] Previous childcare advice systems only provided a means for parents to receive specific childcare advice for their children, and did not take into consideration customer service advice for physical stores. This made it difficult for store staff to provide optimal customer service, and as a result, customer satisfaction could not be expected to improve. There was also a lack of a system for using collected feedback to improve the quality of advice.

[0656] 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.

[0657] In this invention, the server includes: a means for parents to input basic information about their children; a generative artificial intelligence (AI) system that generates personalized childcare advice based on the child's personality and preferences; a means for saving the generated childcare advice; a means for parents to obtain the childcare advice; a means for collecting feedback from parents; a means for improving the generative AI system based on the collected feedback; a means for inputting basic information about store customers; a means for generating personalized customer service advice based on the customer's past purchase history and interests; a means for providing the generated customer service advice to store staff; and a means for collecting feedback from staff and using it to improve the generative AI system. This not only enables personalized childcare advice to be provided to parents, but also enables store staff to provide personalized customer service advice to customers, which is expected to result in improved customer satisfaction. Furthermore, the feedback can be used to continuously improve the quality of advice.

[0658] "Parents" are guardians responsible for raising their children.

[0659] A "child" is a young person who has not yet reached adulthood.

[0660] "Basic information" refers to information about an individual, such as age, gender, favorite foods, and special skills.

[0661] "Generative AI" is an AI system that generates individually optimized information and advice based on input data.

[0662] "Childcare advice" refers to specific guidance and advice regarding child rearing.

[0663] "Feedback" refers to the user's reactions and opinions regarding the advice provided.

[0664] A "server" is a computing device that processes and stores data.

[0665] A "terminal" is a device through which a user inputs data and receives results.

[0666] A "database" is a system for storing and managing information in an organized manner.

[0667] "Customer" means a person who uses a product or service.

[0668] A "store" is a place that offers goods and services.

[0669] "Customer service advice" is advice for providing effective product suggestions and services to customers.

[0670] "Staff" refers to employees who serve customers and perform administrative duties at the store.

[0671] "Purchase history" is a record of products and services that a customer has purchased in the past.

[0672] An "interest" is a field or thing in which an individual has a particular interest.

[0673] A "prompt sentence" is an instruction sentence input to a generative artificial intelligence.

[0674] A system for implementing this invention includes a means for parents to input basic information about their child, a generative artificial intelligence means for generating personalized child-rearing advice based on the child's personality and preferences, a means for saving the generated child-rearing advice, a means for parents to obtain the child-rearing advice, a means for collecting feedback from parents, a means for improving the generative artificial intelligence means based on the collected feedback, a means for inputting basic information about store customers, a means for generating personalized customer service advice based on the customer's past purchase history and interests, a means for providing the generated customer service advice to store staff, and a means for collecting feedback from staff and using it to improve the generative artificial intelligence means.

[0675] The system is built with a server-side backend using Python and Django, and uses OpenAI's GPT-3 model. The server stores the received data in a database (e.g., PostgreSQL) and generates personalized advice using generative artificial intelligence. The server also collects user feedback and stores it in the database.

[0676] The user (parent or store staff) accesses the system using a device (such as a smartphone or tablet) and enters the necessary information. The device converts the entered information into JSON format and sends it to the server via an HTTP request. The server stores the received information in a database, generates advice using generative artificial intelligence, and then sends the advice back to the device for display. The user acts based on the displayed advice and then sends feedback to the server via the device.

[0677] As a concrete example, a parent enters information about a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing). This information is sent by the device to a server and stored in a database. Based on the stored information, the server uses a generative artificial intelligence model to generate individual parenting advice. For example, specific advice such as "He likes drawing, so if he cries, draw a picture with him to calm him down" is generated.

[0678] Another example of application in a physical store is when a staff member inputs basic customer information (for example, a 30-year-old male who has previously purchased cameras and related accessories, and whose interest category is cameras and photography). An example of a configured prompt might be, "The customer's age is 30, their gender is male, their past purchase history is cameras and related accessories, and their interest category is cameras and photography. Please provide the best customer service advice for this customer." The server uses an AI model based on this prompt to generate advice such as, "Introduce this customer to the latest camera models and carefully explain photography techniques and related products." The staff member serves the customer based on this advice, and also collects feedback to reflect in future visits.

[0679] As described above, this system can be applied not only to providing childcare advice but also to customer service advice in physical stores, which is expected to increase customer satisfaction and continuously improve the quality of service.

[0680] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0681] Step 1:

[0682] The user (parent or store staff) uses a terminal to enter basic information. For example, the user uses a smartphone to enter information such as the age, gender, favorite foods, and past purchase history of the child or customer into an input form displayed on the application. This information becomes the input data.

[0683] Step 2:

[0684] The terminal converts the basic information entered into JSON format. A program running on the terminal serializes the data entered into the form into JSON data in key-value format. This JSON data is output.

[0685] Step 3:

[0686] The device sends basic information in JSON format to the server via an HTTP request. The device sends a POST request to the specified endpoint via an internet connection. The JSON data is the input, and the request reaching the server is the output.

[0687] Step 4:

[0688] The server saves the received JSON data to the database. The server parses the contents of the received request and stores the data in the appropriate fields using the API provided by the database. Saving to the database is the output.

[0689] Step 5:

[0690] The server provides prompts to a generative artificial intelligence model (e.g., GPT-3) based on the information stored in the database, generating personalized advice. The server reads the stored data, generates appropriate prompts, and sends the prompts to the AI ​​model. The generated advice is the output.

[0691] Step 6:

[0692] The server saves the generated advice back to the database. The server retrieves the generated text data and stores it in the corresponding field of the database. This saving to the database is the output.

[0693] Step 7:

[0694] The user uses a terminal to send a request to the server to obtain advice. The user sends an HTTP GET request to the server by, for example, pressing a button on an application. The request is the input, and the advice is the output.

[0695] Step 8:

[0696] The server receives a request from the device, fetches the generated advice from the database, and returns the content to the device in JSON format. The server performs a database query, obtains the generated advice, and returns it as a response in JSON format. This response is the output.

[0697] Step 9:

[0698] The user checks the advice displayed on the device and enters feedback as necessary. The user acts based on the displayed advice and enters the results and opinions in the application's input form. The input feedback is input data.

[0699] Step 10:

[0700] The device converts the feedback data into JSON format and sends it back to the server via an HTTP request. The device serializes the feedback into JSON data and sends a POST request to the specified endpoint. The request is the output.

[0701] Step 11:

[0702] The server stores the received feedback in a database to help improve the generative artificial intelligence model. The server parses the feedback data, stores it in a database, and then uses it to retrain the model and improve its performance. The database storage and model improvement are the outputs.

[0703] 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.

[0704] The system of the present invention allows parents to input basic information about their children, generates personalized parenting advice based on the child's personality and preferences, and further combines it with an emotion engine that recognizes the user's emotions, aiming to continuously improve the system based on feedback. The system is composed of a server, terminals, and users.

[0705] Enter your child's basic information

[0706] User

[0707] Parents enter their child's basic information (age, gender, favorite food, special skills, etc.) through a mobile or web app. For example, they enter the information of a 4-year-old boy (favorite food: apples, special skill: drawing).

[0708] Submission of Children's Information

[0709] Terminal

[0710] The entered child information is converted to JSON format and sent to the server. Specifically, the JSON data is sent as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[0711] Receiving and storing children's information

[0712] server

[0713] It processes the incoming POST request, parses the JSON data, connects to the database, saves the parsed information in the "children" table, and returns the ID of the newly created child as a response.

[0714] Emotion recognition

[0715] Terminal

[0716] When a parent operates the application, the emotion engine is activated and recognizes emotions by analyzing the user's voice input and facial expressions. The recognized emotion data is sent to the server.

[0717] Generating parenting advice

[0718] server

[0719] Based on the child's information and emotion data from the emotion engine, generative artificial intelligence (e.g., GPT-2 model) is used to generate parenting advice. For example, if a parent is feeling stressed, advice suggesting specific relaxation methods is generated.

[0720] Saving Parenting Advice

[0721] server

[0722] The generated parenting advice is saved in the "children" table of the database, specifically by saving the advice text in association with the corresponding child's ID.

[0723] Obtaining and viewing parenting advice

[0724] User

[0725] Parents obtain and display parenting advice. Advice generated by a request from the server is sent to the device.

[0726] Terminal

[0727] The device displays the parenting advice it has acquired on the user interface in a format that is easy for parents to understand.

[0728] Enter and submit feedback

[0729] User

[0730] Parents can input their feedback on the parenting advice, for example, by entering an evaluation such as "This advice was very helpful."

[0731] Terminal

[0732] Convert the feedback data into JSON format and send it to the server. Use an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[0733] Receiving and storing feedback

[0734] server

[0735] Save the received feedback in the "feedback" table of the database. Save the feedback content along with the child's ID in the "feedback" table of the database.

[0736] Model Improvements

[0737] server

[0738] The collected feedback is used to improve the generative artificial intelligence model. This improvement process involves analyzing the feedback data and retraining the model as needed. The feedback is then incorporated into the next advice generation process.

[0739] As a specific example, if a user inputs the information of a 4-year-old boy and facial expression recognition determines that he is feeling stressed, the generative AI will suggest a way to relax by "enjoying drawing together." If the parent who received this advice evaluates it as effective and sends it back to the server, the server will use that feedback to improve the AI ​​model and increase the accuracy of future advice.

[0740] As described above, the system of the present invention is an embodiment that provides individualized parenting advice specific to parents and children, and further recognizes the user's emotions and provides appropriate advice according to the context.

[0741] The processing flow will be explained below.

[0742] Step 1:

[0743] Users enter basic information about their children (age, gender, favorite foods, special skills, etc.) through a mobile or web app. For example, a parent enters information about a 4-year-old boy.

[0744] Step 2:

[0745] The device converts the entered child information into JSON format and sends it to the server. Specifically, it sends JSON data containing the child's basic information as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[0746] Step 3:

[0747] The server processes the received POST request, parses the JSON data, and saves the parsed data (age, gender, favorite food, special skills, etc.) in the "children" table in the database. Once saved, it returns the created child's ID as a response.

[0748] Step 4:

[0749] When a parent operates an application on the device, the emotion engine is activated. The emotion engine recognizes the user's voice input and facial expressions and analyzes their emotional state. For example, an emotion recognition algorithm can analyze the parent's face and tone of voice to determine if the user is feeling stressed.

[0750] Step 5:

[0751] The device converts the recognized emotion data into JSON format and sends it to the server. It uses an HTTP POST request to send the emotion data to a specific endpoint on the server (e.g., / submit_emotion).

[0752] Step 6:

[0753] The server stores the received emotion data in a database, in the "emotions" table of the database, in association with the child's ID.

[0754] Step 7:

[0755] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. For example, specific parenting advice is generated using the child's age, gender, favorite foods, special skills, and the parent's emotional state as input. If the parent is feeling stressed, advice including relaxation methods is generated.

[0756] Step 8:

[0757] The server stores the generated parenting advice in a database table called "children," associating the advice text with the corresponding child's ID.

[0758] Step 9:

[0759] A user submits a request to get parenting advice through the application, for example, a parent clicks a button and selects "Get Advice."

[0760] Step 10:

[0761] The device sends a GET request to the server to get parenting advice for a specific child. This is sent as a request to a specific endpoint on the server (e.g., / get_advice / {child_id}).

[0762] Step 11:

[0763] The server processes the received GET request, retrieves parenting advice associated with the child's ID from the database, and returns this advice in a JSON format as a response.

[0764] Step 12:

[0765] The device displays the parenting advice received from the server on the user interface in a format that is easy for parents to understand.

[0766] Step 13:

[0767] The user inputs feedback on the parenting advice, such as whether the advice was helpful or not, and what needs to be improved.

[0768] Step 14:

[0769] The device converts the feedback data into JSON format and sends it to the server. It uses an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[0770] Step 15:

[0771] The server stores the received feedback in a database, storing the feedback along with the child's ID in a "feedback" table in the database.

[0772] Step 16:

[0773] The server uses the collected feedback to improve the generative AI model. This process includes analyzing the feedback data, retraining the model, and evaluating its performance. The improved model is used to generate advice in future years.

[0774] Example 2

[0775] 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."

[0776] Conventional parenting advice systems struggle to provide advice that fully takes into account the individual personality and preferences of children and the emotional state of parents. They also lack a mechanism for effectively utilizing parental feedback to improve the system. This reduces the effectiveness of parenting advice and leads to insufficient support for parents.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0778] In this invention, the server includes means for a parent to input basic information about their child, means for transmitting the input information about the child to the server, means for receiving and storing the transmitted information, means for recognizing a user's emotions, generative artificial intelligence means for generating individualized parenting advice based on the child's personality and preferences, means for storing the generated parenting advice, means for providing the generated parenting advice to the parent, means for the parent to obtain the parenting advice, means for collecting feedback from the parent, and means for improving the generative artificial intelligence means based on the collected feedback. This makes it possible to provide useful parenting advice based on individual child information and the parent's emotional state, and to continuously improve the system by utilizing feedback from the parent.

[0779] "Means for parents to enter basic information about their children" refers to an interface that allows parents to enter information such as their children's age, gender, favorite foods, and special skills using a mobile or web app.

[0780] "Means for sending the entered child information to the server" refers to the function of converting the data entered by the user into JSON format and sending it to the specified endpoint on the server using an HTTP POST request.

[0781] "Means of receiving and storing the transmitted information" refers to the server's ability to process the received POST request, parse the JSON data, connect to a database, and store the information in the appropriate tables.

[0782] "Means for recognizing user emotions" refers to the function in which the emotion engine built into the application analyzes the user's voice input and facial expressions, and generates emotional data as a result.

[0783] "Generative artificial intelligence means for generating individualized childcare advice based on a child's personality and preferences" refers to a function that generates childcare advice using a generative artificial intelligence model (e.g., GPT-2) based on the child's basic information and the user's emotional data.

[0784] "Means for saving the generated parenting advice" refers to a function for saving the generated parenting advice in an appropriate table in a database and managing it in association with the ID of the corresponding child.

[0785] "Means for providing generated parenting advice to parents" refers to a function in which, when a parent requests parenting advice from an application, the server obtains the relevant advice, sends it to the terminal, and displays it on the user interface.

[0786] "Means for parents to obtain parenting advice" refers to the interface through which parents request and obtain up-to-date parenting advice via functionality within the application.

[0787] "Means for collecting feedback from parents" refers to a function that allows parents to enter ratings and comments on parenting advice within the application, convert them into JSON format, and send them to the server.

