System

Abstract

A system is provided.SOLUTION: A system comprising: user interface means for a user to enter text data; server means for parsing the text data received from the user interface means; natural language processing means for generating an appropriate response from the text data parsed by the server means; and means for displaying the response generated by the natural language processing means on the user interface means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, many people suffer from stress, anxiety, and low self-esteem. There is a need to provide a means for users with these problems to easily receive psychological support in their daily lives. However, conventional methods require users to consult with a specialist directly, which is very time- and cost-intensive. Therefore, a system is needed that allows users to easily receive encouragement and advice instantly. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system including a user interface means for a user to input text data, a server means for analyzing the text data received from the user interface means, a natural language processing means for generating an appropriate response from the text data analyzed by the server means, and a means for displaying the response generated by the natural language processing means on the user interface means. Specifically, a user inputs their worries or anxieties via a terminal, and the input is sent to the server for analysis. Emotions and keywords are extracted from the analyzed text data by the natural language processing means, and appropriate encouragement or advice is generated. The generated response is then displayed on the user's terminal via the server, allowing the user to receive instant psychological support.

[0006] "User interface means" refers to means that provides an interface for a user to input text data and send it to the system.

[0007] The "server means" is a computer system that analyzes the text data received from the user interface means and performs appropriate processing.

[0008] "Natural language processing means" is a general term for technologies and algorithms that perform sentiment analysis and keyword extraction based on text data analyzed by server means and generate appropriate responses.

[0009] "Text data" refers to textual information that users enter to describe their worries and anxieties.

[0010] "Sentiment analysis" is a technology that determines a user's emotions from the content of text data.

[0011] A "response" is a message of encouragement or advice that is generated by natural language processing means and returned to the user.

[0012] "Keyword extraction" is the process of extracting important words and phrases from text data. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The system of this invention is a chat AI platform for increasing users' self-esteem, and consists of the following four main components:

[0035] 1. User Interface Means

[0036] 2. Server Means

[0037] 3. Natural Language Processing Methods

[0038] 4. Database Means

[0039] User Interface Means

[0040] The user interface means provides a screen on which a user can input and send their worries and anxieties. The user accesses the platform using a smartphone or computer and inputs their worries and anxieties into a text box. When the user presses the send button, the text data is sent to the server means.

[0041] Server Means

[0042] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the text data into an appropriate format (e.g., JSON format) and sends it to the natural language processing means. At this time, it may also obtain the user's past dialogue history and related data from the database means and use them for analysis.

[0043] Natural language processing tools

[0044] The natural language processing means receives the text data sent from the server means, analyzes it, and generates a response. First, it performs sentiment analysis on the text data and determines whether it is positive, negative, or neutral. Next, it extracts keywords and important phrases to understand the user's intent. Based on this information, it uses pre-prepared response templates and machine learning models to generate appropriate encouragement or advice. Once generated, the response is returned to the server means.

[0045] Database Means

[0046] The database means stores user data, past dialogue history, response templates, etc. This allows the server means and natural language processing means to quickly obtain the necessary information, accelerating analysis and response.

[0047] Specific examples

[0048] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[0049] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[0050] 2. The terminal sends this text data to the server means as an HTTP request.

[0051] 3. The server formats the received text data into JSON format and sends it to the natural language processing means.

[0052] 4. The natural language processing means performs sentiment analysis on the received text data, determining it as negative, and extracting the keywords "work" and "stress."

[0053] 5. Based on the analysis results, the natural language processing means generates a response message saying, "You're working hard. It's important to take a short break!" and sends it back to the server means.

[0054] 6. The server sends the received response message to the terminal as an HTTP response.

[0055] 7. The terminal displays the received response message on the user interface means, and the user reads the message.

[0056] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user uses the user interface means of the terminal to enter their worries and anxieties into the text box and presses the send button.

[0060] Step 2:

[0061] The terminal generates an HTTP request including the text data entered by the user and sends it to the server.

[0062] Step 3:

[0063] The server analyzes the HTTP request received, extracts the text data, and formats the extracted text data in JSON format.

[0064] Step 4:

[0065] The server transmits the formatted JSON format text data to the natural language processing means.

[0066] Step 5:

[0067] A natural language processing means analyzes the received text data and performs a sentiment analysis, which determines whether the text is positive, negative, or neutral.

[0068] Step 6:

[0069] Natural language processing means extract keywords and important phrases from the text data.

[0070] Step 7:

[0071] Natural language processing tools use the analysis results to generate appropriate responses, which are generated using pre-defined templates and machine learning models.

[0072] Step 8:

[0073] The natural language processing means returns the generated response to the server.

[0074] Step 9:

[0075] The server receives the response sent back from the natural language processing means and formats it as an HTTP response.

[0076] Step 10:

[0077] The server sends an HTTP response to the device.

[0078] Step 11:

[0079] The terminal analyzes the HTTP response received from the server and displays the response message on the user interface means.

[0080] Step 12:

[0081] The user reads the response message displayed on the device screen and receives encouragement and advice.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] In modern society, many people experience stress from work and life, but there is a lack of appropriate support to reduce stress and improve self-esteem. Conventional systems have found it difficult to accurately analyze users' emotions and intentions and provide appropriate encouragement and advice in a timely manner. There is a need to solve this problem and provide an efficient and effective method to improve users' self-esteem.

[0085] 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.

[0086] In this invention, the server includes means for formatting text data entered by a user into an appropriate format and sending it to natural language processing means, natural language processing means for analyzing emotions in the text data, extracting keywords, and generating an appropriate response, and means for generating a response using a machine learning model or a response template, thereby making it possible to accurately analyze user emotions and quickly provide an appropriate response.

[0087] "User interface means" refers to an interface device that allows a user to input and transmit text data, and specifically operates on a smartphone or computer application or web browser.

[0088] The "server means" is a device or program that receives text data sent from the user interface means, formats it into an appropriate format, and performs the necessary analysis processing.

[0089] The "natural language processing means" is a device or program that performs a series of processes, such as analyzing text data received from the server means, performing sentiment analysis and keyword extraction, and generating an appropriate response message.

[0090] "Sentiment analysis" is an analytical process that determines the emotions in text data as positive, negative, or neutral.

[0091] "Keyword extraction" is the process of extracting important words and phrases from text data.

[0092] A "response template" is a template of a response message prepared in advance, which is used by the natural language processing means when generating an appropriate response.

[0093] A "machine learning model" is a model that learns from large amounts of data and performs tasks such as prediction and classification, and is used in this system to generate appropriate response messages.

[0094] An "HTTP request" is a protocol used by a client to send data to a server.

[0095] "HTTP response" is a protocol used by a server to send response data to a client.

[0096] The "database means" is a device or program that stores information such as user data, past dialogue history, and response templates, and enables the server or natural language processing means to quickly obtain the information required.

[0097] The system of this invention is a chat AI platform that enhances users' self-esteem and is composed of the following four major components: 1. user interface means, 2. server means, 3. natural language processing means, and 4. database means.

[0098] User Interface Means

[0099] Users access the system's user interface from a smartphone or computer browser. This interface provides a text box and a send button where users can enter and submit their worries and anxieties. The text data entered by the user is sent to the system by pressing the send button.

[0100] Server Means

[0101] The server receives text data sent from the user interface means and formats it into an appropriate format (e.g., JSON format). If necessary, it retrieves the user's past interaction history and other related data from the database means and uses them for analysis. The formatted data is then sent to the natural language processing means. On the server side, this data is sent and received using HTTP requests and responses.

[0102] Natural language processing tools

[0103] The natural language processing means performs sentiment analysis on the text data sent from the server means and determines whether it is positive, negative, or neutral. It also extracts keywords and important phrases from the text data. Based on this information, it uses pre-prepared response templates and machine learning models to generate appropriate messages of encouragement or advice. For example, it generates a response message such as, "You're working hard. It's important to take a short break!"

[0104] Database Means

[0105] The database stores user data, past interaction history, response templates, etc. This allows the server means and natural language processing means to quickly retrieve the required information, facilitating analysis and response. To manage this storage process, a relational database management system (RDBMS) or a non-relational database (such as NoSQL) can be used.

[0106] Specific examples

[0107] As a concrete example, if the user enters "I've been busy at work lately and I'm stressed," the following will be processed:

[0108] 1. The user inputs "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[0109] 2. The terminal sends this text data to the server means as an HTTP request.

[0110] 3. The server formats the received text data into JSON format and sends it to the natural language processing means.

[0111] 4. The natural language processing means performs sentiment analysis on the received text data, determining it as negative, and extracting the keywords "work" and "stress."

[0112] 5. Based on the analysis results, the natural language processing means generates a response message saying, "You're working hard. It's important to take a short break!" and sends it back to the server means.

[0113] 6. The server sends the received response message to the terminal as an HTTP response.

[0114] 7. The terminal displays the received response message on the user interface means, and the user reads the message.

[0115] Prompt Sentence Examples

[0116] Here are some example input prompts for a generative AI model:

[0117] User: I've been busy at work lately and feeling stressed.

[0118] Generative AI model: You work hard, it's important to take a break!

[0119] In this way, users can receive appropriate encouragement and advice to boost their self-esteem and improve the quality of their daily lives.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1:

[0122] The user inputs text data into the user interface means on the terminal and presses the send button.

[0123] Input: User's worries or concerns (e.g., "I've been busy at work lately and I'm feeling stressed.")

[0124] How it works: A user enters a problem into a text box in a browser or application and clicks the submit button.

[0125] Output: The entered text data is passed to the terminal by the send action.

[0126] Step 2:

[0127] The terminal transmits the input text data to the server means as an HTTP request.

[0128] Input: Text data when the send button is pressed

[0129] How it works: Using JavaScript etc., it collects the entered text data and sends it to the server as an Ajax request.

[0130] Output: Text data formatted in HTTP request format is sent to the server.

