System

Abstract

To solve the problem that it is difficult to think of an appropriate phrase in a "communication notebook," which is communication with a guardian, and it takes time to generate the phrase.SOLUTION: A system includes: means for receiving contents of a communication notebook transmitted by a guardian; server means for inputting a prompt sentence used for the contents of the communication notebook into a generative AI model; means for returning a phrase generated by the server means to a terminal; and means for displaying the generated phrase on the terminal.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] The "communication notebook," which is a form of communication with parents, can either improve relationships with parents or worsen them depending on how it is used. However, it is difficult to instantly come up with appropriate phrases, and creating one takes time. [Means for solving the problem]

[0005] This invention uses AI that has learned the speaking techniques of veteran teachers to instantly grasp the diverse needs of parents and increase their sense of security and trust with accurate and thoughtful phrases. Furthermore, it instantly creates recommended reply sentences, eliminating the need to worry about starting from scratch, and shortens work time by simply correcting them, thereby maximizing communication effectiveness over time. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In one embodiment of the present invention, a learning database is provided for AI to learn the speaking techniques of veteran teachers. This database contains phrases actually used by veteran teachers, as well as appropriate phrases for each situation. The AI ​​learns from this database and generates appropriate phrases for the contents of the communication book from parents.

[0029] "Example 2"

[0030] Furthermore, the AI ​​has an algorithm that instantly understands the needs of parents. Specifically, it analyzes the contents of the parent's communication book and infers the parent's feelings and requests in response to the contents. Based on this inference, the AI ​​generates phrases that increase the parent's sense of security and trust.

[0031] "Example 3"

[0032] The AI ​​also has the ability to instantly create recommended reply suggestions. Specifically, the AI ​​generates appropriate reply suggestions based on the contents of the parent's communication book and the parent's estimated feelings and requests regarding that content. These reply suggestions can be used as a reference when teachers create replies to the communication book.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The AI ​​learns the speaking techniques of veteran teachers from a learning database, which contains phrases actually used by veteran teachers and appropriate phrases for each situation.

[0036] Step 2: Once the parent enters the information into the communication book, the AI ​​analyzes it.

[0037] Step 3: Based on the analysis results, the AI ​​selects appropriate phrases from the learning database and generates a reply message for the parent.

[0038] "Example 2"

[0039] Step 1: When the parent enters the information into the communication book, the AI ​​analyzes it.

[0040] Step 2: The AI ​​uses the analysis results to infer the parents' feelings and requests.

[0041] Step 3: Based on the estimation results, the AI ​​generates phrases that will increase parents' sense of security and trust.

[0042] "Example 3"

[0043] Step 1: When the parent enters the information into the communication book, the AI ​​analyzes it.

[0044] Step 2: The AI ​​generates appropriate reply suggestions based on the analysis results and the parent's feelings and requests.

[0045] Step 3: The generated reply ideas can be used as a reference for teachers to write their replies in the communication notebook.

[0046] Example 1

[0047] Next, a description will be given of Example 1 of Form 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."

[0048] In traditional educational settings, communication with parents is an important element, but it takes time and effort for teachers to respond individually. Furthermore, while there is a need to respond accurately and quickly to the diverse needs of parents, there is a lack of effective means to achieve this. It is particularly difficult for newly appointed or inexperienced teachers to acquire the same speaking skills and responsiveness as veteran teachers. This presents a challenge, making it difficult to increase parents' sense of security and trust.

[0049] 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.

[0050] In this invention, the server includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a terminal means that receives the contents of the contact book entered by the user, a server means that inputs the data received from the terminal as a prompt sentence to the generating AI model, a means that returns the generated phrase to the terminal, and a means that the terminal displays the generated phrase to the user. This makes it possible to respond quickly and accurately to the contents of the contact book from parents. Furthermore, by having the artificial intelligence that has learned the speaking techniques of veteran teachers generate appropriate phrases, parents' sense of security and trust can be increased.

[0051] A "veteran educator" is a teacher who has many years of experience in the educational field and possesses strong leadership and communication skills.

[0052] "Artificial intelligence" refers to the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[0053] A "terminal" is a device operated by a user, and refers to hardware such as a smartphone or PC.

[0054] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to generate appropriate output from input data.

[0055] A "prompt sentence" is text data that is input into a generative AI model; it is the sentence that the model analyzes and uses to generate the appropriate output.

[0056] A "server" is a computer system that processes and stores data on a network.

[0057] A "phrase" is a short sentence or expression used in a particular situation or context.

[0058] A "communication notebook" is a communication tool that allows parents and teachers to share information about their children's status and other important matters.

[0059] This invention is a system that optimizes communication with parents by using artificial intelligence that has learned the speaking skills of veteran educators. Specific embodiments of this system will be described below.

[0060] System configuration

[0061] server

[0062] The server maintains a learning database for learning the speaking techniques of veteran educators. This database contains phrases actually used by veteran educators, as well as appropriate phrases for each situation. The server uses a generative AI model to learn from this database. Specifically, the server uses Python and utilizes machine learning libraries such as TENSORFLOW (registered trademark) and PyTorch to analyze the phrases in the database and train the model. The trained model generates appropriate phrases for the contents of parental communication notebooks.

[0063] Terminal

[0064] The device is responsible for sending the parental contact information entered by the user to the server. The device collects text data entered by the user through a web browser or dedicated application and sends it to the server. For example, the device uses a programming language such as JavaScript (registered trademark) or Swift to build a user interface and call an API that sends the input data to the server.

[0065] User

[0066] The user uses the device to input the contents of the parent's contact book. The contents entered by the user are sent to the server via the device. The server uses a generative AI model to generate an appropriate phrase for the input contents and responds to the user.

[0067] Specific examples

[0068] Prompt Sentence Examples

[0069] 1. Prompt: "My child seems to be having a bad day at school lately."

[0070] Generated phrase: "Thank you for your concern about your child. We will keep a close eye on him."

[0071] 2. Prompt: "I can't seem to keep up with the class."

[0072] Generated phrase: "We apologize for any inconvenience caused. We will review the lesson and provide individual support."

[0073] 3. Prompt: "He doesn't seem to get along with the other kids."

[0074] Generated phrase: "We understand your concerns about your child's social skills. We will help them make friends through whole-class activities."

[0075] In this way, the server uses the generative AI model to generate appropriate phrases in response to the user's input, enabling quick and accurate communication with parents, increasing their sense of security and trust.

[0076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0077] Step 1:

[0078] The user uses the terminal to input the contents of the address book.

[0079] Input: The user types text into an input field in a contacts application.

[0080] Specific operation: The user opens a contacts application using a device such as a smartphone or PC and enters information such as, "My child seems to be in a bad mood at school recently."

[0081] Output: The entered text data is temporarily stored in the terminal.

[0082] Step 2:

[0083] The terminal transmits the input data to the server.

[0084] Input: Text data entered by the user.

[0085] Specific operation: The device uses a programming language such as JavaScript or Swift to generate an HTTP request and send input data to the server. For example, it sends data to the endpoint "POST / api / v1 / messages".

[0086] Output: The server receives the HTTP request and retrieves the text data.

[0087] Step 3:

[0088] The server receives the data and inputs it as a prompt to the generative AI model.

[0089] Input: Text data sent from the terminal.

[0090] How it works: The server analyzes the received text data and inputs it as prompts to the generative AI model. Specifically, it uses Python to input data to the model using machine learning libraries such as TensorFlow and PyTorch.

[0091] Output: The generative AI model receives the prompt and begins parsing it.

[0092] Step 4:

[0093] A generative AI model generates appropriate phrases.

[0094] Input: The prompt text entered by the server.

[0095] How it works: The generative AI model analyzes the prompt and generates appropriate phrases based on phrases in the database, such as "Thank you for your concern about your child. We will keep a close eye on them."

[0096] Output: The generated phrase is returned to the server.

[0097] Step 5:

[0098] The server sends the generated phrase back to the device.

[0099] Input: The phrase returned by the generative AI model.

[0100] Specific operation: The server receives the generated phrase and returns it to the terminal as an HTTP response. Specifically, it returns the generated phrase along with the status code "200 OK".

[0101] Output: The device receives the HTTP response and retrieves the generated phrase.

[0102] Step 6:

[0103] The terminal displays the generated phrase to the user.

[0104] Input: The generated phrase returned by the server.

[0105] Specific operation: The device displays the phrase received from the server on the user interface. The user checks the generated phrase and replies to the parent if necessary. For example, it could display "Thank you for your concern about your child. We will keep a close eye on them."

[0106] Output: User reviews the generated phrase and responds appropriately to the parent.

[0107] (Application example 1)

[0108] Next, a description will be given of Application Example 1 of Embodiment 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."

[0109] Customer service in traditional brick-and-mortar stores relies heavily on the experience and skills of staff, and it can be difficult for new or inexperienced staff to instantly provide the right phrases and methods of response. Furthermore, there was a lack of support for smooth customer interaction, making it necessary to improve customer satisfaction.

[0110] 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.

[0111] In this invention, the server includes AI means that has learned the speaking techniques of veteran teachers, means that instantly grasp the diverse needs of parents, means that increase parents' peace of mind and trust with accurate and thoughtful phrases, means that instantly create recommended reply sentences, means that are installed in smart glasses to support store staff in their conversations with customers, means that recognize the customer's speech and suggest appropriate phrases in real time, and means that display the generated phrases on the display of the smart glasses. This enables store staff to smoothly communicate with customers and improve customer satisfaction.

[0112] "AI means that have learned the speaking techniques of veteran teachers" refers to an artificial intelligence means that learns phrases used by veteran teachers and appropriate phrases depending on the situation, and generates appropriate responses based on this.

[0113] "Means for instantly grasping the diverse needs of parents" refers to means for quickly understanding communications and requests from parents and responding accordingly.

[0114] "Means of increasing parents' sense of security and trust through accurate and thoughtful phrases" refers to means of providing parents with appropriate and heartfelt phrases to increase their sense of security and trust.

[0115] The "means for instantly creating suggested reply sentences" is a means for quickly generating appropriate reply sentences based on the content of the message from the parent.

[0116] "A means to be installed on smart glasses to support staff in physical stores in their interactions with customers" refers to a means to be installed on smart glasses worn by staff in physical stores in order to support their interactions with customers.

[0117] "A means for recognizing the speech of a customer and suggesting appropriate phrases in real time" refers to a means for converting what a customer says into text using speech recognition technology and suggesting appropriate phrases in real time based on that text.

[0118] The "means for displaying the generated phrase on the display of the smart glasses" refers to a means for displaying the generated appropriate phrase on the display of the smart glasses so that the staff can confirm it.

[0119] As an embodiment of the present invention, a system is described that is installed in smart glasses to support staff in a brick-and-mortar store in interacting with customers.

[0120] First, the system uses the following hardware and software:

[0121] Hardware: Smart glasses (with built-in camera and microphone)

[0122] Software: OpenAI® API, OpenCV, SpeechRecognition library

[0123] System configuration

[0124] 1. Speech Recognition: The microphone in the smart glasses is used to record the customer's speech and then the SpeechRecognition library is used to convert it into text, allowing us to obtain what the customer is saying in real time.

[0125] 2. Phrase generation: The converted text is sent to the OpenAI API to generate appropriate phrases. A generative AI model is used to select the most appropriate phrases from a database of expert speaking skills.

[0126] 3. Display: The generated phrase is displayed on the smart glasses screen, allowing staff to check the appropriate phrase in real time and smoothly communicate with the customer.

[0127] Specific examples

[0128] For example, if a customer says, "Tell me more about this product," the system works as follows:

[0129] 1. The microphone in the smart glasses records what the customer says, and the SpeechRecognition library converts it into text: "Tell me more about this product."

[0130] 2. Based on the converted text, the OpenAI API generates an appropriate phrase, such as "This product uses the latest technology and is particularly durable."

[0131] 3. The generated phrase is displayed on the smart glasses display, and the staff member checks it and explains it to the customer.

[0132] Prompt Sentence Examples

[0133] If a customer says, "Tell me more about this product," here are some example prompts:

[0134] Generate the right phrases in your customer conversations: Tell me more about this product

[0135] In this way, store staff can smoothly communicate with customers, improving customer satisfaction.

[0136] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0137] Step 1:

[0138] The microphone in the smart glasses records what the customer says. The input is the customer's voice and the output is the recorded voice data. The microphone in the smart glasses captures what the customer says and saves it as voice data.

[0139] Step 2:

[0140] The device converts the recorded voice data into text using the SpeechRecognition library. The input is the recorded voice data, and the output is text data. The SpeechRecognition library analyzes the voice data and generates the corresponding text.

[0141] Step 3:

[0142] The device sends the converted text data to the OpenAI API, which generates an appropriate phrase. The input is the text data, and the output is the generated phrase. The OpenAI API generates a prompt based on the text data and uses a generative AI model to generate the optimal phrase.

[0143] Step 4:

[0144] The server displays the generated phrase on the smart glasses display. The input is the generated phrase and the output is the phrase displayed on the display. The smart glasses display displays the generated phrase so that the staff can check it.

[0145] Step 5:

[0146] The user (staff member) responds appropriately to the customer based on the phrases displayed on the display. The input is the phrase displayed on the display, and the output is the response to the customer. The staff member uses the displayed phrases as a reference to provide an appropriate explanation or response to the customer.

[0147] Example 2

[0148] Next, a description will be given of Example 2 of Form 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."

[0149] Conventional communication systems for parents have difficulty accurately grasping parents' feelings and specific requests and responding appropriately. As a result, parents' anxieties and dissatisfaction remain unresolved, making it difficult to build trusting relationships. Furthermore, while it is necessary to generate swift and accurate replies, there has been a lack of automated methods for this.

[0150] 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.

[0151] In this invention, the server includes means for receiving the contents of the contact book sent from the parent / guardian, means for preprocessing the received contents, means for analyzing the preprocessed contents to estimate the parent / guardian's feelings, means for estimating the parent / guardian's requests, means for generating phrases that increase the parent / guardian's sense of security and trust based on the estimation results, and means for sending the generated phrases to the parent / guardian. This makes it possible to accurately grasp the parent / guardian's feelings and requests and automatically generate a quick and appropriate reply.

[0152] "Guardian" refers to a person who is responsible for a child's education and life.

[0153] "Communication book" refers to a document or digital medium for exchanging information between parents and educational institutions.

[0154] "Server" refers to a computer system that receives, processes, and transmits data over a network.

[0155] "Preprocessing" refers to a series of operations that shape data and convert it into a format that is easier to analyze before data analysis.

[0156] "Analysis" refers to the process of examining data in detail to understand its structure and meaning.

[0157] "Emotions" refers to psychological states that are estimated based on what parents write in their communication books.

[0158] "Requests" refer to specific requests and wishes extracted from the information parents write in their communication books.

[0159] A "phrase" refers to a part or whole of a sentence that is generated to reply to the parent.

[0160] "Generation" refers to the process of creating new data or information using specific algorithms or models.

[0161] "Sending" refers to the act of communication to deliver the generated phrases and data to the parent's device.

[0162] MODE FOR CARRYING OUT THE INVENTION

[0163] The present invention is a system for optimizing communication between parents and educational institutions. A specific embodiment of this system will be described below.

[0164] System configuration

[0165] This system mainly consists of three elements: a server, a terminal, and a user. The server receives and analyzes the contents of the contact book sent by parents and generates appropriate reply phrases. The terminal is a device that parents use to send contact book entries and receive replies from the server. Users refer to parents and staff at educational institutions.

[0166] Hardware and software used

[0167] Server: A powerful computer system that receives, processes, and transmits data.

[0168] Device: A device that can connect to the internet, such as a smartphone, tablet, or PC.

[0169] software:

[0170] NLTK (Natural Language Toolkit): A library for preprocessing text.

[0171] VADER (Valence Aware Dictionary and sEntiment Reasoner): An algorithm for sentiment analysis.

[0172] BERT (Bidirectional Encoder Representations from Transformers): A natural language processing model for estimating parental preferences.

[0173] GPT-3 (registered trademark): A generative AI model that generates phrases that increase parental reassurance and trust.

[0174] Data processing and calculation

[0175] 1. Receiving the contents of the contact book: The server receives the contents of the contact book sent by the parent. The received contents are stored in the database.

[0176] 2. Text preprocessing: The server uses NLTK to tokenize the contacts content, remove stop words, and perform stemming.

[0177] 3. Sentiment Analysis: The server analyzes the sentiment of the preprocessed text using VADER and classifies it as positive, negative, or neutral.

[0178] 4. Desire Estimation: The server uses the BERT model to extract parents' specific desires and concerns from the contents of the communication book.

[0179] 5. Phrase generation: The server uses GPT-3 to generate phrases that increase parents' sense of security and trust based on the results of sentiment analysis and desire estimation.

[0180] 6. Sending the generated phrase: The server sends the generated phrase to the parent's device.

[0181] Specific examples

[0182] For example, if a parent writes in the communication book, "My child seems to be in a bad mood at school recently. Is there something wrong?", the system will operate as follows:

[0183] 1. Receiving the contents of the communication book: The server receives the communication book from the parent and stores it in the database.

[0184] 2. Text preprocessing: The server uses NLTK to tokenize the contacts content, remove stop words, and perform stemming.

[0185] 3. Sentiment Analysis: The server uses VADER to analyze the contents of the contact list to determine whether they contain negative sentiment.

[0186] 4. Desire Estimation: The server uses the BERT model to extract the parent's desire (checking the child's problem).

[0187] 5. Phrase generation: The server sends a prompt to GPT-3, which generates the phrase, "We're sorry for causing you concern about your child's condition. Please rest assured that we will monitor the situation at school and provide any necessary support."

[0188] 6. Sending the generated phrase: The server sends the generated phrase to the parent's device.

[0189] Prompt Sentence Examples

[0190] An example of a prompt sentence to input to the generative AI model is as follows:

[0191] "Content from a parent's note: 'My child seems unmotivated at school recently. Is something wrong?' Parent's emotion: Negative. Parent's request: Confirmation of the child's problem. Generate phrases that will reassure the parent."

[0192] By inputting this prompt into GPT-3, an appropriate phrase will be generated.

[0193] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0194] Program processing flow

[0195] Step 1: Receive contacts

[0196] Input: Contents of the contact book sent by the parent or guardian from the device

[0197] Specific operation: The server receives the HTTP request and saves the contents of the contact list to a database.

[0198] Output: Contents of the contacts saved in the database

[0199] Step 2: Text preprocessing

[0200] Input: Contacts stored in the database

[0201] Specific operation: The server calls the NLTK library, splits the contents of the contacts into tokens, removes unnecessary words, and converts them into their base forms.

[0202] Output: Preprocessed text data

[0203] Step 3: Sentiment Analysis

[0204] Input: Preprocessed text data

[0205] What it does: The server uses the VADER library to calculate the sentiment score of the text and classify the sentiment based on the highest score.

[0206] Output: Sentiment analysis results (positive, negative, neutral)

[0207] Step 4: Estimate demand

[0208] Input: Preprocessed text data

[0209] Specific operation: The server loads the BERT model, gives it the text from the contacts book as input, and performs inference to extract requests and concerns.

[0210] Output: Extracted needs and concerns

[0211] Step 5: Phrase Generation

[0212] Input: Requests and concerns extracted as a result of sentiment analysis

[0213] Specific operation: The server generates a prompt sentence for GPT-3 and sends an API request to retrieve the appropriate phrase.

[0214] Output: Generated phrase

[0215] Step 6: Submit your generated phrase

[0216] Input: Generated phrase

[0217] Specific operation: The server constructs a message containing the generated phrase and sends it to the parent's device as an HTTP response.

[0218] Output: Phrase sent to parent's device

[0219] Specific examples

[0220] For example, if a parent writes in the communication book, "My child seems to be in a bad mood at school recently. Is there something wrong?", the process is as follows:

[0221] Step 1: Receive contacts

[0222] Input: Contents of the contact book sent by the parent or guardian from the device

[0223] Specific operation: The server receives the HTTP request and saves the contents of the contact list to a database.

[0224] Output: Contents of the contacts saved in the database

[0225] Step 2: Text preprocessing

[0226] Input: Contacts stored in the database

[0227] Specific operation: The server calls the NLTK library, splits the contents of the contacts into tokens, removes unnecessary words, and converts them into their base forms.

[0228] Output: Preprocessed text data

[0229] Step 3: Sentiment Analysis

[0230] Input: Preprocessed text data

[0231] What it does: The server uses the VADER library to calculate the sentiment score of the text and classify the sentiment based on the highest score.

[0232] Output: Sentiment analysis result (negative)

[0233] Step 4: Estimate demand

[0234] Input: Preprocessed text data

[0235] Specific operation: The server loads the BERT model, gives it the text from the contacts book as input, and performs inference to extract requests and concerns.

[0236] Output: Extracted needs (confirmation of child's problems)

[0237] Step 5: Phrase Generation

[0238] Input: Sentiment analysis results (negative), extracted requests (confirmation of child's problems)

[0239] Specific operation: The server generates a prompt sentence for GPT-3 and sends an API request to retrieve the appropriate phrase.

[0240] Output: The phrase "We're sorry you're worried about your child's condition. Rest assured we'll monitor their situation at school and provide any support they need."

[0241] Step 6: Submit your generated phrase

[0242] Input: Generated phrase

[0243] Specific operation: The server constructs a message containing the generated phrase and sends it to the parent's device as an HTTP response.

[0244] Output: Phrase sent to parent's device

[0245] (Application example 2)

[0246] Next, a description will be given of Application Example 2 of Form 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."

[0247] Conventional customer support systems have had the problem of being unable to respond quickly and appropriately to customer feedback and inquiries. In particular, it has been difficult to accurately grasp the customer's feelings and requests and generate reassuring phrases based on them. This has led to problems such as being unable to sufficiently improve customer satisfaction or build trusting relationships.

[0248] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes artificial intelligence means that has learned the speaking techniques of veteran educators, means for instantly grasping the needs of various users, means for increasing users' peace of mind and trust with accurate and clever phrases, means for instantly creating recommended reply suggestions, means for analyzing feedback from users, means for generating reassuring phrases based on the analysis results, and means for presenting the generated phrases. This makes it possible to respond quickly and appropriately to feedback and inquiries from customers and accurately grasp their feelings and requests. Furthermore, it becomes possible to effectively improve customer satisfaction and build trusting relationships.

[0249] "Artificial intelligence means that has learned the speaking techniques of veteran educators" is an artificial intelligence function that can learn the communication techniques and speaking styles of experienced educators and use them as a basis to engage in natural conversations.

[0250] "Means for instantly grasping the needs of diverse users" refers to technology that analyzes information input by users and quickly understands their needs and requests.

[0251] "Means of increasing users' sense of security and trust through accurate and clever phrases" refers to a technique for choosing appropriate words according to the user's emotions and situation to increase the user's sense of security and trust.

[0252] "A means for instantly generating suggested reply sentences" is a technology for automatically generating appropriate reply sentences in response to inquiries and feedback from users.

[0253] "Means for analyzing user feedback" refers to technology for analyzing opinions and impressions provided by users and understanding their content.

[0254] The "means for generating reassuring phrases based on the analysis results" is a technology that generates appropriate phrases to reassure users based on the analysis results of the feedback.

[0255] "Means for presenting generated phrases" refers to technology for displaying generated phrases to users and system operators.

[0256] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The function of each element will be explained below.

[0257] server

[0258] The server has the following main functions:

[0259] 1. AI means that has learned the speaking skills of veteran educators:

[0260] The server is equipped with artificial intelligence (AI) that has learned the communication techniques of experienced educators. This AI has a model for conducting natural conversations and facilitates communication with users.

[0261] 2. A way to instantly understand the needs of diverse users:

[0262] The server analyzes input from users and uses natural language processing (NLP) techniques to quickly understand their needs and desires, specifically using NLP libraries such as spaCy.

[0263] 3. Use precise and clever phrases to increase user confidence and trust:

[0264] The server selects appropriate words based on the user's emotions and situation, and generates phrases that will increase the user's sense of security and trust. These phrases are generated using generative AI models such as OpenAI GPT-3.

[0265] 4. How to instantly generate suggested responses:

[0266] The server automatically generates appropriate reply sentences in response to inquiries and feedback from users, a function that is also realized using a generative AI model.