[0788] "Means for improving the generative artificial intelligence means based on the collected feedback" refers to the function of the server analyzing the collected feedback data, retraining the generative artificial intelligence model as necessary, and improving the advice generation process from the next time onwards.

[0789] MODE FOR CARRYING OUT THE INVENTION

[0790] The system of the present invention allows parents to input basic information about their children, generates personalized parenting advice based on the child's personality and preferences, and further combines it with an emotion engine that recognizes the user's emotions, aiming to continuously improve the system based on feedback. The system is composed of a server, terminals, and users.

[0791] Enter your child's basic information

[0792] User

[0793] Parents use a mobile or web app to enter basic information about their child, including the child's age, gender, favorite food, and special skills. For example, a parent might enter information such as "A 4-year-old boy whose favorite food is apples and whose special skill is drawing."

[0794] Submission of Children's Information

[0795] Terminal

[0796] The device converts the entered child information into JSON format and sends it to the server using an HTTP POST request. The destination endpoint is, for example, " / submit_info."

[0797] Receiving and storing children's information

[0798] server

[0799] The server processes the incoming POST request, parses the JSON data, and extracts the information that is stored in the "children" table in the database. Once saved, the newly created child's ID is returned in the response.

[0800] Emotion recognition

[0801] Terminal

[0802] The emotion engine built into the device is activated and analyzes the parent's voice input and facial expressions. The emotion engine recognizes the user's emotional state and generates emotion data. This data is sent to the server. For example, it may recognize that the parent is feeling stressed.

[0803] Generating parenting advice

[0804] server

[0805] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. The generated advice is a specific relaxation method, such as "How to relax by enjoying drawing together." This advice is generated in text format.

[0806] Saving Parenting Advice

[0807] server

[0808] The generated parenting advice is stored in the database in a "children" table, where the advice is associated with the child's ID.

[0809] Obtaining and viewing parenting advice

[0810] User

[0811] A parent sends a request to the server to get parenting advice through the application, for example by clicking a "Get the latest parenting advice" button.

[0812] Terminal

[0813] The device displays the parenting advice it receives from the server on its user interface. The displayed content is presented in a format that is easy for parents to understand. For example, the screen might say, "How to relax by enjoying drawing together."

[0814] Enter and submit feedback

[0815] User

[0816] Parents can enter their feedback on the parenting advice into a feedback form within the application, such as "This advice was very helpful."

[0817] Terminal

[0818] The feedback data is converted to JSON format and sent to the server using an HTTP POST request to an endpoint such as " / submit_feedback".

[0819] Receiving and storing feedback

[0820] server

[0821] The server processes the received feedback data and stores it in a database table called "feedback," associating the child's ID with the feedback content.

[0822] Model Improvements

[0823] server

[0824] The server analyzes the collected feedback data and retrains the generative AI model as needed, improving the advice generation process from the next time onwards. This improvement process allows the system to provide more appropriate advice that reflects parental feedback.

[0825] Examples of concrete examples and prompts

[0826] Examples of specific prompts include:

[0827] "Child information: 4-year-old boy, favorite food: apples, special skill: drawing. Parent's emotion: stress. Based on this information, please suggest specific parenting advice for the parents."

[0828] In this way, the system of the present invention is an embodiment that provides individualized parenting advice specific to parents and children, and further recognizes the user's emotions to provide appropriate advice according to the context.

[0829] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0830] Step 1:

[0831] Enter your child's basic information

[0832] User: Parents use a mobile or web app to enter basic information about their child, including the child's age, gender, favorite food, and special skills. Specifically, a parent might enter information such as "4 years old, male, favorite food: apples, special skill: drawing" into a form.

[0833] Input: Child's basic information (age, gender, favorite food, special skills)

[0834] Output: Entered child information

[0835] Step 2:

[0836] Submission of Children's Information

[0837] Terminal: Converts the entered child information into JSON format. Sends the generated JSON data as an HTTP POST request to the specified endpoint on the server. Specifically, the endpoint " / submit_info" is used.

[0838] Input: Basic information about the child entered by the user

[0839] Data processing: Convert to JSON format

[0840] Output: Child information in JSON format, sent as an HTTP POST request

[0841] Step 3:

[0842] Receiving and storing children's information

[0843] Server: Processes the incoming POST request, parses the JSON data, extracts the information, stores it in the "children" table in the database, and returns the newly created child's ID as a response.

[0844] Input: Child information in JSON format

[0845] Data operations: Parse JSON data and extract information

[0846] Output: Extracted information saved in database, newly generated child ID

[0847] Step 4:

[0848] Emotion recognition

[0849] Device: The emotion engine runs and analyzes the parent's voice input and facial expressions to recognize emotions. The recognized emotion data is sent to the server.

[0850] Input: Parent's voice input and facial expression data

[0851] Data Computation: Analysis and Emotional Data Generation by Emotion Engine

[0852] Output: Emotion data, sent to server

[0853] Step 5:

[0854] Generating parenting advice

[0855] Server: Generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. Specifically, it generates advice such as "How to relax by enjoying drawing together."

[0856] Input: Child's basic information, emotional data

[0857] Data Computation: Generating Parenting Advice Using Generative Artificial Intelligence Models

[0858] Output: Generated parenting advice

[0859] Step 6:

[0860] Saving Parenting Advice

[0861] Server: The generated parenting advice is saved in the "children" table of the database, associated with the ID of the child to whom the advice applies.

[0862] Input: Generated parenting advice

[0863] Data processing: linking advice to child ID

[0864] Output: Parenting advice stored in a database

[0865] Step 7:

[0866] Obtaining and viewing parenting advice

[0867] User: A parent sends a request to the server to get parenting advice through the application by clicking the "Get the latest parenting advice" button.

[0868] Input: Get request

[0869] Output: Parenting advice request

[0870] Device: The parenting advice obtained from the server is displayed on the user interface. The screen displays "How to relax by enjoying drawing together."

[0871] Input: Parenting advice from the server

[0872] Output: Parenting advice displayed in a user interface

[0873] Step 8:

[0874] Enter and submit feedback

[0875] User: Parents enter their feedback on the parenting advice into a feedback form within the application, such as "This advice was very helpful."

[0876] Input: Feedback

[0877] Output: Input feedback

[0878] Terminal: Convert the feedback data into JSON format and send it to the server using an HTTP POST request with the endpoint " / submit_feedback".

[0879] Input: Feedback

[0880] Data processing: Convert to JSON format

[0881] Output: Feedback data in JSON format, sent to the server

[0882] Step 9:

[0883] Receiving and storing feedback

[0884] Server: Processes the received feedback data and stores it in the "feedback" table in the database. The child's ID and the feedback content are associated.

[0885] Input: Feedback data in JSON format

[0886] Data calculation: Parsing and storing feedback data

[0887] Output: Feedback stored in a database

[0888] Step 10:

[0889] Model Improvements

[0890] Server: Analyzes the collected feedback data and retrains the generative AI model. This improvement process improves the accuracy of the advice generation process in future.

[0891] Input: Collected feedback data

[0892] Data Computation: Analyzing feedback data and retraining models

[0893] Output: An improved generative artificial intelligence model

[0894] (Application example 2)

[0895] 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."

[0896] Conventional parenting advice systems provide general advice without taking into account the parents' emotional state, making it difficult to provide appropriate advice tailored to the parents' circumstances and emotions. Furthermore, while feedback to improve the accuracy of advice provided by generative AI is common, specific improvements that reflect the parents' evaluations are insufficient. As a result, there is a need for a system that can reduce the stress and burden felt by parents and provide appropriate parenting methods for their children.

[0897] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0898] In this invention, the server includes means for parents to input basic information about their children, generative artificial intelligence means for generating personalized child-rearing advice based on the child's personality and preferences, means for saving the generated child-rearing advice, means for parents to obtain the child-rearing advice, means for collecting feedback from the parents, means for improving the generative artificial intelligence means based on the collected feedback, emotion recognition means for recognizing the emotions of the parents, and means for adjusting the generation of child-rearing advice based on emotion data recognized by the emotion recognition means. This makes it possible to provide child-rearing advice that takes the parent's emotional state into consideration and to continuously improve the accuracy of the advice based on the feedback.

[0899] "Parent" means a guardian who raises a child.

[0900] "Child's basic information" refers to important information such as the child's age, gender, favorite foods, and special skills.

[0901] "Generative artificial intelligence" refers to AI technology that can generate personalized advice and content based on input information.

[0902] "Emotion recognition means" refers to technology or devices that recognize emotions by analyzing the user's voice input and facial expressions.

[0903] "Parenting advice" refers to specific suggestions and advice regarding child rearing.

[0904] "Feedback" refers to the act of a user returning information, including ratings and opinions, regarding the advice provided.

[0905] A "database" refers to a system for structuring, storing, and managing various types of data.

[0906] "Server" refers to a computer system that provides services to clients over a network.

[0907] The system for implementing this invention comprises a server, a terminal, and a user. The specific flow is as follows.

[0908] First, the user (parent) enters basic information about their child, including age, gender, favorite food, and special skills. This allows parents to register their child's personality and preferences in the system. For example, a 4-year-old boy's favorite food is apples and his special skill is drawing can be entered through a mobile or web app.

[0909] Next, the device converts the entered child information into JSON format and sends it to the server. Specifically, it sends the JSON data as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[0910] The server processes the incoming POST request, parses the JSON data, stores the parsed information in the "children" table, and responds with the ID of the newly created child.

[0911] When a parent operates the application, emotion recognition is activated. This technology recognizes the user's emotions by analyzing the user's voice input and facial expressions. For example, if the parent is feeling stressed, the emotional data is recognized and sent to the server.

[0912] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's information and emotion data from the emotion engine. For example, if a parent is feeling stressed, advice will be generated suggesting specific relaxation methods, such as "relaxing by enjoying drawing together."

[0913] The generated parenting advice is then stored in the database in a "children" table, with the advice text associated with the corresponding child's ID.

[0914] The parent obtains parenting advice, and the advice generated by the server is sent to the device upon request. The obtained parenting advice is displayed on the device's user interface. The displayed content is presented in a format that is easy for the parent to understand.

[0915] Furthermore, parents can input their feedback on the parenting advice. For example, they can input a rating such as "This advice was very helpful." The feedback data is converted into JSON format and sent to the server. The JSON feedback data is sent to a specific endpoint (e.g., / submit_feedback) on the server using an HTTP POST request.

[0916] The server stores the received feedback in a database table called "feedback," along with the child's ID.

[0917] Finally, the server improves the generative artificial intelligence model based on the collected feedback. This improvement process involves analyzing the feedback data and retraining the model if necessary, so that the feedback can be incorporated into the next advice generation process.

[0918] An example of a specific prompt is as follows:

[0919] "I would like some suggestions for how a 4-year-old boy can relax through drawing when he is feeling stressed."

[0920] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0921] Step 1:

[0922] Parents enter basic information about their children. Parents (users) enter information such as their children's age, gender, favorite foods, and special skills through a mobile or web app. The entered information is temporarily stored in the device's local database. Input: Basic information about the child. Output: Entered information.

[0923] Step 2:

[0924] The device converts the entered child information into JSON format and sends it to the server. Specifically, the data is processed by organizing the entered information into a JSON object and sending it as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info). Input: Basic information about the child. Output: JSON format data and HTTP request.

[0925] Step 3:

[0926] The server receives the HTTP POST request and parses the JSON data. The parsed information is structured and stored in the server's database. Specifically, it is stored in the "children" table, and the newly generated child's ID is returned to the terminal as a response. Input: Basic information about the child in JSON format. Output: Data stored in the "children" table and the child's ID.

[0927] Step 4:

[0928] The emotion recognition means is activated while the parent (user) is operating the application. The device's camera and microphone are used to analyze the user's voice and facial expressions, and emotional data is acquired. The acquired emotional data is sent to the server in real time. Input: Parent's voice or facial expressions. Output: Emotional data and HTTP request.

[0929] Step 5:

[0930] The server parses the received emotion data and stores it in a database. In addition, it uses generative AI (e.g., GPT-2 model) to generate parenting advice based on the child's basic information and emotion data. Specific prompts are created and input into the generative AI to generate advice. Input: Emotion data and basic information about the child. Output: Generated parenting advice.

[0931] Step 6:

[0932] The server saves the generated parenting advice in the database. It associates the advice with the ID of the child in the "children" table and saves it. Input: The generated parenting advice. Output: The advice saved in the database.

[0933] Step 7:

[0934] A parent (user) sends a request to the server through the application to get parenting advice. The server searches for the corresponding advice and sends it to the device as an HTTP response. Input: Database query request. Output: Parenting advice sent as an HTTP response.

[0935] Step 8:

[0936] The device displays the received parenting advice on a user interface, allowing parents to view the information in an easy-to-understand format. Input: Advice received as an HTTP response. Output: Displayed parenting advice.

[0937] Step 9:

[0938] Parents (users) input feedback on the parenting advice provided. Feedback ratings and comments are entered through the application. The entered feedback is temporarily saved on the device. Input: Feedback content. Output: Entered feedback.

[0939] Step 10:

[0940] The device converts the feedback data into JSON format and sends it to the server. It then sends it again as an HTTP POST request to a specific endpoint on the server (e.g., / submit_feedback). Input: Feedback content. Output: JSON format data and HTTP request.

[0941] Step 11:

[0942] The server parses the received feedback and saves it in the "feedback" table of the database. It associates the child's ID with the feedback content and saves it. Input: Feedback data in JSON format. Output: Data saved in the "feedback" table.

[0943] Step 12:

[0944] The server improves the generative AI model based on the collected feedback. Specifically, it analyzes the feedback data and retrains the model as necessary. This allows the feedback to be utilized in the next advice generation process. Input: Feedback data. Output: Improved generative AI model.

[0945] 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.

[0946] 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.

[0947] 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.

[0948] [Third embodiment]

[0949] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0950] 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.

[0951] 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).

[0952] 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.

[0953] 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.

[0954] 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).

[0955] 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.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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."

[0961] The system of the present invention aims to allow parents to input basic information about their children, generate personalized parenting advice based on the child's personality and preferences, and continuously improve the system based on feedback. The system consists of a server, terminals, and users.

[0962] Enter your child's basic information

[0963] User

[0964] Parents enter basic information about their children, such as their age, gender, favorite foods, and special skills, through a mobile or web app.

[0965] Submission of Children's Information

[0966] Terminal

[0967] The entered child information is converted into JSON format and sent to the server. The data entered by the parent on the device is first converted into JSON format and sent to the server via an HTTP request.