[0131] Step 3:

[0132] The server formats the received text data into JSON format and sends it to the natural language processing means.

[0133] Input: Text data of the HTTP request sent from the terminal

[0134] How it works: A server-side program parses the data, converts it to JSON format, and, if necessary, retrieves past interaction history and related data from a database.

[0135] Output: The formatted JSON data is sent to the natural language processing means.

[0136] Step 4:

[0137] A natural language processing means analyzes the sentiment of the text data, extracts keywords, and generates a response message.

[0138] Input: JSON format text data sent from the server

[0139] Operation:

[0140] 1. Sentiment analysis: Classifying the sentiment of text as "positive," "negative," or "neutral."

[0141] 2. Keyword extraction: Extract important words and phrases such as "work" and "stress."

[0142] 3. Response generation: Use machine learning models or response templates to generate appropriate response messages (e.g., "You're working hard! It's important to take a break!").

[0143] Output: The generated response message is sent back to the server.

[0144] Step 5:

[0145] The server transmits the response message received from the natural language processing means to the terminal as an HTTP response.

[0146] Input: A response message generated by a natural language processing tool

[0147] How it works: A server-side program receives the response message and formats it as an HTTP response.

[0148] Output: The formatted response message is sent to the terminal.

[0149] Step 6:

[0150] The terminal displays the received response message on the user interface means.

[0151] Input: HTTP response message sent from the server

[0152] What it does: Uses HTML and JavaScript to display the received message in the appropriate location on the screen.

[0153] Output: A state is provided that allows the user to view the response message.

[0154] (Application example 1)

[0155] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0156] In modern factory environments, workers often feel stressed due to high workloads and mistakes. As a result, problems such as reduced work efficiency and increased employee turnover can occur. In response to this, a means is needed to provide workers with mental support quickly and at the right time, but conventional systems have been inadequate in this regard. Conventional systems have had difficulty providing appropriate support for the real-time stress and anxiety felt by workers.

[0157] 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.

[0158] In this invention, the server includes a user interface means for a user to input text data, a processing device means for analyzing the text data received from the user interface means, an automatic response generation means for generating an appropriate response from the text data analyzed by the processing device means, a means for displaying the response generated by the automatic response generation means on the user interface means, and a means for evaluating the emotional state of a worker during work and providing appropriate advice or encouragement. This makes it possible to provide quick and appropriate encouragement or advice in response to real-time stress or anxiety felt by the worker.

[0159] "User interface means" refers to means for providing a device or software screen that a user uses to input text data.

[0160] The "processor means" is a means for analyzing text data received from the user interface means and arranging it into an appropriate format.

[0161] The "automatic response generation means" is a means for generating an appropriate response based on the analyzed text data.

[0162] The "display means" is a means for displaying the generated response on the user interface means.

[0163] The "means for assessing the emotional state of a worker" is a means for analyzing emotions from text entered by a worker and assessing the worker's state.

[0164] The "means for providing an advice or encouragement response" is a means for generating and providing an appropriate advice or encouragement message based on the evaluated emotional state.

[0165] In the system that realizes this application example, a user uses a smartphone to input the stress or anxiety they feel while working, and the AI ​​responds by providing appropriate encouraging or advising messages. The system is composed of a user interface means, a processing device means, an automatic response generation means, a display means, a means for evaluating the emotional state of a worker, and a means for providing an advice or encouraging response.

[0166] System Components

[0167] Hardware

[0168] Smartphones: Used as a user interface means.

[0169] Server Hardware: Required for the operation of the Processing Unit, Auto-Response Generation Unit, and Database.

[0170] software

[0171] User interface means: Provides an application screen for workers to input their emotions.

[0172] Processing unit means: Using Python and Flask as a framework, it receives input text data, parses it, and formats it appropriately.

[0173] Automated response generation method: Hugging Face's Transformers are used to perform natural language processing using NLU models such as BERT.

[0174] Display means: The generated response message is displayed on the smartphone screen.

[0175] Sentiment analysis and response generation: Analyzes the sentiment of the input text data and generates an appropriate response based on the results.

[0176] System operation explanation

[0177] User Interface Means

[0178] Workers use a dedicated application on their smartphones to enter text about the stress or anxiety they feel while working, such as "I'm worried because I make a lot of mistakes while working."

[0179] Processing device means

[0180] The text entered is sent to the server using the Python and Flask frameworks, which then converts the received text data into JSON format and sends it to an automated response generator.

[0181] Auto-response generator

[0182] It uses Hugging Face's Transformers library for natural language processing, leveraging models such as BERT to analyze the sentiment of the text and generate appropriate responses, such as advice like, "Learning from mistakes is part of growing. Take a break and try again!"

[0183] Display means

[0184] The generated response message is sent from the server to the user interface means and displayed on the smartphone screen, allowing the worker to receive prompt and appropriate encouragement or advice.

[0185] Examples of concrete examples and prompts

[0186] Specific examples

[0187] User input: "I'm worried because I make a lot of mistakes while working."

[0188] The system's response: "Learning from your mistakes is part of growing. Take a break and try again!"

[0189] Prompt Sentence Examples

[0190] "You're inputting the anxiety that a worker feels while working. For example, if a worker says, 'I'm worried because I make a lot of mistakes while working,' you should respond with positive encouragement rather than dismissing it."

[0191] The system allows factory workers to reduce stress and anxiety in real time and improve their self-esteem.

[0192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0193] Step 1:

[0194] The user enters text data

[0195] Users use a dedicated application on their smartphone to enter text about the stress or anxiety they feel at work, such as "I'm worried because I make a lot of mistakes at work," and then press the send button.

[0196] Input: Worker input text

[0197] Output: Request to send text data

[0198] Step 2:

[0199] The device sends text data to the server.

[0200] The terminal sends the text data entered by the user to the server as an HTTP request. The text data is formatted in JSON format and sent.

[0201] Input: Text data

[0202] Output: JSON format text data

[0203] Step 3:

[0204] The server receives and processes the text data.

[0205] The server analyzes the JSON-formatted text data received from the terminal and performs the necessary processing. Specifically, it prepares the data for transmission to the natural language processing means, including the process of formatting it into an appropriate format.

[0206] Input: JSON format text data

[0207] Output: Formatted text data

[0208] Step 4:

[0209] The server processes natural language

[0210] The server performs sentiment analysis on the formatted text data using Hugging Face's Transformers library, using BERT as the model to classify the sentiment of the input text as positive, negative, or neutral.

[0211] Input: Formatted text data

[0212] Output: Sentiment analysis results (positive, negative, neutral)

[0213] Step 5:

[0214] The server generates a response message

[0215] The server generates an appropriate response message based on the results of the sentiment analysis. For example, if a negative sentiment is detected, it generates a message like, "Learning from mistakes is part of growing. Take a break and try again!"

[0216] Input: Sentiment analysis results

[0217] Output: Response message

[0218] Step 6:

[0219] The server sends a response message to the terminal.

[0220] The server sends the generated response message to the terminal as an HTTP response. The message is sent in JSON format.

[0221] Input: Response message

[0222] Output: JSON formatted response message

[0223] Step 7:

[0224] The terminal displays a response message

[0225] The terminal displays the response message received from the server on the user interface means, and the worker checks the advice and encouragement message on the screen of his or her smartphone.

[0226] Input: JSON formatted response message

[0227] Output: Response message displayed on the smartphone screen

[0228] In this way, workers can receive appropriate encouragement and advice in real time to alleviate stress and anxiety, which can reduce the mental burden on them while they are working and improve their motivation.

[0229] 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.

[0230] The system of this invention is a chat AI platform designed to enhance users' self-esteem, and is composed of the following five main components:

[0231] 1. User Interface Means

[0232] 2. Server Means

[0233] 3. Natural Language Processing Methods

[0234] 4. Emotion Engine

[0235] 5. Database Means

[0236] User Interface Means

[0237] The user interface means provides a screen on which a user can input and send their worries and anxieties. The user accesses the platform using a smartphone or computer and inputs their worries and anxieties into a text box. When the user presses the send button, the text data is sent to the server means.

[0238] Server Means

[0239] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the text data into an appropriate format (e.g., JSON format) and sends it to the natural language processing means and emotion engine. At this time, it may also obtain the user's past dialogue history and related data from the database means and use them for analysis.

[0240] Natural language processing tools

[0241] The natural language processing means receives the text data sent from the server means and generates a response in conjunction with the analysis results from the emotion engine. It extracts keywords and important phrases and understands the user's intent. Based on this information, it generates appropriate encouragement or advice using pre-prepared response templates and machine learning models. The generated response is returned to the server means.

[0242] Emotion Engine

[0243] The emotion engine receives the text data sent to the natural language processing means and performs emotion analysis. First, the emotion engine determines whether the text data contains positive, negative, or neutral emotions. It also extracts emotion-related keywords and important phrases from the text data. These results are passed to the natural language processing means, which uses them to generate an appropriate response.

[0244] Database Means

[0245] The database means stores user data, past dialogue history, response templates, etc. This allows the server means, natural language processing means, and emotion engine to quickly obtain the necessary information, accelerating analysis and response.

[0246] Specific examples

[0247] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[0248] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[0249] 2. The terminal sends this text data to the server means as an HTTP request.

[0250] 3. The server formats the received text data into JSON format and sends it to the natural language processing means and emotion engine.

[0251] 4. The emotion engine analyzes the received text data and determines it to be negative. It also extracts "work" and "stress" as emotion-related keywords.

[0252] 5. Based on the analysis results of the emotion engine and the text data, the natural language processing means generates a response message such as, "You're working hard. It's important to take a break!"

[0253] 6. The natural language processing means returns the generated response message to the server means.

[0254] 7. The server sends the received response message to the terminal as an HTTP response.

[0255] 8. The terminal displays the response message received from the server on the user interface means.

[0256] 9. The user reads the response message displayed on the device screen and receives encouragement and advice.