[0267] 5. How we analyze your feedback:

[0268] The server uses NLP technology to analyze the opinions and comments provided by users and understand their content.

[0269] 6. How to generate reassuring phrases based on the analysis results:

[0270] The server analyzes the feedback and generates appropriate phrases to reassure the user, using a generative AI model.

[0271] 7. Means of presenting generated phrases:

[0272] The server has an interface for displaying the generated phrases to users and system operators.

[0273] Terminal

[0274] The terminal is a device through which the user interacts with the server. Examples include smartphones and tablets. The terminal displays phrases and reply messages sent from the server and sends input from the user to the server.

[0275] User

[0276] A user is an individual who uses the system. The user communicates with the server through a terminal and sends feedback and inquiries. By receiving replies and phrases from the server, the user gains a sense of security and trust.

[0277] Specific examples

[0278] For example, if a user sends feedback such as "I'm a little dissatisfied with the service recently. I felt that the response was slow," the server analyzes this feedback and calculates an emotion score. It then uses the generative AI model to generate a phrase such as "We apologize for the inconvenience. We will do our best to respond quickly, so we appreciate your continued support," and displays it on the device.

[0279] Prompt Sentence Examples

[0280] "If the customer's sentiment score is -0.5, generate a reassuring phrase."

[0281] "If the customer's sentiment score is 0.8, generate a phrase that increases confidence."

[0282] In this way, the system can respond quickly and appropriately to user feedback and accurately grasp the user's feelings and requests, which in turn helps to improve user satisfaction and build trusting relationships effectively.

[0283] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0284] Step 1:

[0285] The user inputs and sends feedback or inquiries using a terminal. The input feedback is sent to the server as text data.

[0286] Step 2:

[0287] The server analyzes the received feedback using natural language processing (NLP) techniques. Specifically, it uses an NLP library such as spaCy to calculate the sentiment score of the feedback. The input is the text data of the feedback, and the output is the sentiment score.

[0288] Step 3:

[0289] The server uses a generative AI model (e.g., OpenAI GPT-3) based on the analysis results to generate a reassuring phrase. The input is the emotion score, and the output is the generated phrase. Specifically, the emotion score is incorporated into the prompt sentence and input to the generative AI model.

[0290] Step 4:

[0291] The server sends the generated phrase to the terminal. The input is the generated phrase, and the output is the phrase displayed on the terminal. Specifically, the server sends the generated phrase to the terminal as text data.

[0292] Step 5:

[0293] The terminal displays the phrases received from the server to the user. The input is the phrase sent from the server, and the output is the phrase displayed on the terminal screen. Specifically, the terminal displays the received text data on the screen.

[0294] Step 6:

[0295] The user checks the displayed phrase and provides additional feedback or inquiries as necessary. The input is the displayed phrase, and the output is new feedback or inquiries. Specifically, the user operates the terminal to input and send new feedback.

[0296] In this way, the system can respond quickly and appropriately to user feedback and accurately grasp the user's feelings and requests, which in turn helps to improve user satisfaction and build trusting relationships effectively.

[0297] Example 3

[0298] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0299] With conventional communication book systems, it was difficult for teachers to quickly create appropriate replies to messages from parents, making it difficult to gain the parents' peace of mind and trust. Furthermore, teachers had to individually think up replies, which took time and effort, hindering efficient communication. Furthermore, it was difficult to accurately grasp the parents' feelings and requests, which sometimes meant they were unable to respond appropriately.

[0300] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0301] In this invention, the server includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a means for instantly grasping the diverse needs of parents, a means for increasing parents' sense of security and trust with accurate and clever phrases, a means for analyzing the contents of the parent's communication book and generating appropriate reply suggestions based on the estimated feelings and requests, and a means for providing the generated reply suggestions to the user. This enables teachers to quickly create appropriate reply suggestions, enabling efficient and effective communication with parents.

[0302] "Artificial intelligence means that has learned the speaking techniques of veteran educators" is an artificial intelligence function that learns the communication techniques and speaking styles of experienced educators and uses them as a basis for dialogue with parents.

[0303] "A means of instantly grasping the diverse needs of parents" is a function that analyzes the content of communications from parents and quickly understands the diverse requests and emotions contained therein.

[0304] "Means of increasing parents' sense of security and trust through accurate and thoughtful phrases" is a function that increases parents' sense of security and trust by using appropriate and heartfelt expressions according to the parents' feelings and requests.

[0305] "Means for analyzing the contents of the communication book from the parents and generating appropriate reply drafts based on the inferred feelings and requests" is a function for analyzing the contents of the communication book from the parents, inferring the parents' feelings and requests based on the contents, and generating reply drafts accordingly.

[0306] The "means for providing the generated reply draft to the user" is a function for presenting the generated reply draft to the user, such as a teacher, so that the user can check, modify, and use it.

[0307] This invention is a system for teachers to create quick and appropriate replies to messages from parents. The system includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a means for instantly grasping the diverse needs of parents, a means for increasing parents' sense of security and trust with accurate and clever phrases, a means for analyzing the contents of messages from parents and generating appropriate reply suggestions based on estimated feelings and requests, and a means for providing the generated reply suggestions to a user.

[0308] Hardware and software used

[0309] Hardware: Servers, devices (PCs, tablets, smartphones)

[0310] Software: Generative AI models (e.g., GPT-4 (registered trademark)), contact management system

[0311] Specific operation of the system

[0312] 1. The user (teacher) uses a device to access the contact book management system. The user logs in to the contact book management system website or application using a PC, tablet, or smartphone.

[0313] Example: A teacher accesses the contact management system website from a school PC and enters login information to log in to the system.

[0314] 2. The user enters the contents of the contact book from the parent / guardian. The user enters the contents of the contact book received from the parent / guardian into the text box.

[0315] Example: A teacher types, "My child has been feeling a bit underwhelmed at school lately. Is there anything that might be bothering you?"

[0316] 3. The server receives the contents of the address book and sends it to the generative AI model. The server receives the contents of the address book entered by the user and sends them to the generative AI model as a prompt sentence.

[0317] Example: The server sends the following text to a generative AI model: "My child has been feeling unwell at school lately. Is there something that is bothering you?"

[0318] 4. The generative AI model analyzes the contents of the communication notebook and infers the parent's feelings and requests. The generative AI model analyzes the contents of the communication notebook and infers the parent's feelings (e.g., worry) and requests (e.g., providing information about the child's situation).

[0319] Example: A generative AI model infers the emotion "worried" and the request "to provide information about my child's condition."

[0320] 5. The generative AI model generates appropriate reply suggestions based on the estimated emotions and requests.The generative AI model generates appropriate reply suggestions based on the estimated emotions and requests.

[0321] Example: A generative AI model generates the following reply suggestions:

[0322] Dear Parents,

[0323] Thank you for always being concerned about your child. I understand that your child has been feeling unwell recently, but I haven't noticed any major issues at school. However, he seems a little tired, so it might be a good idea to make time for him to rest at home. If you have any concerns, please feel free to contact me at any time.

[0324] Thank you very much.

[0325] teacher

[0326] 6. The server sends the generated reply sentence suggestions back to the contact management system. The server sends the reply sentence suggestions received from the generative AI model back to the contact management system.

[0327] Example: The server generates a reply suggestion and displays it in the user interface of a contact management system.

[0328] 7. The user checks the reply draft and makes corrections as necessary. The user checks the reply draft and makes corrections as necessary.

[0329] Example: The teacher checks the proposed reply and changes the part that reads, "It might be good if you could make time to rest at home as well." to "It might be good if you could make time to relax at home as well."

[0330] 8. The user finalizes the reply and sends it to the parent.

[0331] Example: The teacher finalizes the revised reply and sends it to the parents through the communication management system.

[0332] In this way, this system helps teachers to create quick and appropriate replies to parent / guardian correspondence, enabling efficient and effective communication with parents / guardians. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[0333] Step 1:

[0334] A user accesses the contact management system.

[0335] Input: Login information that allows users to log into the contact management system website or application using a PC, tablet, or smartphone.

[0336] Data processing: The server authenticates the user's login information and grants access.

[0337] Output: The user logs into the contact management system and the system's main screen is displayed.

[0338] Specific operation: The teacher accesses the contact list management system website from a school PC and logs in by entering their username and password.

[0339] Step 2:

[0340] The user inputs the contents of the communication book from the parent.

[0341] Input: Contents of the communication book received from the parents.

[0342] Data processing: The user enters the contents of the contact book into the text box of the contact book management system.

[0343] Output: The entered contact list contents are sent to the server.

[0344] What happens: A teacher types into a text box, "My child has been feeling a bit down at school lately. Is there anything that's worrying you?"

[0345] Step 3:

[0346] The server receives the contents of the contact list and sends them to the generative AI model.

[0347] Input: The contacts entered by the user.

[0348] Data processing: The server sends the contents of the address book as a prompt to the generative AI model.

[0349] Output: The generative AI model receives the contents of the address book.

[0350] What happens: The server sends the following text to the generative AI model: "My child seems a bit depressed at school lately. Is there something that's bothering you?"

[0351] Step 4:

[0352] The generative AI model analyzes the contents of the communication book and infers the parents' emotions and requests.

[0353] Input: Contents of the contact book.

[0354] Data processing: A generative AI model analyzes the contents of the communication notebook and infers the parents' emotions (e.g., worries) and requests (e.g., providing information about their child's situation).

[0355] Output: Estimated emotions and desires.

[0356] Specific behavior: The generative AI model infers the emotion of "worry" and the request for "information about the child's condition."

[0357] Step 5:

[0358] The generative AI model generates appropriate reply suggestions based on the estimated emotions and requests.

[0359] Input: Estimated emotions and desires.

[0360] Data processing: The generative AI model generates appropriate reply suggestions based on the estimated emotions and requests.

[0361] Output: The generated reply suggestions.

[0362] How it works: The generative AI model generates reply suggestions like the following:

[0363] Dear Parents,

[0364] Thank you for always being concerned about your child. I understand that your child has been feeling unwell recently, but I haven't noticed any major issues at school. However, he seems a little tired, so it might be a good idea to make time for him to rest at home. If you have any concerns, please feel free to contact me at any time.

[0365] Thank you very much.

[0366] teacher

[0367] Step 6:

[0368] The server returns the generated reply sentence proposal to the contact book management system.

[0369] Input: The generated reply suggestion.

[0370] Data processing: The server sends the reply text suggestions received from the generative AI model back to the contact management system.

[0371] Output: A reply suggestion is displayed in the user interface of the contact management system.

[0372] Specific operation: The server displays the generated reply sentence proposal on the user interface of the contact management system.

[0373] Step 7:

[0374] The user reviews the reply proposal and makes corrections as necessary.

[0375] Input: The generated reply suggestion.

[0376] Data processing: The user reviews the reply proposal and makes corrections as necessary.

[0377] Output: Revised reply proposal.

[0378] Specific action: The teacher checks the proposed reply and amends the part that reads, "It might be good if you could make time to rest at home as well." to "It might be good if you could make time to relax at home as well."

[0379] Step 8:

[0380] The user sends the final reply to the parent.

[0381] Input: Revised reply proposal.

[0382] Data processing: The user finalizes the reply text and sends it to the parent.

[0383] Output: The final response sent to the parent.

[0384] Specific operations: The teacher finalizes the revised reply text and sends it to the parents through the communication management system.

[0385] (Application example 3)

[0386] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0387] Conventional customer support systems have had the challenge of making it difficult to respond quickly and appropriately to customer inquiries. In particular, it has been difficult to accurately grasp the customer's feelings and requests and generate appropriate replies based on them. Another problem with communication with parents is that teachers have to spend a great deal of time and effort creating replies to messages in the communication book. To solve these problems, an automatic reply generation system using AI is needed.

[0388] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0389] In this invention, the server includes AI means that has learned the speaking techniques of veteran teachers, means that instantly grasp the diverse needs of parents, means that increase parents' peace of mind and trust with accurate and clever phrases, means that instantly create recommended reply suggestions, means that analyze the content of inquiries from customers and infer their feelings and requests, means that generate appropriate reply suggestions based on the inferred feelings and requests, and means that output the generated reply suggestions. This makes it possible to provide quick and appropriate replies to inquiries from customers and parents.

[0390] "AI means that have learned the speaking techniques of veteran teachers" is an artificial intelligence function that learns and imitates the communication techniques of experienced teachers.

[0391] "Means of instantly grasping the diverse needs of parents" refers to a function that allows for quick understanding of inquiries and requests from parents and responding appropriately.

[0392] "Means to increase parents' sense of security and trust with accurate and clever phrases" is a function that generates appropriate language to give parents a sense of security and trust.

[0393] "A means for instantly creating suggested reply sentences" is a function that quickly generates appropriate reply sentences in response to inquiries from parents or customers.

[0394] "Means for analyzing the content of inquiries from customers and inferring their emotions and requests" is a function that analyzes messages from customers and infers the emotions and requests contained therein.

[0395] The "means for generating an appropriate reply sentence proposal based on the estimated emotions and requests" is a function for generating an appropriate reply sentence based on the analyzed emotions and requests.

[0396] The "means for outputting the generated reply sentence proposal" is a function for displaying or transmitting the generated reply sentence.

[0397] To implement this invention, it is necessary to build a system in which the elements of the server, terminal, and user work together. A specific embodiment of this system is shown below.

[0398] server

[0399] The server has the following main functions:

[0400] 1. AI means that have learned the speaking techniques of veteran teachers: It has the ability to learn and imitate the communication techniques of experienced teachers, allowing it to generate appropriate language for parents and customers.

[0401] 2. A means of instantly understanding the diverse needs of parents: It has the ability to quickly understand inquiries and requests from parents and customers and respond appropriately.

[0402] 3. A means of increasing parents' sense of security and trust with accurate and clever phrases: The system has the function of generating appropriate language to give parents and customers a sense of security and trust.

[0403] 4. A means of instantly generating suggested reply sentences: The system has the ability to quickly generate appropriate reply sentences in response to inquiries from parents and customers.

[0404] 5. A means of analyzing customer inquiries and inferring their emotions and requests: This system has the ability to analyze messages from customers and infer the emotions and requests contained within them. Specifically, it uses the TextBlob library to perform emotion analysis.

[0405] 6. A means to generate appropriate reply suggestions based on estimated emotions and requests: The system has the function to generate appropriate reply suggestions based on the analyzed emotions and requests. Specifically, the system generates reply suggestions using OpenAI's generative AI model (e.g., GPT-3).

[0406] 7. Means for outputting generated reply text: has a function for displaying or sending the generated reply text.

[0407] Terminal

[0408] The terminal provides an interface for the user to communicate with the server. Specifically, it has the following functions:

[0409] 1. Message input function: Provides an interface for users to input inquiry messages.

[0410] 2. Reply display function: Provides an interface for displaying reply text sent from the server.

[0411] User

[0412] The user uses the terminal to communicate with the server and perform the following operations:

[0413] 1. Input of query message: The user inputs a query message using the terminal and sends it to the server.

[0414] 2. Confirmation of reply text: The user confirms the reply text sent from the server on the terminal.

[0415] Specific examples

[0416] For example, if a customer sends a message saying, "My item arrived but it's damaged. What should I do?", the server will input the following prompt into the generative AI model:

[0417] "Negative message from customer: My item arrived damaged. What should I do?\nGenerate an appropriate response."

[0418] The generative AI model generates an appropriate reply based on the prompt, and the server sends the reply to the device, where the user can review it and take appropriate action.

[0419] In this way, the server, terminal, and user work together to enable quick and appropriate replies to inquiries from customers and guardians.

[0420] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0421] Step 1:

[0422] A user uses a terminal to input an inquiry message and transmits it to the server.

[0423] Input: The inquiry message entered by the user (e.g., "My item arrived, but it was damaged. What should I do?")

[0424] Output: The query message sent to the server

[0425] Specific operation: The user uses the message input function of the device to enter the inquiry content and presses the send button to send the message to the server.

[0426] Step 2:

[0427] The server analyzes the received inquiry message and infers the emotion and request.

[0428] Input: The query message sent by the user

[0429] Output: Estimated emotions and requests (e.g., negative emotions, request for product exchange)

[0430] Specific operation: The server uses the TextBlob library to analyze the sentiment of the message, obtain a sentiment score, and infer the message's request from the message content.

[0431] Step 3:

[0432] The server generates a prompt sentence based on the estimated emotion and desire.

[0433] Input: Estimated emotions, requests, and inquiry messages

[0434] Output: Generated prompt (e.g., "Negative message from customer: My item arrived, but it was damaged. What should I do?\nPlease generate an appropriate response.")

[0435] Specific operation: The server generates prompt sentences based on the emotion scores and formats them for input into the generative AI model.

[0436] Step 4:

[0437] The server uses the generative AI model to generate an appropriate reply.

[0438] Input: Generated prompt text

[0439] Output: Generated reply (e.g. "We apologize. We will process the replacement of the damaged item immediately.")

[0440] Specific operation: The server inputs a prompt sentence into OpenAI's generative AI model (e.g., GPT-3) to generate a reply sentence. The generated reply sentence is then retrieved.

[0441] Step 5:

[0442] The server sends the generated reply text to the terminal.

[0443] Input: Generated reply text

[0444] Output: Reply text sent to the terminal

[0445] Specific operation: The server performs communication processing to send the generated reply text to the terminal.

[0446] Step 6:

[0447] The user checks the reply text on the terminal.

[0448] Input: Reply text sent from the server

[0449] Output: Reply text displayed on the terminal

[0450] Specific operation: The user uses the reply display function of the device to check the reply text sent from the server.

[0451] 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.

[0452] "Example 1"

[0453] One embodiment of the present invention is a system in which AI learns the speaking techniques of veteran teachers, instantly grasps the diverse needs of parents, and increases their sense of security and trust with accurate and thoughtful phrases. This system includes an emotion engine. The emotion engine analyzes the contents of a communication book from parents and recognizes their emotions. For example, if a parent writes in the communication book about their concerns about their child's learning, the emotion engine recognizes the anxiety and generates appropriate phrases to address it.

[0454] "Example 2"

[0455] In another embodiment, there is a system in which an emotion engine generates recommended reply suggestions based on the user's emotions and presents the generated results to the teacher. Specifically, the emotion engine recognizes the parent's emotions and generates appropriate reply suggestions based on those emotions. For example, if a parent writes in a communication book about their anxiety about their child's studies, the emotion engine recognizes the anxiety and generates appropriate reply suggestions that address that anxiety. The generated results can be used as a reference for the teacher when creating a reply for the communication book.

[0456] "Example 3"

[0457] Yet another embodiment is a system in which AI sequentially learns and optimizes communication with parents. This system includes an emotion engine. The emotion engine analyzes the contents of a communication book from parents and recognizes their emotions. It then generates appropriate phrases corresponding to those emotions, thereby increasing the parents' sense of security and trust. For example, if a parent writes in the communication book about their concerns about their child's learning, the emotion engine recognizes the anxiety and generates appropriate phrases to address it. As a result, the parents' sense of security and trust are increased.

[0458] The processing flow of each embodiment will be described below.

[0459] "Example 1"

[0460] Step 1: The AI ​​learns the speaking techniques of veteran teachers.

[0461] Step 2: Analyze the contents of the communication book from parents.

[0462] Step 3: The emotion engine recognizes the parent's emotion.

[0463] Step 4: Generate an appropriate phrase based on that emotion.

[0464] "Example 2"

[0465] Step 1: The emotion engine recognizes the user's emotion.

[0466] Step 2: Generate appropriate reply suggestions based on the sentiment.

[0467] Step 3: Present the results to the teacher.

[0468] "Example 3"

[0469] Step 1: The AI ​​learns incrementally.

[0470] Step 2: Analyze the contents of the communication book from parents.

[0471] Step 3: The emotion engine recognizes the parent's emotion.

[0472] Step 4: Generate an appropriate phrase based on that emotion.

[0473] Step 5: As a result, parents' sense of security and trust increases.

[0474] Example 1

[0475] Next, a description will be given of Example 1 of Form 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."

[0476] In the past, teachers had to spend a lot of time and effort responding to parents individually, and it was particularly difficult to accurately grasp the parents' feelings and respond appropriately. Furthermore, it took a long time for newly hired teachers to acquire the speaking skills and experience of teachers, which was insufficient in today's educational environment, where quick and accurate responses are required.

[0477] 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.

[0478] In this invention, the server includes artificial intelligence means that has learned the speaking techniques of veteran educators, means for instantly grasping the diverse needs of parents, means for increasing parents' peace of mind and trust with accurate and clever phrases, emotion analysis means for analyzing the contents of the parent's communication book and recognizing their emotions, means for selecting appropriate phrases based on the emotions recognized by the emotion analysis means, artificial intelligence model means for generating reply messages using the selected phrases, and means for sending the generated reply messages to parents. This makes it possible to accurately grasp the parents' emotions and provide a quick and appropriate reply.

[0479] A "veteran educator" is a teacher who has many years of experience and a wealth of knowledge, and is highly regarded in the educational field.

[0480] "Artificial intelligence means" refers to technologies or systems that use machine learning and data analysis to automatically perform specific tasks.

[0481] "Parental needs" refers to the requests and expectations that parents have regarding their children's education and lifestyle.

[0482] "Emotion analysis means" refers to technologies and systems for recognizing and classifying emotions from text data and voice data.

[0483] "Generative AI model means" refers to an AI technology or system for generating natural language sentences based on input data.

[0484] A "reply letter" is a letter written in response to a contact or inquiry from a parent.

[0485] A "communication notebook" is a communication tool that allows parents and teachers to share information about their children's learning and living situations.

[0486] This invention is a system that uses artificial intelligence means that has learned the speaking skills of veteran educators to generate appropriate replies to the contents of a parent's contact book. A specific embodiment of this system will be described below.

[0487] System configuration

[0488] This system consists of three main components: a server, a terminal, and a user.

[0489] 1. Server

[0490] The server has an artificial intelligence system that has learned the speaking techniques of veteran educators. Specifically, it analyzes teacher lesson records and reply contents in the communication book, and registers phrases and their usage in a learning database.

[0491] The server uses emotion analysis means to analyze the contents of the communication book from parents and recognize their emotions. The emotion analysis means analyzes the text data and assigns emotion tags such as "anxiety," "worry," and "joy."

[0492] The server selects appropriate phrases from a learning database based on the emotions recognized by the emotion analysis means.

[0493] The server has a generative artificial intelligence model means for generating a reply sentence using the selected phrase. The generative AI model generates a sentence in natural language based on the input data.

[0494] The server sends the generated reply message to the parent's terminal.

[0495] 2. Terminal

[0496] The terminal is a device that parents use to enter information into the contact book and send it to the server. Specifically, this applies to smartphones, tablets, and PCs.

[0497] The terminal receives the reply message sent from the server and displays it to the parent.

[0498] 3. Users

[0499] The user is a guardian, and uses a terminal to input the contents of the address book and receive a reply message from the server.

[0500] Specific examples

[0501] For example, when a parent types "My child seems to be feeling anxious about his / her studies at school recently" into the communication book on their device and presses the send button, the content is sent to the server. The server uses emotion analysis to tag the message with the emotion "anxiety," and selects the phrase "We understand that you are worried about your child's academic progress, but we are always watching over and supporting your child's growth" from the learning database. The generative AI model uses this phrase to generate a reply message, and the server sends the generated reply message to the parent's device.

[0502] Prompt Sentence Examples

[0503] "Analyze the contents of a parent's communication notebook and generate appropriate phrases. The contents of the communication notebook are, 'My child seems to be feeling anxious about his studies at school recently.'"

[0504] In this way, the system can accurately grasp the parent's feelings and provide a quick and appropriate response.

[0505] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0506] Step 1:

[0507] Preparing the database

[0508] The server collects phrases used by experienced educators and appropriate phrases for each situation, and stores them in a learning database. Specifically, it analyzes the teacher's lesson notes and replies in the communication book, and registers the phrases and their usage in the database. The input is the teacher's lesson notes and replies in the communication book, and the output is the phrases registered in the learning database and their usage.

[0509] Step 2:

[0510] Receiving communication from parents

[0511] The device receives the contents of the contact book from the parent / guardian and sends them as text data to the server. Specifically, the parent / guardian enters "My child seems to be feeling anxious about his / her studies at school recently" into the contact book via the device and presses the send button. The input is the contents of the contact book entered by the parent / guardian, and the output is the text data sent to the server.