[0968] Receiving and storing children's information

[0969] server

[0970] The received child information is saved in the database. The server connects to the database and saves the received child's basic information in each field appropriately.

[0971] Generating parenting advice

[0972] server

[0973] Generative AI (e.g., GPT-2 model) is used to generate parenting advice based on information about the child. Information about the child (such as age, gender, favorite foods, and special skills) is fed into the AI ​​model to generate individually customized advice.

[0974] Saving Parenting Advice

[0975] server

[0976] Save the generated parenting advice in a database. Save the generated text in a specific field in the database for future reference.

[0977] Obtaining and viewing parenting advice

[0978] User

[0979] The parent retrieves and displays the parenting advice. The parent again uses the device to send a request to retrieve the advice from the server.

[0980] Terminal

[0981] The fetched parenting advice is displayed in a user interface in a format that is easy for parents to understand.

[0982] Enter and submit feedback

[0983] User

[0984] Parents can input and submit feedback on the child-rearing advice. Parents who receive the advice can also input feedback on the content, such as an evaluation and areas for improvement.

[0985] Terminal

[0986] The feedback data is converted to JSON format and sent to the server. The input feedback is converted back to JSON format so that it can be securely sent to the server.

[0987] Receiving and storing feedback

[0988] server

[0989] The feedback received is stored in a database to help improve the generative artificial intelligence model. The feedback is stored in a database and later used as material for retraining and improving the AI ​​model.

[0990] Specific examples

[0991] Example 1

[0992] A parent enters information about a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing). This information is sent by the device to a server and stored in a database.

[0993] The server uses a generative artificial intelligence model based on the stored information to generate individualized parenting advice, such as, "She likes drawing, so if she cries, draw a picture with her to calm her down."

[0994] Parents receive this advice through their devices, and if they are satisfied with the displayed information, they can enter feedback such as "It was very helpful." This feedback is then sent back to the server and stored in a database.

[0995] The server analyzes the collected feedback and uses it to improve the performance of the generative artificial intelligence model.

[0996] As described above, the system of the present invention is an embodiment that can provide individual child-rearing advice specific to parents and children and can achieve continuous improvement.

[0997] The processing flow will be explained below.

[0998] Step 1:

[0999] The user enters basic information about the child (age, gender, favorite food, special skills, etc.) through the mobile app or web app, enters the information in the input form provided in the user interface, and clicks the submit button.

[1000] Step 2:

[1001] The device converts the entered child information into JSON format and sends it to the server. Specifically, it sends the JSON data as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[1002] Step 3:

[1003] The server processes the incoming POST request, parses the JSON data, connects to the database, saves the parsed information to the "children" table in the database, and returns the newly created child's ID as a response.

[1004] Step 4:

[1005] The server generates parenting advice using generative AI based on the child's basic information stored in the database. The generative AI receives input such as the child's age, gender, favorite foods, and special skills, and generates appropriate parenting advice text.

[1006] Step 5:

[1007] The server stores the generated parenting advice in the "children" table of the database, specifically by associating the advice text with the corresponding child's ID.

[1008] Step 6:

[1009] The user sends a request to the server through the application to get parenting advice for a child. This request is sent using the GET method.

[1010] Step 7:

[1011] The device displays the parenting advice received from the server on the user interface. Specifically, the device extracts the parenting advice from the JSON data returned from the server and displays it in a format that is easy for parents to understand.

[1012] Step 8:

[1013] The user inputs feedback on the parenting advice, which is then converted back to JSON format by the device.

[1014] Step 9:

[1015] The device sends the feedback data to the server by using an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[1016] Step 10:

[1017] The server stores the received feedback in a database, storing the feedback along with the child's ID in a "feedback" table in the database.

[1018] Step 11:

[1019] The server uses the collected feedback to improve the generative artificial intelligence model. This improvement process includes analyzing the feedback data and retraining the model as needed. The feedback is then incorporated into the next advice generation process.

[1020] Example 1

[1021] 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."

[1022] Today's parents often find it difficult to find individualized parenting methods that suit each child's personality and preferences. Furthermore, existing parenting advice is often general and may not be applicable to each family's specific situation. Therefore, there is a need for a system that allows parents to quickly and effectively obtain appropriate parenting advice.

[1023] 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.

[1024] In this invention, the server includes: means for a parent to input basic information about their child using a mobile app or web app; means for the device to convert the input information about the child into JSON format and send it to the server; means for the server to save the received information about the child in a database; means for the server to generate personalized parenting advice based on the information about the child using generative artificial intelligence; means for saving the generated parenting advice in a database; means for the parent to obtain the parenting advice; means for the device to display the fetched parenting advice; means for the parent to input and send feedback on the generated parenting advice; means for the device to convert the feedback data into JSON format and send it to the server; and means for the server to save the received feedback in a database to help improve the model of the generative artificial intelligence. This enables parents to quickly obtain appropriate parenting advice tailored to the individual characteristics of their child.

[1025] "Parents" are those who enter basic information about their children, receive parenting advice, and provide feedback.

[1026] A "mobile or web app" is a software application that runs on a smartphone or computer and allows parents to enter basic information about their children.

[1027] "Terminal" refers to a device or equipment that converts the information entered by the parent into JSON format and sends it to the server.

[1028] "JSON format" is a lightweight data description format widely used for data exchange, and is an abbreviation for JavaScript Object Notation.

[1029] An "HTTP request" is a form of communication protocol used by a client to send data to a server.

[1030] "Server" refers to a computer system that receives and stores data sent by users and generates child-rearing advice using generative artificial intelligence.

[1031] The "database" is a data management system for systematically storing basic information about children, generated parenting advice, feedback, and so on.

[1032] "Generative AI" refers to AI techniques that generate new information or suggestions based on data, and examples include the GPT-2 model.

[1033] "Parenting advice" refers to specific parenting advice and suggestions generated by generative artificial intelligence based on basic information about a child.

[1034] "Feedback" refers to opinions such as evaluations and improvements that users provide regarding the generated parenting advice.

[1035] "Fetch" refers to the operation of retrieving and obtaining data or information from a server.

[1036] "User interface" refers to the screen and operation method displayed on the terminal, and is the interface through which parents input information and check advice.

[1037] The system of the present invention consists of three main elements: a server, a terminal, and a user. The system as a whole is designed to smoothly carry out a series of processes: parents input basic information about their children, receive parenting advice generated based on that information, and provide feedback.

[1038] Enter your child's basic information

[1039] User

[1040] Parents use a mobile or web app to enter basic information about their child, such as their age, gender, favorite foods, and special skills. Specifically, parents fill out the required information in the application form and press the submit button. This information is stored on the device in JSON format.

[1041] Submission of Children's Information

[1042] Terminal

[1043] The device converts the child information entered by the user into JSON format and sends it to the server via an HTTP request. Specifically, the device makes a POST request to the backend endpoint (e.g., http: / / example.com / api / childinfo).

[1044] Receiving and storing children's information

[1045] server

[1046] The server receives the JSON data sent via the HTTP request, validates it, and then saves it to a database. The server connects to a database (e.g. MySQL) and stores the information in tables in the database, with the appropriate fields.

[1047] Generating parenting advice

[1048] server

[1049] The server retrieves basic information about the child from the database and supplies this to a generative artificial intelligence (e.g., GPT-2 model) to generate individualized parenting advice. Specifically, the server calls the AI ​​model's API and supplies a prompt to generate specific advice. An example of a prompt is, "Please tell me some parenting advice for a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing)."

[1050] Saving Parenting Advice

[1051] server

[1052] The server saves the generated parenting advice back into the database, and the generated text data is stored in a field of an appropriate table so that the user can refer to it later.

[1053] Obtaining and viewing parenting advice

[1054] User

[1055] The parent then uses the device to send a request for parenting advice and view the advice. The request is initiated by pressing the "Get Advice" button in the app.

[1056] Terminal

[1057] The device displays the parenting advice received from the server on a user interface. The device also receives the advice text returned from the server and displays it in a text view within the app.

[1058] Enter and submit feedback

[1059] User

[1060] Parents can provide feedback on the displayed parenting advice within the app by entering their ratings and comments in the app's feedback form and pressing the submit button.

[1061] Terminal

[1062] The device converts the feedback data into JSON format and sends it to the server via an HTTP request. The device also sends the feedback data as a POST request to the server's feedback endpoint.

[1063] Receiving and storing feedback

[1064] server

[1065] The server receives the feedback, validates it, and then stores it in a database. The stored feedback is used to improve the generative artificial intelligence model and is used as material for retraining and improving the AI ​​model later.

[1066] Through the above process, the system is able to provide individualized parenting advice tailored to parents and children, and to continuously improve its accuracy.

[1067] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1068] Step 1:

[1069] User

[1070] Parents use a mobile or web app to enter basic information about their child (age, gender, favorite foods, special skills, etc.). Specifically, they fill in the required information in the input form within the app and press the submit button. The input data is in the form of text, options, numbers, etc., and as the parent enters the information, individual basic information about the child is accumulated. The input is received from the application front end, converted into JSON format, and prepared for sending to the next process.

[1071] Input: Child's basic information (age, gender, favorite food, special skills, etc.)

[1072] Output: Child basic information in JSON format

[1073] Step 2:

[1074] Terminal

[1075] The device converts the child information entered by the user into JSON format and sends this data to the server via an HTTP request. Specifically, the data in the input form is converted into structured data (JSON format) by a program such as JavaScript. The device then makes a POST request to the backend endpoint (e.g., http: / / example.com / api / childinfo).

[1076] Input: Child basic information in JSON format

[1077] Output: HTTP POST request

[1078] Step 3:

[1079] server

[1080] The server receives the JSON data sent via the HTTP request. This data is validated and saved to the database. Specifically, the server parses the received JSON data, checks for invalid data, and stores it in the appropriate table in the database (e.g. MySQL). Validation includes checking for required fields and validating the data format.

[1081] Input: HTTP POST request (child's basic information in JSON format)

[1082] Output: Basic information about the child stored in the database

[1083] Step 4:

[1084] server

[1085] The server retrieves basic information about the child from the database and supplies it to a generative artificial intelligence (e.g., GPT-2 model) to generate individualized parenting advice. Specifically, the server uses the child's basic information retrieved from the database as part of a prompt sentence and calls the AI ​​model's API to generate advice. An example prompt sentence is, "Please tell me some parenting advice for a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing)."

[1086] Input: Basic information about the child retrieved from the database

[1087] Output: Generated parenting advice

[1088] Step 5:

[1089] server

[1090] The server stores the generated child-rearing advice in the database again, specifically, stores the generated text data in a field of an appropriate table so that the user can refer to it later.

[1091] Input: Generated parenting advice

[1092] Output: Parenting advice stored in a database

[1093] Step 6:

[1094] User

[1095] The parent again uses the device to send a request for parenting advice by pressing the "Get Advice" button in the app. The user's request is sent to the server via the device and processed there.

[1096] Input: Parenting advice request

[1097] Output: None

[1098] Terminal

[1099] The device displays the parenting advice received from the server in a user interface. Specifically, it retrieves the advice text returned from the server and displays it in a text view within the app.

[1100] Input: Parenting advice from the server

[1101] Output: Advice displayed in the user interface

[1102] Step 7:

[1103] User

[1104] Parents can provide feedback on the displayed parenting advice within the app by entering their ratings and comments in the app's feedback form and pressing the submit button.

[1105] Input: Feedback on parenting advice

[1106] Output: Feedback data in JSON format

[1107] Terminal

[1108] The terminal converts the feedback data into JSON format and sends it to the server via an HTTP request. Specifically, the terminal converts the feedback data back into JSON format and sends a POST request to the feedback endpoint.

[1109] Input: Feedback data in JSON format

[1110] Output: HTTP POST request

[1111] Step 8:

[1112] server

[1113] The server receives the feedback, validates it, and then stores it in a database. The stored feedback is used to improve the generative artificial intelligence model. Specifically, the feedback data is analyzed and used to retrain the AI ​​model and adjust its parameters.

[1114] Input: HTTP POST request (feedback data in JSON format)

[1115] Output: Feedback data stored in a database

[1116] (Application example 1)

[1117] 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."

[1118] Previous childcare advice systems only provided a means for parents to receive specific childcare advice for their children, and did not take into consideration customer service advice for physical stores. This made it difficult for store staff to provide optimal customer service, and as a result, customer satisfaction could not be expected to improve. There was also a lack of a system for using collected feedback to improve the quality of advice.

[1119] 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.

[1120] In this invention, the server includes: a means for parents to input basic information about their children; a generative artificial intelligence (AI) system that generates personalized childcare advice based on the child's personality and preferences; a means for saving the generated childcare advice; a means for parents to obtain the childcare advice; a means for collecting feedback from parents; a means for improving the generative AI system based on the collected feedback; a means for inputting basic information about store customers; a means for generating personalized customer service advice based on the customer's past purchase history and interests; a means for providing the generated customer service advice to store staff; and a means for collecting feedback from staff and using it to improve the generative AI system. This not only enables personalized childcare advice to be provided to parents, but also enables store staff to provide personalized customer service advice to customers, which is expected to result in improved customer satisfaction. Furthermore, the feedback can be used to continuously improve the quality of advice.

[1121] "Parents" are guardians responsible for raising their children.

[1122] A "child" is a young person who has not yet reached adulthood.

[1123] "Basic information" refers to information about an individual, such as age, gender, favorite foods, and special skills.

[1124] "Generative AI" is an AI system that generates individually optimized information and advice based on input data.

[1125] "Childcare advice" refers to specific guidance and advice regarding child rearing.

[1126] "Feedback" refers to the user's reactions and opinions regarding the advice provided.

[1127] A "server" is a computing device that processes and stores data.

[1128] A "terminal" is a device through which a user inputs data and receives results.

[1129] A "database" is a system for storing and managing information in an organized manner.

[1130] "Customer" means a person who uses a product or service.

[1131] A "store" is a place that offers goods and services.

[1132] "Customer service advice" is advice for providing effective product suggestions and services to customers.

[1133] "Staff" refers to employees who serve customers and perform administrative duties at the store.

[1134] "Purchase history" is a record of products and services that a customer has purchased in the past.

[1135] An "interest" is a field or thing in which an individual has a particular interest.

[1136] A "prompt sentence" is an instruction sentence input to a generative artificial intelligence.

[1137] A system for implementing this invention includes a means for parents to input basic information about their child, a generative artificial intelligence means for generating personalized child-rearing advice based on the child's personality and preferences, a means for saving the generated child-rearing advice, a means for parents to obtain the child-rearing advice, a means for collecting feedback from parents, a means for improving the generative artificial intelligence means based on the collected feedback, a means for inputting basic information about store customers, a means for generating personalized customer service advice based on the customer's past purchase history and interests, a means for providing the generated customer service advice to store staff, and a means for collecting feedback from staff and using it to improve the generative artificial intelligence means.