[0257] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[0258] The processing flow will be explained below.

[0259] Step 1:

[0260] The user uses the user interface means of the terminal to enter their worries and anxieties into the text box and presses the send button.

[0261] Step 2:

[0262] The terminal generates an HTTP request including the text data entered by the user and sends it to the server.

[0263] Step 3:

[0264] The server receives the HTTP request, analyzes it, extracts the text data, and formats it into JSON format.

[0265] Step 4:

[0266] The server sends the formatted JSON formatted text data to the natural language processing means and emotion engine.

[0267] Step 5:

[0268] The sentiment engine receives the text data and analyzes the sentiment of the text, first determining whether the text has a positive, negative, or neutral sentiment.

[0269] Step 6:

[0270] The emotion engine extracts emotion-related keywords and key phrases from the text data.

[0271] Step 7:

[0272] The emotion engine passes the analysis results to the natural language processing means, which include the emotion judgment result of the text and extracted keywords.

[0273] Step 8:

[0274] The natural language processing means generates an appropriate response message based on the analysis results from the emotion engine and the original text data, using pre-prepared templates and machine learning models.

[0275] Step 9:

[0276] The natural language processing means returns the generated response message to the server.

[0277] Step 10:

[0278] The server formats the response message received from the natural language processing means as an HTTP response.

[0279] Step 11:

[0280] The server sends an HTTP response to the device.

[0281] Step 12:

[0282] The terminal analyzes the HTTP response received from the server and displays the response message on the user interface means.

[0283] Step 13:

[0284] The user reads the response message displayed on the device screen and receives encouragement and advice.

[0285] Specific examples

[0286] Consider the example where a user types, "Work has been busy and stressful lately."

[0287] The specific process flow:

[0288] Step 1:

[0289] The user uses the user interface means of the terminal to input "I've been busy at work recently and I'm feeling stressed," and presses the send button.

[0290] Step 2:

[0291] The terminal generates an HTTP request including this text data and sends it to the server.

[0292] Step 3:

[0293] The server receives the HTTP request, analyzes it, extracts the text data "I've been busy at work lately and I'm feeling stressed," and formats it into JSON format.

[0294] Step 4:

[0295] The server sends the formatted JSON formatted text data to the natural language processing means and emotion engine.

[0296] Step 5:

[0297] The emotion engine receives the text "I've been busy at work lately and feeling stressed," performs emotion analysis, and determines that the emotion is negative.

[0298] Step 6:

[0299] The emotion engine extracts emotion-related keywords such as "work" and "stress" from the text data.

[0300] Step 7:

[0301] The emotion engine passes the emotion determination result and keywords of the text to the natural language processing means.

[0302] Step 8:

[0303] Based on the analysis results from the emotion engine and the original text data "I've been busy at work lately and feeling stressed," the natural language processing means generates a response message saying, "You're working really hard. It's important to take a short break!"

[0304] Step 9:

[0305] The natural language processing means returns the generated response message to the server.

[0306] Step 10:

[0307] The server formats the response message received from the natural language processing means as an HTTP response.

[0308] Step 11:

[0309] The server sends the HTTP response to the user's device.

[0310] Step 12:

[0311] The terminal analyzes the HTTP response received from the server and displays a response message on the user interface means saying, "You're working hard. It's important to take a short break!"

[0312] Step 13:

[0313] The user reads the response message displayed on the device screen and receives encouragement and advice.

[0314] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[0315] Example 2

[0316] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0317] Conventional chat systems have limitations in generating immediate and appropriate responses to user input, and it is particularly difficult to properly understand the user's emotions and provide responses that are in tune with those emotions. Furthermore, if the user interface is not intuitive, users may feel stressed when using the system, which may not lead to an improvement in self-esteem.

[0318] 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.

[0319] In this invention, the server includes user interface means for a user to input text data, server means for analyzing the text data received from the user interface means, natural language processing means for generating an appropriate response from the text data analyzed by the server means, an emotion engine for analyzing emotions based on the text data, and means for displaying the response generated by the natural language processing means and the emotion engine on the user interface means. This makes it possible to quickly and appropriately generate a response that is in line with the user's emotions and increase the user's sense of self-affirmation.

[0320] "User interface means" means a screen or device used by a user to input text data and transmit that data to the system.

[0321] The "server means" refers to a device or system that has the role of analyzing text data received from the user interface means and, if necessary, transmitting the data to the natural language processing means and the emotion engine.

[0322] "Natural language processing means" refers to the technology or method for analyzing text data provided by the server means and generating appropriate responses. This means is capable of extracting keywords and important phrases and understanding the user's intent.

[0323] An "emotion engine" is a technology or method that analyzes the emotions in text data and determines the emotional state, such as positive, negative, or neutral.

[0324] "Database means" refers to a system that stores user data, past dialogue history, response templates, etc., and can quickly retrieve them as needed.

[0325] A "response message" is a response to the user, such as encouragement or advice, generated by natural language processing means and an emotion engine.

[0326] "Text data" refers to character data such as worries or anxieties entered by the user.

[0327] "Sentiment analysis" is the process of determining a user's emotions from the content of text data and classifying that emotional state as positive, negative, neutral, etc.

[0328] A "response template" is a predefined example or format of a response used by a natural language processing tool.

[0329] A "generative AI model" is a model generated using machine learning technology for performing natural language processing, sentiment analysis, and other tasks.

[0330] A "prompt" is text or instructions that are input to a generative AI model.

[0331] The system of the present invention is a chat AI platform for enhancing users' self-esteem, and is composed of several main components: a user interface means, a server means, a natural language processing means, an emotion engine, and a database means.

[0332] Hardware and Software

[0333] To realize this system, the following hardware and software are required.

[0334] Hardware: smartphones, computers, servers

[0335] Software: Natural language processing libraries (e.g., NLTK, spaCy), sentiment analysis libraries (e.g., VADER, TextBlob), database systems (e.g., MySQL, MongoDB), web servers (e.g., Apache, Nginx)

[0336] User Interface

[0337] Users access the platform using a smartphone or computer. They enter their worries and anxieties into a text box via a browser or a dedicated application and press the send button. This data is sent as text data to a server.

[0338] Server Means

[0339] The server receives text data sent from the user interface means, converts the received text data into an appropriate format (e.g., JSON format), and sends it to the natural language processing means and emotion engine. It also retrieves the user's past dialogue history and related data from the database means as needed and uses them for analysis.

[0340] Natural language processing tools

[0341] The natural language processing means analyzes the text data sent from the server means. Specifically, it extracts keywords and important phrases and understands the user's intent. Based on this information, it uses a machine learning model (e.g., a generative AI model) to generate an appropriate response message of encouragement or advice. The generated response message is returned to the server means.

[0342] Emotion Engine

[0343] The emotion engine receives the text data and performs emotion analysis. The emotion engine uses an emotion analysis library to determine positive, negative, or neutral emotions from the content of the text data. It also extracts emotion-related keywords and provides the results to the natural language processing means.

[0344] Database Means

[0345] The database means stores user data, past dialogue history, response templates, etc. This allows the server means, natural language processing means, and emotion engine to quickly obtain the necessary information, accelerating analysis and response.

[0346] Specific examples

[0347] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[0348] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[0349] 2. The terminal sends this text data to the server means as an HTTP request.

[0350] 3. The server formats the received text data into JSON format and sends it to the natural language processing means and emotion engine.

[0351] 4. The emotion engine analyzes the received text data and determines it to be negative. It also extracts "work" and "stress" as emotion-related keywords.

[0352] 5. Based on the analysis results of the emotion engine and the text data, the natural language processing means generates a response message such as, "You're working hard. It's important to take a break!"

[0353] 6. The natural language processing means returns the generated response message to the server means.

[0354] 7. The server sends the received response message to the terminal as an HTTP response.

[0355] 8. The terminal displays the response message received from the server on the user interface means.

[0356] 9. The user reads the response message displayed on the device screen and receives encouragement and advice.

[0357] Prompt Sentence Examples

[0358] Below are some example prompts to input to a generative AI model:

[0359] "If a user types, 'I've been busy and stressed at work lately,' how should we respond?"

[0360] In this way, users can receive prompt and appropriate encouragement and advice, which can increase their self-esteem.

[0361] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0362] Step 1:

[0363] The user inputs text data into the user interface means of the terminal and presses the send button. The input text data is a sentence such as "I've been busy at work lately and I'm feeling stressed." The user interface means receives this input and captures the clicking of the send button as an event.

[0364] Step 2:

[0365] The terminal transmits the text data received from the user interface means to the server means. Specifically, the text data is transmitted using an HTTP POST request. For example, the text data is packed in JSON format and transmitted. The URL of this request is specified by the API endpoint of the server.

[0366] Step 3:

[0367] The server receives the HTTP POST request, extracts the text data, and formats it in a parsable form (e.g., JSON again). At this point, it performs error checking to remove any incomplete data, and adds metadata such as the user ID and timestamp. The formatted text data is then sent to the natural language processing tool and emotion engine.

[0368] Step 4:

[0369] The server retrieves the user's past interaction history and related data from the database means as needed. Specifically, it retrieves past interaction data associated with the user ID using SQL queries or NoSQL queries, and provides the data to the natural language processing means and emotion engine as additional information for analyzing the data.

[0370] Step 5:

[0371] The natural language processing (NLP) means analyzes the text data to extract keywords and important phrases. It uses a NLP library (e.g., NLTK or spaCy) to tokenize the text and tag it with parts of speech to extract keywords like "work" and "stress." The results of this analysis are also sent to the sentiment engine.

[0372] Step 6:

[0373] The sentiment engine receives the text data and performs sentiment analysis. For example, it uses a sentiment analysis library such as VADER or TextBlob to calculate a sentiment score for each word and classify it as positive, negative, or neutral. The classification results and sentiment-related keywords (e.g., "stress") are sent back to the natural language processing means.