[0512] Step 3:

[0513] Emotion analysis using an emotion engine

[0514] The server inputs the contents of the received contact book into the emotion engine, analyzes the text data, and recognizes the parent's emotions. The emotion engine assigns emotion tags such as "anxiety," "worry," and "joy" to the contents of the contact book. The input is the text data of the contact book, and the output is data with emotion tags. Specifically, it recognizes the emotion tag "anxiety" from the phrase "I feel anxious."

[0515] Step 4:

[0516] Choosing the right phrase

[0517] The server selects appropriate phrases from the learning database based on the emotion tags recognized by the emotion engine. The input is data with emotion tags attached, and the output is the selected phrase. Specifically, the phrase selected for "anxiety" is "We understand that you may be worried about your child's academic progress, but we are always watching over and supporting your child's growth."

[0518] Step 5:

[0519] Generating replies using a generative AI model

[0520] The server inputs the selected phrase into the generative AI model and generates a reply to the parent. The input is the selected phrase, and the output is the generated reply. Specifically, the generative AI model generates the reply, "We understand that you are worried about your child's learning progress, but we are always watching over and supporting your child's growth."

[0521] Step 6:

[0522] Sending a reply

[0523] The server sends the generated reply to the parent's device. The input is the generated reply, and the output is the reply sent to the parent's device. Specifically, the server sends the generated reply to the parent's device via email or a messaging app.

[0524] (Application example 1)

[0525] Next, a description will be given of Application Example 1 of Embodiment 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."

[0526] Conventional security services have had the challenge of being unable to respond quickly and appropriately to user inquiries and concerns. In particular, there is a need to accurately understand the user's emotions and provide appropriate phrases in response to them, but there has been a lack of effective means to achieve this.

[0527] 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.

[0528] In this invention, the server includes AI means that has learned the speaking techniques of veteran teachers, means that instantly grasp the needs of various users, means that increase users' sense of security and trust with accurate and clever phrases, means that instantly create recommended reply sentences, emotion analysis means, and means that generate appropriate phrases based on the emotion analysis results. This makes it possible to respond quickly and appropriately to inquiries and concerns from users.

[0529] "AI means that have learned the speaking techniques of veteran teachers" is an artificial intelligence means that learns effective communication techniques used by experienced educators and generates appropriate phrases based on them.

[0530] "Means for instantly grasping the diverse needs of users" refers to means for quickly understanding inquiries and requests from users and responding accordingly.

[0531] "Means of increasing users' sense of security and trust through accurate and clever phrases" refers to means of choosing appropriate words according to the user's emotions and situation to increase the user's sense of security and trust.

[0532] The "means for instantly generating recommended reply sentences" is a means for quickly generating appropriate reply sentences in response to inquiries or requests from users.

[0533] The "emotion analysis means" is a means for analyzing the emotion from the content input by the user and determining whether the emotion is positive or negative.

[0534] The "means for generating appropriate phrases based on emotion analysis results" refers to means for generating appropriate phrases for a user based on emotion information obtained by the emotion analysis means.

[0535] This invention is a system for responding promptly and appropriately to inquiries and concerns from users in security services. The system includes an AI means that has learned the speaking skills of veteran teachers, a means for instantly grasping the needs of diverse users, a means for increasing users' peace of mind and trust with accurate and thoughtful phrases, a means for instantly creating recommended reply sentences, a means for sentiment analysis, and a means for generating appropriate phrases based on the results of sentiment analysis.

[0536] System configuration

[0537] 1. AI methods that learned the speaking skills of veteran teachers

[0538] The server uses a generative AI model that has been trained on the speaking skills of veteran teachers. This model learns effective communication techniques used by teachers and generates appropriate phrases based on those.

[0539] 2. A means of instantly grasping the needs of diverse users

[0540] The server uses natural language processing (NLP) technology to quickly understand user inquiries and requests, analyze the user's input, and respond appropriately.

[0541] 3. A way to increase user confidence and trust with accurate and clever phrases

[0542] The server analyzes the user's emotions using emotion analysis means and generates appropriate phrases based on the results, thereby improving the user's sense of security and trust.

[0543] 4. A way to instantly generate suggested replies

[0544] The server uses an algorithm to quickly generate appropriate reply text in response to user inquiries and requests, enabling it to respond to users quickly and appropriately.

[0545] 5. Emotion analysis method

[0546] The server uses a sentiment analysis algorithm to analyze the sentiment of the user's input, thereby determining whether the sentiment is positive or negative.

[0547] 6. A method for generating appropriate phrases based on the results of sentiment analysis

[0548] The server uses a generative AI model to generate appropriate phrases for the user based on the emotional information obtained by the emotion analysis means.

[0549] Hardware and software used

[0550] Hardware: Servers, smartphones

[0551] Software: Python, OpenAI API, TextBlob library

[0552] Specific examples of processing

[0553] When a user enters "I'm worried about whether the security system is working properly" through a smartphone application, the server performs the following process.

[0554] 1. Sentiment Analysis: Use the TextBlob library to analyze the sentiment of the user's input text. Determine whether the sentiment is positive or negative.

[0555] 2. Phrase generation: Based on the results of sentiment analysis, the generative AI model generates appropriate phrases.

[0556] 3. Generate a response: Return the generated phrase to the user.

[0557] Prompt Sentence Examples

[0558] User: I'm not sure if the security system is working properly.

[0559] AI: I know how you feel.

[0560] In this way, appropriate phrases can be provided to ease the user's anxiety.

[0561] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0562] Step 1:

[0563] Users enter their inquiries and concerns through a smartphone application.

[0564] Input: User text (e.g., "I'm worried about whether my security system is working properly.")

[0565] Output: User input text data

[0566] Step 2:

[0567] The terminal transmits the user's input text data to the server.

[0568] Input: User-entered text data

[0569] Output: Text data sent to the server

[0570] Step 3:

[0571] The server passes the received text data to emotion analysis means, which performs emotion analysis.

[0572] Input: Text data sent to the server

[0573] Data processing: Analyze the sentiment of the text using the TextBlob library to determine positive or negative sentiment.

[0574] Output: Sentiment analysis result (e.g. "anxiety")

[0575] Step 4:

[0576] Based on the results of the emotion analysis, the server inputs a prompt sentence into the generative AI model to generate an appropriate phrase.

[0577] Input: Sentiment analysis result (e.g., "anxiety")

[0578] Data calculation: Input a prompt sentence into the generative AI model to generate an appropriate phrase.

[0579] Output: Generated phrase (e.g., "I know how you feel.")

[0580] Step 5:

[0581] The server sends the generated phrase to the device.

[0582] Input: Generated phrase

[0583] Output: Phrase sent to the terminal

[0584] Step 6:

[0585] The terminal displays the received phrase to the user.

[0586] Input: Phrase sent to the terminal

[0587] Output: The phrase displayed to the user (e.g., "I know how you feel.")

[0588] In this way, appropriate phrases can be provided to ease the user's anxiety.

[0589] Example 2

[0590] Next, a description will be given of Example 2 of Form 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."

[0591] Conventional parent-teacher communication systems have had difficulty quickly and accurately grasping parents' feelings and requests and generating appropriate responses accordingly. This has resulted in problems such as parents being unable to increase their sense of security and trust, and the quality of communication declining.

[0592] 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.

[0593] In this invention, the server includes means for receiving messages from parents, means for analyzing the received messages to estimate the feelings and requests of the parents, means for generating appropriate reply suggestions based on the estimated feelings, and means for presenting the generated reply suggestions to the teacher. This makes it possible to quickly and accurately grasp the feelings and requests of the parents and to generate appropriate replies accordingly.

[0594] The "means for receiving messages from guardians" is a function that allows a guardian to use a terminal to send a message input by the guardian to a server, and the server to receive the message.

[0595] "Means for analyzing received messages to infer the emotions and requests of parents" refers to a function that uses a natural language processing algorithm to analyze the content of received messages and infer the emotions (joy, anxiety, anger, etc.) and requests of parents.

[0596] The "means for generating appropriate reply suggestions based on estimated emotions" is a function for generating appropriate reply suggestions using a generative AI model in accordance with the estimated emotions of the parent.

[0597] The "means for presenting the generated reply draft to the teacher" is a function for sending the generated reply draft to the teacher's terminal so that the teacher can check it.

[0598] A "natural language processing algorithm" is an algorithm that analyzes text data and extracts emotions and intentions from its content.

[0599] A "generative AI model" is an artificial intelligence model for generating natural-sounding sentences based on an input prompt.

[0600] This invention is a system for optimizing communication between parents and teachers. It receives messages from parents, analyzes their contents to infer the parents' feelings and requests, and generates appropriate reply suggestions to present to the teacher.

[0601] Hardware and software used

[0602] Hardware: Servers, terminals (PCs, tablets)

[0603] Software: Generative AI models (e.g., GPT-4), sentiment analysis engines (e.g., natural language processing algorithms)

[0604] Specific operation of the system

[0605] 1. Data Collection:

[0606] The user (guardian) enters a message into the address book and sends it from the device to the server.

[0607] Specific actions: A parent uses a device (PC or tablet) to type "I'm worried because my child has recently started to dislike doing his or her homework" into the communication application and clicks the send button.

[0608] 2. Data reception:

[0609] The server receives the contact book message sent from the terminal.

[0610] Specific operation: The server receives the HTTP request and saves the message data in a database.

[0611] 3. Emotion analysis:

[0612] The server passes the received message to an emotion analysis engine, which analyzes the parents' emotions and requests.

[0613] Specific operation: The server uses a natural language processing algorithm to analyze the message text and extract emotions such as "anxiety" from the text.

[0614] 4. Emotion estimation:

[0615] The server uses the analysis results to estimate the parents' feelings and requests.

[0616] Specific operation: The server analyzes the analysis results and confirms that "anxiety" has a high score.

[0617] 5. Generate reply suggestions:

[0618] The server uses a generative AI model (GPT-4) to generate appropriate reply suggestions based on the estimated emotions.

[0619] Specific operation: The server sends a prompt to the generative AI model. For example, the prompt is sent as follows: "If a parent writes in the communication book, 'I'm worried that my child has recently started to dislike doing his / her homework,' please generate an appropriate reply." The generative AI model generates a reply such as, "We understand that you are feeling anxious about your child's homework. We are here to support you, so please let us know if you have any questions."

[0620] 6. Teacher Presentation:

[0621] The server sends the generated reply draft to the teacher's terminal, which the teacher uses as a reference when creating a reply for the communication book.

[0622] Specific operation: The server sends data containing the generated reply sentence suggestions to the teacher's device. A notification is displayed on the teacher's device, and the teacher opens the contact application to check the reply sentence suggestions.

[0623] Examples and prompts

[0624] Examples:

[0625] If a parent writes in a communication book, "I'm worried because my child has recently started to dislike doing his homework," the emotion analysis engine will detect "anxiety."

[0626] The generative AI model generates a suggested reply such as, "We understand that you may be feeling anxious about your child's homework. We are here to support you, so please let us know if you have any questions."

[0627] Example prompt sentence:

[0628] "When a parent writes in the communication book, 'I'm worried because my child has recently started to dislike doing his homework,' generate an appropriate response suggestion."

[0629] This system makes it possible to quickly and accurately grasp the feelings and needs of parents and generate appropriate responses accordingly, thereby increasing parents' sense of security and trust.

[0630] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0631] Step 1:

[0632] Data collection

[0633] The user (guardian) enters a message into the address book and sends it from the device to the server.

[0634] Input: A message typed by a parent or guardian on a device (PC or tablet) (e.g., "I'm worried because my child has recently started to dislike doing his or her homework.").

[0635] Output: Message data sent from the device to the server.

[0636] Specific operation: The parent enters a message in the contacts application and clicks the send button. The device sends the message to the server.

[0637] Step 2:

[0638] Data reception

[0639] The server receives the contact book message sent from the terminal.

[0640] Input: Message data sent from the terminal.

[0641] Output: Message data stored on the server.

[0642] Specific operation: The server receives the HTTP request and saves the message data in a database.

[0643] Step 3:

[0644] Emotion analysis

[0645] The server passes the received message to an emotion analysis engine, which analyzes the parents' emotions and requests.

[0646] Input: Message data stored on the server.

[0647] Output: The sentiment data returned by the sentiment analysis engine (e.g., "anxiety").

[0648] Specific operation: The server uses a natural language processing algorithm to analyze the message text and extract emotions such as "anxiety" from the text.

[0649] Step 4:

[0650] Emotion estimation

[0651] The server uses the analysis results to estimate the parents' feelings and requests.

[0652] Input: Sentiment data returned from the sentiment analysis engine.

[0653] Output: Estimated emotion or desire (e.g., "anxiety").

[0654] Specific operation: The server analyzes the analysis results and confirms that "anxiety" has a high score.

[0655] Step 5:

[0656] Generate reply suggestions

[0657] The server uses a generative AI model (GPT-4) to generate appropriate reply suggestions based on the estimated emotions.

[0658] Input: Inferred emotion or desire (e.g., "anxiety").

[0659] Output: A generated response suggestion (e.g., "We understand you're feeling anxious about your child's homework. We're here to help, so please let us know if you have any questions.").

[0660] Specific operation: The server sends a prompt to the generative AI model. For example, it sends a prompt such as, "If a parent writes in the communication book, 'My child has recently started to dislike doing his homework, and I'm worried,' please generate an appropriate reply." The generative AI model then generates an appropriate reply.

[0661] Step 6:

[0662] Presenting to the teacher

[0663] The server sends the generated reply draft to the teacher's terminal, which the teacher uses as a reference when creating a reply for the communication book.

[0664] Input: The generated reply suggestion.

[0665] Output: Response proposal displayed on the teacher's device.

[0666] Specific operation: The server sends data containing the generated reply sentence suggestions to the teacher's device. A notification is displayed on the teacher's device, and the teacher opens the contact application to check the reply sentence suggestions.

[0667] (Application example 2)

[0668] Next, a description will be given of Application Example 2 of Form 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."

[0669] In communication between parents and teachers, it is necessary to accurately grasp the parents' feelings and requests and provide appropriate replies quickly. However, it takes time and effort for teachers to respond individually, and there are challenges in that it is difficult to alleviate parents' anxiety and sufficiently increase their trust. Furthermore, advanced emotion analysis and natural language generation technology are required to generate appropriate replies that reflect the parents' feelings.

[0670] 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.

[0671] In this invention, the server includes AI means that has learned the speaking techniques of veteran teachers, means that instantly grasp the diverse needs of parents, means that increase parents' peace of mind and trust with accurate and clever phrases, means that instantly create recommended reply suggestions, means that analyze the contents of the parent's communication book and infer their emotions and requests, means that generate reply suggestions based on the parents' emotions using an emotion engine, means that input prompt sentences using a generative AI model and generate appropriate reply sentences, and means that present the generated reply sentences to the teacher. This makes it possible to accurately grasp parents' emotions and requests and provide quick and appropriate replies.

[0672] "AI means that have learned the speaking techniques of veteran teachers" refers to artificial intelligence means that learns the communication techniques and speaking styles of experienced teachers and imitates them.

[0673] "Means for instantly grasping the diverse needs of parents" refers to means for quickly analyzing and understanding information and requests from parents.

[0674] "Means of increasing parents' sense of security and trust through accurate and thoughtful phrases" refers to means of providing appropriate and heartfelt words to parents to increase their sense of security and trust.

[0675] The "means for instantly creating suggested reply sentences" is a means for quickly generating appropriate reply sentences in response to inquiries or requests from parents.

[0676] "Means for analyzing the contents of communication notebooks from parents and inferring their feelings and requests" refers to a means for analyzing the text written in communication notebooks and inferring the feelings and specific requests of parents.

[0677] The "means for generating a reply message corresponding to the parent's emotions using an emotion engine" refers to a means for using an emotion analysis engine to recognize the parent's emotions and generate an appropriate reply message corresponding to those emotions.

[0678] "Means for inputting a prompt sentence using a generative AI model and generating an appropriate reply sentence" refers to means for inputting a prompt sentence into a generative AI model and generating an appropriate reply sentence based on it.

[0679] The "means for presenting the generated reply text to the teacher" is a means for displaying the generated reply text to the teacher and providing it as a reference.

[0680] The system for carrying out the present invention includes a series of means for facilitating communication between parents and teachers and increasing parents' sense of security and trust. Specific embodiments of this system will be described below.

[0681] System Configuration

[0682] The system consists of a server and user terminals (smartphones, tablets, etc.). The server includes the following means:

[0683] 1. AI means that learn the speaking skills of veteran teachers: Artificial intelligence that learns the communication techniques of experienced teachers and imitates them.

[0684] 2. A means of instantly grasping the diverse needs of parents: A means of quickly analyzing and understanding information and requests from parents.

[0685] 3. A means of increasing parents' sense of security and trust with accurate and thoughtful phrases: A means of providing parents with appropriate and heartfelt words to increase their sense of security and trust.

[0686] 4. A means of instantly generating suggested reply sentences: A means of quickly generating appropriate reply sentences in response to inquiries or requests from parents.

[0687] 5. A means of analyzing the contents of communication diaries from parents and inferring their feelings and requests: A means of analyzing the text written in communication diaries and inferring the feelings and specific requests of parents.

[0688] 6. A means for generating reply suggestions based on the parent's emotions using an emotion engine: An emotion analysis engine for recognizing the parent's emotions and generating appropriate reply suggestions based on those emotions.

[0689] 7. A means for inputting a prompt sentence using a generative AI model and generating an appropriate reply sentence: A means for inputting a prompt sentence into a generative AI model and generating an appropriate reply sentence based on it.

[0690] 8. Means for presenting the generated reply sentence to the teacher: Means for displaying the generated reply sentence to the teacher and providing it for reference.

[0691] Processing flow

[0692] The server receives and analyzes the parent's contact information. It uses a natural language processing library, such as TextBlob, to estimate the parent's feelings and requests. It then uses an emotion engine to generate a reply message based on the parent's feelings. A generative AI model (e.g., GPT-3) is used to generate an appropriate reply message based on the parent's prompt.

[0693] The generated reply text is displayed on the teacher's device, and the teacher uses it as a reference to create a reply to the parent. This enables the teacher to accurately understand the parent's feelings and requests and provide a quick and appropriate reply.

[0694] Specific examples

[0695] For example, if a parent writes in the communication book, "I'm worried about my child's learning," the server analyzes this text and determines that the emotion is negative.Then, the generative AI model inputs a prompt saying, "When the parent's emotion is negative, please generate an appropriate reply sentence," and generates an appropriate reply sentence.

[0696] Example prompt sentence:

[0697] "Generate an appropriate response when the parent's emotion is negative."

[0698] In this way, communication between parents and teachers can be facilitated, and parents' sense of security and trust can be increased.

[0699] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0700] Step 1:

[0701] The server receives the contents of the contact book from the parent.

[0702] Input: Text data written by the parent in the communication book.

[0703] Output: The text of the contact list.

[0704] Specific operation: The server receives the contents of the contact book sent from the user terminal in text format and stores them in a database.

[0705] Step 2:

[0706] The server analyzes the contents of the received contact book and estimates the user's feelings and requests.

[0707] Input: Contacts text.

[0708] Output: Sentiment polarity (positive / negative) and subjectivity score.

[0709] Specific operation: The server uses a natural language processing library such as TextBlob to analyze the contents of the contact list and calculate the sentiment polarity and subjectivity.

[0710] Step 3:

[0711] The server uses an emotion engine to generate a reply message according to the parent's emotion.

[0712] Input: Sentiment polarity and subjectivity scores.

[0713] Output: The prompt statement.

[0714] Specific operation: The server generates a prompt sentence to be input to the generative AI model based on the polarity of the emotion. For example, it generates a prompt sentence such as, "When the parent's emotion is negative, please generate an appropriate reply sentence."

[0715] Step 4:

[0716] The server inputs a prompt sentence using a generative AI model and generates an appropriate reply sentence.

[0717] Input: prompt statement.

[0718] Output: The generated reply text.

[0719] Specific operation: The server uses OpenAI's API to input a prompt sentence into a generative AI model (e.g., GPT-3) and generates an appropriate reply sentence based on it.

[0720] Step 5:

[0721] The server presents the generated reply text to the teacher's terminal.

[0722] Input: The generated reply text.

[0723] Output: Reply text displayed on the teacher's terminal.

[0724] Specific operation: The server sends the generated reply text to the teacher's terminal and displays it so that the teacher can check it.

[0725] Step 6:

[0726] The teacher will use the provided response text as a reference to create a final response to the parents.

[0727] Input: The generated reply text.

[0728] Output: Final response to parents.

[0729] Specific operation: The teacher uses the reply text provided by the server as a reference to create a final reply to the parent / guardian and writes it in the communication book.

[0730] Example 3

[0731] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0732] In traditional educational settings, it took a great deal of time and effort for teachers to communicate smoothly with parents. It was also difficult to accurately grasp parents' feelings and requests and respond appropriately, placing a heavy burden on teachers. Furthermore, insufficient communication with parents could lead to a decline in parents' sense of security and trust.

[0733] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes artificial intelligence means that has learned the speaking techniques of veteran educators, means for instantly grasping diverse parental needs, means for increasing parental comfort and trust with accurate and clever phrases, means for analyzing the contents of a parent's message book from parents and generating appropriate reply suggestions based on the parents' estimated feelings and requests regarding the contents, means for providing the generated reply suggestions as a reference for teachers when writing replies to the message book, means for sequentially learning and optimizing communication with parents, means for analyzing the contents of the parent's message book from parents using an emotion engine and recognizing the parents' emotions, and means for generating appropriate phrases according to the emotions. This enables teachers to accurately grasp parents' emotions and requests and quickly provide appropriate replies.

[0734] "Artificial intelligence means" refers to a system that includes machine learning algorithms and natural language processing technology to analyze the contents of communication notebooks from parents and generate appropriate reply suggestions.

[0735] "Means for instantly understanding parents' needs" refers to technology that analyzes the contents of communication notebooks from parents and quickly grasps their requests and feelings based on those contents.

[0736] "Means to increase parents' sense of security and trust with accurate and clever phrases" is a technology that generates appropriate words and expressions that correspond to parents' emotions and requests, thereby increasing their sense of security and trust.

[0737] The "means for generating reply proposals" is a technology for automatically creating appropriate reply proposals based on the contents of the parent's communication book and their feelings and requests.

[0738] "Means to provide as a reference when creating a reply to the communication book" refers to a technology that presents the generated reply draft to the teacher and provides it as reference information when the teacher creates the actual reply.

[0739] "Means for sequential learning and optimizing communication with parents" refers to technology that improves communication with parents by collecting teacher replies and parental reactions and continuously training an artificial intelligence model based on them.

[0740] The "emotion engine" is a technology that analyzes the contents of communication notebooks from parents and recognizes the emotions contained within them.

[0741] The "means for generating appropriate phrases" is a technology for generating appropriate words or expressions for the guardian in accordance with the emotions recognized by the emotion engine.

[0742] This invention is a system for teachers to communicate smoothly with parents, and aims to analyze the contents of a parent's contact book and generate appropriate reply suggestions. This system includes elements of a server, a terminal, and a user.

[0743] System configuration

[0744] server:

[0745] The server plays a central role in receiving and analyzing the contents of the communication book from parents. The server has the following functions:

[0746] 1. Data Collection:

[0747] The server receives the contents of the communication book from the parents and stores them in a database in text format.

[0748] 2. Emotion analysis:

[0749] The server uses an emotion engine (e.g., natural language processing API) to analyze the contents of the contact book and recognize the parent's emotions.

[0750] 3. Generate reply suggestions:

[0751] The server uses a generative AI model (e.g., generative AI model) to generate appropriate reply sentence suggestions based on the parent's feelings and requests.

[0752] 4. Incremental learning:

[0753] The server collects the replies that teachers actually send and the reactions of parents, and learns from them sequentially, allowing the AI ​​model to optimize communication with parents.

[0754] Device:

[0755] The terminal is a device used by teachers and parents to input and display the contents of the contact book. The terminal has the following functions:

[0756] 1. Input function:

[0757] It provides an interface for parents to input the contents of the contact book and send it to the server.