[1138] The system is built with a server-side backend using Python and Django, and uses OpenAI's GPT-3 model. The server stores the received data in a database (e.g., PostgreSQL) and generates personalized advice using generative artificial intelligence. The server also collects user feedback and stores it in the database.

[1139] The user (parent or store staff) accesses the system using a device (such as a smartphone or tablet) and enters the necessary information. The device converts the entered information into JSON format and sends it to the server via an HTTP request. The server stores the received information in a database, generates advice using generative artificial intelligence, and then sends the advice back to the device for display. The user acts based on the displayed advice and then sends feedback to the server via the device.

[1140] As a concrete example, a parent enters information about a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing). This information is sent by the device to a server and stored in a database. Based on the stored information, the server uses a generative artificial intelligence model to generate individual parenting advice. For example, specific advice such as "He likes drawing, so if he cries, draw a picture with him to calm him down" is generated.

[1141] Another example of application in a physical store is when a staff member inputs basic customer information (for example, a 30-year-old male who has previously purchased cameras and related accessories, and whose interest category is cameras and photography). An example of a configured prompt might be, "The customer's age is 30, their gender is male, their past purchase history is cameras and related accessories, and their interest category is cameras and photography. Please provide the best customer service advice for this customer." The server uses an AI model based on this prompt to generate advice such as, "Introduce this customer to the latest camera models and carefully explain photography techniques and related products." The staff member serves the customer based on this advice, and also collects feedback to reflect in future visits.

[1142] As described above, this system can be applied not only to providing childcare advice but also to customer service advice in physical stores, which is expected to increase customer satisfaction and continuously improve the quality of service.

[1143] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1144] Step 1:

[1145] The user (parent or store staff) uses a terminal to enter basic information. For example, the user uses a smartphone to enter information such as the age, gender, favorite foods, and past purchase history of the child or customer into an input form displayed on the application. This information becomes the input data.

[1146] Step 2:

[1147] The terminal converts the basic information entered into JSON format. A program running on the terminal serializes the data entered into the form into JSON data in key-value format. This JSON data is output.

[1148] Step 3:

[1149] The device sends basic information in JSON format to the server via an HTTP request. The device sends a POST request to the specified endpoint via an internet connection. The JSON data is the input, and the request reaching the server is the output.

[1150] Step 4:

[1151] The server saves the received JSON data to the database. The server parses the contents of the received request and stores the data in the appropriate fields using the API provided by the database. Saving to the database is the output.

[1152] Step 5:

[1153] The server provides prompts to a generative artificial intelligence model (e.g., GPT-3) based on the information stored in the database, generating personalized advice. The server reads the stored data, generates appropriate prompts, and sends the prompts to the AI ​​model. The generated advice is the output.

[1154] Step 6:

[1155] The server saves the generated advice back to the database. The server retrieves the generated text data and stores it in the corresponding field of the database. This saving to the database is the output.

[1156] Step 7:

[1157] The user uses a terminal to send a request to the server to obtain advice. The user sends an HTTP GET request to the server by, for example, pressing a button on an application. The request is the input, and the advice is the output.

[1158] Step 8:

[1159] The server receives a request from the device, fetches the generated advice from the database, and returns the content to the device in JSON format. The server performs a database query, obtains the generated advice, and returns it as a response in JSON format. This response is the output.

[1160] Step 9:

[1161] The user checks the advice displayed on the device and enters feedback as necessary. The user acts based on the displayed advice and enters the results and opinions in the application's input form. The input feedback is input data.

[1162] Step 10:

[1163] The device converts the feedback data into JSON format and sends it back to the server via an HTTP request. The device serializes the feedback into JSON data and sends a POST request to the specified endpoint. The request is the output.

[1164] Step 11:

[1165] The server stores the received feedback in a database to help improve the generative artificial intelligence model. The server parses the feedback data, stores it in a database, and then uses it to retrain the model and improve its performance. The database storage and model improvement are the outputs.

[1166] 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.

[1167] The system of the present invention allows parents to input basic information about their children, generates personalized parenting advice based on the child's personality and preferences, and further combines it with an emotion engine that recognizes the user's emotions, aiming to continuously improve the system based on feedback. The system is composed of a server, terminals, and users.

[1168] Enter your child's basic information

[1169] User

[1170] Parents enter their child's basic information (age, gender, favorite food, special skills, etc.) through a mobile or web app. For example, they enter the information of a 4-year-old boy (favorite food: apples, special skill: drawing).

[1171] Submission of Children's Information

[1172] Terminal

[1173] The entered child information is converted to JSON format and sent to the server. Specifically, the JSON data is sent as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[1174] Receiving and storing children's information

[1175] server

[1176] It processes the incoming POST request, parses the JSON data, connects to the database, saves the parsed information in the "children" table, and returns the ID of the newly created child as a response.

[1177] Emotion recognition

[1178] Terminal

[1179] When a parent operates the application, the emotion engine is activated and recognizes emotions by analyzing the user's voice input and facial expressions. The recognized emotion data is sent to the server.

[1180] Generating parenting advice

[1181] server

[1182] Based on the child's information and emotion data from the emotion engine, generative artificial intelligence (e.g., GPT-2 model) is used to generate parenting advice. For example, if a parent is feeling stressed, advice suggesting specific relaxation methods is generated.

[1183] Saving Parenting Advice

[1184] server

[1185] The generated parenting advice is saved in the "children" table of the database, specifically by saving the advice text in association with the corresponding child's ID.

[1186] Obtaining and viewing parenting advice

[1187] User

[1188] Parents obtain and display parenting advice. Advice generated by a request from the server is sent to the device.

[1189] Terminal

[1190] The device displays the parenting advice it has acquired on the user interface in a format that is easy for parents to understand.

[1191] Enter and submit feedback

[1192] User

[1193] Parents can input their feedback on the parenting advice, for example, by entering an evaluation such as "This advice was very helpful."

[1194] Terminal

[1195] Convert the feedback data into JSON format and send it to the server. Use an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[1196] Receiving and storing feedback

[1197] server

[1198] Save the received feedback in the "feedback" table of the database. Save the feedback content along with the child's ID in the "feedback" table of the database.

[1199] Model Improvements

[1200] server

[1201] The collected feedback is used to improve the generative artificial intelligence model. This improvement process involves analyzing the feedback data and retraining the model as needed. The feedback is then incorporated into the next advice generation process.

[1202] As a specific example, if a user inputs the information of a 4-year-old boy and facial expression recognition determines that he is feeling stressed, the generative AI will suggest a way to relax by "enjoying drawing together." If the parent who received this advice evaluates it as effective and sends it back to the server, the server will use that feedback to improve the AI ​​model and increase the accuracy of future advice.

[1203] As described above, the system of the present invention is an embodiment that provides individualized parenting advice specific to parents and children, and further recognizes the user's emotions and provides appropriate advice according to the context.

[1204] The processing flow will be explained below.

[1205] Step 1:

[1206] Users enter basic information about their children (age, gender, favorite foods, special skills, etc.) through a mobile or web app. For example, a parent enters information about a 4-year-old boy.

[1207] Step 2:

[1208] The device converts the entered child information into JSON format and sends it to the server. Specifically, it sends JSON data containing the child's basic information as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[1209] Step 3:

[1210] The server processes the received POST request, parses the JSON data, and saves the parsed data (age, gender, favorite food, special skills, etc.) in the "children" table in the database. Once saved, it returns the created child's ID as a response.

[1211] Step 4:

[1212] When a parent operates an application on the device, the emotion engine is activated. The emotion engine recognizes the user's voice input and facial expressions and analyzes their emotional state. For example, an emotion recognition algorithm can analyze the parent's face and tone of voice to determine if the user is feeling stressed.

[1213] Step 5:

[1214] The device converts the recognized emotion data into JSON format and sends it to the server. It uses an HTTP POST request to send the emotion data to a specific endpoint on the server (e.g., / submit_emotion).

[1215] Step 6:

[1216] The server stores the received emotion data in a database, in the "emotions" table of the database, in association with the child's ID.

[1217] Step 7:

[1218] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. For example, specific parenting advice is generated using the child's age, gender, favorite foods, special skills, and the parent's emotional state as input. If the parent is feeling stressed, advice including relaxation methods is generated.

[1219] Step 8:

[1220] The server stores the generated parenting advice in a database table called "children," associating the advice text with the corresponding child's ID.

[1221] Step 9:

[1222] A user submits a request to get parenting advice through the application, for example, a parent clicks a button and selects "Get Advice."

[1223] Step 10:

[1224] The device sends a GET request to the server to get parenting advice for a specific child. This is sent as a request to a specific endpoint on the server (e.g., / get_advice / {child_id}).

[1225] Step 11:

[1226] The server processes the received GET request, retrieves parenting advice associated with the child's ID from the database, and returns this advice in a JSON format as a response.

[1227] Step 12:

[1228] The device displays the parenting advice received from the server on the user interface in a format that is easy for parents to understand.

[1229] Step 13:

[1230] The user inputs feedback on the parenting advice, such as whether the advice was helpful or not, and what needs to be improved.

[1231] Step 14:

[1232] The device converts the feedback data into JSON format and sends it to the server. It uses an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[1233] Step 15:

[1234] The server stores the received feedback in a database, storing the feedback along with the child's ID in a "feedback" table in the database.

[1235] Step 16:

[1236] The server uses the collected feedback to improve the generative AI model. This process includes analyzing the feedback data, retraining the model, and evaluating its performance. The improved model is used to generate advice in future years.

[1237] Example 2

[1238] 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."

[1239] Conventional parenting advice systems struggle to provide advice that fully takes into account the individual personality and preferences of children and the emotional state of parents. They also lack a mechanism for effectively utilizing parental feedback to improve the system. This reduces the effectiveness of parenting advice and leads to insufficient support for parents.

[1240] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1241] In this invention, the server includes means for a parent to input basic information about their child, means for transmitting the input information about the child to the server, means for receiving and storing the transmitted information, means for recognizing a user's emotions, generative artificial intelligence means for generating individualized parenting advice based on the child's personality and preferences, means for storing the generated parenting advice, means for providing the generated parenting advice to the parent, means for the parent to obtain the parenting advice, means for collecting feedback from the parent, and means for improving the generative artificial intelligence means based on the collected feedback. This makes it possible to provide useful parenting advice based on individual child information and the parent's emotional state, and to continuously improve the system by utilizing feedback from the parent.

[1242] "Means for parents to enter basic information about their children" refers to an interface that allows parents to enter information such as their children's age, gender, favorite foods, and special skills using a mobile or web app.

[1243] "Means for sending the entered child information to the server" refers to the function of converting the data entered by the user into JSON format and sending it to the specified endpoint on the server using an HTTP POST request.

[1244] "Means of receiving and storing the transmitted information" refers to the server's ability to process the received POST request, parse the JSON data, connect to a database, and store the information in the appropriate tables.

[1245] "Means for recognizing user emotions" refers to the function in which the emotion engine built into the application analyzes the user's voice input and facial expressions, and generates emotional data as a result.

[1246] "Generative artificial intelligence means for generating individualized childcare advice based on a child's personality and preferences" refers to a function that generates childcare advice using a generative artificial intelligence model (e.g., GPT-2) based on the child's basic information and the user's emotional data.

[1247] "Means for saving the generated parenting advice" refers to a function for saving the generated parenting advice in an appropriate table in a database and managing it in association with the ID of the corresponding child.

[1248] "Means for providing generated parenting advice to parents" refers to a function in which, when a parent requests parenting advice from an application, the server obtains the relevant advice, sends it to the terminal, and displays it on the user interface.

[1249] "Means for parents to obtain parenting advice" refers to the interface through which parents request and obtain up-to-date parenting advice via functionality within the application.

[1250] "Means for collecting feedback from parents" refers to a function that allows parents to enter ratings and comments on parenting advice within the application, convert them into JSON format, and send them to the server.

[1251] "Means for improving the generative artificial intelligence means based on the collected feedback" refers to the function of the server analyzing the collected feedback data, retraining the generative artificial intelligence model as necessary, and improving the advice generation process from the next time onwards.

[1252] MODE FOR CARRYING OUT THE INVENTION

[1253] The system of the present invention allows parents to input basic information about their children, generates personalized parenting advice based on the child's personality and preferences, and further combines it with an emotion engine that recognizes the user's emotions, aiming to continuously improve the system based on feedback. The system is composed of a server, terminals, and users.

[1254] Enter your child's basic information

[1255] User

[1256] Parents use a mobile or web app to enter basic information about their child, including the child's age, gender, favorite food, and special skills. For example, a parent might enter information such as "A 4-year-old boy whose favorite food is apples and whose special skill is drawing."

[1257] Submission of Children's Information

[1258] Terminal

[1259] The device converts the entered child information into JSON format and sends it to the server using an HTTP POST request. The destination endpoint is, for example, " / submit_info."

[1260] Receiving and storing children's information

[1261] server

[1262] The server processes the incoming POST request, parses the JSON data, and extracts the information that is stored in the "children" table in the database. Once saved, the newly created child's ID is returned in the response.

[1263] Emotion recognition

[1264] Terminal

[1265] The emotion engine built into the device is activated and analyzes the parent's voice input and facial expressions. The emotion engine recognizes the user's emotional state and generates emotion data. This data is sent to the server. For example, it may recognize that the parent is feeling stressed.

[1266] Generating parenting advice

[1267] server

[1268] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. The generated advice is a specific relaxation method, such as "How to relax by enjoying drawing together." This advice is generated in text format.

[1269] Saving Parenting Advice

[1270] server

[1271] The generated parenting advice is stored in the database in a "children" table, where the advice is associated with the child's ID.

[1272] Obtaining and viewing parenting advice

[1273] User

[1274] A parent sends a request to the server to get parenting advice through the application, for example by clicking a "Get the latest parenting advice" button.

[1275] Terminal

[1276] The device displays the parenting advice it receives from the server on its user interface. The displayed content is presented in a format that is easy for parents to understand. For example, the screen might say, "How to relax by enjoying drawing together."

[1277] Enter and submit feedback

[1278] User

[1279] Parents can enter their feedback on the parenting advice into a feedback form within the application, such as "This advice was very helpful."

[1280] Terminal

[1281] The feedback data is converted to JSON format and sent to the server using an HTTP POST request to an endpoint such as " / submit_feedback".