[0374] Step 7:

[0375] The natural language processing means receives the results from the emotion engine and uses a generative AI model (e.g., GPT-3) to generate an appropriate response message, such as an encouraging message like, "You're working hard. It's important to take a break!" The generated response message is sent back to the server in JSON format.

[0376] Step 8:

[0377] The server sends the response message received from the natural language processing means to the terminal as an HTTP response. The response includes a status code (e.g., 200 OK) to indicate that the processing was completed successfully.

[0378] Step 9:

[0379] The device receives the HTTP response, analyzes the response message, and displays it in the user interface. Specifically, it uses HTML and JavaScript to display a message below the text box or in the conversation window: "You're working hard. It's important to take a break!"

[0380] Step 10:

[0381] Users can check the response messages displayed on the device screen and receive encouragement and advice, which is expected to increase their self-esteem and improve the quality of their daily lives.

[0382] (Application example 2)

[0383] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0384] Conventional chat AI platforms lack the accuracy and speed of emotion recognition to generate responses that enhance users' self-esteem. They also lack specific support measures to improve the user experience in virtual stores. This can result in users not receiving appropriate advice or encouragement, resulting in a poor quality shopping experience.

[0385] 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.

[0386] In this invention, the server includes a user interface means for a user to input text data, a server means for analyzing the text data received from the user interface means, an emotion engine means for analyzing the emotion of the text data analyzed by the server means, a natural language processing means including a generative AI model for generating an appropriate response from the text data analyzed by the server means, and a means for displaying the response generated by the natural language processing means on the user interface means. This makes it possible to quickly and appropriately provide encouragement or advice according to the user's emotion, thereby improving the quality of the shopping experience in the virtual store.

[0387] A "user interface means" is a method or device by which a user inputs and transmits textual data.

[0388] The "server means" is a central data processing device that processes and analyzes data received from the user interface means and performs necessary processing.

[0389] The "emotion engine means" is a device or method that analyzes the emotions of text data and determines emotions such as positive, negative, or neutral.

[0390] A "generative AI model" is a method or device that uses machine learning algorithms to automatically generate appropriate responses from input data.

[0391] A "natural language processing means" is a method or device for analyzing and understanding text data entered by a user and generating an appropriate response.

[0392] A "prompt sentence" is an input sentence that serves as the basis for a generative AI model to generate a response, and it affects the quality and appropriateness of the generated response.

[0393] The system of the present invention provides a chat AI assistant to enhance users' shopping experience and is composed of the following main means:

[0394] 1. User Interface Means

[0395] The user interface means provides a screen on which a user can input and send text data. The user can access the virtual store using, for example, a smartphone or a computer, and enter questions or concerns into the text box while shopping. When the user presses the send button, the input text data is sent to the server means.

[0396] 2. Server Means

[0397] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the received text data into an appropriate format (e.g., JSON format) and sends it to the emotion engine means and natural language processing means. It may also retrieve the user's past dialogue history and related data from a database and use them for analysis.

[0398] 3. Emotional Engine Means

[0399] The emotion engine means receives the text data sent from the server means and performs emotion analysis. This means determines positive, negative, or neutral emotions from the content of the text data. It also extracts emotion-related keywords and important phrases from the text data. These results are passed to the natural language processing means and are used to generate an appropriate response.

[0400] 4. Natural Language Processing Methods

[0401] The natural language processing means generates an appropriate response using a generative AI model based on data from the server means and the emotion engine means. This means generates a response including encouragement or advice using a prompt sentence according to the user's emotional state. For example, a prompt sentence such as "I think that's a good choice. Please take your time to choose." is used.

[0402] 5. Response Display Means

[0403] The generated response is sent to the user interface means via the server means and displayed on the user interface means, allowing the user to read the displayed message and receive appropriate advice or encouragement.

[0404] Hardware and software used

[0405] Server: Used as the central device for data processing and analysis. Uses Flask (a lightweight Python web application framework).

[0406] Sentiment Engine: For sentiment analysis, we use the pipeline (sentiment-analysis) model from the Transformers library.

[0407] Natural language processing means: For response generation, we use the pipeline (text-generation) model from the Transformers library.

[0408] Database: Use an appropriate data management solution (e.g., SQL or NoSQL database) to store user data and past interaction history.

[0409] Specific examples

[0410] A user types, "I want to find accessories that go well with this dress, but I'm not sure what to buy."

[0411] The server means receives this message, performs emotion analysis using the emotion engine means, and determines the emotion as "neutral."

[0412] Natural language processing generates an appropriate response based on the sentiment analysis results, for example, using a prompt such as "I think that's a good choice. Please take your time to choose."

[0413] The generated response is sent to the user through the server means and displayed in the user interface.

[0414] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0415] Step 1:

[0416] The user inputs "I want to buy accessories that go well with this dress, but I'm not sure what to buy" into the user interface means on the terminal and presses the send button.

[0417] Input: User's text data.

[0418] Output: Text data is ready to be sent.

[0419] Step 2:

[0420] The terminal transmits this text data to the server means as an HTTP request.

[0421] Input: User's text data.

[0422] Output: HTTP request with text data.

[0423] Step 3:

[0424] The server formats the received text data into JSON format and sends it to the emotion engine means and natural language processing means.

[0425] Input: HTTP request containing the user's text data.

[0426] Output: Data in JSON format that is sent to the sentiment engine means and natural language processing means.

[0427] Step 4:

[0428] The emotion engine means performs emotion analysis on the received text data to determine whether the emotion is positive, negative, or neutral, and extracts important keywords.

[0429] Input: Text data in JSON format.

[0430] Output: Sentiment analysis results and keywords.

[0431] Step 5:

[0432] The natural language processing means receives the analysis results from the emotion engine means and uses the generative AI model to generate an appropriate response based on the prompt sentence. In this case, the prompt sentence used is, "I think that's a good choice. Please take your time to choose."

[0433] Input: Sentiment analysis results and keywords.

[0434] Output: The generated response message.

[0435] Step 6:

[0436] The server transmits the response message received from the natural language processing means to the terminal as an HTTP response.

[0437] Input: The generated response message.

[0438] Output: The HTTP response containing the response message.

[0439] Step 7:

[0440] The terminal displays the response message received from the server on the user interface means, and the user can read the displayed message and receive appropriate advice or encouragement.

[0441] Input: The HTTP response containing the response message.

[0442] Output: The response message displayed in the user interface.

[0443] 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.

[0444] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0445] 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.

[0446] [Second embodiment]

[0447] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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).

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0459] The system of this invention is a chat AI platform for increasing users' self-esteem, and consists of the following four main components:

[0460] 1. User Interface Means

[0461] 2. Server Means

[0462] 3. Natural Language Processing Methods

[0463] 4. Database Means

[0464] User Interface Means

[0465] The user interface means provides a screen on which a user can input and send their worries and anxieties. The user accesses the platform using a smartphone or computer and inputs their worries and anxieties into a text box. When the user presses the send button, the text data is sent to the server means.

[0466] Server Means

[0467] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the text data into an appropriate format (e.g., JSON format) and sends it to the natural language processing means. At this time, it may also obtain the user's past dialogue history and related data from the database means and use them for analysis.

[0468] Natural language processing tools

[0469] The natural language processing means receives the text data sent from the server means, analyzes it, and generates a response. First, it performs sentiment analysis on the text data and determines whether it is positive, negative, or neutral. Next, it extracts keywords and important phrases to understand the user's intent. Based on this information, it uses pre-prepared response templates and machine learning models to generate appropriate encouragement or advice. Once generated, the response is returned to the server means.

[0470] Database Means

[0471] The database means stores user data, past dialogue history, response templates, etc. This allows the server means and natural language processing means to quickly obtain the necessary information, accelerating analysis and response.

[0472] Specific examples

[0473] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[0474] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[0475] 2. The terminal sends this text data to the server means as an HTTP request.

[0476] 3. The server formats the received text data into JSON format and sends it to the natural language processing means.

[0477] 4. The natural language processing means performs sentiment analysis on the received text data, determining it as negative, and extracting the keywords "work" and "stress."

[0478] 5. Based on the analysis results, the natural language processing means generates a response message saying, "You're working hard. It's important to take a short break!" and sends it back to the server means.

[0479] 6. The server sends the received response message to the terminal as an HTTP response.

[0480] 7. The terminal displays the received response message on the user interface means, and the user reads the message.

[0481] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[0482] The processing flow will be explained below.

[0483] Step 1:

[0484] The user uses the user interface means of the terminal to enter their worries and anxieties into the text box and presses the send button.

[0485] Step 2:

[0486] The terminal generates an HTTP request including the text data entered by the user and sends it to the server.

[0487] Step 3:

[0488] The server analyzes the HTTP request received, extracts the text data, and formats the extracted text data in JSON format.

[0489] Step 4:

[0490] The server transmits the formatted JSON format text data to the natural language processing means.

[0491] Step 5:

[0492] A natural language processing means analyzes the received text data and performs a sentiment analysis, which determines whether the text is positive, negative, or neutral.

[0493] Step 6:

[0494] Natural language processing means extract keywords and important phrases from the text data.

[0495] Step 7:

[0496] Natural language processing tools use the analysis results to generate appropriate responses, which are generated using pre-defined templates and machine learning models.

[0497] Step 8:

[0498] The natural language processing means returns the generated response to the server.

[0499] Step 9:

[0500] The server receives the response sent back from the natural language processing means and formats it as an HTTP response.

[0501] Step 10:

[0502] The server sends an HTTP response to the device.

[0503] Step 11:

[0504] The terminal analyzes the HTTP response received from the server and displays the response message on the user interface means.

[0505] Step 12:

[0506] The user reads the response message displayed on the device screen and receives encouragement and advice.