[0758] 2.Display function:

[0759] The teacher is provided with an interface to review the reply suggestions provided by the server and to create the actual reply.

[0760] User:

[0761] The users are teachers and parents, and they communicate using the system.

[0762] Specific examples

[0763] For example, if a parent writes in the communication book, "I'm worried because my child has been refusing to do his / her homework lately," the server processes the information in the following manner.

[0764] 1. Data Collection:

[0765] The parent or guardian uses the device to enter a message into the contacts app and presses the send button.

[0766] The server receives the sent messages and stores them in a database.

[0767] 2. Emotion analysis:

[0768] The server creates an API request to send the text data from the contacts to the emotion engine.

[0769] The emotion engine analyzes text data and identifies "anxiety."

[0770] The server receives the emotion data returned from the emotion engine and stores it in a database.

[0771] 3. Generate reply suggestions:

[0772] The server generates a prompt sentence including emotion data and the contents of the contact book.

[0773] Example prompt: "The following is a note from a parent. Please generate an appropriate response based on this. Note: 'I'm worried because my child has been refusing to do his homework lately.'"

[0774] The server sends this prompt to the generative AI model.

[0775] The generative AI model generates reply sentence suggestions based on the prompt sentence and returns them to the server.

[0776] The server stores the generated reply suggestions in a database.

[0777] 4. Incremental learning:

[0778] The teacher uses the terminal to create an actual reply while referring to the reply proposal provided by the server, and sends it to the parent.

[0779] Parents respond to replies (e.g., message again, feedback).

[0780] The server stores the teacher's replies and the parent's reactions in a database.

[0781] The server periodically feeds this data back into the generative AI model to update the model.

[0782] In this way, the server provides a system that facilitates communication with parents and reduces the burden on teachers. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0783] Step 1: Data collection

[0784] The server receives the contents of the address book from the parent / guardian. When the parent / guardian uses the device to enter a message into the address book app and presses the send button, the message is sent to the server. The server saves the received message in text format in a database. The input is the contents of the address book from the parent / guardian, and the output is the text data saved in the database.

[0785] Step 2: Sentiment Analysis

[0786] The server sends the saved contents of the address book to the emotion engine. The server creates an API request to send the text data of the address book to the emotion engine. The emotion engine analyzes the text data and identifies the parent's emotion (e.g., "anxiety," "joy," "anger," etc.). The server receives the emotion data returned from the emotion engine and stores it in a database. The input is the text data of the address book, and the output is the emotion data returned from the emotion engine.

[0787] Step 3: Generate reply suggestions

[0788] The server sends the emotion data and the contents of the contact book to the generative AI model. The server generates a prompt sentence that includes the emotion data and the contents of the contact book. An example of a prompt sentence is, "The contents of the contact book from the parent are shown below. Please generate an appropriate reply sentence proposal based on this. Contents of the contact book: 'I'm worried because my child has been refusing to do his homework lately.'" The server sends this prompt sentence to the generative AI model. The generative AI model generates a reply sentence proposal based on the prompt sentence and returns it to the server. The server stores the generated reply sentence proposal in a database. The input is a prompt sentence that includes the emotion data and the contents of the contact book, and the output is the reply sentence proposal returned by the generative AI model.

[0789] Step 4: Incremental learning

[0790] The server collects the replies that the teacher actually sent and the reactions of the parents. The teacher uses their device to create an actual reply using the reply suggestions provided by the server as a reference, and sends it to the parent. The parent then sends a reaction to the reply (e.g., a follow-up message, feedback). The server stores the teacher's replies and the parent's reactions in a database. The server periodically feeds this data back into the generative AI model to update the model. The input is the teacher's replies and the parent's reactions, and the output is an updated generative AI model.

[0791] (Application example 3)

[0792] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0793] In physical stores, responding quickly and appropriately to customer inquiries and feedback is important for improving customer satisfaction. However, it is difficult for store staff to respond appropriately to all inquiries, and responses can be delayed, especially when the inquiry contains emotional elements. For this reason, there is a need for a system that can instantly analyze customer emotions and requests and provide appropriate reply suggestions.

[0794] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0795] In this invention, the server includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a means for instantly grasping diverse customer needs, a means for increasing customer peace of mind and trust with accurate and clever phrases, a means for instantly creating recommended reply suggestions, a means for analyzing customer inquiries and feedback and generating appropriate reply suggestions based on their emotions and requests, and a means for inputting prompt sentences into a generation AI model and generating appropriate reply suggestions, thereby enabling a quick and appropriate response to customer inquiries and feedback.

[0796] "Artificial intelligence means that has learned the speaking techniques of veteran educators" is an artificial intelligence function that learns the communication techniques and speaking styles of experienced educators and responds appropriately based on that.

[0797] "Means of instantly grasping diverse customer needs" refers to technology that analyzes customer inquiries and feedback to quickly understand customer requests and expectations.

[0798] "Means of increasing customer confidence and trust through accurate and clever phrases" refers to a method of choosing appropriate words according to the customer's emotions and situation to increase the customer's sense of confidence and trust.

[0799] "A means for instantly generating recommended reply suggestions" is a technology for quickly generating appropriate reply sentences in response to inquiries and feedback from customers.

[0800] "A means of analyzing customer inquiries and feedback and generating appropriate reply suggestions based on their emotions and requests" is a technology that analyzes customer messages, understands their emotions and requests, and then generates the most appropriate reply.

[0801] "Means for inputting a prompt sentence into a generative AI model and generating an appropriate reply sentence proposal" refers to a technology for providing a specific input sentence (prompt sentence) to a generative AI model and generating an appropriate reply sentence based on that.

[0802] A system for carrying out this invention has the following configuration: The server includes artificial intelligence means that has learned the speaking techniques of veteran educators, means that instantly grasp the needs of diverse customers, means that increase customer peace of mind and trust with accurate and clever phrases, means that instantly create recommended reply suggestions, means that analyze inquiries and feedback from customers and generate appropriate reply suggestions based on their emotions and requests, and means that input prompt sentences into a generation AI model and generate appropriate reply suggestions.

[0803] Hardware and software used

[0804] Hardware: Smartphone

[0805] Software: Python, TensorFlow, NLP library (e.g., spaCy), sentiment analysis engine (e.g., Google® Cloud Natural Language API)

[0806] Data processing and calculation

[0807] 1. Data acquisition: Customer inquiries and feedback are acquired as text data. The text entered by the customer is sent to the server via a smartphone application.

[0808] 2. Preprocessing: The acquired text data is preprocessed using an NLP library, specifically tokenizing, removing stop words, stemming, etc.

[0809] 3. Sentiment Analysis: Recognize customer sentiment from text data using a sentiment analysis engine, for example, by using Google Cloud Natural Language API to obtain a sentiment score for the text.

[0810] 4. Use of a generative AI model: A generative AI model (e.g., GPT-3) is fed a prompt that includes the customer's query and the perceived sentiment to generate appropriate reply suggestions.

[0811] Specific examples

[0812] For example, if a customer inquires through a smartphone app, "The product I recently purchased is broken. Can you replace it?", the server analyzes the text and recognizes that the customer's emotion is "dissatisfied." The server then inputs the following prompt sentence into the generative AI model:

[0813] Customer inquiry: "My recent purchase broke. Can I get a replacement?"

[0814] Customer Sentiment: "Dissatisfied"

[0815] Based on this, the generative AI model generates a suggested reply such as, "We apologize for the inconvenience. We will process the exchange immediately." The store clerk can then use this suggested reply as a reference to respond quickly and appropriately.

[0816] In this way, customer service at physical stores becomes more efficient and customer satisfaction increases.

[0817] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0818] Step 1:

[0819] Users enter inquiries and feedback through a smartphone app.

[0820] Input: Text data entered by the user (e.g., "The item I recently purchased is broken. Can you replace it?")

[0821] Output: Text data sent from the smartphone app to the server

[0822] Step 2:

[0823] The server preprocesses the received text data using an NLP library.

[0824] Input: Text data sent to the server

[0825] Output: Preprocessed text data (tokenized, stopword removed, stemmed, etc.)

[0826] Specific operation: The server uses Python and an NLP library (e.g., spaCy) to tokenize the text data, remove unnecessary words, and convert them into their base forms.

[0827] Step 3:

[0828] The server inputs the preprocessed text data into a sentiment analysis engine to recognize customer emotions.

[0829] Input: Preprocessed text data

[0830] Output: Sentiment analysis result (e.g., "dissatisfied")

[0831] Specific operation: The server uses the Google Cloud Natural Language API to obtain the sentiment score of the text data and recognizes the customer sentiment as "dissatisfied."

[0832] Step 4:

[0833] The server inputs the prompt sentence into the generative AI model and generates an appropriate reply sentence proposal.

[0834] Input: Prompt sentence (e.g., "Customer Question: 'My recent purchase broke. Can you replace it?' Customer Sentiment: 'Unhappy'")

[0835] Output: Generated reply suggestions (e.g., "We apologize for the inconvenience. We will process your replacement immediately.")

[0836] Specific operation: The server inputs the prompt sentence into a generative AI model (e.g., GPT-3) and generates appropriate reply sentence suggestions.

[0837] Step 5:

[0838] The server sends the generated reply text to the smartphone app and displays it to the user.

[0839] Input: Generated reply suggestion

[0840] Output: Reply suggestions displayed on the smartphone app

[0841] Specific operation: The server sends the generated reply draft to the smartphone app and displays it for the user to review.

[0842] Step 6:

[0843] The user refers to the generated reply text proposal and responds to the customer promptly and appropriately.

[0844] Input: Reply suggestions displayed on smartphone app

[0845] Output: Appropriate response to the customer

[0846] Specific actions: The user (store clerk) refers to the reply suggestions displayed on the smartphone app and responds to the customer quickly and appropriately.

[0847] 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.

[0848] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0849] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0850] 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.

[0851] [Second embodiment]

[0852] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0853] 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.

[0854] 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).

[0855] 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.

[0856] 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.

[0857] 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).

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0864] "Example 1"

[0865] In one embodiment of the present invention, a learning database is provided for AI to learn the speaking techniques of veteran teachers. This database contains phrases actually used by veteran teachers, as well as appropriate phrases for each situation. The AI ​​learns from this database and generates appropriate phrases for the contents of the communication book from parents.

[0866] "Example 2"

[0867] Furthermore, the AI ​​has an algorithm that instantly understands the needs of parents. Specifically, it analyzes the contents of the parent's communication book and infers the parent's feelings and requests in response to the contents. Based on this inference, the AI ​​generates phrases that increase the parent's sense of security and trust.

[0868] "Example 3"

[0869] The AI ​​also has the ability to instantly create recommended reply suggestions. Specifically, the AI ​​generates appropriate reply suggestions based on the contents of the parent's communication book and the parent's estimated feelings and requests regarding that content. These reply suggestions can be used as a reference when teachers create replies to the communication book.

[0870] The processing flow of each embodiment will be described below.

[0871] "Example 1"

[0872] Step 1: The AI ​​learns the speaking techniques of veteran teachers from a learning database, which contains phrases actually used by veteran teachers and appropriate phrases for each situation.

[0873] Step 2: Once the parent enters the information into the communication book, the AI ​​analyzes it.

[0874] Step 3: Based on the analysis results, the AI ​​selects appropriate phrases from the learning database and generates a reply message for the parent.

[0875] "Example 2"

[0876] Step 1: When the parent enters the information into the communication book, the AI ​​analyzes it.

[0877] Step 2: The AI ​​uses the analysis results to infer the parents' feelings and requests.

[0878] Step 3: Based on the estimation results, the AI ​​generates phrases that will increase parents' sense of security and trust.

[0879] "Example 3"

[0880] Step 1: When the parent enters the information into the communication book, the AI ​​analyzes it.

[0881] Step 2: The AI ​​generates appropriate reply suggestions based on the analysis results and the parent's feelings and requests.

[0882] Step 3: The generated reply ideas can be used as a reference for teachers to write their replies in the communication notebook.

[0883] Example 1

[0884] Next, a description will be given of Example 1 of Form 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."

[0885] In traditional educational settings, communication with parents is an important element, but it takes time and effort for teachers to respond individually. Furthermore, while there is a need to respond accurately and quickly to the diverse needs of parents, there is a lack of effective means to achieve this. It is particularly difficult for newly appointed or inexperienced teachers to acquire the same speaking skills and responsiveness as veteran teachers. This presents a challenge, making it difficult to increase parents' sense of security and trust.

[0886] 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.

[0887] In this invention, the server includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a terminal means that receives the contents of the contact book entered by the user, a server means that inputs the data received from the terminal as a prompt sentence to the generating AI model, a means that returns the generated phrase to the terminal, and a means that the terminal displays the generated phrase to the user. This makes it possible to respond quickly and accurately to the contents of the contact book from parents. Furthermore, by having the artificial intelligence that has learned the speaking techniques of veteran teachers generate appropriate phrases, parents' sense of security and trust can be increased.

[0888] A "veteran educator" is a teacher who has many years of experience in the educational field and possesses strong leadership and communication skills.

[0889] "Artificial intelligence" refers to the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[0890] A "terminal" is a device operated by a user, and refers to hardware such as a smartphone or PC.

[0891] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to generate appropriate output from input data.

[0892] A "prompt sentence" is text data that is input into a generative AI model; it is the sentence that the model analyzes and uses to generate the appropriate output.

[0893] A "server" is a computer system that processes and stores data on a network.

[0894] A "phrase" is a short sentence or expression used in a particular situation or context.

[0895] A "communication notebook" is a communication tool that allows parents and teachers to share information about their children's status and other important matters.

[0896] This invention is a system that optimizes communication with parents by using artificial intelligence that has learned the speaking skills of veteran educators. Specific embodiments of this system will be described below.

[0897] System configuration

[0898] server

[0899] The server maintains a learning database for learning the speaking techniques of veteran educators. This database contains phrases actually used by veteran educators, as well as appropriate phrases for each situation. The server uses a generative AI model to learn from this database. Specifically, the server uses Python and utilizes machine learning libraries such as TensorFlow and PyTorch to analyze the phrases in the database and train the model. The trained model then generates appropriate phrases for the contents of parental communication notebooks.

[0900] Terminal

[0901] The device is responsible for sending the parental contact information entered by the user to the server. The device collects text data entered by the user through a web browser or dedicated application and sends it to the server. For example, the device uses programming languages ​​such as JavaScript or Swift to build a user interface and call APIs that send the input data to the server.

[0902] User

[0903] The user uses the device to input the contents of the parent's contact book. The contents entered by the user are sent to the server via the device. The server uses a generative AI model to generate an appropriate phrase for the input contents and responds to the user.

[0904] Specific examples

[0905] Prompt Sentence Examples

[0906] 1. Prompt: "My child seems to be having a bad day at school lately."

[0907] Generated phrase: "Thank you for your concern about your child. We will keep a close eye on him."

[0908] 2. Prompt: "I can't seem to keep up with the class."

[0909] Generated phrase: "We apologize for any inconvenience caused. We will review the lesson and provide individual support."

[0910] 3. Prompt: "He doesn't seem to get along with the other kids."

[0911] Generated phrase: "We understand your concerns about your child's social skills. We will help them make friends through whole-class activities."

[0912] In this way, the server uses the generative AI model to generate appropriate phrases in response to the user's input, enabling quick and accurate communication with parents, increasing their sense of security and trust.

[0913] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0914] Step 1:

[0915] The user uses the terminal to input the contents of the address book.

[0916] Input: The user types text into an input field in a contacts application.

[0917] Specific operation: The user opens a contacts application using a device such as a smartphone or PC and enters information such as, "My child seems to be in a bad mood at school recently."

[0918] Output: The entered text data is temporarily stored in the terminal.

[0919] Step 2:

[0920] The terminal transmits the input data to the server.

[0921] Input: Text data entered by the user.

[0922] Specific operation: The device uses a programming language such as JavaScript or Swift to generate an HTTP request and send input data to the server. For example, it sends data to the endpoint "POST / api / v1 / messages".

[0923] Output: The server receives the HTTP request and retrieves the text data.

[0924] Step 3:

[0925] The server receives the data and inputs it as a prompt to the generative AI model.

[0926] Input: Text data sent from the terminal.

[0927] How it works: The server analyzes the received text data and inputs it as prompts to the generative AI model. Specifically, it uses Python to input data to the model using machine learning libraries such as TensorFlow and PyTorch.

[0928] Output: The generative AI model receives the prompt and begins parsing it.

[0929] Step 4:

[0930] A generative AI model generates appropriate phrases.

[0931] Input: The prompt text entered by the server.

[0932] How it works: The generative AI model analyzes the prompt and generates appropriate phrases based on phrases in the database, such as "Thank you for your concern about your child. We will keep a close eye on them."

[0933] Output: The generated phrase is returned to the server.

[0934] Step 5:

[0935] The server sends the generated phrase back to the device.

[0936] Input: The phrase returned by the generative AI model.

[0937] Specific operation: The server receives the generated phrase and returns it to the terminal as an HTTP response. Specifically, it returns the generated phrase along with the status code "200 OK".

[0938] Output: The device receives the HTTP response and retrieves the generated phrase.

[0939] Step 6:

[0940] The terminal displays the generated phrase to the user.

[0941] Input: The generated phrase returned by the server.

[0942] Specific operation: The device displays the phrase received from the server on the user interface. The user checks the generated phrase and replies to the parent if necessary. For example, it could display "Thank you for your concern about your child. We will keep a close eye on them."

[0943] Output: User reviews the generated phrase and responds appropriately to the parent.

[0944] (Application example 1)

[0945] Next, a description will be given of Application Example 1 of Form 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."

[0946] Customer service in traditional brick-and-mortar stores relies heavily on the experience and skills of staff, and it can be difficult for new or inexperienced staff to instantly provide the right phrases and methods of response. Furthermore, there was a lack of support for smooth customer interaction, making it necessary to improve customer satisfaction.

[0947] 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.

[0948] In this invention, the server includes AI means that has learned the speaking techniques of veteran teachers, means that instantly grasp the diverse needs of parents, means that increase parents' peace of mind and trust with accurate and thoughtful phrases, means that instantly create recommended reply sentences, means that are installed in smart glasses to support store staff in their conversations with customers, means that recognize the customer's speech and suggest appropriate phrases in real time, and means that display the generated phrases on the display of the smart glasses. This enables store staff to smoothly communicate with customers and improve customer satisfaction.

[0949] "AI means that have learned the speaking techniques of veteran teachers" refers to an artificial intelligence means that learns phrases used by veteran teachers and appropriate phrases depending on the situation, and generates appropriate responses based on this.

[0950] "Means for instantly grasping the diverse needs of parents" refers to means for quickly understanding communications and requests from parents and responding accordingly.

[0951] "Means of increasing parents' sense of security and trust through accurate and thoughtful phrases" refers to means of providing parents with appropriate and heartfelt phrases to increase their sense of security and trust.

[0952] The "means for instantly creating suggested reply sentences" is a means for quickly generating appropriate reply sentences based on the content of the message from the parent.

[0953] "A means to be installed on smart glasses to support staff in physical stores in their interactions with customers" refers to a means to be installed on smart glasses worn by staff in physical stores in order to support their interactions with customers.

[0954] "A means for recognizing the speech of a customer and suggesting appropriate phrases in real time" refers to a means for converting what a customer says into text using speech recognition technology and suggesting appropriate phrases in real time based on that text.

[0955] The "means for displaying the generated phrase on the display of the smart glasses" refers to a means for displaying the generated appropriate phrase on the display of the smart glasses so that the staff can confirm it.

[0956] As an embodiment of the present invention, a system is described that is installed in smart glasses to support staff in a brick-and-mortar store in interacting with customers.

[0957] First, the system uses the following hardware and software:

[0958] Hardware: Smart glasses (with built-in camera and microphone)

[0959] Software: OpenAI API, OpenCV, SpeechRecognition library

[0960] System configuration

[0961] 1. Speech Recognition: The microphone in the smart glasses is used to record the customer's speech and then the SpeechRecognition library is used to convert it into text, allowing us to obtain what the customer is saying in real time.

[0962] 2. Phrase generation: The converted text is sent to the OpenAI API to generate appropriate phrases. A generative AI model is used to select the most appropriate phrases from a database of expert speaking skills.

[0963] 3. Display: The generated phrase is displayed on the smart glasses screen, allowing staff to check the appropriate phrase in real time and smoothly communicate with the customer.

[0964] Specific examples

[0965] For example, if a customer says, "Tell me more about this product," the system works as follows:

[0966] 1. The microphone in the smart glasses records what the customer says, and the SpeechRecognition library converts it into text: "Tell me more about this product."

[0967] 2. Based on the converted text, the OpenAI API generates an appropriate phrase, such as "This product uses the latest technology and is particularly durable."

[0968] 3. The generated phrase is displayed on the smart glasses display, and the staff member checks it and explains it to the customer.

[0969] Prompt Sentence Examples

[0970] If a customer says, "Tell me more about this product," here are some example prompts:

[0971] Generate the right phrases in your customer conversations: Tell me more about this product

[0972] In this way, store staff can smoothly communicate with customers, improving customer satisfaction.

[0973] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0974] Step 1:

[0975] The microphone in the smart glasses records what the customer says. The input is the customer's voice and the output is the recorded voice data. The microphone in the smart glasses captures what the customer says and saves it as voice data.

[0976] Step 2:

[0977] The device converts the recorded voice data into text using the SpeechRecognition library. The input is the recorded voice data, and the output is text data. The SpeechRecognition library analyzes the voice data and generates the corresponding text.

[0978] Step 3:

[0979] The device sends the converted text data to the OpenAI API, which generates an appropriate phrase. The input is the text data, and the output is the generated phrase. The OpenAI API generates a prompt based on the text data and uses a generative AI model to generate the optimal phrase.

[0980] Step 4:

[0981] The server displays the generated phrase on the smart glasses display. The input is the generated phrase and the output is the phrase displayed on the display. The smart glasses display displays the generated phrase so that the staff can check it.

[0982] Step 5:

[0983] The user (staff member) responds appropriately to the customer based on the phrases displayed on the display. The input is the phrase displayed on the display, and the output is the response to the customer. The staff member uses the displayed phrases as a reference to provide an appropriate explanation or response to the customer.

[0984] Example 2

[0985] Next, a description will be given of Example 2 of Form 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."

[0986] Conventional communication systems for parents have difficulty accurately grasping parents' feelings and specific requests and responding appropriately. As a result, parents' anxieties and dissatisfaction remain unresolved, making it difficult to build trusting relationships. Furthermore, while it is necessary to generate swift and accurate replies, there has been a lack of automated methods for this.

[0987] 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.

[0988] In this invention, the server includes means for receiving the contents of the contact book sent from the parent / guardian, means for preprocessing the received contents, means for analyzing the preprocessed contents to estimate the parent / guardian's feelings, means for estimating the parent / guardian's requests, means for generating phrases that increase the parent / guardian's sense of security and trust based on the estimation results, and means for sending the generated phrases to the parent / guardian. This makes it possible to accurately grasp the parent / guardian's feelings and requests and automatically generate a quick and appropriate reply.

[0989] "Guardian" refers to a person who is responsible for a child's education and life.

[0990] "Communication book" refers to a document or digital medium for exchanging information between parents and educational institutions.

[0991] "Server" refers to a computer system that receives, processes, and transmits data over a network.

[0992] "Preprocessing" refers to a series of operations that shape data and convert it into a format that is easier to analyze before data analysis.

[0993] "Analysis" refers to the process of examining data in detail to understand its structure and meaning.

[0994] "Emotions" refers to psychological states that are estimated based on what parents write in their communication books.

[0995] "Requests" refer to specific requests and wishes extracted from the information parents write in their communication books.

[0996] A "phrase" refers to a part or whole of a sentence that is generated to reply to the parent.

[0997] "Generation" refers to the process of creating new data or information using specific algorithms or models.

[0998] "Sending" refers to the act of communication to deliver the generated phrases and data to the parent's device.

[0999] MODE FOR CARRYING OUT THE INVENTION

[1000] The present invention is a system for optimizing communication between parents and educational institutions. A specific embodiment of this system will be described below.

[1001] System configuration

[1002] This system mainly consists of three elements: a server, a terminal, and a user. The server receives and analyzes the contents of the contact book sent by parents and generates appropriate reply phrases. The terminal is a device that parents use to send contact book entries and receive replies from the server. Users refer to parents and staff at educational institutions.