[1282] Receiving and storing feedback

[1283] server

[1284] The server processes the received feedback data and stores it in a database table called "feedback," associating the child's ID with the feedback content.

[1285] Model Improvements

[1286] server

[1287] The server analyzes the collected feedback data and retrains the generative AI model as needed, improving the advice generation process from the next time onwards. This improvement process allows the system to provide more appropriate advice that reflects parental feedback.

[1288] Examples of concrete examples and prompts

[1289] Examples of specific prompts include:

[1290] "Child information: 4-year-old boy, favorite food: apples, special skill: drawing. Parent's emotion: stress. Based on this information, please suggest specific parenting advice for the parents."

[1291] In this way, the system of the present invention is an embodiment that provides individualized parenting advice specific to parents and children, and further recognizes the user's emotions to provide appropriate advice according to the context.

[1292] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1293] Step 1:

[1294] Enter your child's basic information

[1295] User: Parents use a mobile or web app to enter basic information about their child, including the child's age, gender, favorite food, and special skills. Specifically, a parent might enter information such as "4 years old, male, favorite food: apples, special skill: drawing" into a form.

[1296] Input: Child's basic information (age, gender, favorite food, special skills)

[1297] Output: Entered child information

[1298] Step 2:

[1299] Submission of Children's Information

[1300] Terminal: Converts the entered child information into JSON format. Sends the generated JSON data as an HTTP POST request to the specified endpoint on the server. Specifically, the endpoint " / submit_info" is used.

[1301] Input: Basic information about the child entered by the user

[1302] Data processing: Convert to JSON format

[1303] Output: Child information in JSON format, sent as an HTTP POST request

[1304] Step 3:

[1305] Receiving and storing children's information

[1306] Server: Processes the incoming POST request, parses the JSON data, extracts the information, stores it in the "children" table in the database, and returns the newly created child's ID as a response.

[1307] Input: Child information in JSON format

[1308] Data operations: Parse JSON data and extract information

[1309] Output: Extracted information saved in database, newly generated child ID

[1310] Step 4:

[1311] Emotion recognition

[1312] Device: The emotion engine runs and analyzes the parent's voice input and facial expressions to recognize emotions. The recognized emotion data is sent to the server.

[1313] Input: Parent's voice input and facial expression data

[1314] Data Computation: Analysis and Emotional Data Generation by Emotion Engine

[1315] Output: Emotion data, sent to server

[1316] Step 5:

[1317] Generating parenting advice

[1318] Server: Generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. Specifically, it generates advice such as "How to relax by enjoying drawing together."

[1319] Input: Child's basic information, emotional data

[1320] Data Computation: Generating Parenting Advice Using Generative Artificial Intelligence Models

[1321] Output: Generated parenting advice

[1322] Step 6:

[1323] Saving Parenting Advice

[1324] Server: The generated parenting advice is saved in the "children" table of the database, associated with the ID of the child to whom the advice applies.

[1325] Input: Generated parenting advice

[1326] Data processing: linking advice to child ID

[1327] Output: Parenting advice stored in a database

[1328] Step 7:

[1329] Obtaining and viewing parenting advice

[1330] User: A parent sends a request to the server to get parenting advice through the application by clicking the "Get the latest parenting advice" button.

[1331] Input: Get request

[1332] Output: Parenting advice request

[1333] Device: The parenting advice obtained from the server is displayed on the user interface. The screen displays "How to relax by enjoying drawing together."

[1334] Input: Parenting advice from the server

[1335] Output: Parenting advice displayed in a user interface

[1336] Step 8:

[1337] Enter and submit feedback

[1338] User: Parents enter their feedback on the parenting advice into a feedback form within the application, such as "This advice was very helpful."

[1339] Input: Feedback

[1340] Output: Input feedback

[1341] Terminal: Convert the feedback data into JSON format and send it to the server using an HTTP POST request with the endpoint " / submit_feedback".

[1342] Input: Feedback

[1343] Data processing: Convert to JSON format

[1344] Output: Feedback data in JSON format, sent to the server

[1345] Step 9:

[1346] Receiving and storing feedback

[1347] Server: Processes the received feedback data and stores it in the "feedback" table in the database. The child's ID and the feedback content are associated.

[1348] Input: Feedback data in JSON format

[1349] Data calculation: Parsing and storing feedback data

[1350] Output: Feedback stored in a database

[1351] Step 10:

[1352] Model Improvements

[1353] Server: Analyzes the collected feedback data and retrains the generative AI model. This improvement process improves the accuracy of the advice generation process in future.

[1354] Input: Collected feedback data

[1355] Data Computation: Analyzing feedback data and retraining models

[1356] Output: An improved generative artificial intelligence model

[1357] (Application example 2)

[1358] 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."

[1359] Conventional parenting advice systems provide general advice without taking into account the parents' emotional state, making it difficult to provide appropriate advice tailored to the parents' circumstances and emotions. Furthermore, while feedback to improve the accuracy of advice provided by generative AI is common, specific improvements that reflect the parents' evaluations are insufficient. As a result, there is a need for a system that can reduce the stress and burden felt by parents and provide appropriate parenting methods for their children.

[1360] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1361] In this invention, the server includes means for parents to input basic information about their children, generative artificial intelligence means for generating personalized child-rearing advice based on the child's personality and preferences, means for saving the generated child-rearing advice, means for parents to obtain the child-rearing advice, means for collecting feedback from the parents, means for improving the generative artificial intelligence means based on the collected feedback, emotion recognition means for recognizing the emotions of the parents, and means for adjusting the generation of child-rearing advice based on emotion data recognized by the emotion recognition means. This makes it possible to provide child-rearing advice that takes the parent's emotional state into consideration and to continuously improve the accuracy of the advice based on the feedback.

[1362] "Parent" means a guardian who raises a child.

[1363] "Child's basic information" refers to important information such as the child's age, gender, favorite foods, and special skills.

[1364] "Generative artificial intelligence" refers to AI technology that can generate personalized advice and content based on input information.

[1365] "Emotion recognition means" refers to technology or devices that recognize emotions by analyzing the user's voice input and facial expressions.

[1366] "Parenting advice" refers to specific suggestions and advice regarding child rearing.

[1367] "Feedback" refers to the act of a user returning information, including ratings and opinions, regarding the advice provided.

[1368] A "database" refers to a system for structuring, storing, and managing various types of data.

[1369] "Server" refers to a computer system that provides services to clients over a network.

[1370] The system for implementing this invention comprises a server, a terminal, and a user. The specific flow is as follows.

[1371] First, the user (parent) enters basic information about their child, including age, gender, favorite food, and special skills. This allows parents to register their child's personality and preferences in the system. For example, a 4-year-old boy's favorite food is apples and his special skill is drawing can be entered through a mobile or web app.

[1372] Next, the device converts the entered child information into JSON format and sends it to the server. Specifically, it sends the JSON data as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[1373] The server processes the incoming POST request, parses the JSON data, stores the parsed information in the "children" table, and responds with the ID of the newly created child.

[1374] When a parent operates the application, emotion recognition is activated. This technology recognizes the user's emotions by analyzing the user's voice input and facial expressions. For example, if the parent is feeling stressed, the emotional data is recognized and sent to the server.

[1375] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's information and emotion data from the emotion engine. For example, if a parent is feeling stressed, advice will be generated suggesting specific relaxation methods, such as "relaxing by enjoying drawing together."

[1376] The generated parenting advice is then stored in the database in a "children" table, with the advice text associated with the corresponding child's ID.

[1377] The parent obtains parenting advice, and the advice generated by the server is sent to the device upon request. The obtained parenting advice is displayed on the device's user interface. The displayed content is presented in a format that is easy for the parent to understand.

[1378] Furthermore, parents can input their feedback on the parenting advice. For example, they can input a rating such as "This advice was very helpful." The feedback data is converted into JSON format and sent to the server. The JSON feedback data is sent to a specific endpoint (e.g., / submit_feedback) on the server using an HTTP POST request.

[1379] The server stores the received feedback in a database table called "feedback," along with the child's ID.

[1380] Finally, the server improves the generative artificial intelligence model based on the collected feedback. This improvement process involves analyzing the feedback data and retraining the model if necessary, so that the feedback can be incorporated into the next advice generation process.

[1381] An example of a specific prompt is as follows:

[1382] "I would like some suggestions for how a 4-year-old boy can relax through drawing when he is feeling stressed."

[1383] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1384] Step 1:

[1385] Parents enter basic information about their children. Parents (users) enter information such as their children's age, gender, favorite foods, and special skills through a mobile or web app. The entered information is temporarily stored in the device's local database. Input: Basic information about the child. Output: Entered information.

[1386] Step 2:

[1387] The device converts the entered child information into JSON format and sends it to the server. Specifically, the data is processed by organizing the entered information into a JSON object and sending it as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info). Input: Basic information about the child. Output: JSON format data and HTTP request.

[1388] Step 3:

[1389] The server receives the HTTP POST request and parses the JSON data. The parsed information is structured and stored in the server's database. Specifically, it is stored in the "children" table, and the newly generated child's ID is returned to the terminal as a response. Input: Basic information about the child in JSON format. Output: Data stored in the "children" table and the child's ID.

[1390] Step 4:

[1391] The emotion recognition means is activated while the parent (user) is operating the application. The device's camera and microphone are used to analyze the user's voice and facial expressions, and emotional data is acquired. The acquired emotional data is sent to the server in real time. Input: Parent's voice or facial expressions. Output: Emotional data and HTTP request.

[1392] Step 5:

[1393] The server parses the received emotion data and stores it in a database. In addition, it uses generative AI (e.g., GPT-2 model) to generate parenting advice based on the child's basic information and emotion data. Specific prompts are created and input into the generative AI to generate advice. Input: Emotion data and basic information about the child. Output: Generated parenting advice.

[1394] Step 6:

[1395] The server saves the generated parenting advice in the database. It associates the advice with the ID of the child in the "children" table and saves it. Input: The generated parenting advice. Output: The advice saved in the database.

[1396] Step 7:

[1397] A parent (user) sends a request to the server through the application to get parenting advice. The server searches for the corresponding advice and sends it to the device as an HTTP response. Input: Database query request. Output: Parenting advice sent as an HTTP response.

[1398] Step 8:

[1399] The device displays the received parenting advice on a user interface, allowing parents to view the information in an easy-to-understand format. Input: Advice received as an HTTP response. Output: Displayed parenting advice.

[1400] Step 9:

[1401] Parents (users) input feedback on the parenting advice provided. Feedback ratings and comments are entered through the application. The entered feedback is temporarily saved on the device. Input: Feedback content. Output: Entered feedback.

[1402] Step 10:

[1403] The device converts the feedback data into JSON format and sends it to the server. It then sends it again as an HTTP POST request to a specific endpoint on the server (e.g., / submit_feedback). Input: Feedback content. Output: JSON format data and HTTP request.

[1404] Step 11:

[1405] The server parses the received feedback and saves it in the "feedback" table of the database. It associates the child's ID with the feedback content and saves it. Input: Feedback data in JSON format. Output: Data saved in the "feedback" table.

[1406] Step 12:

[1407] The server improves the generative AI model based on the collected feedback. Specifically, it analyzes the feedback data and retrains the model as necessary. This allows the feedback to be utilized in the next advice generation process. Input: Feedback data. Output: Improved generative AI model.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] [Fourth embodiment]

[1412] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1413] 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.

[1414] 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).

[1415] 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.

[1416] 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.

[1417] 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).

[1418] 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.

[1419] 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.

[1420] 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.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] 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."

[1425] The system of the present invention aims to allow parents to input basic information about their children, generate personalized parenting advice based on the child's personality and preferences, and continuously improve the system based on feedback. The system consists of a server, terminals, and users.

[1426] Enter your child's basic information

[1427] User

[1428] Parents enter basic information about their children, such as their age, gender, favorite foods, and special skills, through a mobile or web app.

[1429] Submission of Children's Information

[1430] Terminal

[1431] The entered child information is converted into JSON format and sent to the server. The data entered by the parent on the device is first converted into JSON format and sent to the server via an HTTP request.

[1432] Receiving and storing children's information

[1433] server

[1434] The received child information is saved in the database. The server connects to the database and saves the received child's basic information in each field appropriately.

[1435] Generating parenting advice

[1436] server

[1437] Generative AI (e.g., GPT-2 model) is used to generate parenting advice based on information about the child. Information about the child (such as age, gender, favorite foods, and special skills) is fed into the AI ​​model to generate individually customized advice.

[1438] Saving Parenting Advice

[1439] server

[1440] Save the generated parenting advice in a database. Save the generated text in a specific field in the database for future reference.

[1441] Obtaining and viewing parenting advice

[1442] User

[1443] The parent retrieves and displays the parenting advice. The parent again uses the device to send a request to retrieve the advice from the server.

[1444] Terminal

[1445] The fetched parenting advice is displayed in a user interface in a format that is easy for parents to understand.

[1446] Enter and submit feedback

[1447] User

[1448] Parents can input and submit feedback on the child-rearing advice. Parents who receive the advice can also input feedback on the content, such as an evaluation and areas for improvement.

[1449] Terminal

[1450] The feedback data is converted to JSON format and sent to the server. The input feedback is converted back to JSON format so that it can be securely sent to the server.

[1451] Receiving and storing feedback

[1452] server

[1453] The feedback received is stored in a database to help improve the generative artificial intelligence model. The feedback is stored in a database and later used as material for retraining and improving the AI ​​model.

[1454] Specific examples

[1455] Example 1

[1456] A parent enters information about a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing). This information is sent by the device to a server and stored in a database.

[1457] The server uses a generative artificial intelligence model based on the stored information to generate individualized parenting advice, such as, "She likes drawing, so if she cries, draw a picture with her to calm her down."

[1458] Parents receive this advice through their devices, and if they are satisfied with the displayed information, they can enter feedback such as "It was very helpful." This feedback is then sent back to the server and stored in a database.

[1459] The server analyzes the collected feedback and uses it to improve the performance of the generative artificial intelligence model.

[1460] As described above, the system of the present invention is an embodiment that can provide individual child-rearing advice specific to parents and children and can achieve continuous improvement.

[1461] The processing flow will be explained below.

[1462] Step 1:

[1463] The user enters basic information about the child (age, gender, favorite food, special skills, etc.) through the mobile app or web app, enters the information in the input form provided in the user interface, and clicks the submit button.

[1464] Step 2:

[1465] The device converts the entered child information into JSON format and sends it to the server. Specifically, it sends the JSON data as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[1466] Step 3:

[1467] The server processes the incoming POST request, parses the JSON data, connects to the database, saves the parsed information to the "children" table in the database, and returns the newly created child's ID as a response.