[0507] Example 1

[0508] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0509] In modern society, many people experience stress from work and life, but there is a lack of appropriate support to reduce stress and improve self-esteem. Conventional systems have found it difficult to accurately analyze users' emotions and intentions and provide appropriate encouragement and advice in a timely manner. There is a need to solve this problem and provide an efficient and effective method to improve users' self-esteem.

[0510] 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.

[0511] In this invention, the server includes means for formatting text data entered by a user into an appropriate format and sending it to natural language processing means, natural language processing means for analyzing emotions in the text data, extracting keywords, and generating an appropriate response, and means for generating a response using a machine learning model or a response template, thereby making it possible to accurately analyze user emotions and quickly provide an appropriate response.

[0512] "User interface means" refers to an interface device that allows a user to input and transmit text data, and specifically operates on a smartphone or computer application or web browser.

[0513] The "server means" is a device or program that receives text data sent from the user interface means, formats it into an appropriate format, and performs the necessary analysis processing.

[0514] The "natural language processing means" is a device or program that performs a series of processes, such as analyzing text data received from the server means, performing sentiment analysis and keyword extraction, and generating an appropriate response message.

[0515] "Sentiment analysis" is an analytical process that determines the emotions in text data as positive, negative, or neutral.

[0516] "Keyword extraction" is the process of extracting important words and phrases from text data.

[0517] A "response template" is a template of a response message prepared in advance, which is used by the natural language processing means when generating an appropriate response.

[0518] A "machine learning model" is a model that learns from large amounts of data and performs tasks such as prediction and classification, and is used in this system to generate appropriate response messages.

[0519] An "HTTP request" is a protocol used by a client to send data to a server.

[0520] "HTTP response" is a protocol used by a server to send response data to a client.

[0521] The "database means" is a device or program that stores information such as user data, past dialogue history, and response templates, and enables the server or natural language processing means to quickly obtain the information required.

[0522] The system of this invention is a chat AI platform that enhances users' self-esteem and is composed of the following four major components: 1. user interface means, 2. server means, 3. natural language processing means, and 4. database means.

[0523] User Interface Means

[0524] Users access the system's user interface from a smartphone or computer browser. This interface provides a text box and a send button where users can enter and submit their worries and anxieties. The text data entered by the user is sent to the system by pressing the send button.

[0525] Server Means

[0526] The server receives text data sent from the user interface means and formats it into an appropriate format (e.g., JSON format). If necessary, it retrieves the user's past interaction history and other related data from the database means and uses them for analysis. The formatted data is then sent to the natural language processing means. On the server side, this data is sent and received using HTTP requests and responses.

[0527] Natural language processing tools

[0528] The natural language processing means performs sentiment analysis on the text data sent from the server means and determines whether it is positive, negative, or neutral. It also extracts keywords and important phrases from the text data. Based on this information, it uses pre-prepared response templates and machine learning models to generate appropriate messages of encouragement or advice. For example, it generates a response message such as, "You're working hard. It's important to take a short break!"

[0529] Database Means

[0530] The database stores user data, past interaction history, response templates, etc. This allows the server means and natural language processing means to quickly retrieve the required information, facilitating analysis and response. To manage this storage process, a relational database management system (RDBMS) or a non-relational database (such as NoSQL) can be used.

[0531] Specific examples

[0532] As a concrete example, if the user enters "I've been busy at work lately and I'm stressed," the following will be processed:

[0533] 1. The user inputs "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[0534] 2. The terminal sends this text data to the server means as an HTTP request.

[0535] 3. The server formats the received text data into JSON format and sends it to the natural language processing means.

[0536] 4. The natural language processing means performs sentiment analysis on the received text data, determining it as negative, and extracting the keywords "work" and "stress."

[0537] 5. Based on the analysis results, the natural language processing means generates a response message saying, "You're working hard. It's important to take a short break!" and sends it back to the server means.

[0538] 6. The server sends the received response message to the terminal as an HTTP response.

[0539] 7. The terminal displays the received response message on the user interface means, and the user reads the message.

[0540] Prompt Sentence Examples

[0541] Here are some example input prompts for a generative AI model:

[0542] User: I've been busy at work lately and feeling stressed.

[0543] Generative AI model: You work hard, it's important to take a break!

[0544] In this way, users can receive appropriate encouragement and advice to boost their self-esteem and improve the quality of their daily lives.

[0545] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0546] Step 1:

[0547] The user inputs text data into the user interface means on the terminal and presses the send button.

[0548] Input: User's worries or concerns (e.g., "I've been busy at work lately and I'm feeling stressed.")

[0549] How it works: A user enters a problem into a text box in a browser or application and clicks the submit button.

[0550] Output: The entered text data is passed to the terminal by the send action.

[0551] Step 2:

[0552] The terminal transmits the input text data to the server means as an HTTP request.

[0553] Input: Text data when the send button is pressed

[0554] How it works: Using JavaScript etc., it collects the entered text data and sends it to the server as an Ajax request.

[0555] Output: Text data formatted in HTTP request format is sent to the server.

[0556] Step 3:

[0557] The server formats the received text data into JSON format and sends it to the natural language processing means.

[0558] Input: Text data of the HTTP request sent from the terminal

[0559] How it works: A server-side program parses the data, converts it to JSON format, and, if necessary, retrieves past interaction history and related data from a database.

[0560] Output: The formatted JSON data is sent to the natural language processing means.

[0561] Step 4:

[0562] A natural language processing means analyzes the sentiment of the text data, extracts keywords, and generates a response message.

[0563] Input: JSON format text data sent from the server

[0564] Operation:

[0565] 1. Sentiment analysis: Classifying the sentiment of text as "positive," "negative," or "neutral."

[0566] 2. Keyword extraction: Extract important words and phrases such as "work" and "stress."

[0567] 3. Response generation: Use machine learning models or response templates to generate appropriate response messages (e.g., "You're working hard! It's important to take a break!").

[0568] Output: The generated response message is sent back to the server.

[0569] Step 5:

[0570] The server transmits the response message received from the natural language processing means to the terminal as an HTTP response.

[0571] Input: A response message generated by a natural language processing tool

[0572] How it works: A server-side program receives the response message and formats it as an HTTP response.

[0573] Output: The formatted response message is sent to the terminal.

[0574] Step 6:

[0575] The terminal displays the received response message on the user interface means.

[0576] Input: HTTP response message sent from the server

[0577] What it does: Uses HTML and JavaScript to display the received message in the appropriate location on the screen.

[0578] Output: A state is provided that allows the user to view the response message.

[0579] (Application example 1)

[0580] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0581] In modern factory environments, workers often feel stressed due to high workloads and mistakes. As a result, problems such as reduced work efficiency and increased employee turnover can occur. In response to this, a means is needed to provide workers with mental support quickly and at the right time, but conventional systems have been inadequate in this regard. Conventional systems have had difficulty providing appropriate support for the real-time stress and anxiety felt by workers.

[0582] 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.

[0583] In this invention, the server includes a user interface means for a user to input text data, a processing device means for analyzing the text data received from the user interface means, an automatic response generation means for generating an appropriate response from the text data analyzed by the processing device means, a means for displaying the response generated by the automatic response generation means on the user interface means, and a means for evaluating the emotional state of a worker during work and providing appropriate advice or encouragement. This makes it possible to provide quick and appropriate encouragement or advice in response to real-time stress or anxiety felt by the worker.

[0584] "User interface means" refers to means for providing a device or software screen that a user uses to input text data.

[0585] The "processor means" is a means for analyzing text data received from the user interface means and arranging it into an appropriate format.

[0586] The "automatic response generation means" is a means for generating an appropriate response based on the analyzed text data.

[0587] The "display means" is a means for displaying the generated response on the user interface means.

[0588] The "means for assessing the emotional state of a worker" is a means for analyzing emotions from text entered by a worker and assessing the worker's state.

[0589] The "means for providing an advice or encouragement response" is a means for generating and providing an appropriate advice or encouragement message based on the evaluated emotional state.

[0590] In the system that realizes this application example, a user uses a smartphone to input the stress or anxiety they feel while working, and the AI ​​responds by providing appropriate encouraging or advising messages. The system is composed of a user interface means, a processing device means, an automatic response generation means, a display means, a means for evaluating the emotional state of a worker, and a means for providing an advice or encouraging response.

[0591] System Components

[0592] Hardware

[0593] Smartphones: Used as a user interface means.

[0594] Server Hardware: Required for the operation of the Processing Unit, Auto-Response Generation Unit, and Database.

[0595] software

[0596] User interface means: Provides an application screen for workers to input their emotions.

[0597] Processing unit means: Using Python and Flask as a framework, it receives input text data, parses it, and formats it appropriately.

[0598] Automated response generation method: Hugging Face's Transformers are used to perform natural language processing using NLU models such as BERT.

[0599] Display means: The generated response message is displayed on the smartphone screen.

[0600] Sentiment analysis and response generation: Analyzes the sentiment of the input text data and generates an appropriate response based on the results.

[0601] System operation explanation

[0602] User Interface Means

[0603] Workers use a dedicated application on their smartphones to enter text about the stress or anxiety they feel while working, such as "I'm worried because I make a lot of mistakes while working."

[0604] Processing device means

[0605] The text entered is sent to the server using the Python and Flask frameworks, which then converts the received text data into JSON format and sends it to an automated response generator.

[0606] Auto-response generator

[0607] It uses Hugging Face's Transformers library for natural language processing, leveraging models such as BERT to analyze the sentiment of the text and generate appropriate responses, such as advice like, "Learning from mistakes is part of growing. Take a break and try again!"

[0608] Display means

[0609] The generated response message is sent from the server to the user interface means and displayed on the smartphone screen, allowing the worker to receive prompt and appropriate encouragement or advice.

[0610] Examples of concrete examples and prompts

[0611] Specific examples

[0612] User input: "I'm worried because I make a lot of mistakes while working."