[1003] Hardware and software used

[1004] Server: A powerful computer system that receives, processes, and transmits data.

[1005] Device: A device that can connect to the internet, such as a smartphone, tablet, or PC.

[1006] software:

[1007] NLTK (Natural Language Toolkit): A library for preprocessing text.

[1008] VADER (Valence Aware Dictionary and sEntiment Reasoner): An algorithm for sentiment analysis.

[1009] BERT (Bidirectional Encoder Representations from Transformers): A natural language processing model for estimating parental preferences.

[1010] GPT-3: A generative AI model that generates phrases that increase parental reassurance and trust.

[1011] Data processing and calculation

[1012] 1. Receiving the contents of the contact book: The server receives the contents of the contact book sent by the parent. The received contents are stored in the database.

[1013] 2. Text preprocessing: The server uses NLTK to tokenize the contacts content, remove stop words, and perform stemming.

[1014] 3. Sentiment Analysis: The server analyzes the sentiment of the preprocessed text using VADER and classifies it as positive, negative, or neutral.

[1015] 4. Desire Estimation: The server uses the BERT model to extract parents' specific desires and concerns from the contents of the communication book.

[1016] 5. Phrase generation: The server uses GPT-3 to generate phrases that increase parents' sense of security and trust based on the results of sentiment analysis and desire estimation.

[1017] 6. Sending the generated phrase: The server sends the generated phrase to the parent's device.

[1018] Specific examples

[1019] For example, if a parent writes in the communication book, "My child seems to be in a bad mood at school recently. Is there something wrong?", the system will operate as follows:

[1020] 1. Receiving the contents of the communication book: The server receives the communication book from the parent and stores it in the database.

[1021] 2. Text preprocessing: The server uses NLTK to tokenize the contacts content, remove stop words, and perform stemming.

[1022] 3. Sentiment Analysis: The server uses VADER to analyze the contents of the contact list to determine whether they contain negative sentiment.

[1023] 4. Desire Estimation: The server uses the BERT model to extract the parent's desire (checking the child's problem).

[1024] 5. Phrase generation: The server sends a prompt to GPT-3, which generates the phrase, "We're sorry for causing you concern about your child's condition. Please rest assured that we will monitor the situation at school and provide any necessary support."

[1025] 6. Sending the generated phrase: The server sends the generated phrase to the parent's device.

[1026] Prompt Sentence Examples

[1027] An example of a prompt sentence to input to the generative AI model is as follows:

[1028] "Content from a parent's note: 'My child seems unmotivated at school recently. Is something wrong?' Parent's emotion: Negative. Parent's request: Confirmation of the child's problem. Generate phrases that will reassure the parent."

[1029] By inputting this prompt into GPT-3, an appropriate phrase will be generated.

[1030] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1031] Program processing flow

[1032] Step 1: Receive contacts

[1033] Input: Contents of the contact book sent by the parent or guardian from the device

[1034] Specific operation: The server receives the HTTP request and saves the contents of the contact list to a database.

[1035] Output: Contents of the contacts saved in the database

[1036] Step 2: Text preprocessing

[1037] Input: Contacts stored in the database

[1038] Specific operation: The server calls the NLTK library, splits the contents of the contacts into tokens, removes unnecessary words, and converts them into their base forms.

[1039] Output: Preprocessed text data

[1040] Step 3: Sentiment Analysis

[1041] Input: Preprocessed text data

[1042] What it does: The server uses the VADER library to calculate the sentiment score of the text and classify the sentiment based on the highest score.

[1043] Output: Sentiment analysis results (positive, negative, neutral)

[1044] Step 4: Estimate demand

[1045] Input: Preprocessed text data

[1046] Specific operation: The server loads the BERT model, gives it the text from the contacts book as input, and performs inference to extract requests and concerns.

[1047] Output: Extracted needs and concerns

[1048] Step 5: Phrase Generation

[1049] Input: Requests and concerns extracted as a result of sentiment analysis

[1050] Specific operation: The server generates a prompt sentence for GPT-3 and sends an API request to retrieve the appropriate phrase.

[1051] Output: Generated phrase

[1052] Step 6: Submit your generated phrase

[1053] Input: Generated phrase

[1054] Specific operation: The server constructs a message containing the generated phrase and sends it to the parent's device as an HTTP response.

[1055] Output: Phrase sent to parent's device

[1056] Specific examples

[1057] For example, if a parent writes in the communication book, "My child seems to be in a bad mood at school recently. Is there something wrong?", the process is as follows:

[1058] Step 1: Receive contacts

[1059] Input: Contents of the contact book sent by the parent or guardian from the device

[1060] Specific operation: The server receives the HTTP request and saves the contents of the contact list to a database.

[1061] Output: Contents of the contacts saved in the database

[1062] Step 2: Text preprocessing

[1063] Input: Contacts stored in the database

[1064] Specific operation: The server calls the NLTK library, splits the contents of the contacts into tokens, removes unnecessary words, and converts them into their base forms.

[1065] Output: Preprocessed text data

[1066] Step 3: Sentiment Analysis

[1067] Input: Preprocessed text data

[1068] What it does: The server uses the VADER library to calculate the sentiment score of the text and classify the sentiment based on the highest score.

[1069] Output: Sentiment analysis result (negative)

[1070] Step 4: Estimate demand

[1071] Input: Preprocessed text data

[1072] Specific operation: The server loads the BERT model, gives it the text from the contacts book as input, and performs inference to extract requests and concerns.

[1073] Output: Extracted needs (confirmation of child's problems)

[1074] Step 5: Phrase Generation

[1075] Input: Sentiment analysis results (negative), extracted requests (confirmation of child's problems)

[1076] Specific operation: The server generates a prompt sentence for GPT-3 and sends an API request to retrieve the appropriate phrase.

[1077] Output: The phrase "We're sorry you're worried about your child's condition. Rest assured we'll monitor their situation at school and provide any support they need."

[1078] Step 6: Submit your generated phrase

[1079] Input: Generated phrase

[1080] Specific operation: The server constructs a message containing the generated phrase and sends it to the parent's device as an HTTP response.

[1081] Output: Phrase sent to parent's device

[1082] (Application example 2)

[1083] Next, a description will be given of Application Example 2 of Form 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."

[1084] Conventional customer support systems have had the problem of being unable to respond quickly and appropriately to customer feedback and inquiries. In particular, it has been difficult to accurately grasp the customer's feelings and requests and generate reassuring phrases based on them. This has led to problems such as being unable to sufficiently improve customer satisfaction or build trusting relationships.

[1085] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes artificial intelligence means that has learned the speaking techniques of veteran educators, means for instantly grasping the needs of various users, means for increasing users' peace of mind and trust with accurate and clever phrases, means for instantly creating recommended reply suggestions, means for analyzing feedback from users, means for generating reassuring phrases based on the analysis results, and means for presenting the generated phrases. This makes it possible to respond quickly and appropriately to feedback and inquiries from customers and accurately grasp their feelings and requests. Furthermore, it becomes possible to effectively improve customer satisfaction and build trusting relationships.

[1086] "Artificial intelligence means that has learned the speaking techniques of veteran educators" is an artificial intelligence function that can learn the communication techniques and speaking styles of experienced educators and use them as a basis to engage in natural conversations.

[1087] "Means for instantly grasping the needs of diverse users" refers to technology that analyzes information input by users and quickly understands their needs and requests.

[1088] "Means of increasing users' sense of security and trust through accurate and clever phrases" refers to a technique for choosing appropriate words according to the user's emotions and situation to increase the user's sense of security and trust.

[1089] "A means for instantly generating suggested reply sentences" is a technology for automatically generating appropriate reply sentences in response to inquiries and feedback from users.

[1090] "Means for analyzing user feedback" refers to technology for analyzing opinions and impressions provided by users and understanding their content.

[1091] The "means for generating reassuring phrases based on the analysis results" is a technology that generates appropriate phrases to reassure users based on the analysis results of the feedback.

[1092] "Means for presenting generated phrases" refers to technology for displaying generated phrases to users and system operators.

[1093] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The function of each element will be explained below.

[1094] server

[1095] The server has the following main functions:

[1096] 1. AI means that has learned the speaking skills of veteran educators:

[1097] The server is equipped with artificial intelligence (AI) that has learned the communication techniques of experienced educators. This AI has a model for conducting natural conversations and facilitates communication with users.

[1098] 2. A way to instantly understand the needs of diverse users:

[1099] The server analyzes input from users and uses natural language processing (NLP) techniques to quickly understand their needs and desires, specifically using NLP libraries such as spaCy.

[1100] 3. Use precise and clever phrases to increase user confidence and trust:

[1101] The server selects appropriate words based on the user's emotions and situation, and generates phrases that will increase the user's sense of security and trust. These phrases are generated using generative AI models such as OpenAI GPT-3.

[1102] 4. How to instantly generate suggested responses:

[1103] The server automatically generates appropriate reply sentences in response to inquiries and feedback from users, a function that is also realized using a generative AI model.

[1104] 5. How we analyze your feedback:

[1105] The server uses NLP technology to analyze the opinions and comments provided by users and understand their content.

[1106] 6. How to generate reassuring phrases based on the analysis results:

[1107] The server analyzes the feedback and generates appropriate phrases to reassure the user, using a generative AI model.

[1108] 7. Means of presenting generated phrases:

[1109] The server has an interface for displaying the generated phrases to users and system operators.

[1110] Terminal

[1111] The terminal is a device through which the user interacts with the server. Examples include smartphones and tablets. The terminal displays phrases and reply messages sent from the server and sends input from the user to the server.

[1112] User

[1113] A user is an individual who uses the system. The user communicates with the server through a terminal and sends feedback and inquiries. By receiving replies and phrases from the server, the user gains a sense of security and trust.

[1114] Specific examples

[1115] For example, if a user sends feedback such as "I'm a little dissatisfied with the service recently. I felt that the response was slow," the server analyzes this feedback and calculates an emotion score. It then uses the generative AI model to generate a phrase such as "We apologize for the inconvenience. We will do our best to respond quickly, so we appreciate your continued support," and displays it on the device.

[1116] Prompt Sentence Examples

[1117] "If the customer's sentiment score is -0.5, generate a reassuring phrase."

[1118] "If the customer's sentiment score is 0.8, generate a phrase that increases confidence."

[1119] In this way, the system can respond quickly and appropriately to user feedback and accurately grasp the user's feelings and requests, which in turn helps to improve user satisfaction and build trusting relationships effectively.

[1120] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1121] Step 1:

[1122] The user inputs and sends feedback or inquiries using a terminal. The input feedback is sent to the server as text data.

[1123] Step 2:

[1124] The server analyzes the received feedback using natural language processing (NLP) techniques. Specifically, it uses an NLP library such as spaCy to calculate the sentiment score of the feedback. The input is the text data of the feedback, and the output is the sentiment score.

[1125] Step 3:

[1126] The server uses a generative AI model (e.g., OpenAI GPT-3) based on the analysis results to generate a reassuring phrase. The input is the emotion score, and the output is the generated phrase. Specifically, the emotion score is incorporated into the prompt sentence and input to the generative AI model.

[1127] Step 4:

[1128] The server sends the generated phrase to the terminal. The input is the generated phrase, and the output is the phrase displayed on the terminal. Specifically, the server sends the generated phrase to the terminal as text data.

[1129] Step 5:

[1130] The terminal displays the phrases received from the server to the user. The input is the phrase sent from the server, and the output is the phrase displayed on the terminal screen. Specifically, the terminal displays the received text data on the screen.

[1131] Step 6:

[1132] The user checks the displayed phrase and provides additional feedback or inquiries as necessary. The input is the displayed phrase, and the output is new feedback or inquiries. Specifically, the user operates the terminal to input and send new feedback.

[1133] In this way, the system can respond quickly and appropriately to user feedback and accurately grasp the user's feelings and requests, which in turn helps to improve user satisfaction and build trusting relationships effectively.

[1134] Example 3

[1135] Next, a description will be given of Example 3 of Form Example 3. 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."

[1136] With conventional communication book systems, it was difficult for teachers to quickly create appropriate replies to messages from parents, making it difficult to gain the parents' peace of mind and trust. Furthermore, teachers had to individually think up replies, which took time and effort, hindering efficient communication. Furthermore, it was difficult to accurately grasp the parents' feelings and requests, which sometimes meant they were unable to respond appropriately.

[1137] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1138] In this invention, the server includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a means for instantly grasping the diverse needs of parents, a means for increasing parents' sense of security and trust with accurate and clever phrases, a means for analyzing the contents of the parent's communication book and generating appropriate reply suggestions based on the estimated feelings and requests, and a means for providing the generated reply suggestions to the user. This enables teachers to quickly create appropriate reply suggestions, enabling efficient and effective communication with parents.

[1139] "Artificial intelligence means that has learned the speaking techniques of veteran educators" is an artificial intelligence function that learns the communication techniques and speaking styles of experienced educators and uses them as a basis for dialogue with parents.

[1140] "A means of instantly grasping the diverse needs of parents" is a function that analyzes the content of communications from parents and quickly understands the diverse requests and emotions contained therein.

[1141] "Means of increasing parents' sense of security and trust through accurate and thoughtful phrases" is a function that increases parents' sense of security and trust by using appropriate and heartfelt expressions according to the parents' feelings and requests.

[1142] "Means for analyzing the contents of the communication book from the parents and generating appropriate reply drafts based on the inferred feelings and requests" is a function for analyzing the contents of the communication book from the parents, inferring the parents' feelings and requests based on the contents, and generating reply drafts accordingly.

[1143] The "means for providing the generated reply draft to the user" is a function for presenting the generated reply draft to the user, such as a teacher, so that the user can check, modify, and use it.

[1144] This invention is a system for teachers to create quick and appropriate replies to messages from parents. The system includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a means for instantly grasping the diverse needs of parents, a means for increasing parents' sense of security and trust with accurate and clever phrases, a means for analyzing the contents of messages from parents and generating appropriate reply suggestions based on estimated feelings and requests, and a means for providing the generated reply suggestions to a user.

[1145] Hardware and software used

[1146] Hardware: Servers, devices (PCs, tablets, smartphones)

[1147] Software: Generative AI models (e.g., GPT-4), contact management systems

[1148] Specific operation of the system

[1149] 1. The user (teacher) uses a device to access the contact book management system. The user logs in to the contact book management system website or application using a PC, tablet, or smartphone.

[1150] Example: A teacher accesses the contact management system website from a school PC and enters login information to log in to the system.

[1151] 2. The user enters the contents of the contact book from the parent / guardian. The user enters the contents of the contact book received from the parent / guardian into the text box.

[1152] Example: A teacher types, "My child has been feeling a bit underwhelmed at school lately. Is there anything that might be bothering you?"

[1153] 3. The server receives the contents of the address book and sends it to the generative AI model. The server receives the contents of the address book entered by the user and sends them to the generative AI model as a prompt sentence.

[1154] Example: The server sends the following text to a generative AI model: "My child has been feeling unwell at school lately. Is there something that is bothering you?"

[1155] 4. The generative AI model analyzes the contents of the communication notebook and infers the parent's feelings and requests. The generative AI model analyzes the contents of the communication notebook and infers the parent's feelings (e.g., worry) and requests (e.g., providing information about the child's situation).

[1156] Example: A generative AI model infers the emotion "worried" and the request "to provide information about my child's condition."

[1157] 5. The generative AI model generates appropriate reply suggestions based on the estimated emotions and requests.The generative AI model generates appropriate reply suggestions based on the estimated emotions and requests.

[1158] Example: A generative AI model generates the following reply suggestions:

[1159] Dear Parents,

[1160] Thank you for always being concerned about your child. I understand that your child has been feeling unwell recently, but I haven't noticed any major issues at school. However, he seems a little tired, so it might be a good idea to make time for him to rest at home. If you have any concerns, please feel free to contact me at any time.

[1161] Thank you very much.

[1162] teacher

[1163] 6. The server sends the generated reply sentence suggestions back to the contact management system. The server sends the reply sentence suggestions received from the generative AI model back to the contact management system.

[1164] Example: The server generates a reply suggestion and displays it in the user interface of a contact management system.

[1165] 7. The user checks the reply draft and makes corrections as necessary. The user checks the reply draft and makes corrections as necessary.

[1166] Example: The teacher checks the proposed reply and changes the part that reads, "It might be good if you could make time to rest at home as well." to "It might be good if you could make time to relax at home as well."

[1167] 8. The user finalizes the reply and sends it to the parent.

[1168] Example: The teacher finalizes the revised reply and sends it to the parents through the communication management system.

[1169] In this way, this system helps teachers to create quick and appropriate replies to parent / guardian correspondence, enabling efficient and effective communication with parents / guardians. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1170] Step 1:

[1171] A user accesses the contact management system.

[1172] Input: Login information that allows users to log into the contact management system website or application using a PC, tablet, or smartphone.

[1173] Data processing: The server authenticates the user's login information and grants access.

[1174] Output: The user logs into the contact management system and the system's main screen is displayed.

[1175] Specific operation: The teacher accesses the contact list management system website from a school PC and logs in by entering their username and password.

[1176] Step 2:

[1177] The user inputs the contents of the communication book from the parent.

[1178] Input: Contents of the communication book received from the parents.

[1179] Data processing: The user enters the contents of the contact book into the text box of the contact book management system.

[1180] Output: The entered contact list contents are sent to the server.

[1181] What happens: A teacher types into a text box, "My child has been feeling a bit down at school lately. Is there anything that's worrying you?"

[1182] Step 3:

[1183] The server receives the contents of the contact list and sends them to the generative AI model.

[1184] Input: The contacts entered by the user.

[1185] Data processing: The server sends the contents of the address book as a prompt to the generative AI model.

[1186] Output: The generative AI model receives the contents of the address book.

[1187] What happens: The server sends the following text to the generative AI model: "My child seems a bit depressed at school lately. Is there something that's bothering you?"

[1188] Step 4:

[1189] The generative AI model analyzes the contents of the communication book and infers the parents' emotions and requests.

[1190] Input: Contents of the contact book.

[1191] Data processing: A generative AI model analyzes the contents of the communication notebook and infers the parents' emotions (e.g., worries) and requests (e.g., providing information about their child's situation).

[1192] Output: Estimated emotions and desires.

[1193] Specific behavior: The generative AI model infers the emotion of "worry" and the request for "information about the child's condition."

[1194] Step 5:

[1195] The generative AI model generates appropriate reply suggestions based on the estimated emotions and requests.

[1196] Input: Estimated emotions and desires.

[1197] Data processing: The generative AI model generates appropriate reply suggestions based on the estimated emotions and requests.

[1198] Output: The generated reply suggestions.

[1199] How it works: The generative AI model generates reply suggestions like the following:

[1200] Dear Parents,

[1201] Thank you for always being concerned about your child. I understand that your child has been feeling unwell recently, but I haven't noticed any major issues at school. However, he seems a little tired, so it might be a good idea to make time for him to rest at home. If you have any concerns, please feel free to contact me at any time.

[1202] Thank you very much.

[1203] teacher

[1204] Step 6:

[1205] The server returns the generated reply sentence proposal to the contact book management system.

[1206] Input: The generated reply suggestion.

[1207] Data processing: The server sends the reply text suggestions received from the generative AI model back to the contact management system.

[1208] Output: A reply suggestion is displayed in the user interface of the contact management system.

[1209] Specific operation: The server displays the generated reply sentence proposal on the user interface of the contact management system.

[1210] Step 7:

[1211] The user reviews the reply proposal and makes corrections as necessary.

[1212] Input: The generated reply suggestion.

[1213] Data processing: The user reviews the reply proposal and makes corrections as necessary.

[1214] Output: Revised reply proposal.

[1215] Specific action: The teacher checks the proposed reply and amends the part that reads, "It might be good if you could make time to rest at home as well." to "It might be good if you could make time to relax at home as well."

[1216] Step 8:

[1217] The user sends the final reply to the parent.

[1218] Input: Revised reply proposal.

[1219] Data processing: The user finalizes the reply text and sends it to the parent.

[1220] Output: The final response sent to the parent.

[1221] Specific operations: The teacher finalizes the revised reply text and sends it to the parents through the communication management system.

[1222] (Application example 3)

[1223] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1224] Conventional customer support systems have had the challenge of making it difficult to respond quickly and appropriately to customer inquiries. In particular, it has been difficult to accurately grasp the customer's feelings and requests and generate appropriate replies based on them. Another problem with communication with parents is that teachers have to spend a great deal of time and effort creating replies to messages in the communication book. To solve these problems, an automatic reply generation system using AI is needed.

[1225] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1226] In this invention, the server includes AI means that has learned the speaking techniques of veteran teachers, means that instantly grasp the diverse needs of parents, means that increase parents' peace of mind and trust with accurate and clever phrases, means that instantly create recommended reply suggestions, means that analyze the content of inquiries from customers and infer their feelings and requests, means that generate appropriate reply suggestions based on the inferred feelings and requests, and means that output the generated reply suggestions. This makes it possible to provide quick and appropriate replies to inquiries from customers and parents.

[1227] "AI means that have learned the speaking techniques of veteran teachers" is an artificial intelligence function that learns and imitates the communication techniques of experienced teachers.

[1228] "Means of instantly grasping the diverse needs of parents" refers to a function that allows for quick understanding of inquiries and requests from parents and responding appropriately.

[1229] "Means to increase parents' sense of security and trust with accurate and clever phrases" is a function that generates appropriate language to give parents a sense of security and trust.

[1230] "A means for instantly creating suggested reply sentences" is a function that quickly generates appropriate reply sentences in response to inquiries from parents or customers.

[1231] "Means for analyzing the content of inquiries from customers and inferring their emotions and requests" is a function that analyzes messages from customers and infers the emotions and requests contained therein.

[1232] The "means for generating an appropriate reply sentence proposal based on the estimated emotions and requests" is a function for generating an appropriate reply sentence based on the analyzed emotions and requests.

[1233] The "means for outputting the generated reply sentence proposal" is a function for displaying or transmitting the generated reply sentence.

[1234] To implement this invention, it is necessary to build a system in which the elements of the server, terminal, and user work together. A specific embodiment of this system is shown below.

[1235] server

[1236] The server has the following main functions:

[1237] 1. AI means that have learned the speaking techniques of veteran teachers: It has the ability to learn and imitate the communication techniques of experienced teachers, allowing it to generate appropriate language for parents and customers.

[1238] 2. A means of instantly understanding the diverse needs of parents: It has the ability to quickly understand inquiries and requests from parents and customers and respond appropriately.

[1239] 3. A means of increasing parents' sense of security and trust with accurate and clever phrases: The system has the function of generating appropriate language to give parents and customers a sense of security and trust.

[1240] 4. A means of instantly generating suggested reply sentences: The system has the ability to quickly generate appropriate reply sentences in response to inquiries from parents and customers.

[1241] 5. A means of analyzing customer inquiries and inferring their emotions and requests: This system has the ability to analyze messages from customers and infer the emotions and requests contained within them. Specifically, it uses the TextBlob library to perform emotion analysis.

[1242] 6. A means to generate appropriate reply suggestions based on estimated emotions and requests: The system has the function to generate appropriate reply suggestions based on the analyzed emotions and requests. Specifically, the system generates reply suggestions using OpenAI's generative AI model (e.g., GPT-3).

[1243] 7. Means for outputting generated reply text: has a function for displaying or sending the generated reply text.

[1244] Terminal

[1245] The terminal provides an interface for the user to communicate with the server. Specifically, it has the following functions:

[1246] 1. Message input function: Provides an interface for users to input inquiry messages.

[1247] 2. Reply display function: Provides an interface for displaying reply text sent from the server.

[1248] User

[1249] The user uses the terminal to communicate with the server and perform the following operations:

[1250] 1. Input of query message: The user inputs a query message using the terminal and sends it to the server.

[1251] 2. Confirmation of reply text: The user confirms the reply text sent from the server on the terminal.

[1252] Specific examples

[1253] For example, if a customer sends a message saying, "My item arrived but it's damaged. What should I do?", the server will input the following prompt into the generative AI model:

[1254] "Negative message from customer: My item arrived damaged. What should I do?\nGenerate an appropriate response."