[1468] Step 4:

[1469] The server generates parenting advice using generative AI based on the child's basic information stored in the database. The generative AI receives input such as the child's age, gender, favorite foods, and special skills, and generates appropriate parenting advice text.

[1470] Step 5:

[1471] The server stores the generated parenting advice in the "children" table of the database, specifically by associating the advice text with the corresponding child's ID.

[1472] Step 6:

[1473] The user sends a request to the server through the application to get parenting advice for a child. This request is sent using the GET method.

[1474] Step 7:

[1475] The device displays the parenting advice received from the server on the user interface. Specifically, the device extracts the parenting advice from the JSON data returned from the server and displays it in a format that is easy for parents to understand.

[1476] Step 8:

[1477] The user inputs feedback on the parenting advice, which is then converted back to JSON format by the device.

[1478] Step 9:

[1479] The device sends the feedback data to the server by using an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[1480] Step 10:

[1481] The server stores the received feedback in a database, storing the feedback along with the child's ID in a "feedback" table in the database.

[1482] Step 11:

[1483] The server uses the collected feedback to improve the generative artificial intelligence model. This improvement process includes analyzing the feedback data and retraining the model as needed. The feedback is then incorporated into the next advice generation process.

[1484] Example 1

[1485] 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."

[1486] Today's parents often find it difficult to find individualized parenting methods that suit each child's personality and preferences. Furthermore, existing parenting advice is often general and may not be applicable to each family's specific situation. Therefore, there is a need for a system that allows parents to quickly and effectively obtain appropriate parenting advice.

[1487] 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.

[1488] In this invention, the server includes: means for a parent to input basic information about their child using a mobile app or web app; means for the device to convert the input information about the child into JSON format and send it to the server; means for the server to save the received information about the child in a database; means for the server to generate personalized parenting advice based on the information about the child using generative artificial intelligence; means for saving the generated parenting advice in a database; means for the parent to obtain the parenting advice; means for the device to display the fetched parenting advice; means for the parent to input and send feedback on the generated parenting advice; means for the device to convert the feedback data into JSON format and send it to the server; and means for the server to save the received feedback in a database to help improve the model of the generative artificial intelligence. This enables parents to quickly obtain appropriate parenting advice tailored to the individual characteristics of their child.

[1489] "Parents" are those who enter basic information about their children, receive parenting advice, and provide feedback.

[1490] A "mobile or web app" is a software application that runs on a smartphone or computer and allows parents to enter basic information about their children.

[1491] "Terminal" refers to a device or equipment that converts the information entered by the parent into JSON format and sends it to the server.

[1492] "JSON format" is a lightweight data description format widely used for data exchange, and is an abbreviation for JavaScript Object Notation.

[1493] An "HTTP request" is a form of communication protocol used by a client to send data to a server.

[1494] "Server" refers to a computer system that receives and stores data sent by users and generates child-rearing advice using generative artificial intelligence.

[1495] The "database" is a data management system for systematically storing basic information about children, generated parenting advice, feedback, and so on.

[1496] "Generative AI" refers to AI techniques that generate new information or suggestions based on data, and examples include the GPT-2 model.

[1497] "Parenting advice" refers to specific parenting advice and suggestions generated by generative artificial intelligence based on basic information about a child.

[1498] "Feedback" refers to opinions such as evaluations and improvements that users provide regarding the generated parenting advice.

[1499] "Fetch" refers to the operation of retrieving and obtaining data or information from a server.

[1500] "User interface" refers to the screen and operation method displayed on the terminal, and is the interface through which parents input information and check advice.

[1501] The system of the present invention consists of three main elements: a server, a terminal, and a user. The system as a whole is designed to smoothly carry out a series of processes: parents input basic information about their children, receive parenting advice generated based on that information, and provide feedback.

[1502] Enter your child's basic information

[1503] User

[1504] Parents use a mobile or web app to enter basic information about their child, such as their age, gender, favorite foods, and special skills. Specifically, parents fill out the required information in the application form and press the submit button. This information is stored on the device in JSON format.

[1505] Submission of Children's Information

[1506] Terminal

[1507] The device converts the child information entered by the user into JSON format and sends it to the server via an HTTP request. Specifically, the device makes a POST request to the backend endpoint (e.g., http: / / example.com / api / childinfo).

[1508] Receiving and storing children's information

[1509] server

[1510] The server receives the JSON data sent via the HTTP request, validates it, and then saves it to a database. The server connects to a database (e.g. MySQL) and stores the information in tables in the database, with the appropriate fields.

[1511] Generating parenting advice

[1512] server

[1513] The server retrieves basic information about the child from the database and supplies this to a generative artificial intelligence (e.g., GPT-2 model) to generate individualized parenting advice. Specifically, the server calls the AI ​​model's API and supplies a prompt to generate specific advice. An example of a prompt is, "Please tell me some parenting advice for a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing)."

[1514] Saving Parenting Advice

[1515] server

[1516] The server saves the generated parenting advice back into the database, and the generated text data is stored in a field of an appropriate table so that the user can refer to it later.

[1517] Obtaining and viewing parenting advice

[1518] User

[1519] The parent then uses the device to send a request for parenting advice and view the advice. The request is initiated by pressing the "Get Advice" button in the app.

[1520] Terminal

[1521] The device displays the parenting advice received from the server on a user interface. The device also receives the advice text returned from the server and displays it in a text view within the app.

[1522] Enter and submit feedback

[1523] User

[1524] Parents can provide feedback on the displayed parenting advice within the app by entering their ratings and comments in the app's feedback form and pressing the submit button.

[1525] Terminal

[1526] The device converts the feedback data into JSON format and sends it to the server via an HTTP request. The device also sends the feedback data as a POST request to the server's feedback endpoint.

[1527] Receiving and storing feedback

[1528] server

[1529] The server receives the feedback, validates it, and then stores it in a database. The stored feedback is used to improve the generative artificial intelligence model and is used as material for retraining and improving the AI ​​model later.

[1530] Through the above process, the system is able to provide individualized parenting advice tailored to parents and children, and to continuously improve its accuracy.

[1531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1532] Step 1:

[1533] User

[1534] Parents use a mobile or web app to enter basic information about their child (age, gender, favorite foods, special skills, etc.). Specifically, they fill in the required information in the input form within the app and press the submit button. The input data is in the form of text, options, numbers, etc., and as the parent enters the information, individual basic information about the child is accumulated. The input is received from the application front end, converted into JSON format, and prepared for sending to the next process.

[1535] Input: Child's basic information (age, gender, favorite food, special skills, etc.)

[1536] Output: Child basic information in JSON format

[1537] Step 2:

[1538] Terminal

[1539] The device converts the child information entered by the user into JSON format and sends this data to the server via an HTTP request. Specifically, the data in the input form is converted into structured data (JSON format) by a program such as JavaScript. The device then makes a POST request to the backend endpoint (e.g., http: / / example.com / api / childinfo).

[1540] Input: Child basic information in JSON format

[1541] Output: HTTP POST request

[1542] Step 3:

[1543] server

[1544] The server receives the JSON data sent via the HTTP request. This data is validated and saved to the database. Specifically, the server parses the received JSON data, checks for invalid data, and stores it in the appropriate table in the database (e.g. MySQL). Validation includes checking for required fields and validating the data format.

[1545] Input: HTTP POST request (child's basic information in JSON format)

[1546] Output: Basic information about the child stored in the database

[1547] Step 4:

[1548] server

[1549] The server retrieves basic information about the child from the database and supplies it to a generative artificial intelligence (e.g., GPT-2 model) to generate individualized parenting advice. Specifically, the server uses the child's basic information retrieved from the database as part of a prompt sentence and calls the AI ​​model's API to generate advice. An example prompt sentence is, "Please tell me some parenting advice for a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing)."

[1550] Input: Basic information about the child retrieved from the database

[1551] Output: Generated parenting advice

[1552] Step 5:

[1553] server

[1554] The server stores the generated child-rearing advice in the database again, specifically, stores the generated text data in a field of an appropriate table so that the user can refer to it later.

[1555] Input: Generated parenting advice

[1556] Output: Parenting advice stored in a database

[1557] Step 6:

[1558] User

[1559] The parent again uses the device to send a request for parenting advice by pressing the "Get Advice" button in the app. The user's request is sent to the server via the device and processed there.

[1560] Input: Parenting advice request

[1561] Output: None

[1562] Terminal

[1563] The device displays the parenting advice received from the server in a user interface. Specifically, it retrieves the advice text returned from the server and displays it in a text view within the app.

[1564] Input: Parenting advice from the server

[1565] Output: Advice displayed in the user interface

[1566] Step 7:

[1567] User

[1568] Parents can provide feedback on the displayed parenting advice within the app by entering their ratings and comments in the app's feedback form and pressing the submit button.

[1569] Input: Feedback on parenting advice

[1570] Output: Feedback data in JSON format

[1571] Terminal

[1572] The terminal converts the feedback data into JSON format and sends it to the server via an HTTP request. Specifically, the terminal converts the feedback data back into JSON format and sends a POST request to the feedback endpoint.

[1573] Input: Feedback data in JSON format

[1574] Output: HTTP POST request

[1575] Step 8:

[1576] server

[1577] The server receives the feedback, validates it, and then stores it in a database. The stored feedback is used to improve the generative artificial intelligence model. Specifically, the feedback data is analyzed and used to retrain the AI ​​model and adjust its parameters.

[1578] Input: HTTP POST request (feedback data in JSON format)

[1579] Output: Feedback data stored in a database

[1580] (Application example 1)

[1581] 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."

[1582] Previous childcare advice systems only provided a means for parents to receive specific childcare advice for their children, and did not take into consideration customer service advice for physical stores. This made it difficult for store staff to provide optimal customer service, and as a result, customer satisfaction could not be expected to improve. There was also a lack of a system for using collected feedback to improve the quality of advice.

[1583] 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.

[1584] In this invention, the server includes: a means for parents to input basic information about their children; a generative artificial intelligence (AI) system that generates personalized childcare advice based on the child's personality and preferences; a means for saving the generated childcare advice; a means for parents to obtain the childcare advice; a means for collecting feedback from parents; a means for improving the generative AI system based on the collected feedback; a means for inputting basic information about store customers; a means for generating personalized customer service advice based on the customer's past purchase history and interests; a means for providing the generated customer service advice to store staff; and a means for collecting feedback from staff and using it to improve the generative AI system. This not only enables personalized childcare advice to be provided to parents, but also enables store staff to provide personalized customer service advice to customers, which is expected to result in improved customer satisfaction. Furthermore, the feedback can be used to continuously improve the quality of advice.

[1585] "Parents" are guardians responsible for raising their children.

[1586] A "child" is a young person who has not yet reached adulthood.

[1587] "Basic information" refers to information about an individual, such as age, gender, favorite foods, and special skills.

[1588] "Generative AI" is an AI system that generates individually optimized information and advice based on input data.

[1589] "Childcare advice" refers to specific guidance and advice regarding child rearing.

[1590] "Feedback" refers to the user's reactions and opinions regarding the advice provided.

[1591] A "server" is a computing device that processes and stores data.

[1592] A "terminal" is a device through which a user inputs data and receives results.

[1593] A "database" is a system for storing and managing information in an organized manner.

[1594] "Customer" means a person who uses a product or service.

[1595] A "store" is a place that offers goods and services.

[1596] "Customer service advice" is advice for providing effective product suggestions and services to customers.

[1597] "Staff" refers to employees who serve customers and perform administrative duties at the store.

[1598] "Purchase history" is a record of products and services that a customer has purchased in the past.

[1599] An "interest" is a field or thing in which an individual has a particular interest.

[1600] A "prompt sentence" is an instruction sentence input to a generative artificial intelligence.

[1601] A system for implementing this invention includes a means for parents to input basic information about their child, a generative artificial intelligence means for generating personalized child-rearing advice based on the child's personality and preferences, a means for saving the generated child-rearing advice, a means for parents to obtain the child-rearing advice, a means for collecting feedback from parents, a means for improving the generative artificial intelligence means based on the collected feedback, a means for inputting basic information about store customers, a means for generating personalized customer service advice based on the customer's past purchase history and interests, a means for providing the generated customer service advice to store staff, and a means for collecting feedback from staff and using it to improve the generative artificial intelligence means.

[1602] The system is built with a server-side backend using Python and Django, and uses OpenAI's GPT-3 model. The server stores the received data in a database (e.g., PostgreSQL) and generates personalized advice using generative artificial intelligence. The server also collects user feedback and stores it in the database.

[1603] The user (parent or store staff) accesses the system using a device (such as a smartphone or tablet) and enters the necessary information. The device converts the entered information into JSON format and sends it to the server via an HTTP request. The server stores the received information in a database, generates advice using generative artificial intelligence, and then sends the advice back to the device for display. The user acts based on the displayed advice and then sends feedback to the server via the device.

[1604] As a concrete example, a parent enters information about a 4-year-old boy (age: 4, gender: male, favorite food: apples, special skill: drawing). This information is sent by the device to a server and stored in a database. Based on the stored information, the server uses a generative artificial intelligence model to generate individual parenting advice. For example, specific advice such as "He likes drawing, so if he cries, draw a picture with him to calm him down" is generated.

[1605] Another example of application in a physical store is when a staff member inputs basic customer information (for example, a 30-year-old male who has previously purchased cameras and related accessories, and whose interest category is cameras and photography). An example of a configured prompt might be, "The customer's age is 30, their gender is male, their past purchase history is cameras and related accessories, and their interest category is cameras and photography. Please provide the best customer service advice for this customer." The server uses an AI model based on this prompt to generate advice such as, "Introduce this customer to the latest camera models and carefully explain photography techniques and related products." The staff member serves the customer based on this advice, and also collects feedback to reflect in future visits.

[1606] As described above, this system can be applied not only to providing childcare advice but also to customer service advice in physical stores, which is expected to increase customer satisfaction and continuously improve the quality of service.

[1607] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1608] Step 1:

[1609] The user (parent or store staff) uses a terminal to enter basic information. For example, the user uses a smartphone to enter information such as the age, gender, favorite foods, and past purchase history of the child or customer into an input form displayed on the application. This information becomes the input data.

[1610] Step 2:

[1611] The terminal converts the basic information entered into JSON format. A program running on the terminal serializes the data entered into the form into JSON data in key-value format. This JSON data is output.

[1612] Step 3:

[1613] The device sends basic information in JSON format to the server via an HTTP request. The device sends a POST request to the specified endpoint via an internet connection. The JSON data is the input, and the request reaching the server is the output.