[0613] The system's response: "Learning from your mistakes is part of growing. Take a break and try again!"

[0614] Prompt Sentence Examples

[0615] "You're inputting the anxiety that a worker feels while working. For example, if a worker says, 'I'm worried because I make a lot of mistakes while working,' you should respond with positive encouragement rather than dismissing it."

[0616] The system allows factory workers to reduce stress and anxiety in real time and improve their self-esteem.

[0617] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0618] Step 1:

[0619] The user enters text data

[0620] Users use a dedicated application on their smartphone to enter text about the stress or anxiety they feel at work, such as "I'm worried because I make a lot of mistakes at work," and then press the send button.

[0621] Input: Worker input text

[0622] Output: Request to send text data

[0623] Step 2:

[0624] The device sends text data to the server.

[0625] The terminal sends the text data entered by the user to the server as an HTTP request. The text data is formatted in JSON format and sent.

[0626] Input: Text data

[0627] Output: JSON format text data

[0628] Step 3:

[0629] The server receives and processes the text data.

[0630] The server analyzes the JSON-formatted text data received from the terminal and performs the necessary processing. Specifically, it prepares the data for transmission to the natural language processing means, including the process of formatting it into an appropriate format.

[0631] Input: JSON format text data

[0632] Output: Formatted text data

[0633] Step 4:

[0634] The server processes natural language

[0635] The server performs sentiment analysis on the formatted text data using Hugging Face's Transformers library, using BERT as the model to classify the sentiment of the input text as positive, negative, or neutral.

[0636] Input: Formatted text data

[0637] Output: Sentiment analysis results (positive, negative, neutral)

[0638] Step 5:

[0639] The server generates a response message

[0640] The server generates an appropriate response message based on the results of the sentiment analysis. For example, if a negative sentiment is detected, it generates a message like, "Learning from mistakes is part of growing. Take a break and try again!"

[0641] Input: Sentiment analysis results

[0642] Output: Response message

[0643] Step 6:

[0644] The server sends a response message to the terminal.

[0645] The server sends the generated response message to the terminal as an HTTP response. The message is sent in JSON format.

[0646] Input: Response message

[0647] Output: JSON formatted response message

[0648] Step 7:

[0649] The terminal displays a response message

[0650] The terminal displays the response message received from the server on the user interface means, and the worker checks the advice and encouragement message on the screen of his or her smartphone.

[0651] Input: JSON formatted response message

[0652] Output: Response message displayed on the smartphone screen

[0653] In this way, workers can receive appropriate encouragement and advice in real time to alleviate stress and anxiety, which can reduce the mental burden on them while they are working and improve their motivation.

[0654] 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.

[0655] The system of this invention is a chat AI platform designed to enhance users' self-esteem, and is composed of the following five main components:

[0656] 1. User Interface Means

[0657] 2. Server Means

[0658] 3. Natural Language Processing Methods

[0659] 4. Emotion Engine

[0660] 5. Database Means

[0661] User Interface Means

[0662] The user interface means provides a screen on which a user can input and send their worries and anxieties. The user accesses the platform using a smartphone or computer and inputs their worries and anxieties into a text box. When the user presses the send button, the text data is sent to the server means.

[0663] Server Means

[0664] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the text data into an appropriate format (e.g., JSON format) and sends it to the natural language processing means and emotion engine. At this time, it may also obtain the user's past dialogue history and related data from the database means and use them for analysis.

[0665] Natural language processing tools

[0666] The natural language processing means receives the text data sent from the server means and generates a response in conjunction with the analysis results from the emotion engine. It extracts keywords and important phrases and understands the user's intent. Based on this information, it generates appropriate encouragement or advice using pre-prepared response templates and machine learning models. The generated response is returned to the server means.

[0667] Emotion Engine

[0668] The emotion engine receives the text data sent to the natural language processing means and performs emotion analysis. First, the emotion engine determines whether the text data contains positive, negative, or neutral emotions. It also extracts emotion-related keywords and important phrases from the text data. These results are passed to the natural language processing means, which uses them to generate an appropriate response.

[0669] Database Means

[0670] The database means stores user data, past dialogue history, response templates, etc. This allows the server means, natural language processing means, and emotion engine to quickly obtain the necessary information, accelerating analysis and response.

[0671] Specific examples

[0672] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[0673] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[0674] 2. The terminal sends this text data to the server means as an HTTP request.

[0675] 3. The server formats the received text data into JSON format and sends it to the natural language processing means and emotion engine.

[0676] 4. The emotion engine analyzes the received text data and determines it to be negative. It also extracts "work" and "stress" as emotion-related keywords.

[0677] 5. Based on the analysis results of the emotion engine and the text data, the natural language processing means generates a response message such as, "You're working hard. It's important to take a break!"

[0678] 6. The natural language processing means returns the generated response message to the server means.

[0679] 7. The server sends the received response message to the terminal as an HTTP response.

[0680] 8. The terminal displays the response message received from the server on the user interface means.

[0681] 9. The user reads the response message displayed on the device screen and receives encouragement and advice.

[0682] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[0683] The processing flow will be explained below.

[0684] Step 1:

[0685] The user uses the user interface means of the terminal to enter their worries and anxieties into the text box and presses the send button.

[0686] Step 2:

[0687] The terminal generates an HTTP request including the text data entered by the user and sends it to the server.

[0688] Step 3:

[0689] The server receives the HTTP request, analyzes it, extracts the text data, and formats it into JSON format.

[0690] Step 4:

[0691] The server sends the formatted JSON formatted text data to the natural language processing means and emotion engine.

[0692] Step 5:

[0693] The sentiment engine receives the text data and analyzes the sentiment of the text, first determining whether the text has a positive, negative, or neutral sentiment.

[0694] Step 6:

[0695] The emotion engine extracts emotion-related keywords and key phrases from the text data.

[0696] Step 7:

[0697] The emotion engine passes the analysis results to the natural language processing means, which include the emotion judgment result of the text and extracted keywords.

[0698] Step 8:

[0699] The natural language processing means generates an appropriate response message based on the analysis results from the emotion engine and the original text data, using pre-prepared templates and machine learning models.

[0700] Step 9:

[0701] The natural language processing means returns the generated response message to the server.

[0702] Step 10:

[0703] The server formats the response message received from the natural language processing means as an HTTP response.

[0704] Step 11:

[0705] The server sends an HTTP response to the device.

[0706] Step 12:

[0707] The terminal analyzes the HTTP response received from the server and displays the response message on the user interface means.

[0708] Step 13:

[0709] The user reads the response message displayed on the device screen and receives encouragement and advice.

[0710] Specific examples

[0711] Consider the example where a user types, "Work has been busy and stressful lately."

[0712] The specific process flow:

[0713] Step 1:

[0714] The user uses the user interface means of the terminal to input "I've been busy at work recently and I'm feeling stressed," and presses the send button.

[0715] Step 2:

[0716] The terminal generates an HTTP request including this text data and sends it to the server.

[0717] Step 3:

[0718] The server receives the HTTP request, analyzes it, extracts the text data "I've been busy at work lately and I'm feeling stressed," and formats it into JSON format.

[0719] Step 4:

[0720] The server sends the formatted JSON formatted text data to the natural language processing means and emotion engine.

[0721] Step 5:

[0722] The emotion engine receives the text "I've been busy at work lately and feeling stressed," performs emotion analysis, and determines that the emotion is negative.

[0723] Step 6:

[0724] The emotion engine extracts emotion-related keywords such as "work" and "stress" from the text data.

[0725] Step 7:

[0726] The emotion engine passes the emotion determination result and keywords of the text to the natural language processing means.

[0727] Step 8:

[0728] Based on the analysis results from the emotion engine and the original text data "I've been busy at work lately and feeling stressed," the natural language processing means generates a response message saying, "You're working really hard. It's important to take a short break!"

[0729] Step 9:

[0730] The natural language processing means returns the generated response message to the server.

[0731] Step 10:

[0732] The server formats the response message received from the natural language processing means as an HTTP response.

[0733] Step 11:

[0734] The server sends the HTTP response to the user's device.

[0735] Step 12:

[0736] The terminal analyzes the HTTP response received from the server and displays a response message on the user interface means saying, "You're working hard. It's important to take a short break!"

[0737] Step 13:

[0738] The user reads the response message displayed on the device screen and receives encouragement and advice.

[0739] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[0740] Example 2

[0741] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0742] Conventional chat systems have limitations in generating immediate and appropriate responses to user input, and it is particularly difficult to properly understand the user's emotions and provide responses that are in tune with those emotions. Furthermore, if the user interface is not intuitive, users may feel stressed when using the system, which may not lead to an improvement in self-esteem.

[0743] 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.

[0744] In this invention, the server includes user interface means for a user to input text data, server means for analyzing the text data received from the user interface means, natural language processing means for generating an appropriate response from the text data analyzed by the server means, an emotion engine for analyzing emotions based on the text data, and means for displaying the response generated by the natural language processing means and the emotion engine on the user interface means. This makes it possible to quickly and appropriately generate a response that is in line with the user's emotions and increase the user's sense of self-affirmation.

[0745] "User interface means" means a screen or device used by a user to input text data and transmit that data to the system.

[0746] The "server means" refers to a device or system that has the role of analyzing text data received from the user interface means and, if necessary, transmitting the data to the natural language processing means and the emotion engine.

[0747] "Natural language processing means" refers to the technology or method for analyzing text data provided by the server means and generating appropriate responses. This means is capable of extracting keywords and important phrases and understanding the user's intent.

[0748] An "emotion engine" is a technology or method that analyzes the emotions in text data and determines the emotional state, such as positive, negative, or neutral.

[0749] "Database means" refers to a system that stores user data, past dialogue history, response templates, etc., and can quickly retrieve them as needed.

[0750] A "response message" is a response to the user, such as encouragement or advice, generated by natural language processing means and an emotion engine.