[1255] The generative AI model generates an appropriate reply based on the prompt, and the server sends the reply to the device, where the user can review it and take appropriate action.

[1256] In this way, the server, terminal, and user work together to enable quick and appropriate replies to inquiries from customers and guardians.

[1257] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1258] Step 1:

[1259] A user uses a terminal to input an inquiry message and transmits it to the server.

[1260] Input: The inquiry message entered by the user (e.g., "My item arrived, but it was damaged. What should I do?")

[1261] Output: The query message sent to the server

[1262] Specific operation: The user uses the message input function of the device to enter the inquiry content and presses the send button to send the message to the server.

[1263] Step 2:

[1264] The server analyzes the received inquiry message and infers the emotion and request.

[1265] Input: The query message sent by the user

[1266] Output: Estimated emotions and requests (e.g., negative emotions, request for product exchange)

[1267] Specific operation: The server uses the TextBlob library to analyze the sentiment of the message, obtain a sentiment score, and infer the message's request from the message content.

[1268] Step 3:

[1269] The server generates a prompt sentence based on the estimated emotion and desire.

[1270] Input: Estimated emotions, requests, and inquiry messages

[1271] Output: Generated prompt (e.g., "Negative message from customer: My item arrived, but it was damaged. What should I do?\nPlease generate an appropriate response.")

[1272] Specific operation: The server generates prompt sentences based on the emotion scores and formats them for input into the generative AI model.

[1273] Step 4:

[1274] The server uses the generative AI model to generate an appropriate reply.

[1275] Input: Generated prompt text

[1276] Output: Generated reply (e.g. "We apologize. We will process the replacement of the damaged item immediately.")

[1277] Specific operation: The server inputs a prompt sentence into OpenAI's generative AI model (e.g., GPT-3) to generate a reply sentence. The generated reply sentence is then retrieved.

[1278] Step 5:

[1279] The server sends the generated reply text to the terminal.

[1280] Input: Generated reply text

[1281] Output: Reply text sent to the terminal

[1282] Specific operation: The server performs communication processing to send the generated reply text to the terminal.

[1283] Step 6:

[1284] The user checks the reply text on the terminal.

[1285] Input: Reply text sent from the server

[1286] Output: Reply text displayed on the terminal

[1287] Specific operation: The user uses the reply display function of the device to check the reply text sent from the server.

[1288] 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.

[1289] "Example 1"

[1290] One embodiment of the present invention is a system in which AI learns the speaking techniques of veteran teachers, instantly grasps the diverse needs of parents, and increases their sense of security and trust with accurate and thoughtful phrases. This system includes an emotion engine. The emotion engine analyzes the contents of a communication book from parents and recognizes their emotions. For example, if a parent writes in the communication book about their concerns about their child's learning, the emotion engine recognizes the anxiety and generates appropriate phrases to address it.

[1291] "Example 2"

[1292] In another embodiment, there is a system in which an emotion engine generates recommended reply suggestions based on the user's emotions and presents the generated results to the teacher. Specifically, the emotion engine recognizes the parent's emotions and generates appropriate reply suggestions based on those emotions. For example, if a parent writes in a communication book about their anxiety about their child's studies, the emotion engine recognizes the anxiety and generates appropriate reply suggestions that address that anxiety. The generated results can be used as a reference for the teacher when creating a reply for the communication book.

[1293] "Example 3"

[1294] Yet another embodiment is a system in which AI sequentially learns and optimizes communication with parents. This system includes an emotion engine. The emotion engine analyzes the contents of a communication book from parents and recognizes their emotions. It then generates appropriate phrases corresponding to those emotions, thereby increasing the parents' sense of security and trust. For example, if a parent writes in the communication book about their concerns about their child's learning, the emotion engine recognizes the anxiety and generates appropriate phrases to address it. As a result, the parents' sense of security and trust are increased.

[1295] The processing flow of each embodiment will be described below.

[1296] "Example 1"

[1297] Step 1: The AI ​​learns the speaking techniques of veteran teachers.

[1298] Step 2: Analyze the contents of the communication book from parents.

[1299] Step 3: The emotion engine recognizes the parent's emotion.

[1300] Step 4: Generate an appropriate phrase based on that emotion.

[1301] "Example 2"

[1302] Step 1: The emotion engine recognizes the user's emotion.

[1303] Step 2: Generate appropriate reply suggestions based on the sentiment.

[1304] Step 3: Present the results to the teacher.

[1305] "Example 3"

[1306] Step 1: The AI ​​learns incrementally.

[1307] Step 2: Analyze the contents of the communication book from parents.

[1308] Step 3: The emotion engine recognizes the parent's emotion.

[1309] Step 4: Generate an appropriate phrase based on that emotion.

[1310] Step 5: As a result, parents' sense of security and trust increases.

[1311] Example 1

[1312] Next, a description will be given of Example 1 of Form 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."

[1313] In the past, teachers had to spend a lot of time and effort responding to parents individually, and it was particularly difficult to accurately grasp the parents' feelings and respond appropriately. Furthermore, it took a long time for newly hired teachers to acquire the speaking skills and experience of teachers, which was insufficient in today's educational environment, where quick and accurate responses are required.

[1314] 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.

[1315] In this invention, the server includes artificial intelligence means that has learned the speaking techniques of veteran educators, means for instantly grasping the diverse needs of parents, means for increasing parents' peace of mind and trust with accurate and clever phrases, emotion analysis means for analyzing the contents of the parent's communication book and recognizing their emotions, means for selecting appropriate phrases based on the emotions recognized by the emotion analysis means, artificial intelligence model means for generating reply messages using the selected phrases, and means for sending the generated reply messages to parents. This makes it possible to accurately grasp the parents' emotions and provide a quick and appropriate reply.

[1316] A "veteran educator" is a teacher who has many years of experience and a wealth of knowledge, and is highly regarded in the educational field.

[1317] "Artificial intelligence means" refers to technologies or systems that use machine learning and data analysis to automatically perform specific tasks.

[1318] "Parental needs" refers to the requests and expectations that parents have regarding their children's education and lifestyle.

[1319] "Emotion analysis means" refers to technologies and systems for recognizing and classifying emotions from text data and voice data.

[1320] "Generative AI model means" refers to an AI technology or system for generating natural language sentences based on input data.

[1321] A "reply letter" is a letter written in response to a contact or inquiry from a parent.

[1322] A "communication notebook" is a communication tool that allows parents and teachers to share information about their children's learning and living situations.

[1323] This invention is a system that uses artificial intelligence means that has learned the speaking skills of veteran educators to generate appropriate replies to the contents of a parent's contact book. A specific embodiment of this system will be described below.

[1324] System configuration

[1325] This system consists of three main components: a server, a terminal, and a user.

[1326] 1. Server

[1327] The server has an artificial intelligence system that has learned the speaking techniques of veteran educators. Specifically, it analyzes teacher lesson records and reply contents in the communication book, and registers phrases and their usage in a learning database.

[1328] The server uses emotion analysis means to analyze the contents of the communication book from parents and recognize their emotions. The emotion analysis means analyzes the text data and assigns emotion tags such as "anxiety," "worry," and "joy."

[1329] The server selects appropriate phrases from a learning database based on the emotions recognized by the emotion analysis means.

[1330] The server has a generative artificial intelligence model means for generating a reply sentence using the selected phrase. The generative AI model generates a sentence in natural language based on the input data.

[1331] The server sends the generated reply message to the parent's terminal.

[1332] 2. Terminal

[1333] The terminal is a device that parents use to enter information into the contact book and send it to the server. Specifically, this applies to smartphones, tablets, and PCs.

[1334] The terminal receives the reply message sent from the server and displays it to the parent.

[1335] 3. Users

[1336] The user is a guardian, and uses a terminal to input the contents of the address book and receive a reply message from the server.

[1337] Specific examples

[1338] For example, when a parent types "My child seems to be feeling anxious about his / her studies at school recently" into the communication book on their device and presses the send button, the content is sent to the server. The server uses emotion analysis to tag the message with the emotion "anxiety," and selects the phrase "We understand that you are worried about your child's academic progress, but we are always watching over and supporting your child's growth" from the learning database. The generative AI model uses this phrase to generate a reply message, and the server sends the generated reply message to the parent's device.

[1339] Prompt Sentence Examples

[1340] "Analyze the contents of a parent's communication notebook and generate appropriate phrases. The contents of the communication notebook are, 'My child seems to be feeling anxious about his studies at school recently.'"

[1341] In this way, the system can accurately grasp the parent's feelings and provide a quick and appropriate response.

[1342] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1343] Step 1:

[1344] Preparing the database

[1345] The server collects phrases used by experienced educators and appropriate phrases for each situation, and stores them in a learning database. Specifically, it analyzes the teacher's lesson notes and replies in the communication book, and registers the phrases and their usage in the database. The input is the teacher's lesson notes and replies in the communication book, and the output is the phrases registered in the learning database and their usage.

[1346] Step 2:

[1347] Receiving communication from parents

[1348] The device receives the contents of the contact book from the parent / guardian and sends them as text data to the server. Specifically, the parent / guardian enters "My child seems to be feeling anxious about his / her studies at school recently" into the contact book via the device and presses the send button. The input is the contents of the contact book entered by the parent / guardian, and the output is the text data sent to the server.

[1349] Step 3:

[1350] Emotion analysis using an emotion engine

[1351] The server inputs the contents of the received contact book into the emotion engine, analyzes the text data, and recognizes the parent's emotions. The emotion engine assigns emotion tags such as "anxiety," "worry," and "joy" to the contents of the contact book. The input is the text data of the contact book, and the output is data with emotion tags. Specifically, it recognizes the emotion tag "anxiety" from the phrase "I feel anxious."

[1352] Step 4:

[1353] Choosing the right phrase

[1354] The server selects appropriate phrases from the learning database based on the emotion tags recognized by the emotion engine. The input is data with emotion tags attached, and the output is the selected phrase. Specifically, the phrase selected for "anxiety" is "We understand that you may be worried about your child's academic progress, but we are always watching over and supporting your child's growth."

[1355] Step 5:

[1356] Generating replies using a generative AI model

[1357] The server inputs the selected phrase into the generative AI model and generates a reply to the parent. The input is the selected phrase, and the output is the generated reply. Specifically, the generative AI model generates the reply, "We understand that you are worried about your child's learning progress, but we are always watching over and supporting your child's growth."

[1358] Step 6:

[1359] Sending a reply

[1360] The server sends the generated reply to the parent's device. The input is the generated reply, and the output is the reply sent to the parent's device. Specifically, the server sends the generated reply to the parent's device via email or a messaging app.

[1361] (Application example 1)

[1362] Next, a description will be given of Application Example 1 of Form 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."

[1363] Conventional security services have had the challenge of being unable to respond quickly and appropriately to user inquiries and concerns. In particular, there is a need to accurately understand the user's emotions and provide appropriate phrases in response to them, but there has been a lack of effective means to achieve this.

[1364] 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.

[1365] In this invention, the server includes AI means that has learned the speaking techniques of veteran teachers, means that instantly grasp the needs of various users, means that increase users' sense of security and trust with accurate and clever phrases, means that instantly create recommended reply sentences, emotion analysis means, and means that generate appropriate phrases based on the emotion analysis results. This makes it possible to respond quickly and appropriately to inquiries and concerns from users.

[1366] "AI means that have learned the speaking techniques of veteran teachers" is an artificial intelligence means that learns effective communication techniques used by experienced educators and generates appropriate phrases based on them.

[1367] "Means for instantly grasping the diverse needs of users" refers to means for quickly understanding inquiries and requests from users and responding accordingly.

[1368] "Means of increasing users' sense of security and trust through accurate and clever phrases" refers to means of choosing appropriate words according to the user's emotions and situation to increase the user's sense of security and trust.

[1369] The "means for instantly generating recommended reply sentences" is a means for quickly generating appropriate reply sentences in response to inquiries or requests from users.

[1370] The "emotion analysis means" is a means for analyzing the emotion from the content input by the user and determining whether the emotion is positive or negative.

[1371] The "means for generating appropriate phrases based on emotion analysis results" refers to means for generating appropriate phrases for a user based on emotion information obtained by the emotion analysis means.

[1372] This invention is a system for responding promptly and appropriately to inquiries and concerns from users in security services. The system includes an AI means that has learned the speaking skills of veteran teachers, a means for instantly grasping the needs of diverse users, a means for increasing users' peace of mind and trust with accurate and thoughtful phrases, a means for instantly creating recommended reply sentences, a means for sentiment analysis, and a means for generating appropriate phrases based on the results of sentiment analysis.

[1373] System configuration

[1374] 1. AI methods that learned the speaking skills of veteran teachers

[1375] The server uses a generative AI model that has been trained on the speaking skills of veteran teachers. This model learns effective communication techniques used by teachers and generates appropriate phrases based on those.

[1376] 2. A means of instantly grasping the needs of diverse users

[1377] The server uses natural language processing (NLP) technology to quickly understand user inquiries and requests, analyze the user's input, and respond appropriately.

[1378] 3. A way to increase user confidence and trust with accurate and clever phrases

[1379] The server analyzes the user's emotions using emotion analysis means and generates appropriate phrases based on the results, thereby improving the user's sense of security and trust.

[1380] 4. A way to instantly generate suggested replies

[1381] The server uses an algorithm to quickly generate appropriate reply text in response to user inquiries and requests, enabling it to respond to users quickly and appropriately.

[1382] 5. Emotion analysis method

[1383] The server uses a sentiment analysis algorithm to analyze the sentiment of the user's input, thereby determining whether the sentiment is positive or negative.

[1384] 6. A method for generating appropriate phrases based on the results of sentiment analysis

[1385] The server uses a generative AI model to generate appropriate phrases for the user based on the emotional information obtained by the emotion analysis means.

[1386] Hardware and software used

[1387] Hardware: Servers, smartphones

[1388] Software: Python, OpenAI API, TextBlob library

[1389] Specific examples of processing

[1390] When a user enters "I'm worried about whether the security system is working properly" through a smartphone application, the server performs the following process.

[1391] 1. Sentiment Analysis: Use the TextBlob library to analyze the sentiment of the user's input text. Determine whether the sentiment is positive or negative.

[1392] 2. Phrase generation: Based on the results of sentiment analysis, the generative AI model generates appropriate phrases.

[1393] 3. Generate a response: Return the generated phrase to the user.

[1394] Prompt Sentence Examples

[1395] User: I'm not sure if the security system is working properly.

[1396] AI: I know how you feel.

[1397] In this way, appropriate phrases can be provided to ease the user's anxiety.

[1398] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1399] Step 1:

[1400] Users enter their inquiries and concerns through a smartphone application.

[1401] Input: User text (e.g., "I'm worried about whether my security system is working properly.")

[1402] Output: User input text data

[1403] Step 2:

[1404] The terminal transmits the user's input text data to the server.

[1405] Input: User-entered text data

[1406] Output: Text data sent to the server

[1407] Step 3:

[1408] The server passes the received text data to emotion analysis means, which performs emotion analysis.

[1409] Input: Text data sent to the server

[1410] Data processing: Analyze the sentiment of the text using the TextBlob library to determine positive or negative sentiment.

[1411] Output: Sentiment analysis result (e.g. "anxiety")

[1412] Step 4:

[1413] Based on the results of the emotion analysis, the server inputs a prompt sentence into the generative AI model to generate an appropriate phrase.

[1414] Input: Sentiment analysis result (e.g., "anxiety")

[1415] Data calculation: Input a prompt sentence into the generative AI model to generate an appropriate phrase.

[1416] Output: Generated phrase (e.g., "I know how you feel.")

[1417] Step 5:

[1418] The server sends the generated phrase to the device.

[1419] Input: Generated phrase

[1420] Output: Phrase sent to the terminal

[1421] Step 6:

[1422] The terminal displays the received phrase to the user.

[1423] Input: Phrase sent to the terminal

[1424] Output: The phrase displayed to the user (e.g., "I know how you feel.")

[1425] In this way, appropriate phrases can be provided to ease the user's anxiety.

[1426] Example 2

[1427] Next, a description will be given of Example 2 of Form 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."

[1428] Conventional parent-teacher communication systems have had difficulty quickly and accurately grasping parents' feelings and requests and generating appropriate responses accordingly. This has resulted in problems such as parents being unable to increase their sense of security and trust, and the quality of communication declining.

[1429] 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.

[1430] In this invention, the server includes means for receiving messages from parents, means for analyzing the received messages to estimate the feelings and requests of the parents, means for generating appropriate reply suggestions based on the estimated feelings, and means for presenting the generated reply suggestions to the teacher. This makes it possible to quickly and accurately grasp the feelings and requests of the parents and to generate appropriate replies accordingly.

[1431] The "means for receiving messages from guardians" is a function that allows a guardian to use a terminal to send a message input by the guardian to a server, and the server to receive the message.

[1432] "Means for analyzing received messages to infer the emotions and requests of parents" refers to a function that uses a natural language processing algorithm to analyze the content of received messages and infer the emotions (joy, anxiety, anger, etc.) and requests of parents.

[1433] The "means for generating appropriate reply suggestions based on estimated emotions" is a function for generating appropriate reply suggestions using a generative AI model in accordance with the estimated emotions of the parent.

[1434] The "means for presenting the generated reply draft to the teacher" is a function for sending the generated reply draft to the teacher's terminal so that the teacher can check it.

[1435] A "natural language processing algorithm" is an algorithm that analyzes text data and extracts emotions and intentions from its content.

[1436] A "generative AI model" is an artificial intelligence model for generating natural-sounding sentences based on an input prompt.

[1437] This invention is a system for optimizing communication between parents and teachers. It receives messages from parents, analyzes their contents to infer the parents' feelings and requests, and generates appropriate reply suggestions to present to the teacher.

[1438] Hardware and software used

[1439] Hardware: Servers, terminals (PCs, tablets)

[1440] Software: Generative AI models (e.g., GPT-4), sentiment analysis engines (e.g., natural language processing algorithms)

[1441] Specific operation of the system

[1442] 1. Data Collection:

[1443] The user (guardian) enters a message into the address book and sends it from the device to the server.

[1444] Specific actions: A parent uses a device (PC or tablet) to type "I'm worried because my child has recently started to dislike doing his or her homework" into the communication application and clicks the send button.

[1445] 2. Data reception:

[1446] The server receives the contact book message sent from the terminal.

[1447] Specific operation: The server receives the HTTP request and saves the message data in a database.

[1448] 3. Emotion analysis:

[1449] The server passes the received message to an emotion analysis engine, which analyzes the parents' emotions and requests.

[1450] Specific operation: The server uses a natural language processing algorithm to analyze the message text and extract emotions such as "anxiety" from the text.

[1451] 4. Emotion estimation:

[1452] The server uses the analysis results to estimate the parents' feelings and requests.

[1453] Specific operation: The server analyzes the analysis results and confirms that "anxiety" has a high score.

[1454] 5. Generate reply suggestions:

[1455] The server uses a generative AI model (GPT-4) to generate appropriate reply suggestions based on the estimated emotions.

[1456] Specific operation: The server sends a prompt to the generative AI model. For example, the prompt is sent as follows: "If a parent writes in the communication book, 'I'm worried that my child has recently started to dislike doing his / her homework,' please generate an appropriate reply." The generative AI model generates a reply such as, "We understand that you are feeling anxious about your child's homework. We are here to support you, so please let us know if you have any questions."

[1457] 6. Teacher Presentation:

[1458] The server sends the generated reply draft to the teacher's terminal, which the teacher uses as a reference when creating a reply for the communication book.

[1459] Specific operation: The server sends data containing the generated reply sentence suggestions to the teacher's device. A notification is displayed on the teacher's device, and the teacher opens the contact application to check the reply sentence suggestions.

[1460] Examples and prompts

[1461] Examples:

[1462] If a parent writes in a communication book, "I'm worried because my child has recently started to dislike doing his homework," the emotion analysis engine will detect "anxiety."

[1463] The generative AI model generates a suggested reply such as, "We understand that you may be feeling anxious about your child's homework. We are here to support you, so please let us know if you have any questions."

[1464] Example prompt sentence:

[1465] "When a parent writes in the communication book, 'I'm worried because my child has recently started to dislike doing his homework,' generate an appropriate response suggestion."

[1466] This system makes it possible to quickly and accurately grasp the feelings and needs of parents and generate appropriate responses accordingly, thereby increasing parents' sense of security and trust.

[1467] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1468] Step 1:

[1469] Data collection

[1470] The user (guardian) enters a message into the address book and sends it from the device to the server.

[1471] Input: A message typed by a parent or guardian on a device (PC or tablet) (e.g., "I'm worried because my child has recently started to dislike doing his or her homework.").

[1472] Output: Message data sent from the device to the server.

[1473] Specific operation: The parent enters a message in the contacts application and clicks the send button. The device sends the message to the server.

[1474] Step 2:

[1475] Data reception

[1476] The server receives the contact book message sent from the terminal.

[1477] Input: Message data sent from the terminal.

[1478] Output: Message data stored on the server.

[1479] Specific operation: The server receives the HTTP request and saves the message data in a database.

[1480] Step 3:

[1481] Emotion analysis

[1482] The server passes the received message to an emotion analysis engine, which analyzes the parents' emotions and requests.

[1483] Input: Message data stored on the server.

[1484] Output: The sentiment data returned by the sentiment analysis engine (e.g., "anxiety").

[1485] Specific operation: The server uses a natural language processing algorithm to analyze the message text and extract emotions such as "anxiety" from the text.

[1486] Step 4:

[1487] Emotion estimation

[1488] The server uses the analysis results to estimate the parents' feelings and requests.

[1489] Input: Sentiment data returned from the sentiment analysis engine.

[1490] Output: Estimated emotion or desire (e.g., "anxiety").

[1491] Specific operation: The server analyzes the analysis results and confirms that "anxiety" has a high score.

[1492] Step 5:

[1493] Generate reply suggestions

[1494] The server uses a generative AI model (GPT-4) to generate appropriate reply suggestions based on the estimated emotions.

[1495] Input: Inferred emotion or desire (e.g., "anxiety").

[1496] Output: A generated response suggestion (e.g., "We understand you're feeling anxious about your child's homework. We're here to help, so please let us know if you have any questions.").

[1497] Specific operation: The server sends a prompt to the generative AI model. For example, it sends a prompt such as, "If a parent writes in the communication book, 'My child has recently started to dislike doing his homework, and I'm worried,' please generate an appropriate reply." The generative AI model then generates an appropriate reply.

[1498] Step 6:

[1499] Presenting to the teacher

[1500] The server sends the generated reply draft to the teacher's terminal, which the teacher uses as a reference when creating a reply for the communication book.

[1501] Input: The generated reply suggestion.

[1502] Output: Response proposal displayed on the teacher's device.

[1503] Specific operation: The server sends data containing the generated reply sentence suggestions to the teacher's device. A notification is displayed on the teacher's device, and the teacher opens the contact application to check the reply sentence suggestions.

[1504] (Application example 2)

[1505] Next, a description will be given of Application Example 2 of Form 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."

[1506] In communication between parents and teachers, it is necessary to accurately grasp the parents' feelings and requests and provide appropriate replies quickly. However, it takes time and effort for teachers to respond individually, and there are challenges in that it is difficult to alleviate parents' anxiety and sufficiently increase their trust. Furthermore, advanced emotion analysis and natural language generation technology are required to generate appropriate replies that reflect the parents' feelings.

[1507] 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.

[1508] In this invention, the server includes AI means that has learned the speaking techniques of veteran teachers, means that instantly grasp the diverse needs of parents, means that increase parents' peace of mind and trust with accurate and clever phrases, means that instantly create recommended reply suggestions, means that analyze the contents of the parent's communication book and infer their emotions and requests, means that generate reply suggestions based on the parents' emotions using an emotion engine, means that input prompt sentences using a generative AI model and generate appropriate reply sentences, and means that present the generated reply sentences to the teacher. This makes it possible to accurately grasp parents' emotions and requests and provide quick and appropriate replies.

[1509] "AI means that have learned the speaking techniques of veteran teachers" refers to artificial intelligence means that learns the communication techniques and speaking styles of experienced teachers and imitates them.

[1510] "Means for instantly grasping the diverse needs of parents" refers to means for quickly analyzing and understanding information and requests from parents.