[1614] Step 4:

[1615] The server saves the received JSON data to the database. The server parses the contents of the received request and stores the data in the appropriate fields using the API provided by the database. Saving to the database is the output.

[1616] Step 5:

[1617] The server provides prompts to a generative artificial intelligence model (e.g., GPT-3) based on the information stored in the database, generating personalized advice. The server reads the stored data, generates appropriate prompts, and sends the prompts to the AI ​​model. The generated advice is the output.

[1618] Step 6:

[1619] The server saves the generated advice back to the database. The server retrieves the generated text data and stores it in the corresponding field of the database. This saving to the database is the output.

[1620] Step 7:

[1621] The user uses a terminal to send a request to the server to obtain advice. The user sends an HTTP GET request to the server by, for example, pressing a button on an application. The request is the input, and the advice is the output.

[1622] Step 8:

[1623] The server receives a request from the device, fetches the generated advice from the database, and returns the content to the device in JSON format. The server performs a database query, obtains the generated advice, and returns it as a response in JSON format. This response is the output.

[1624] Step 9:

[1625] The user checks the advice displayed on the device and enters feedback as necessary. The user acts based on the displayed advice and enters the results and opinions in the application's input form. The input feedback is input data.

[1626] Step 10:

[1627] The device converts the feedback data into JSON format and sends it back to the server via an HTTP request. The device serializes the feedback into JSON data and sends a POST request to the specified endpoint. The request is the output.

[1628] Step 11:

[1629] The server stores the received feedback in a database to help improve the generative artificial intelligence model. The server parses the feedback data, stores it in a database, and then uses it to retrain the model and improve its performance. The database storage and model improvement are the outputs.

[1630] 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.

[1631] The system of the present invention allows parents to input basic information about their children, generates personalized parenting advice based on the child's personality and preferences, and further combines it with an emotion engine that recognizes the user's emotions, aiming to continuously improve the system based on feedback. The system is composed of a server, terminals, and users.

[1632] Enter your child's basic information

[1633] User

[1634] Parents enter their child's basic information (age, gender, favorite food, special skills, etc.) through a mobile or web app. For example, they enter the information of a 4-year-old boy (favorite food: apples, special skill: drawing).

[1635] Submission of Children's Information

[1636] Terminal

[1637] The entered child information is converted to JSON format and sent to the server. Specifically, the JSON data is sent as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[1638] Receiving and storing children's information

[1639] server

[1640] It processes the incoming POST request, parses the JSON data, connects to the database, saves the parsed information in the "children" table, and returns the ID of the newly created child as a response.

[1641] Emotion recognition

[1642] Terminal

[1643] When a parent operates the application, the emotion engine is activated and recognizes emotions by analyzing the user's voice input and facial expressions. The recognized emotion data is sent to the server.

[1644] Generating parenting advice

[1645] server

[1646] Based on the child's information and emotion data from the emotion engine, generative artificial intelligence (e.g., GPT-2 model) is used to generate parenting advice. For example, if a parent is feeling stressed, advice suggesting specific relaxation methods is generated.

[1647] Saving Parenting Advice

[1648] server

[1649] The generated parenting advice is saved in the "children" table of the database, specifically by saving the advice text in association with the corresponding child's ID.

[1650] Obtaining and viewing parenting advice

[1651] User

[1652] Parents obtain and display parenting advice. Advice generated by a request from the server is sent to the device.

[1653] Terminal

[1654] The device displays the parenting advice it has acquired on the user interface in a format that is easy for parents to understand.

[1655] Enter and submit feedback

[1656] User

[1657] Parents can input their feedback on the parenting advice, for example, by entering an evaluation such as "This advice was very helpful."

[1658] Terminal

[1659] Convert the feedback data into JSON format and send it to the server. Use an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[1660] Receiving and storing feedback

[1661] server

[1662] Save the received feedback in the "feedback" table of the database. Save the feedback content along with the child's ID in the "feedback" table of the database.

[1663] Model Improvements

[1664] server

[1665] The collected feedback is used to improve the generative artificial intelligence model. This improvement process involves analyzing the feedback data and retraining the model as needed. The feedback is then incorporated into the next advice generation process.

[1666] As a specific example, if a user inputs the information of a 4-year-old boy and facial expression recognition determines that he is feeling stressed, the generative AI will suggest a way to relax by "enjoying drawing together." If the parent who received this advice evaluates it as effective and sends it back to the server, the server will use that feedback to improve the AI ​​model and increase the accuracy of future advice.

[1667] As described above, the system of the present invention is an embodiment that provides individualized parenting advice specific to parents and children, and further recognizes the user's emotions and provides appropriate advice according to the context.

[1668] The processing flow will be explained below.

[1669] Step 1:

[1670] Users enter basic information about their children (age, gender, favorite foods, special skills, etc.) through a mobile or web app. For example, a parent enters information about a 4-year-old boy.

[1671] Step 2:

[1672] The device converts the entered child information into JSON format and sends it to the server. Specifically, it sends JSON data containing the child's basic information as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[1673] Step 3:

[1674] The server processes the received POST request, parses the JSON data, and saves the parsed data (age, gender, favorite food, special skills, etc.) in the "children" table in the database. Once saved, it returns the created child's ID as a response.

[1675] Step 4:

[1676] When a parent operates an application on the device, the emotion engine is activated. The emotion engine recognizes the user's voice input and facial expressions and analyzes their emotional state. For example, an emotion recognition algorithm can analyze the parent's face and tone of voice to determine if the user is feeling stressed.

[1677] Step 5:

[1678] The device converts the recognized emotion data into JSON format and sends it to the server. It uses an HTTP POST request to send the emotion data to a specific endpoint on the server (e.g., / submit_emotion).

[1679] Step 6:

[1680] The server stores the received emotion data in a database, in the "emotions" table of the database, in association with the child's ID.

[1681] Step 7:

[1682] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. For example, specific parenting advice is generated using the child's age, gender, favorite foods, special skills, and the parent's emotional state as input. If the parent is feeling stressed, advice including relaxation methods is generated.

[1683] Step 8:

[1684] The server stores the generated parenting advice in a database table called "children," associating the advice text with the corresponding child's ID.

[1685] Step 9:

[1686] A user submits a request to get parenting advice through the application, for example, a parent clicks a button and selects "Get Advice."

[1687] Step 10:

[1688] The device sends a GET request to the server to get parenting advice for a specific child. This is sent as a request to a specific endpoint on the server (e.g., / get_advice / {child_id}).

[1689] Step 11:

[1690] The server processes the received GET request, retrieves parenting advice associated with the child's ID from the database, and returns this advice in a JSON format as a response.

[1691] Step 12:

[1692] The device displays the parenting advice received from the server on the user interface in a format that is easy for parents to understand.

[1693] Step 13:

[1694] The user inputs feedback on the parenting advice, such as whether the advice was helpful or not, and what needs to be improved.

[1695] Step 14:

[1696] The device converts the feedback data into JSON format and sends it to the server. It uses an HTTP POST request to send the feedback JSON data to a specific endpoint on the server (e.g., / submit_feedback).

[1697] Step 15:

[1698] The server stores the received feedback in a database, storing the feedback along with the child's ID in a "feedback" table in the database.

[1699] Step 16:

[1700] The server uses the collected feedback to improve the generative AI model. This process includes analyzing the feedback data, retraining the model, and evaluating its performance. The improved model is used to generate advice in future years.

[1701] Example 2

[1702] 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."

[1703] Conventional parenting advice systems struggle to provide advice that fully takes into account the individual personality and preferences of children and the emotional state of parents. They also lack a mechanism for effectively utilizing parental feedback to improve the system. This reduces the effectiveness of parenting advice and leads to insufficient support for parents.

[1704] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1705] In this invention, the server includes means for a parent to input basic information about their child, means for transmitting the input information about the child to the server, means for receiving and storing the transmitted information, means for recognizing a user's emotions, generative artificial intelligence means for generating individualized parenting advice based on the child's personality and preferences, means for storing the generated parenting advice, means for providing the generated parenting advice to the parent, means for the parent to obtain the parenting advice, means for collecting feedback from the parent, and means for improving the generative artificial intelligence means based on the collected feedback. This makes it possible to provide useful parenting advice based on individual child information and the parent's emotional state, and to continuously improve the system by utilizing feedback from the parent.

[1706] "Means for parents to enter basic information about their children" refers to an interface that allows parents to enter information such as their children's age, gender, favorite foods, and special skills using a mobile or web app.

[1707] "Means for sending the entered child information to the server" refers to the function of converting the data entered by the user into JSON format and sending it to the specified endpoint on the server using an HTTP POST request.

[1708] "Means of receiving and storing the transmitted information" refers to the server's ability to process the received POST request, parse the JSON data, connect to a database, and store the information in the appropriate tables.

[1709] "Means for recognizing user emotions" refers to the function in which the emotion engine built into the application analyzes the user's voice input and facial expressions, and generates emotional data as a result.

[1710] "Generative artificial intelligence means for generating individualized childcare advice based on a child's personality and preferences" refers to a function that generates childcare advice using a generative artificial intelligence model (e.g., GPT-2) based on the child's basic information and the user's emotional data.

[1711] "Means for saving the generated parenting advice" refers to a function for saving the generated parenting advice in an appropriate table in a database and managing it in association with the ID of the corresponding child.

[1712] "Means for providing generated parenting advice to parents" refers to a function in which, when a parent requests parenting advice from an application, the server obtains the relevant advice, sends it to the terminal, and displays it on the user interface.

[1713] "Means for parents to obtain parenting advice" refers to the interface through which parents request and obtain up-to-date parenting advice via functionality within the application.

[1714] "Means for collecting feedback from parents" refers to a function that allows parents to enter ratings and comments on parenting advice within the application, convert them into JSON format, and send them to the server.

[1715] "Means for improving the generative artificial intelligence means based on the collected feedback" refers to the function of the server analyzing the collected feedback data, retraining the generative artificial intelligence model as necessary, and improving the advice generation process from the next time onwards.

[1716] MODE FOR CARRYING OUT THE INVENTION

[1717] The system of the present invention allows parents to input basic information about their children, generates personalized parenting advice based on the child's personality and preferences, and further combines it with an emotion engine that recognizes the user's emotions, aiming to continuously improve the system based on feedback. The system is composed of a server, terminals, and users.

[1718] Enter your child's basic information

[1719] User

[1720] Parents use a mobile or web app to enter basic information about their child, including the child's age, gender, favorite food, and special skills. For example, a parent might enter information such as "A 4-year-old boy whose favorite food is apples and whose special skill is drawing."

[1721] Submission of Children's Information

[1722] Terminal

[1723] The device converts the entered child information into JSON format and sends it to the server using an HTTP POST request. The destination endpoint is, for example, " / submit_info."

[1724] Receiving and storing children's information

[1725] server

[1726] The server processes the incoming POST request, parses the JSON data, and extracts the information that is stored in the "children" table in the database. Once saved, the newly created child's ID is returned in the response.

[1727] Emotion recognition

[1728] Terminal

[1729] The emotion engine built into the device is activated and analyzes the parent's voice input and facial expressions. The emotion engine recognizes the user's emotional state and generates emotion data. This data is sent to the server. For example, it may recognize that the parent is feeling stressed.

[1730] Generating parenting advice

[1731] server

[1732] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. The generated advice is a specific relaxation method, such as "How to relax by enjoying drawing together." This advice is generated in text format.

[1733] Saving Parenting Advice

[1734] server

[1735] The generated parenting advice is stored in the database in a "children" table, where the advice is associated with the child's ID.

[1736] Obtaining and viewing parenting advice

[1737] User

[1738] A parent sends a request to the server to get parenting advice through the application, for example by clicking a "Get the latest parenting advice" button.

[1739] Terminal

[1740] The device displays the parenting advice it receives from the server on its user interface. The displayed content is presented in a format that is easy for parents to understand. For example, the screen might say, "How to relax by enjoying drawing together."

[1741] Enter and submit feedback

[1742] User

[1743] Parents can enter their feedback on the parenting advice into a feedback form within the application, such as "This advice was very helpful."

[1744] Terminal

[1745] The feedback data is converted to JSON format and sent to the server using an HTTP POST request to an endpoint such as " / submit_feedback".

[1746] Receiving and storing feedback

[1747] server

[1748] The server processes the received feedback data and stores it in a database table called "feedback," associating the child's ID with the feedback content.

[1749] Model Improvements

[1750] server

[1751] The server analyzes the collected feedback data and retrains the generative AI model as needed, improving the advice generation process from the next time onwards. This improvement process allows the system to provide more appropriate advice that reflects parental feedback.

[1752] Examples of concrete examples and prompts

[1753] Examples of specific prompts include:

[1754] "Child information: 4-year-old boy, favorite food: apples, special skill: drawing. Parent's emotion: stress. Based on this information, please suggest specific parenting advice for the parents."

[1755] In this way, the system of the present invention is an embodiment that provides individualized parenting advice specific to parents and children, and further recognizes the user's emotions to provide appropriate advice according to the context.

[1756] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1757] Step 1:

[1758] Enter your child's basic information

[1759] User: Parents use a mobile or web app to enter basic information about their child, including the child's age, gender, favorite food, and special skills. Specifically, a parent might enter information such as "4 years old, male, favorite food: apples, special skill: drawing" into a form.

[1760] Input: Child's basic information (age, gender, favorite food, special skills)

[1761] Output: Entered child information

[1762] Step 2:

[1763] Submission of Children's Information

[1764] Terminal: Converts the entered child information into JSON format. Sends the generated JSON data as an HTTP POST request to the specified endpoint on the server. Specifically, the endpoint " / submit_info" is used.

[1765] Input: Basic information about the child entered by the user

[1766] Data processing: Convert to JSON format

[1767] Output: Child information in JSON format, sent as an HTTP POST request

[1768] Step 3:

[1769] Receiving and storing children's information

[1770] Server: Processes the incoming POST request, parses the JSON data, extracts the information, stores it in the "children" table in the database, and returns the newly created child's ID as a response.

[1771] Input: Child information in JSON format

[1772] Data operations: Parse JSON data and extract information

[1773] Output: Extracted information saved in database, newly generated child ID

[1774] Step 4:

[1775] Emotion recognition

[1776] Device: The emotion engine runs and analyzes the parent's voice input and facial expressions to recognize emotions. The recognized emotion data is sent to the server.

[1777] Input: Parent's voice input and facial expression data

[1778] Data Computation: Analysis and Emotional Data Generation by Emotion Engine

[1779] Output: Emotion data, sent to server

[1780] Step 5:

[1781] Generating parenting advice

[1782] Server: Generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's basic information and emotional data. Specifically, it generates advice such as "How to relax by enjoying drawing together."