[0751] "Text data" refers to character data such as worries or anxieties entered by the user.

[0752] "Sentiment analysis" is the process of determining a user's emotions from the content of text data and classifying that emotional state as positive, negative, neutral, etc.

[0753] A "response template" is a predefined example or format of a response used by a natural language processing tool.

[0754] A "generative AI model" is a model generated using machine learning technology for performing natural language processing, sentiment analysis, and other tasks.

[0755] A "prompt" is text or instructions that are input to a generative AI model.

[0756] The system of the present invention is a chat AI platform for enhancing users' self-esteem, and is composed of several main components: a user interface means, a server means, a natural language processing means, an emotion engine, and a database means.

[0757] Hardware and Software

[0758] To realize this system, the following hardware and software are required.

[0759] Hardware: smartphones, computers, servers

[0760] Software: Natural language processing libraries (e.g., NLTK, spaCy), sentiment analysis libraries (e.g., VADER, TextBlob), database systems (e.g., MySQL, MongoDB), web servers (e.g., Apache, Nginx)

[0761] User Interface

[0762] Users access the platform using a smartphone or computer. They enter their worries and anxieties into a text box via a browser or a dedicated application and press the send button. This data is sent as text data to a server.

[0763] Server Means

[0764] The server receives text data sent from the user interface means, converts the received text data into an appropriate format (e.g., JSON format), and sends it to the natural language processing means and emotion engine. It also retrieves the user's past dialogue history and related data from the database means as needed and uses them for analysis.

[0765] Natural language processing tools

[0766] The natural language processing means analyzes the text data sent from the server means. Specifically, it extracts keywords and important phrases and understands the user's intent. Based on this information, it uses a machine learning model (e.g., a generative AI model) to generate an appropriate response message of encouragement or advice. The generated response message is returned to the server means.

[0767] Emotion Engine

[0768] The emotion engine receives the text data and performs emotion analysis. The emotion engine uses an emotion analysis library to determine positive, negative, or neutral emotions from the content of the text data. It also extracts emotion-related keywords and provides the results to the natural language processing means.

[0769] Database Means

[0770] The database means stores user data, past dialogue history, response templates, etc. This allows the server means, natural language processing means, and emotion engine to quickly obtain the necessary information, accelerating analysis and response.

[0771] Specific examples

[0772] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[0773] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[0774] 2. The terminal sends this text data to the server means as an HTTP request.

[0775] 3. The server formats the received text data into JSON format and sends it to the natural language processing means and emotion engine.

[0776] 4. The emotion engine analyzes the received text data and determines it to be negative. It also extracts "work" and "stress" as emotion-related keywords.

[0777] 5. Based on the analysis results of the emotion engine and the text data, the natural language processing means generates a response message such as, "You're working hard. It's important to take a break!"

[0778] 6. The natural language processing means returns the generated response message to the server means.

[0779] 7. The server sends the received response message to the terminal as an HTTP response.

[0780] 8. The terminal displays the response message received from the server on the user interface means.

[0781] 9. The user reads the response message displayed on the device screen and receives encouragement and advice.

[0782] Prompt Sentence Examples

[0783] Below are some example prompts to input to a generative AI model:

[0784] "If a user types, 'I've been busy and stressed at work lately,' how should we respond?"

[0785] In this way, users can receive prompt and appropriate encouragement and advice, which can increase their self-esteem.

[0786] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0787] Step 1:

[0788] The user inputs text data into the user interface means of the terminal and presses the send button. The input text data is a sentence such as "I've been busy at work lately and I'm feeling stressed." The user interface means receives this input and captures the clicking of the send button as an event.

[0789] Step 2:

[0790] The terminal transmits the text data received from the user interface means to the server means. Specifically, the text data is transmitted using an HTTP POST request. For example, the text data is packed in JSON format and transmitted. The URL of this request is specified by the API endpoint of the server.

[0791] Step 3:

[0792] The server receives the HTTP POST request, extracts the text data, and formats it in a parsable form (e.g., JSON again). At this point, it performs error checking to remove any incomplete data, and adds metadata such as the user ID and timestamp. The formatted text data is then sent to the natural language processing tool and emotion engine.

[0793] Step 4:

[0794] The server retrieves the user's past interaction history and related data from the database means as needed. Specifically, it retrieves past interaction data associated with the user ID using SQL queries or NoSQL queries, and provides the data to the natural language processing means and emotion engine as additional information for analyzing the data.

[0795] Step 5:

[0796] The natural language processing (NLP) means analyzes the text data to extract keywords and important phrases. It uses a NLP library (e.g., NLTK or spaCy) to tokenize the text and tag it with parts of speech to extract keywords like "work" and "stress." The results of this analysis are also sent to the sentiment engine.

[0797] Step 6:

[0798] The sentiment engine receives the text data and performs sentiment analysis. For example, it uses a sentiment analysis library such as VADER or TextBlob to calculate a sentiment score for each word and classify it as positive, negative, or neutral. The classification results and sentiment-related keywords (e.g., "stress") are sent back to the natural language processing means.

[0799] Step 7:

[0800] The natural language processing means receives the results from the emotion engine and uses a generative AI model (e.g., GPT-3) to generate an appropriate response message, such as an encouraging message like, "You're working hard. It's important to take a break!" The generated response message is sent back to the server in JSON format.

[0801] Step 8:

[0802] The server sends the response message received from the natural language processing means to the terminal as an HTTP response. The response includes a status code (e.g., 200 OK) to indicate that the processing was completed successfully.

[0803] Step 9:

[0804] The device receives the HTTP response, analyzes the response message, and displays it in the user interface. Specifically, it uses HTML and JavaScript to display a message below the text box or in the conversation window: "You're working hard. It's important to take a break!"

[0805] Step 10:

[0806] Users can check the response messages displayed on the device screen and receive encouragement and advice, which is expected to increase their self-esteem and improve the quality of their daily lives.

[0807] (Application example 2)

[0808] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0809] Conventional chat AI platforms lack the accuracy and speed of emotion recognition to generate responses that enhance users' self-esteem. They also lack specific support measures to improve the user experience in virtual stores. This can result in users not receiving appropriate advice or encouragement, resulting in a poor quality shopping experience.

[0810] 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.

[0811] In this invention, the server includes a user interface means for a user to input text data, a server means for analyzing the text data received from the user interface means, an emotion engine means for analyzing the emotion of the text data analyzed by the server means, a natural language processing means including a generative AI model for generating an appropriate response from the text data analyzed by the server means, and a means for displaying the response generated by the natural language processing means on the user interface means. This makes it possible to quickly and appropriately provide encouragement or advice according to the user's emotion, thereby improving the quality of the shopping experience in the virtual store.

[0812] A "user interface means" is a method or device by which a user inputs and transmits textual data.

[0813] The "server means" is a central data processing device that processes and analyzes data received from the user interface means and performs necessary processing.

[0814] The "emotion engine means" is a device or method that analyzes the emotions of text data and determines emotions such as positive, negative, or neutral.

[0815] A "generative AI model" is a method or device that uses machine learning algorithms to automatically generate appropriate responses from input data.

[0816] A "natural language processing means" is a method or device for analyzing and understanding text data entered by a user and generating an appropriate response.

[0817] A "prompt sentence" is an input sentence that serves as the basis for a generative AI model to generate a response, and it affects the quality and appropriateness of the generated response.

[0818] The system of the present invention provides a chat AI assistant to enhance users' shopping experience and is composed of the following main means:

[0819] 1. User Interface Means

[0820] The user interface means provides a screen on which a user can input and send text data. The user can access the virtual store using, for example, a smartphone or a computer, and enter questions or concerns into the text box while shopping. When the user presses the send button, the input text data is sent to the server means.

[0821] 2. Server Means

[0822] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the received text data into an appropriate format (e.g., JSON format) and sends it to the emotion engine means and natural language processing means. It may also retrieve the user's past dialogue history and related data from a database and use them for analysis.

[0823] 3. Emotional Engine Means

[0824] The emotion engine means receives the text data sent from the server means and performs emotion analysis. This means determines positive, negative, or neutral emotions from the content of the text data. It also extracts emotion-related keywords and important phrases from the text data. These results are passed to the natural language processing means and are used to generate an appropriate response.

[0825] 4. Natural Language Processing Methods

[0826] The natural language processing means generates an appropriate response using a generative AI model based on data from the server means and the emotion engine means. This means generates a response including encouragement or advice using a prompt sentence according to the user's emotional state. For example, a prompt sentence such as "I think that's a good choice. Please take your time to choose." is used.

[0827] 5. Response Display Means

[0828] The generated response is sent to the user interface means via the server means and displayed on the user interface means, allowing the user to read the displayed message and receive appropriate advice or encouragement.

[0829] Hardware and software used

[0830] Server: Used as the central device for data processing and analysis. Uses Flask (a lightweight Python web application framework).

[0831] Sentiment Engine: For sentiment analysis, we use the pipeline (sentiment-analysis) model from the Transformers library.

[0832] Natural language processing means: For response generation, we use the pipeline (text-generation) model from the Transformers library.

[0833] Database: Use an appropriate data management solution (e.g., SQL or NoSQL database) to store user data and past interaction history.

[0834] Specific examples

[0835] A user types, "I want to find accessories that go well with this dress, but I'm not sure what to buy."

[0836] The server means receives this message, performs emotion analysis using the emotion engine means, and determines the emotion as "neutral."

[0837] Natural language processing generates an appropriate response based on the sentiment analysis results, for example, using a prompt such as "I think that's a good choice. Please take your time to choose."

[0838] The generated response is sent to the user through the server means and displayed in the user interface.

[0839] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0840] Step 1:

[0841] The user inputs "I want to buy accessories that go well with this dress, but I'm not sure what to buy" into the user interface means on the terminal and presses the send button.

[0842] Input: User's text data.

[0843] Output: Text data is ready to be sent.