[1511] "Means of increasing parents' sense of security and trust through accurate and thoughtful phrases" refers to means of providing appropriate and heartfelt words to parents to increase their sense of security and trust.

[1512] The "means for instantly creating suggested reply sentences" is a means for quickly generating appropriate reply sentences in response to inquiries or requests from parents.

[1513] "Means for analyzing the contents of communication notebooks from parents and inferring their feelings and requests" refers to a means for analyzing the text written in communication notebooks and inferring the feelings and specific requests of parents.

[1514] The "means for generating a reply message corresponding to the parent's emotions using an emotion engine" refers to a means for using an emotion analysis engine to recognize the parent's emotions and generate an appropriate reply message corresponding to those emotions.

[1515] "Means for inputting a prompt sentence using a generative AI model and generating an appropriate reply sentence" refers to means for inputting a prompt sentence into a generative AI model and generating an appropriate reply sentence based on it.

[1516] The "means for presenting the generated reply text to the teacher" is a means for displaying the generated reply text to the teacher and providing it as a reference.

[1517] The system for carrying out the present invention includes a series of means for facilitating communication between parents and teachers and increasing parents' sense of security and trust. Specific embodiments of this system will be described below.

[1518] System Configuration

[1519] The system consists of a server and user terminals (smartphones, tablets, etc.). The server includes the following means:

[1520] 1. AI means that learn the speaking skills of veteran teachers: Artificial intelligence that learns the communication techniques of experienced teachers and imitates them.

[1521] 2. A means of instantly grasping the diverse needs of parents: A means of quickly analyzing and understanding information and requests from parents.

[1522] 3. A means of increasing parents' sense of security and trust with accurate and thoughtful phrases: A means of providing parents with appropriate and heartfelt words to increase their sense of security and trust.

[1523] 4. A means of instantly generating suggested reply sentences: A means of quickly generating appropriate reply sentences in response to inquiries or requests from parents.

[1524] 5. A means of analyzing the contents of communication diaries from parents and inferring their feelings and requests: A means of analyzing the text written in communication diaries and inferring the feelings and specific requests of parents.

[1525] 6. A means for generating reply suggestions based on the parent's emotions using an emotion engine: An emotion analysis engine for recognizing the parent's emotions and generating appropriate reply suggestions based on those emotions.

[1526] 7. A means for inputting a prompt sentence using a generative AI model and generating an appropriate reply sentence: A means for inputting a prompt sentence into a generative AI model and generating an appropriate reply sentence based on it.

[1527] 8. Means for presenting the generated reply sentence to the teacher: Means for displaying the generated reply sentence to the teacher and providing it for reference.

[1528] Processing flow

[1529] The server receives and analyzes the parent's contact information. It uses a natural language processing library, such as TextBlob, to estimate the parent's feelings and requests. It then uses an emotion engine to generate a reply message based on the parent's feelings. A generative AI model (e.g., GPT-3) is used to generate an appropriate reply message based on the parent's prompt.

[1530] The generated reply text is displayed on the teacher's device, and the teacher uses it as a reference to create a reply to the parent. This enables the teacher to accurately understand the parent's feelings and requests and provide a quick and appropriate reply.

[1531] Specific examples

[1532] For example, if a parent writes in the communication book, "I'm worried about my child's learning," the server analyzes this text and determines that the emotion is negative.Then, the generative AI model inputs a prompt saying, "When the parent's emotion is negative, please generate an appropriate reply sentence," and generates an appropriate reply sentence.

[1533] Example prompt sentence:

[1534] "Generate an appropriate response when the parent's emotion is negative."

[1535] In this way, communication between parents and teachers can be facilitated, and parents' sense of security and trust can be increased.

[1536] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1537] Step 1:

[1538] The server receives the contents of the contact book from the parent.

[1539] Input: Text data written by the parent in the communication book.

[1540] Output: The text of the contact list.

[1541] Specific operation: The server receives the contents of the contact book sent from the user terminal in text format and stores them in a database.

[1542] Step 2:

[1543] The server analyzes the contents of the received contact book and estimates the user's feelings and requests.

[1544] Input: Contacts text.

[1545] Output: Sentiment polarity (positive / negative) and subjectivity score.

[1546] Specific operation: The server uses a natural language processing library such as TextBlob to analyze the contents of the contact list and calculate the sentiment polarity and subjectivity.

[1547] Step 3:

[1548] The server uses an emotion engine to generate a reply message according to the parent's emotion.

[1549] Input: Sentiment polarity and subjectivity scores.

[1550] Output: The prompt statement.

[1551] Specific operation: The server generates a prompt sentence to be input to the generative AI model based on the polarity of the emotion. For example, it generates a prompt sentence such as, "When the parent's emotion is negative, please generate an appropriate reply sentence."

[1552] Step 4:

[1553] The server inputs a prompt sentence using a generative AI model and generates an appropriate reply sentence.

[1554] Input: prompt statement.

[1555] Output: The generated reply text.

[1556] Specific operation: The server uses OpenAI's API to input a prompt sentence into a generative AI model (e.g., GPT-3) and generates an appropriate reply sentence based on it.

[1557] Step 5:

[1558] The server presents the generated reply text to the teacher's terminal.

[1559] Input: The generated reply text.

[1560] Output: Reply text displayed on the teacher's terminal.

[1561] Specific operation: The server sends the generated reply text to the teacher's terminal and displays it so that the teacher can check it.

[1562] Step 6:

[1563] The teacher will use the provided response text as a reference to create a final response to the parents.

[1564] Input: The generated reply text.

[1565] Output: Final response to parents.

[1566] Specific operation: The teacher uses the reply text provided by the server as a reference to create a final reply to the parent / guardian and writes it in the communication book.

[1567] Example 3

[1568] Next, a description will be given of Example 3 of Form Example 3. 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."

[1569] In traditional educational settings, it took a great deal of time and effort for teachers to communicate smoothly with parents. It was also difficult to accurately grasp parents' feelings and requests and respond appropriately, placing a heavy burden on teachers. Furthermore, insufficient communication with parents could lead to a decline in parents' sense of security and trust.

[1570] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes artificial intelligence means that has learned the speaking techniques of veteran educators, means for instantly grasping diverse parental needs, means for increasing parental comfort and trust with accurate and clever phrases, means for analyzing the contents of a parent's message book from parents and generating appropriate reply suggestions based on the parents' estimated feelings and requests regarding the contents, means for providing the generated reply suggestions as a reference for teachers when writing replies to the message book, means for sequentially learning and optimizing communication with parents, means for analyzing the contents of the parent's message book from parents using an emotion engine and recognizing the parents' emotions, and means for generating appropriate phrases according to the emotions. This enables teachers to accurately grasp parents' emotions and requests and quickly provide appropriate replies.

[1571] "Artificial intelligence means" refers to a system that includes machine learning algorithms and natural language processing technology to analyze the contents of communication notebooks from parents and generate appropriate reply suggestions.

[1572] "Means for instantly understanding parents' needs" refers to technology that analyzes the contents of communication notebooks from parents and quickly grasps their requests and feelings based on those contents.

[1573] "Means to increase parents' sense of security and trust with accurate and clever phrases" is a technology that generates appropriate words and expressions that correspond to parents' emotions and requests, thereby increasing their sense of security and trust.

[1574] The "means for generating reply proposals" is a technology for automatically creating appropriate reply proposals based on the contents of the parent's communication book and their feelings and requests.

[1575] "Means to provide as a reference when creating a reply to the communication book" refers to a technology that presents the generated reply draft to the teacher and provides it as reference information when the teacher creates the actual reply.

[1576] "Means for sequential learning and optimizing communication with parents" refers to technology that improves communication with parents by collecting teacher replies and parental reactions and continuously training an artificial intelligence model based on them.

[1577] The "emotion engine" is a technology that analyzes the contents of communication notebooks from parents and recognizes the emotions contained within them.

[1578] The "means for generating appropriate phrases" is a technology for generating appropriate words or expressions for the guardian in accordance with the emotions recognized by the emotion engine.

[1579] This invention is a system for teachers to communicate smoothly with parents, and aims to analyze the contents of a parent's contact book and generate appropriate reply suggestions. This system includes elements of a server, a terminal, and a user.

[1580] System configuration

[1581] server:

[1582] The server plays a central role in receiving and analyzing the contents of the communication book from parents. The server has the following functions:

[1583] 1. Data Collection:

[1584] The server receives the contents of the communication book from the parents and stores them in a database in text format.

[1585] 2. Emotion analysis:

[1586] The server uses an emotion engine (e.g., natural language processing API) to analyze the contents of the contact book and recognize the parent's emotions.

[1587] 3. Generate reply suggestions:

[1588] The server uses a generative AI model (e.g., generative AI model) to generate appropriate reply sentence suggestions based on the parent's feelings and requests.

[1589] 4. Incremental learning:

[1590] The server collects the replies that teachers actually send and the reactions of parents, and learns from them sequentially, allowing the AI ​​model to optimize communication with parents.

[1591] Device:

[1592] The terminal is a device used by teachers and parents to input and display the contents of the contact book. The terminal has the following functions:

[1593] 1. Input function:

[1594] It provides an interface for parents to input the contents of the contact book and send it to the server.

[1595] 2.Display function:

[1596] The teacher is provided with an interface to review the reply suggestions provided by the server and to create the actual reply.

[1597] User:

[1598] The users are teachers and parents, and they communicate using the system.

[1599] Specific examples

[1600] For example, if a parent writes in the communication book, "I'm worried because my child has been refusing to do his / her homework lately," the server processes the information in the following manner.

[1601] 1. Data Collection:

[1602] The parent or guardian uses the device to enter a message into the contacts app and presses the send button.

[1603] The server receives the sent messages and stores them in a database.

[1604] 2. Emotion analysis:

[1605] The server creates an API request to send the text data from the contacts to the emotion engine.

[1606] The emotion engine analyzes text data and identifies "anxiety."

[1607] The server receives the emotion data returned from the emotion engine and stores it in a database.

[1608] 3. Generate reply suggestions:

[1609] The server generates a prompt sentence including emotion data and the contents of the contact book.

[1610] Example prompt: "The following is a note from a parent. Please generate an appropriate response based on this. Note: 'I'm worried because my child has been refusing to do his homework lately.'"

[1611] The server sends this prompt to the generative AI model.

[1612] The generative AI model generates reply sentence suggestions based on the prompt sentence and returns them to the server.

[1613] The server stores the generated reply suggestions in a database.

[1614] 4. Incremental learning:

[1615] The teacher uses the terminal to create an actual reply while referring to the reply proposal provided by the server, and sends it to the parent.

[1616] Parents respond to replies (e.g., message again, feedback).

[1617] The server stores the teacher's replies and the parent's reactions in a database.

[1618] The server periodically feeds this data back into the generative AI model to update the model.

[1619] In this way, the server provides a system that facilitates communication with parents and reduces the burden on teachers. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1620] Step 1: Data collection

[1621] The server receives the contents of the address book from the parent / guardian. When the parent / guardian uses the device to enter a message into the address book app and presses the send button, the message is sent to the server. The server saves the received message in text format in a database. The input is the contents of the address book from the parent / guardian, and the output is the text data saved in the database.

[1622] Step 2: Sentiment Analysis

[1623] The server sends the saved contents of the address book to the emotion engine. The server creates an API request to send the text data of the address book to the emotion engine. The emotion engine analyzes the text data and identifies the parent's emotion (e.g., "anxiety," "joy," "anger," etc.). The server receives the emotion data returned from the emotion engine and stores it in a database. The input is the text data of the address book, and the output is the emotion data returned from the emotion engine.

[1624] Step 3: Generate reply suggestions

[1625] The server sends the emotion data and the contents of the contact book to the generative AI model. The server generates a prompt sentence that includes the emotion data and the contents of the contact book. An example of a prompt sentence is, "The contents of the contact book from the parent are shown below. Please generate an appropriate reply sentence proposal based on this. Contents of the contact book: 'I'm worried because my child has been refusing to do his homework lately.'" The server sends this prompt sentence to the generative AI model. The generative AI model generates a reply sentence proposal based on the prompt sentence and returns it to the server. The server stores the generated reply sentence proposal in a database. The input is a prompt sentence that includes the emotion data and the contents of the contact book, and the output is the reply sentence proposal returned by the generative AI model.

[1626] Step 4: Incremental learning

[1627] The server collects the replies that the teacher actually sent and the reactions of the parents. The teacher uses their device to create an actual reply using the reply suggestions provided by the server as a reference, and sends it to the parent. The parent then sends a reaction to the reply (e.g., a follow-up message, feedback). The server stores the teacher's replies and the parent's reactions in a database. The server periodically feeds this data back into the generative AI model to update the model. The input is the teacher's replies and the parent's reactions, and the output is an updated generative AI model.

[1628] (Application example 3)

[1629] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1630] In physical stores, responding quickly and appropriately to customer inquiries and feedback is important for improving customer satisfaction. However, it is difficult for store staff to respond appropriately to all inquiries, and responses can be delayed, especially when the inquiry contains emotional elements. For this reason, there is a need for a system that can instantly analyze customer emotions and requests and provide appropriate reply suggestions.

[1631] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1632] In this invention, the server includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a means for instantly grasping diverse customer needs, a means for increasing customer peace of mind and trust with accurate and clever phrases, a means for instantly creating recommended reply suggestions, a means for analyzing customer inquiries and feedback and generating appropriate reply suggestions based on their emotions and requests, and a means for inputting prompt sentences into a generation AI model and generating appropriate reply suggestions, thereby enabling a quick and appropriate response to customer inquiries and feedback.

[1633] "Artificial intelligence means that has learned the speaking techniques of veteran educators" is an artificial intelligence function that learns the communication techniques and speaking styles of experienced educators and responds appropriately based on that.

[1634] "Means of instantly grasping diverse customer needs" refers to technology that analyzes customer inquiries and feedback to quickly understand customer requests and expectations.

[1635] "Means of increasing customer confidence and trust through accurate and clever phrases" refers to a method of choosing appropriate words according to the customer's emotions and situation to increase the customer's sense of confidence and trust.

[1636] "A means for instantly generating recommended reply suggestions" is a technology for quickly generating appropriate reply sentences in response to inquiries and feedback from customers.

[1637] "A means of analyzing customer inquiries and feedback and generating appropriate reply suggestions based on their emotions and requests" is a technology that analyzes customer messages, understands their emotions and requests, and then generates the most appropriate reply.

[1638] "Means for inputting a prompt sentence into a generative AI model and generating an appropriate reply sentence proposal" refers to a technology for providing a specific input sentence (prompt sentence) to a generative AI model and generating an appropriate reply sentence based on that.

[1639] A system for carrying out this invention has the following configuration: The server includes artificial intelligence means that has learned the speaking techniques of veteran educators, means that instantly grasp the needs of diverse customers, means that increase customer peace of mind and trust with accurate and clever phrases, means that instantly create recommended reply suggestions, means that analyze inquiries and feedback from customers and generate appropriate reply suggestions based on their emotions and requests, and means that input prompt sentences into a generation AI model and generate appropriate reply suggestions.

[1640] Hardware and software used

[1641] Hardware: Smartphone

[1642] Software: Python, TensorFlow, NLP libraries (e.g., spaCy), sentiment analysis engines (e.g., Google Cloud Natural Language API)

[1643] Data processing and calculation

[1644] 1. Data acquisition: Customer inquiries and feedback are acquired as text data. The text entered by the customer is sent to the server via a smartphone application.

[1645] 2. Preprocessing: The acquired text data is preprocessed using an NLP library, specifically tokenizing, removing stop words, stemming, etc.

[1646] 3. Sentiment Analysis: Recognize customer sentiment from text data using a sentiment analysis engine, for example, by using Google Cloud Natural Language API to obtain a sentiment score for the text.

[1647] 4. Use of a generative AI model: A generative AI model (e.g., GPT-3) is fed a prompt that includes the customer's query and the perceived sentiment to generate appropriate reply suggestions.

[1648] Specific examples

[1649] For example, if a customer inquires through a smartphone app, "The product I recently purchased is broken. Can you replace it?", the server analyzes the text and recognizes that the customer's emotion is "dissatisfied." The server then inputs the following prompt sentence into the generative AI model:

[1650] Customer inquiry: "My recent purchase broke. Can I get a replacement?"

[1651] Customer Sentiment: "Dissatisfied"

[1652] Based on this, the generative AI model generates a suggested reply such as, "We apologize for the inconvenience. We will process the exchange immediately." The store clerk can then use this suggested reply as a reference to respond quickly and appropriately.

[1653] In this way, customer service at physical stores becomes more efficient and customer satisfaction increases.

[1654] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1655] Step 1:

[1656] Users enter inquiries and feedback through a smartphone app.

[1657] Input: Text data entered by the user (e.g., "The item I recently purchased is broken. Can you replace it?")

[1658] Output: Text data sent from the smartphone app to the server

[1659] Step 2:

[1660] The server preprocesses the received text data using an NLP library.

[1661] Input: Text data sent to the server

[1662] Output: Preprocessed text data (tokenized, stopword removed, stemmed, etc.)

[1663] Specific operation: The server uses Python and an NLP library (e.g., spaCy) to tokenize the text data, remove unnecessary words, and convert them into their base forms.

[1664] Step 3:

[1665] The server inputs the preprocessed text data into a sentiment analysis engine to recognize customer emotions.

[1666] Input: Preprocessed text data

[1667] Output: Sentiment analysis result (e.g., "dissatisfied")

[1668] Specific operation: The server uses the Google Cloud Natural Language API to obtain the sentiment score of the text data and recognizes the customer sentiment as "dissatisfied."

[1669] Step 4:

[1670] The server inputs the prompt sentence into the generative AI model and generates an appropriate reply sentence proposal.

[1671] Input: Prompt sentence (e.g., "Customer Question: 'My recent purchase broke. Can you replace it?' Customer Sentiment: 'Unhappy'")

[1672] Output: Generated reply suggestions (e.g., "We apologize for the inconvenience. We will process your replacement immediately.")

[1673] Specific operation: The server inputs the prompt sentence into a generative AI model (e.g., GPT-3) and generates appropriate reply sentence suggestions.

[1674] Step 5:

[1675] The server sends the generated reply text to the smartphone app and displays it to the user.

[1676] Input: Generated reply suggestion

[1677] Output: Reply suggestions displayed on the smartphone app

[1678] Specific operation: The server sends the generated reply draft to the smartphone app and displays it for the user to review.

[1679] Step 6:

[1680] The user refers to the generated reply text proposal and responds to the customer promptly and appropriately.

[1681] Input: Reply suggestions displayed on smartphone app

[1682] Output: Appropriate response to the customer

[1683] Specific actions: The user (store clerk) refers to the reply suggestions displayed on the smartphone app and responds to the customer quickly and appropriately.

[1684] 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.

[1685] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is also input. The data generation model 58 converts the input inference data into the instruction indicated by the prompt.

[1686] Therefore, inference is performed and the inference results are output in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1687] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1688] 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.

[1689] [Third embodiment]

[1690] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1691] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1692] 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).

[1693] 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.

[1694] 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.

[1695] 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).

[1696] 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.

[1697] 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.

[1698] 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.

[1699] 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.

[1700] 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.

[1701] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1702] "Example 1"

[1703] In one embodiment of the present invention, a learning database is provided for AI to learn the speaking techniques of veteran teachers. This database contains phrases actually used by veteran teachers, as well as appropriate phrases for each situation. The AI ​​learns from this database and generates appropriate phrases for the contents of the communication book from parents.

[1704] "Example 2"

[1705] Furthermore, the AI ​​has an algorithm that instantly understands the needs of parents. Specifically, it analyzes the contents of the parent's communication book and infers the parent's feelings and requests in response to the contents. Based on this inference, the AI ​​generates phrases that increase the parent's sense of security and trust.

[1706] "Example 3"

[1707] The AI ​​also has the ability to instantly create recommended reply suggestions. Specifically, the AI ​​generates appropriate reply suggestions based on the contents of the parent's communication book and the parent's estimated feelings and requests regarding that content. These reply suggestions can be used as a reference when teachers create replies to the communication book.

[1708] The processing flow of each embodiment will be described below.

[1709] "Example 1"

[1710] Step 1: The AI ​​learns the speaking techniques of veteran teachers from a learning database, which contains phrases actually used by veteran teachers and appropriate phrases for each situation.

[1711] Step 2: Once the parent enters the information into the communication book, the AI ​​analyzes it.

[1712] Step 3: Based on the analysis results, the AI ​​selects appropriate phrases from the learning database and generates a reply message for the parent.

[1713] "Example 2"

[1714] Step 1: When the parent enters the information into the communication book, the AI ​​analyzes it.

[1715] Step 2: The AI ​​uses the analysis results to infer the parents' feelings and requests.

[1716] Step 3: Based on the estimation results, the AI ​​generates phrases that will increase parents' sense of security and trust.

[1717] "Example 3"

[1718] Step 1: When the parent enters the information into the communication book, the AI ​​analyzes it.

[1719] Step 2: The AI ​​generates appropriate reply suggestions based on the analysis results and the parent's feelings and requests.

[1720] Step 3: The generated reply ideas can be used as a reference for teachers to write their replies in the communication notebook.

[1721] Example 1

[1722] Next, a description will be given of Example 1 of Form 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."

[1723] In traditional educational settings, communication with parents is an important element, but it takes time and effort for teachers to respond individually. Furthermore, while there is a need to respond accurately and quickly to the diverse needs of parents, there is a lack of effective means to achieve this. It is particularly difficult for newly appointed or inexperienced teachers to acquire the same speaking skills and responsiveness as veteran teachers. This presents a challenge, making it difficult to increase parents' sense of security and trust.

[1724] 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.

[1725] In this invention, the server includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a terminal means that receives the contents of the contact book entered by the user, a server means that inputs the data received from the terminal as a prompt sentence to the generating AI model, a means that returns the generated phrase to the terminal, and a means that the terminal displays the generated phrase to the user. This makes it possible to respond quickly and accurately to the contents of the contact book from parents. Furthermore, by having the artificial intelligence that has learned the speaking techniques of veteran teachers generate appropriate phrases, parents' sense of security and trust can be increased.

[1726] A "veteran educator" is a teacher who has many years of experience in the educational field and possesses strong leadership and communication skills.

[1727] "Artificial intelligence" refers to the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[1728] A "terminal" is a device operated by a user, and refers to hardware such as a smartphone or PC.

[1729] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to generate appropriate output from input data.

[1730] A "prompt sentence" is text data that is input into a generative AI model; it is the sentence that the model analyzes and uses to generate the appropriate output.

[1731] A "server" is a computer system that processes and stores data on a network.

[1732] A "phrase" is a short sentence or expression used in a particular situation or context.

[1733] A "communication notebook" is a communication tool that allows parents and teachers to share information about their children's status and other important matters.

[1734] This invention is a system that optimizes communication with parents by using artificial intelligence that has learned the speaking skills of veteran educators. Specific embodiments of this system will be described below.

[1735] System configuration

[1736] server

[1737] The server maintains a learning database for learning the speaking techniques of veteran educators. This database contains phrases actually used by veteran educators, as well as appropriate phrases for each situation. The server uses a generative AI model to learn from this database. Specifically, the server uses Python and utilizes machine learning libraries such as TensorFlow and PyTorch to analyze the phrases in the database and train the model. The trained model then generates appropriate phrases for the contents of parental communication notebooks.

[1738] Terminal

[1739] The device is responsible for sending the parental contact information entered by the user to the server. The device collects text data entered by the user through a web browser or dedicated application and sends it to the server. For example, the device uses programming languages ​​such as JavaScript or Swift to build a user interface and call APIs that send the input data to the server.

[1740] User

[1741] The user uses the device to input the contents of the parent's contact book. The contents entered by the user are sent to the server via the device. The server uses a generative AI model to generate an appropriate phrase for the input contents and responds to the user.

[1742] Specific examples

[1743] Prompt Sentence Examples

[1744] 1. Prompt: "My child seems to be having a bad day at school lately."

[1745] Generated phrase: "Thank you for your concern about your child. We will keep a close eye on him."

[1746] 2. Prompt: "I can't seem to keep up with the class."

[1747] Generated phrase: "We apologize for any inconvenience caused. We will review the lesson and provide individual support."

[1748] 3. Prompt: "He doesn't seem to get along with the other kids."