[1783] Input: Child's basic information, emotional data

[1784] Data Computation: Generating Parenting Advice Using Generative Artificial Intelligence Models

[1785] Output: Generated parenting advice

[1786] Step 6:

[1787] Saving Parenting Advice

[1788] Server: The generated parenting advice is saved in the "children" table of the database, associated with the ID of the child to whom the advice applies.

[1789] Input: Generated parenting advice

[1790] Data processing: linking advice to child ID

[1791] Output: Parenting advice stored in a database

[1792] Step 7:

[1793] Obtaining and viewing parenting advice

[1794] User: A parent sends a request to the server to get parenting advice through the application by clicking the "Get the latest parenting advice" button.

[1795] Input: Get request

[1796] Output: Parenting advice request

[1797] Device: The parenting advice obtained from the server is displayed on the user interface. The screen displays "How to relax by enjoying drawing together."

[1798] Input: Parenting advice from the server

[1799] Output: Parenting advice displayed in a user interface

[1800] Step 8:

[1801] Enter and submit feedback

[1802] User: Parents enter their feedback on the parenting advice into a feedback form within the application, such as "This advice was very helpful."

[1803] Input: Feedback

[1804] Output: Input feedback

[1805] Terminal: Convert the feedback data into JSON format and send it to the server using an HTTP POST request with the endpoint " / submit_feedback".

[1806] Input: Feedback

[1807] Data processing: Convert to JSON format

[1808] Output: Feedback data in JSON format, sent to the server

[1809] Step 9:

[1810] Receiving and storing feedback

[1811] Server: Processes the received feedback data and stores it in the "feedback" table in the database. The child's ID and the feedback content are associated.

[1812] Input: Feedback data in JSON format

[1813] Data calculation: Parsing and storing feedback data

[1814] Output: Feedback stored in a database

[1815] Step 10:

[1816] Model Improvements

[1817] Server: Analyzes the collected feedback data and retrains the generative AI model. This improvement process improves the accuracy of the advice generation process in future.

[1818] Input: Collected feedback data

[1819] Data Computation: Analyzing feedback data and retraining models

[1820] Output: An improved generative artificial intelligence model

[1821] (Application example 2)

[1822] 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."

[1823] Conventional parenting advice systems provide general advice without taking into account the parents' emotional state, making it difficult to provide appropriate advice tailored to the parents' circumstances and emotions. Furthermore, while feedback to improve the accuracy of advice provided by generative AI is common, specific improvements that reflect the parents' evaluations are insufficient. As a result, there is a need for a system that can reduce the stress and burden felt by parents and provide appropriate parenting methods for their children.

[1824] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1825] In this invention, the server includes means for parents to input basic information about their children, generative artificial intelligence means for generating personalized child-rearing advice based on the child's personality and preferences, means for saving the generated child-rearing advice, means for parents to obtain the child-rearing advice, means for collecting feedback from the parents, means for improving the generative artificial intelligence means based on the collected feedback, emotion recognition means for recognizing the emotions of the parents, and means for adjusting the generation of child-rearing advice based on emotion data recognized by the emotion recognition means. This makes it possible to provide child-rearing advice that takes the parent's emotional state into consideration and to continuously improve the accuracy of the advice based on the feedback.

[1826] "Parent" means a guardian who raises a child.

[1827] "Child's basic information" refers to important information such as the child's age, gender, favorite foods, and special skills.

[1828] "Generative artificial intelligence" refers to AI technology that can generate personalized advice and content based on input information.

[1829] "Emotion recognition means" refers to technology or devices that recognize emotions by analyzing the user's voice input and facial expressions.

[1830] "Parenting advice" refers to specific suggestions and advice regarding child rearing.

[1831] "Feedback" refers to the act of a user returning information, including ratings and opinions, regarding the advice provided.

[1832] A "database" refers to a system for structuring, storing, and managing various types of data.

[1833] "Server" refers to a computer system that provides services to clients over a network.

[1834] The system for implementing this invention comprises a server, a terminal, and a user. The specific flow is as follows.

[1835] First, the user (parent) enters basic information about their child, including age, gender, favorite food, and special skills. This allows parents to register their child's personality and preferences in the system. For example, a 4-year-old boy's favorite food is apples and his special skill is drawing can be entered through a mobile or web app.

[1836] Next, the device converts the entered child information into JSON format and sends it to the server. Specifically, it sends the JSON data as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info).

[1837] The server processes the incoming POST request, parses the JSON data, stores the parsed information in the "children" table, and responds with the ID of the newly created child.

[1838] When a parent operates the application, emotion recognition is activated. This technology recognizes the user's emotions by analyzing the user's voice input and facial expressions. For example, if the parent is feeling stressed, the emotional data is recognized and sent to the server.

[1839] The server generates parenting advice using generative artificial intelligence (e.g., GPT-2 model) based on the child's information and emotion data from the emotion engine. For example, if a parent is feeling stressed, advice will be generated suggesting specific relaxation methods, such as "relaxing by enjoying drawing together."

[1840] The generated parenting advice is then stored in the database in a "children" table, with the advice text associated with the corresponding child's ID.

[1841] The parent obtains parenting advice, and the advice generated by the server is sent to the device upon request. The obtained parenting advice is displayed on the device's user interface. The displayed content is presented in a format that is easy for the parent to understand.

[1842] Furthermore, parents can input their feedback on the parenting advice. For example, they can input a rating such as "This advice was very helpful." The feedback data is converted into JSON format and sent to the server. The JSON feedback data is sent to a specific endpoint (e.g., / submit_feedback) on the server using an HTTP POST request.

[1843] The server stores the received feedback in a database table called "feedback," along with the child's ID.

[1844] Finally, the server improves the generative artificial intelligence model based on the collected feedback. This improvement process involves analyzing the feedback data and retraining the model if necessary, so that the feedback can be incorporated into the next advice generation process.

[1845] An example of a specific prompt is as follows:

[1846] "I would like some suggestions for how a 4-year-old boy can relax through drawing when he is feeling stressed."

[1847] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1848] Step 1:

[1849] Parents enter basic information about their children. Parents (users) enter information such as their children's age, gender, favorite foods, and special skills through a mobile or web app. The entered information is temporarily stored in the device's local database. Input: Basic information about the child. Output: Entered information.

[1850] Step 2:

[1851] The device converts the entered child information into JSON format and sends it to the server. Specifically, the data is processed by organizing the entered information into a JSON object and sending it as an HTTP POST request to a specific endpoint on the server (e.g., / submit_info). Input: Basic information about the child. Output: JSON format data and HTTP request.

[1852] Step 3:

[1853] The server receives the HTTP POST request and parses the JSON data. The parsed information is structured and stored in the server's database. Specifically, it is stored in the "children" table, and the newly generated child's ID is returned to the terminal as a response. Input: Basic information about the child in JSON format. Output: Data stored in the "children" table and the child's ID.

[1854] Step 4:

[1855] The emotion recognition means is activated while the parent (user) is operating the application. The device's camera and microphone are used to analyze the user's voice and facial expressions, and emotional data is acquired. The acquired emotional data is sent to the server in real time. Input: Parent's voice or facial expressions. Output: Emotional data and HTTP request.

[1856] Step 5:

[1857] The server parses the received emotion data and stores it in a database. In addition, it uses generative AI (e.g., GPT-2 model) to generate parenting advice based on the child's basic information and emotion data. Specific prompts are created and input into the generative AI to generate advice. Input: Emotion data and basic information about the child. Output: Generated parenting advice.

[1858] Step 6:

[1859] The server saves the generated parenting advice in the database. It associates the advice with the ID of the child in the "children" table and saves it. Input: The generated parenting advice. Output: The advice saved in the database.

[1860] Step 7:

[1861] A parent (user) sends a request to the server through the application to get parenting advice. The server searches for the corresponding advice and sends it to the device as an HTTP response. Input: Database query request. Output: Parenting advice sent as an HTTP response.

[1862] Step 8:

[1863] The device displays the received parenting advice on a user interface, allowing parents to view the information in an easy-to-understand format. Input: Advice received as an HTTP response. Output: Displayed parenting advice.

[1864] Step 9:

[1865] Parents (users) input feedback on the parenting advice provided. Feedback ratings and comments are entered through the application. The entered feedback is temporarily saved on the device. Input: Feedback content. Output: Entered feedback.

[1866] Step 10:

[1867] The device converts the feedback data into JSON format and sends it to the server. It then sends it again as an HTTP POST request to a specific endpoint on the server (e.g., / submit_feedback). Input: Feedback content. Output: JSON format data and HTTP request.

[1868] Step 11:

[1869] The server parses the received feedback and saves it in the "feedback" table of the database. It associates the child's ID with the feedback content and saves it. Input: Feedback data in JSON format. Output: Data saved in the "feedback" table.

[1870] Step 12:

[1871] The server improves the generative AI model based on the collected feedback. Specifically, it analyzes the feedback data and retrains the model as necessary. This allows the feedback to be utilized in the next advice generation process. Input: Feedback data. Output: Improved generative AI model.

[1872] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1873] 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.

[1874] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1875] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1876] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1877] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1878] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1879] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1880] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1881] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1882] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1883] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1884] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1885] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1886] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1887] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1888] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1889] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1890] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1891] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1892] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1893] The following is further disclosed regarding the above embodiment.

[1894] (Claim 1)

[1895] A means for parents to enter basic information about their children;

[1896] a generative artificial intelligence means for generating personalized parenting advice based on a child's personality and preferences;

[1897] a means for storing the generated parenting advice;

[1898] avenues for parents to obtain parenting advice;

[1899] a means of gathering parental feedback;

[1900] a means for improving the generative artificial intelligence means based on the collected feedback; and

[1901] A system including:

[1902] (Claim 2)

[1903] 2. The system according to claim 1, wherein the basic information of the child includes age, gender, favorite foods, special skills, etc.

[1904] (Claim 3)

[1905] 2. The system according to claim 1, wherein when providing the generated parenting advice to the parent, the system displays text generated by the generative artificial intelligence means.

[1906] "Example 1"

[1907] (Claim 1)

[1908] A way for parents to enter their child's basic information using a mobile or web app;

[1909] A means for converting the entered child information into JSON format and sending it to the server;

[1910] a means for storing the received child information in a database by the server;

[1911] A means for the server to generate individual childcare advice based on information about the child using generative artificial intelligence;

[1912] a means for storing the generated parenting advice in a database;

[1913] avenues for parents to obtain parenting advice;

[1914] a means for displaying the parenting advice fetched by the device;

[1915] a means for parents to input and submit feedback on the generated parenting advice;

[1916] A means for the terminal to convert the feedback data into JSON format and send it to the server;

[1917] A means for storing the received feedback in a database to help improve the generative artificial intelligence model; and

[1918] A system including:

[1919] (Claim 2)

[1920] 2. The system according to claim 1, wherein the basic information of the child includes age, gender, favorite foods, special skills, etc.

[1921] (Claim 3)

[1922] 2. The system of claim 1, wherein the terminal displays the generated parenting advice on a user interface.

[1923] "Application Example 1"

[1924] (Claim 1)

[1925] A means for parents to enter basic information about their children;

[1926] a generative artificial intelligence means for generating personalized parenting advice based on a child's personality and preferences;

[1927] a means for storing the generated parenting advice;

[1928] avenues for parents to obtain parenting advice;

[1929] a means of gathering parental feedback;

[1930] a means for improving the generative artificial intelligence means based on the collected feedback; and

[1931] a means for inputting basic information about customers of the store;

[1932] A means of generating personalized customer service recommendations based on a customer's past purchase history and interests;

[1933] A means for providing the generated customer service advice to store staff;

[1934] a means of collecting feedback from staff to help improve generative artificial intelligence measures; and

[1935] A system including:

[1936] (Claim 2)

[1937] 2. The system according to claim 1, wherein the basic information of the child includes age, gender, favorite foods, special skills, etc.

[1938] (Claim 3)

[1939] 2. The system according to claim 1, wherein the generated text is displayed when providing the generated childcare advice and customer service advice to the parent and the store staff, respectively.

[1940] "Example 2: Combining Emotion Engines"

[1941] (Claim 1)

[1942] A means for parents to enter basic information about their children;

[1943] means for transmitting the entered child information to a server;

[1944] means for receiving and storing the transmitted information;

[1945] means for recognizing a user's emotion;

[1946] a generative artificial intelligence means for generating personalized parenting advice based on a child's personality and preferences;

[1947] a means for storing the generated parenting advice;

[1948] a means for providing the generated parenting advice to parents;

[1949] avenues for parents to obtain parenting advice;

[1950] a means of gathering parental feedback;

[1951] a means for improving the generative artificial intelligence means based on the collected feedback; and

[1952] A system including:

[1953] (Claim 2)

[1954] 2. The system according to claim 1, wherein the basic information of the child includes age, gender, favorite foods, special skills, etc.

[1955] (Claim 3)

[1956] 2. The system according to claim 1, wherein when providing the generated parenting advice to the parent, the system displays text generated by the generative artificial intelligence means.

[1957] "Application example 2 when combining emotion engines"

[1958] (Claim 1)

[1959] A means for parents to enter basic information about their children;

[1960] a generative artificial intelligence means for generating personalized parenting advice based on a child's personality and preferences;

[1961] a means for storing the generated parenting advice;

[1962] avenues for parents to obtain parenting advice;

[1963] a means of gathering parental feedback;

[1964] a means for improving the generative artificial intelligence means based on the collected feedback; and

[1965] an emotion recognition means for recognizing the parent's emotion;

[1966] a means for adjusting generation of parenting advice based on emotion data recognized by the emotion recognition means;

[1967] A system including:

[1968] (Claim 2)

[1969] 2. The system according to claim 1, wherein the basic information of the child includes age, gender, favorite foods, special skills, etc.

[1970] (Claim 3)

[1971] 2. The system according to claim 1, wherein when providing the generated parenting advice to the parent, the system displays text generated by the generative artificial intelligence means. [Explanation of symbols]

[1972] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for parents to enter basic information about their children; a generative artificial intelligence means for generating personalized parenting advice based on a child's personality and preferences; a means for storing the generated parenting advice; avenues for parents to obtain parenting advice; a means of gathering parental feedback; a means for improving the generative artificial intelligence means based on the collected feedback; and A system including:

2. 2. The system according to claim 1, wherein the basic information of the child includes age, sex, favorite foods, special skills, etc.

3. 2. The system according to claim 1, wherein the system displays text generated by the generative artificial intelligence means when providing the generated parenting advice to the parent.

Images