[1749] Generated phrase: "We understand your concerns about your child's social skills. We will help them make friends through whole-class activities."

[1750] In this way, the server uses the generative AI model to generate appropriate phrases in response to the user's input, enabling quick and accurate communication with parents, increasing their sense of security and trust.

[1751] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1752] Step 1:

[1753] The user uses the terminal to input the contents of the address book.

[1754] Input: The user types text into an input field in a contacts application.

[1755] Specific operation: The user opens a contacts application using a device such as a smartphone or PC and enters information such as, "My child seems to be in a bad mood at school recently."

[1756] Output: The entered text data is temporarily stored in the terminal.

[1757] Step 2:

[1758] The terminal transmits the input data to the server.

[1759] Input: Text data entered by the user.

[1760] Specific operation: The device uses a programming language such as JavaScript or Swift to generate an HTTP request and send input data to the server. For example, it sends data to the endpoint "POST / api / v1 / messages".

[1761] Output: The server receives the HTTP request and retrieves the text data.

[1762] Step 3:

[1763] The server receives the data and inputs it as a prompt to the generative AI model.

[1764] Input: Text data sent from the terminal.

[1765] How it works: The server analyzes the received text data and inputs it as prompts to the generative AI model. Specifically, it uses Python to input data to the model using machine learning libraries such as TensorFlow and PyTorch.

[1766] Output: The generative AI model receives the prompt and begins parsing it.

[1767] Step 4:

[1768] A generative AI model generates appropriate phrases.

[1769] Input: The prompt text entered by the server.

[1770] How it works: The generative AI model analyzes the prompt and generates appropriate phrases based on phrases in the database, such as "Thank you for your concern about your child. We will keep a close eye on them."

[1771] Output: The generated phrase is returned to the server.

[1772] Step 5:

[1773] The server sends the generated phrase back to the device.

[1774] Input: The phrase returned by the generative AI model.

[1775] Specific operation: The server receives the generated phrase and returns it to the terminal as an HTTP response. Specifically, it returns the generated phrase along with the status code "200 OK".

[1776] Output: The device receives the HTTP response and retrieves the generated phrase.

[1777] Step 6:

[1778] The terminal displays the generated phrase to the user.

[1779] Input: The generated phrase returned by the server.

[1780] Specific operation: The device displays the phrase received from the server on the user interface. The user checks the generated phrase and replies to the parent if necessary. For example, it could display "Thank you for your concern about your child. We will keep a close eye on them."

[1781] Output: User reviews the generated phrase and responds appropriately to the parent.

[1782] (Application example 1)

[1783] Next, a description will be given of Application Example 1 of Form 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."

[1784] Customer service in traditional brick-and-mortar stores relies heavily on the experience and skills of staff, and it can be difficult for new or inexperienced staff to instantly provide the right phrases and methods of response. Furthermore, there was a lack of support for smooth customer interaction, making it necessary to improve customer satisfaction.

[1785] 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.

[1786] In this invention, the server includes AI means that has learned the speaking techniques of veteran teachers, means that instantly grasp the diverse needs of parents, means that increase parents' peace of mind and trust with accurate and thoughtful phrases, means that instantly create recommended reply sentences, means that are installed in smart glasses to support store staff in their conversations with customers, means that recognize the customer's speech and suggest appropriate phrases in real time, and means that display the generated phrases on the display of the smart glasses. This enables store staff to smoothly communicate with customers and improve customer satisfaction.

[1787] "AI means that have learned the speaking techniques of veteran teachers" refers to an artificial intelligence means that learns phrases used by veteran teachers and appropriate phrases depending on the situation, and generates appropriate responses based on this.

[1788] "Means for instantly grasping the diverse needs of parents" refers to means for quickly understanding communications and requests from parents and responding accordingly.

[1789] "Means of increasing parents' sense of security and trust through accurate and thoughtful phrases" refers to means of providing parents with appropriate and heartfelt phrases to increase their sense of security and trust.

[1790] The "means for instantly creating suggested reply sentences" is a means for quickly generating appropriate reply sentences based on the content of the message from the parent.

[1791] "A means to be installed on smart glasses to support staff in physical stores in their interactions with customers" refers to a means to be installed on smart glasses worn by staff in physical stores in order to support their interactions with customers.

[1792] "A means for recognizing the speech of a customer and suggesting appropriate phrases in real time" refers to a means for converting what a customer says into text using speech recognition technology and suggesting appropriate phrases in real time based on that text.

[1793] The "means for displaying the generated phrase on the display of the smart glasses" refers to a means for displaying the generated appropriate phrase on the display of the smart glasses so that the staff can confirm it.

[1794] As an embodiment of the present invention, a system is described that is installed in smart glasses to support staff in a brick-and-mortar store in interacting with customers.

[1795] First, the system uses the following hardware and software:

[1796] Hardware: Smart glasses (with built-in camera and microphone)

[1797] Software: OpenAI API, OpenCV, SpeechRecognition library

[1798] System configuration

[1799] 1. Speech Recognition: The microphone in the smart glasses is used to record the customer's speech and then the SpeechRecognition library is used to convert it into text, allowing us to obtain what the customer is saying in real time.

[1800] 2. Phrase generation: The converted text is sent to the OpenAI API to generate appropriate phrases. A generative AI model is used to select the most appropriate phrases from a database of expert speaking skills.

[1801] 3. Display: The generated phrase is displayed on the smart glasses screen, allowing staff to check the appropriate phrase in real time and smoothly communicate with the customer.

[1802] Specific examples

[1803] For example, if a customer says, "Tell me more about this product," the system works as follows:

[1804] 1. The microphone in the smart glasses records what the customer says, and the SpeechRecognition library converts it into text: "Tell me more about this product."

[1805] 2. Based on the converted text, the OpenAI API generates an appropriate phrase, such as "This product uses the latest technology and is particularly durable."

[1806] 3. The generated phrase is displayed on the smart glasses display, and the staff member checks it and explains it to the customer.

[1807] Prompt Sentence Examples

[1808] If a customer says, "Tell me more about this product," here are some example prompts:

[1809] Generate the right phrases in your customer conversations: Tell me more about this product

[1810] In this way, store staff can smoothly communicate with customers, improving customer satisfaction.

[1811] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1812] Step 1:

[1813] The microphone in the smart glasses records what the customer says. The input is the customer's voice and the output is the recorded voice data. The microphone in the smart glasses captures what the customer says and saves it as voice data.

[1814] Step 2:

[1815] The device converts the recorded voice data into text using the SpeechRecognition library. The input is the recorded voice data, and the output is text data. The SpeechRecognition library analyzes the voice data and generates the corresponding text.

[1816] Step 3:

[1817] The device sends the converted text data to the OpenAI API, which generates an appropriate phrase. The input is the text data, and the output is the generated phrase. The OpenAI API generates a prompt based on the text data and uses a generative AI model to generate the optimal phrase.

[1818] Step 4:

[1819] The server displays the generated phrase on the smart glasses display. The input is the generated phrase and the output is the phrase displayed on the display. The smart glasses display displays the generated phrase so that the staff can check it.

[1820] Step 5:

[1821] The user (staff member) responds appropriately to the customer based on the phrases displayed on the display. The input is the phrase displayed on the display, and the output is the response to the customer. The staff member uses the displayed phrases as a reference to provide an appropriate explanation or response to the customer.

[1822] Example 2

[1823] Next, a description will be given of Example 2 of Form 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."

[1824] Conventional communication systems for parents have difficulty accurately grasping parents' feelings and specific requests and responding appropriately. As a result, parents' anxieties and dissatisfaction remain unresolved, making it difficult to build trusting relationships. Furthermore, while it is necessary to generate swift and accurate replies, there has been a lack of automated methods for this.

[1825] 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.

[1826] In this invention, the server includes means for receiving the contents of the contact book sent from the parent / guardian, means for preprocessing the received contents, means for analyzing the preprocessed contents to estimate the parent / guardian's feelings, means for estimating the parent / guardian's requests, means for generating phrases that increase the parent / guardian's sense of security and trust based on the estimation results, and means for sending the generated phrases to the parent / guardian. This makes it possible to accurately grasp the parent / guardian's feelings and requests and automatically generate a quick and appropriate reply.

[1827] "Guardian" refers to a person who is responsible for a child's education and life.

[1828] "Communication book" refers to a document or digital medium for exchanging information between parents and educational institutions.

[1829] "Server" refers to a computer system that receives, processes, and transmits data over a network.

[1830] "Preprocessing" refers to a series of operations that shape data and convert it into a format that is easier to analyze before data analysis.

[1831] "Analysis" refers to the process of examining data in detail to understand its structure and meaning.

[1832] "Emotions" refers to psychological states that are estimated based on what parents write in their communication books.

[1833] "Requests" refer to specific requests and wishes extracted from the information parents write in their communication books.

[1834] A "phrase" refers to a part or whole of a sentence that is generated to reply to the parent.

[1835] "Generation" refers to the process of creating new data or information using specific algorithms or models.

[1836] "Sending" refers to the act of communication to deliver the generated phrases and data to the parent's device.

[1837] MODE FOR CARRYING OUT THE INVENTION

[1838] The present invention is a system for optimizing communication between parents and educational institutions. A specific embodiment of this system will be described below.

[1839] System configuration

[1840] This system mainly consists of three elements: a server, a terminal, and a user. The server receives and analyzes the contents of the contact book sent by parents and generates appropriate reply phrases. The terminal is a device that parents use to send contact book entries and receive replies from the server. Users refer to parents and staff at educational institutions.

[1841] Hardware and software used

[1842] Server: A powerful computer system that receives, processes, and transmits data.

[1843] Device: A device that can connect to the internet, such as a smartphone, tablet, or PC.

[1844] software:

[1845] NLTK (Natural Language Toolkit): A library for preprocessing text.

[1846] VADER (Valence Aware Dictionary and sEntiment Reasoner): An algorithm for sentiment analysis.

[1847] BERT (Bidirectional Encoder Representations from Transformers): A natural language processing model for estimating parental preferences.

[1848] GPT-3: A generative AI model that generates phrases that increase parental reassurance and trust.

[1849] Data processing and calculation

[1850] 1. Receiving the contents of the contact book: The server receives the contents of the contact book sent by the parent. The received contents are stored in the database.

[1851] 2. Text preprocessing: The server uses NLTK to tokenize the contacts content, remove stop words, and perform stemming.

[1852] 3. Sentiment Analysis: The server analyzes the sentiment of the preprocessed text using VADER and classifies it as positive, negative, or neutral.

[1853] 4. Desire Estimation: The server uses the BERT model to extract parents' specific desires and concerns from the contents of the communication book.

[1854] 5. Phrase generation: The server uses GPT-3 to generate phrases that increase parents' sense of security and trust based on the results of sentiment analysis and desire estimation.

[1855] 6. Sending the generated phrase: The server sends the generated phrase to the parent's device.

[1856] Specific examples

[1857] For example, if a parent writes in the communication book, "My child seems to be in a bad mood at school recently. Is there something wrong?", the system will operate as follows:

[1858] 1. Receiving the contents of the communication book: The server receives the communication book from the parent and stores it in the database.

[1859] 2. Text preprocessing: The server uses NLTK to tokenize the contacts content, remove stop words, and perform stemming.

[1860] 3. Sentiment Analysis: The server uses VADER to analyze the contents of the contact list to determine whether they contain negative sentiment.

[1861] 4. Desire Estimation: The server uses the BERT model to extract the parent's desire (checking the child's problem).

[1862] 5. Phrase generation: The server sends a prompt to GPT-3, which generates the phrase, "We're sorry for causing you concern about your child's condition. Please rest assured that we will monitor the situation at school and provide any necessary support."

[1863] 6. Sending the generated phrase: The server sends the generated phrase to the parent's device.

[1864] Prompt Sentence Examples

[1865] An example of a prompt sentence to input to the generative AI model is as follows:

[1866] "Content from a parent's note: 'My child seems unmotivated at school recently. Is something wrong?' Parent's emotion: Negative. Parent's request: Confirmation of the child's problem. Generate phrases that will reassure the parent."

[1867] By inputting this prompt into GPT-3, an appropriate phrase will be generated.

[1868] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1869] Program processing flow

[1870] Step 1: Receive contacts

[1871] Input: Contents of the contact book sent by the parent or guardian from the device

[1872] Specific operation: The server receives the HTTP request and saves the contents of the contact list to a database.

[1873] Output: Contents of the contacts saved in the database

[1874] Step 2: Text preprocessing

[1875] Input: Contacts stored in the database

[1876] Specific operation: The server calls the NLTK library, splits the contents of the contacts into tokens, removes unnecessary words, and converts them into their base forms.

[1877] Output: Preprocessed text data

[1878] Step 3: Sentiment Analysis

[1879] Input: Preprocessed text data

[1880] What it does: The server uses the VADER library to calculate the sentiment score of the text and classify the sentiment based on the highest score.

[1881] Output: Sentiment analysis results (positive, negative, neutral)

[1882] Step 4: Estimate demand

[1883] Input: Preprocessed text data

[1884] Specific operation: The server loads the BERT model, gives it the text from the contacts book as input, and performs inference to extract requests and concerns.

[1885] Output: Extracted needs and concerns

[1886] Step 5: Phrase Generation

[1887] Input: Requests and concerns extracted as a result of sentiment analysis

[1888] Specific operation: The server generates a prompt sentence for GPT-3 and sends an API request to retrieve the appropriate phrase.

[1889] Output: Generated phrase

[1890] Step 6: Submit your generated phrase

[1891] Input: Generated phrase

[1892] Specific operation: The server constructs a message containing the generated phrase and sends it to the parent's device as an HTTP response.

[1893] Output: Phrase sent to parent's device

[1894] Specific examples

[1895] For example, if a parent writes in the communication book, "My child seems to be in a bad mood at school recently. Is there something wrong?", the process is as follows:

[1896] Step 1: Receive contacts

[1897] Input: Contents of the contact book sent by the parent or guardian from the device

[1898] Specific operation: The server receives the HTTP request and saves the contents of the contact list to a database.

[1899] Output: Contents of the contacts saved in the database

[1900] Step 2: Text preprocessing

[1901] Input: Contacts stored in the database

[1902] Specific operation: The server calls the NLTK library, splits the contents of the contacts into tokens, removes unnecessary words, and converts them into their base forms.

[1903] Output: Preprocessed text data

[1904] Step 3: Sentiment Analysis

[1905] Input: Preprocessed text data

[1906] What it does: The server uses the VADER library to calculate the sentiment score of the text and classify the sentiment based on the highest score.

[1907] Output: Sentiment analysis result (negative)

[1908] Step 4: Estimate demand

[1909] Input: Preprocessed text data

[1910] Specific operation: The server loads the BERT model, gives it the text from the contacts book as input, and performs inference to extract requests and concerns.

[1911] Output: Extracted needs (confirmation of child's problems)

[1912] Step 5: Phrase Generation

[1913] Input: Sentiment analysis results (negative), extracted requests (confirmation of child's problems)

[1914] Specific operation: The server generates a prompt sentence for GPT-3 and sends an API request to retrieve the appropriate phrase.

[1915] Output: The phrase "We're sorry you're worried about your child's condition. Rest assured we'll monitor their situation at school and provide any support they need."

[1916] Step 6: Submit your generated phrase

[1917] Input: Generated phrase

[1918] Specific operation: The server constructs a message containing the generated phrase and sends it to the parent's device as an HTTP response.

[1919] Output: Phrase sent to parent's device

[1920] (Application example 2)

[1921] Next, a description will be given of Application Example 2 of Form 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."

[1922] Conventional customer support systems have had the problem of being unable to respond quickly and appropriately to customer feedback and inquiries. In particular, it has been difficult to accurately grasp the customer's feelings and requests and generate reassuring phrases based on them. This has led to problems such as being unable to sufficiently improve customer satisfaction or build trusting relationships.

[1923] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes artificial intelligence means that has learned the speaking techniques of veteran educators, means for instantly grasping the needs of various users, means for increasing users' peace of mind and trust with accurate and clever phrases, means for instantly creating recommended reply suggestions, means for analyzing feedback from users, means for generating reassuring phrases based on the analysis results, and means for presenting the generated phrases. This makes it possible to respond quickly and appropriately to feedback and inquiries from customers and accurately grasp their feelings and requests. Furthermore, it becomes possible to effectively improve customer satisfaction and build trusting relationships.

[1924] "Artificial intelligence means that has learned the speaking techniques of veteran educators" is an artificial intelligence function that can learn the communication techniques and speaking styles of experienced educators and use them as a basis to engage in natural conversations.

[1925] "Means for instantly grasping the needs of diverse users" refers to technology that analyzes information input by users and quickly understands their needs and requests.

[1926] "Means of increasing users' sense of security and trust through accurate and clever phrases" refers to a technique for choosing appropriate words according to the user's emotions and situation to increase the user's sense of security and trust.

[1927] "A means for instantly generating suggested reply sentences" is a technology for automatically generating appropriate reply sentences in response to inquiries and feedback from users.

[1928] "Means for analyzing user feedback" refers to technology for analyzing opinions and impressions provided by users and understanding their content.

[1929] The "means for generating reassuring phrases based on the analysis results" is a technology that generates appropriate phrases to reassure users based on the analysis results of the feedback.

[1930] "Means for presenting generated phrases" refers to technology for displaying generated phrases to users and system operators.

[1931] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The function of each element will be explained below.

[1932] server

[1933] The server has the following main functions:

[1934] 1. AI means that has learned the speaking skills of veteran educators:

[1935] The server is equipped with artificial intelligence (AI) that has learned the communication techniques of experienced educators. This AI has a model for conducting natural conversations and facilitates communication with users.

[1936] 2. A way to instantly understand the needs of diverse users:

[1937] The server analyzes input from users and uses natural language processing (NLP) techniques to quickly understand their needs and desires, specifically using NLP libraries such as spaCy.

[1938] 3. Use precise and clever phrases to increase user confidence and trust:

[1939] The server selects appropriate words based on the user's emotions and situation, and generates phrases that will increase the user's sense of security and trust. These phrases are generated using generative AI models such as OpenAI GPT-3.

[1940] 4. How to instantly generate suggested responses:

[1941] The server automatically generates appropriate reply sentences in response to inquiries and feedback from users, a function that is also realized using a generative AI model.

[1942] 5. How we analyze your feedback:

[1943] The server uses NLP technology to analyze the opinions and comments provided by users and understand their content.

[1944] 6. How to generate reassuring phrases based on the analysis results:

[1945] The server analyzes the feedback and generates appropriate phrases to reassure the user, using a generative AI model.

[1946] 7. Means of presenting generated phrases:

[1947] The server has an interface for displaying the generated phrases to users and system operators.

[1948] Terminal

[1949] The terminal is a device through which the user interacts with the server. Examples include smartphones and tablets. The terminal displays phrases and reply messages sent from the server and sends input from the user to the server.

[1950] User

[1951] A user is an individual who uses the system. The user communicates with the server through a terminal and sends feedback and inquiries. By receiving replies and phrases from the server, the user gains a sense of security and trust.

[1952] Specific examples

[1953] For example, if a user sends feedback such as "I'm a little dissatisfied with the service recently. I felt that the response was slow," the server analyzes this feedback and calculates an emotion score. It then uses the generative AI model to generate a phrase such as "We apologize for the inconvenience. We will do our best to respond quickly, so we appreciate your continued support," and displays it on the device.

[1954] Prompt Sentence Examples

[1955] "If the customer's sentiment score is -0.5, generate a reassuring phrase."

[1956] "If the customer's sentiment score is 0.8, generate a phrase that increases confidence."

[1957] In this way, the system can respond quickly and appropriately to user feedback and accurately grasp the user's feelings and requests, which in turn helps to improve user satisfaction and build trusting relationships effectively.

[1958] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1959] Step 1:

[1960] The user inputs and sends feedback or inquiries using a terminal. The input feedback is sent to the server as text data.

[1961] Step 2:

[1962] The server analyzes the received feedback using natural language processing (NLP) techniques. Specifically, it uses an NLP library such as spaCy to calculate the sentiment score of the feedback. The input is the text data of the feedback, and the output is the sentiment score.

[1963] Step 3:

[1964] The server uses a generative AI model (e.g., OpenAI GPT-3) based on the analysis results to generate a reassuring phrase. The input is the emotion score, and the output is the generated phrase. Specifically, the emotion score is incorporated into the prompt sentence and input to the generative AI model.

[1965] Step 4:

[1966] The server sends the generated phrase to the terminal. The input is the generated phrase, and the output is the phrase displayed on the terminal. Specifically, the server sends the generated phrase to the terminal as text data.

[1967] Step 5:

[1968] The terminal displays the phrases received from the server to the user. The input is the phrase sent from the server, and the output is the phrase displayed on the terminal screen. Specifically, the terminal displays the received text data on the screen.

[1969] Step 6:

[1970] The user checks the displayed phrase and provides additional feedback or inquiries as necessary. The input is the displayed phrase, and the output is new feedback or inquiries. Specifically, the user operates the terminal to input and send new feedback.

[1971] In this way, the system can respond quickly and appropriately to user feedback and accurately grasp the user's feelings and requests, which in turn helps to improve user satisfaction and build trusting relationships effectively.

[1972] Example 3

[1973] Next, a third embodiment of the third embodiment will be described. 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."

[1974] With conventional communication book systems, it was difficult for teachers to quickly create appropriate replies to messages from parents, making it difficult to gain the parents' peace of mind and trust. Furthermore, teachers had to individually think up replies, which took time and effort, hindering efficient communication. Furthermore, it was difficult to accurately grasp the parents' feelings and requests, which sometimes meant they were unable to respond appropriately.

[1975] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1976] In this invention, the server includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a means for instantly grasping the diverse needs of parents, a means for increasing parents' sense of security and trust with accurate and clever phrases, a means for analyzing the contents of the parent's communication book and generating appropriate reply suggestions based on the estimated feelings and requests, and a means for providing the generated reply suggestions to the user. This enables teachers to quickly create appropriate reply suggestions, enabling efficient and effective communication with parents.

[1977] "Artificial intelligence means that has learned the speaking techniques of veteran educators" is an artificial intelligence function that learns the communication techniques and speaking styles of experienced educators and uses them as a basis for dialogue with parents.

[1978] "A means of instantly grasping the diverse needs of parents" is a function that analyzes the content of communications from parents and quickly understands the diverse requests and emotions contained therein.

[1979] "Means of increasing parents' sense of security and trust through accurate and thoughtful phrases" is a function that increases parents' sense of security and trust by using appropriate and heartfelt expressions according to the parents' feelings and requests.

[1980] "Means for analyzing the contents of the communication book from the parents and generating appropriate reply drafts based on the inferred feelings and requests" is a function for analyzing the contents of the communication book from the parents, inferring the parents' feelings and requests based on the contents, and generating reply drafts accordingly.

[1981] The "means for providing the generated reply draft to the user" is a function for presenting the generated reply draft to the user, such as a teacher, so that the user can check, modify, and use it.

[1982] This invention is a system for teachers to create quick and appropriate replies to messages from parents. The system includes an artificial intelligence means that has learned the speaking techniques of veteran educators, a means for instantly grasping the diverse needs of parents, a means for increasing parents' sense of security and trust with accurate and clever phrases, a means for analyzing the contents of messages from parents and generating appropriate reply suggestions based on estimated feelings and requests, and a means for providing the generated reply suggestions to a user.

[1983] Hardware and software used

[1984] Hardware: Ser...

Claims

1. A means for receiving the contents of the communication book sent by the guardian; A server means for inputting a prompt sentence using the contents of the address book into a generation AI model; means for returning the phrase generated by said server means to a terminal; and means for displaying the generated phrase on a terminal.

2. A means for receiving the contents of the communication book sent by the guardian; means for preprocessing the contents of said contact book; A means for analyzing the contents of the preprocessed communication book and estimating the feelings and requests of the guardian; means for generating a phrase for the parent based on the estimated emotion and desire; The system includes a means for transmitting the generated phrase to the parent.

3. A means for receiving the contents of the communication book sent by the guardian; a means for analyzing the contents of the communication book using an emotion engine and estimating the emotions and requests of the guardian; A means for generating a phrase for the parent using a generative AI model based on a prompt sentence using the estimated emotion and request; The system includes a means for transmitting the generated phrase to the parent.

Images