[0844] Step 2:

[0845] The terminal transmits this text data to the server means as an HTTP request.

[0846] Input: User's text data.

[0847] Output: HTTP request with text data.

[0848] Step 3:

[0849] The server formats the received text data into JSON format and sends it to the emotion engine means and natural language processing means.

[0850] Input: HTTP request containing the user's text data.

[0851] Output: Data in JSON format that is sent to the sentiment engine means and natural language processing means.

[0852] Step 4:

[0853] The emotion engine means performs emotion analysis on the received text data to determine whether the emotion is positive, negative, or neutral, and extracts important keywords.

[0854] Input: Text data in JSON format.

[0855] Output: Sentiment analysis results and keywords.

[0856] Step 5:

[0857] The natural language processing means receives the analysis results from the emotion engine means and uses the generative AI model to generate an appropriate response based on the prompt sentence. In this case, the prompt sentence used is, "I think that's a good choice. Please take your time to choose."

[0858] Input: Sentiment analysis results and keywords.

[0859] Output: The generated response message.

[0860] Step 6:

[0861] The server transmits the response message received from the natural language processing means to the terminal as an HTTP response.

[0862] Input: The generated response message.

[0863] Output: The HTTP response containing the response message.

[0864] Step 7:

[0865] The terminal displays the response message received from the server on the user interface means, and the user can read the displayed message and receive appropriate advice or encouragement.

[0866] Input: The HTTP response containing the response message.

[0867] Output: The response message displayed in the user interface.

[0868] 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.

[0869] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0870] 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.

[0871] [Third embodiment]

[0872] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0873] 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.

[0874] 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).

[0875] 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.

[0876] 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.

[0877] 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).

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0884] The system of this invention is a chat AI platform for increasing users' self-esteem, and consists of the following four main components:

[0885] 1. User Interface Means

[0886] 2. Server Means

[0887] 3. Natural Language Processing Methods

[0888] 4. Database Means

[0889] User Interface Means

[0890] The user interface means provides a screen on which a user can input and send their worries and anxieties. The user accesses the platform using a smartphone or computer and inputs their worries and anxieties into a text box. When the user presses the send button, the text data is sent to the server means.

[0891] Server Means

[0892] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the text data into an appropriate format (e.g., JSON format) and sends it to the natural language processing means. At this time, it may also obtain the user's past dialogue history and related data from the database means and use them for analysis.

[0893] Natural language processing tools

[0894] The natural language processing means receives the text data sent from the server means, analyzes it, and generates a response. First, it performs sentiment analysis on the text data and determines whether it is positive, negative, or neutral. Next, it extracts keywords and important phrases to understand the user's intent. Based on this information, it uses pre-prepared response templates and machine learning models to generate appropriate encouragement or advice. Once generated, the response is returned to the server means.

[0895] Database Means

[0896] The database means stores user data, past dialogue history, response templates, etc. This allows the server means and natural language processing means to quickly obtain the necessary information, accelerating analysis and response.

[0897] Specific examples

[0898] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[0899] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[0900] 2. The terminal sends this text data to the server means as an HTTP request.

[0901] 3. The server formats the received text data into JSON format and sends it to the natural language processing means.

[0902] 4. The natural language processing means performs sentiment analysis on the received text data, determining it as negative, and extracting the keywords "work" and "stress."

[0903] 5. Based on the analysis results, the natural language processing means generates a response message saying, "You're working hard. It's important to take a short break!" and sends it back to the server means.

[0904] 6. The server sends the received response message to the terminal as an HTTP response.

[0905] 7. The terminal displays the received response message on the user interface means, and the user reads the message.

[0906] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[0907] The processing flow will be explained below.

[0908] Step 1:

[0909] The user uses the user interface means of the terminal to enter their worries and anxieties into the text box and presses the send button.

[0910] Step 2:

[0911] The terminal generates an HTTP request including the text data entered by the user and sends it to the server.

[0912] Step 3:

[0913] The server analyzes the HTTP request received, extracts the text data, and formats the extracted text data in JSON format.

[0914] Step 4:

[0915] The server transmits the formatted JSON format text data to the natural language processing means.

[0916] Step 5:

[0917] A natural language processing means analyzes the received text data and performs a sentiment analysis, which determines whether the text is positive, negative, or neutral.

[0918] Step 6:

[0919] Natural language processing means extract keywords and important phrases from the text data.

[0920] Step 7:

[0921] Natural language processing tools use the analysis results to generate appropriate responses, which are generated using pre-defined templates and machine learning models.

[0922] Step 8:

[0923] The natural language processing means returns the generated response to the server.

[0924] Step 9:

[0925] The server receives the response sent back from the natural language processing means and formats it as an HTTP response.

[0926] Step 10:

[0927] The server sends an HTTP response to the device.

[0928] Step 11:

[0929] The terminal analyzes the HTTP response received from the server and displays the response message on the user interface means.

[0930] Step 12:

[0931] The user reads the response message displayed on the device screen and receives encouragement and advice.

[0932] Example 1

[0933] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0934] In modern society, many people experience stress from work and life, but there is a lack of appropriate support to reduce stress and improve self-esteem. Conventional systems have found it difficult to accurately analyze users' emotions and intentions and provide appropriate encouragement and advice in a timely manner. There is a need to solve this problem and provide an efficient and effective method to improve users' self-esteem.

[0935] 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.

[0936] In this invention, the server includes means for formatting text data entered by a user into an appropriate format and sending it to natural language processing means, natural language processing means for analyzing emotions in the text data, extracting keywords, and generating an appropriate response, and means for generating a response using a machine learning model or a response template, thereby making it possible to accurately analyze user emotions and quickly provide an appropriate response.

[0937] "User interface means" refers to an interface device that allows a user to input and transmit text data, and specifically operates on a smartphone or computer application or web browser.

[0938] The "server means" is a device or program that receives text data sent from the user interface means, formats it into an appropriate format, and performs the necessary analysis processing.

[0939] The "natural language processing means" is a device or program that performs a series of processes, such as analyzing text data received from the server means, performing sentiment analysis and keyword extraction, and generating an appropriate response message.

[0940] "Sentiment analysis" is an analytical process that determines the emotions in text data as positive, negative, or neutral.

[0941] "Keyword extraction" is the process of extracting important words and phrases from text data.

[0942] A "response template" is a template of a response message prepared in advance, which is used by the natural language processing means when generating an appropriate response.

[0943] A "machine learning model" is a model that learns from large amounts of data and performs tasks such as prediction and classification, and is used in this system to generate appropriate response messages.

[0944] An "HTTP request" is a protocol used by a client to send data to a server.

[0945] "HTTP response" is a protocol used by a server to send response data to a client.

[0946] The "database means" is a device or program that stores information such as user data, past dialogue history, and response templates, and enables the server or natural language processing means to quickly obtain the information required.

[0947] The system of this invention is a chat AI platform that enhances users' self-esteem and is composed of the following four major components: 1. user interface means, 2. server means, 3. natural language processing means, and 4. database means.

[0948] User Interface Means

[0949] Users access the system's user interface from a smartphone or computer browser. This interface provides a text box and a send button where users can enter and submit their worries and anxieties. The text data entered by the user is sent to the system by pressing the send button.

[0950] Server Means

[0951] The server receives text data sent from the user interface means and formats it into an appropriate format (e.g., JSON format). If necessary, it retrieves the user's past interaction history and other related data from the database means and uses them for analysis. The formatted data is then sent to the natural language processing means. On the server side, this data is sent and received using HTTP requests and responses.

[0952] Natural language processing tools

[0953] The natural language processing means performs sentiment analysis on the text data sent from the server means and determines whether it is positive, negative, or neutral. It also extracts keywords and important phrases from the text data. Based on this information, it uses pre-prepared response templates and machine learning models to generate appropriate messages of encouragement or advice. For example, it generates a response message such as, "You're working hard. It's important to take a short break!"

[0954] Database Means

[0955] The database stores user data, past interaction history, response templates, etc. This allows the server means and natural language processing means to quickly retrieve the required information, facilitating analysis and response. To manage this storage process, a relational database management system (RDBMS) or a non-relational database (such as NoSQL) can be used.

[0956] Specific examples

[0957] As a concrete example, if the user enters "I've been busy at work lately and I'm stressed," the following will be processed:

[0958] 1. The user inputs "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[0959] 2. The terminal sends this text data to the server means as an HTTP request.

[0960] 3. The server formats the received text data into JSON format and sends it to the natural language processing means.

[0961] 4. The natural language processing means performs sentiment analysis on the received text data, determining it as negative, and extracting the keywords "work" and "stress."

[0962] 5. Based on the analysis results, the natural language processing means generates a response message saying, "You're working hard. It's important to take a short break!" and sends it back to the server means.

[0963] 6. The server sends the received response message to the terminal as an HTTP response.

[0964] 7. The terminal displays the received response message on the user interface means, and the user reads the message.

[0965] Prompt Sentence Examples

[0966] Here are some example input prompts for a generative AI model:

[0967] User: I've been busy at work lately and feeling stressed.

[0968] Generative AI model: You work hard, it's important to take a break!

[0969] In this way, users can receive appropriate encouragement and advice to boost their self-esteem and improve the quality of their daily lives.

[0970] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0971] Step 1:

[0972] The user inputs text data into the user interface means on the terminal and presses the send button.

[0973] Input: User's worries or concerns (e.g., "I've been busy at work lately and I'm feeling stressed.")

[0974] How it works: A user enters a problem into a text box in a browser or application and clicks the submit button.

[0975] Output: The entered text data is passed to the terminal by the send action.

[0976] Step 2:

[0977] The terminal transmits the input text data to the server means as an HTTP request.

[0978] Input: Text data when the send button is pressed

[0979] How it works: Using JavaScript etc., it collects the entered text data and sends it to the server as an Ajax request.

[0980] Output: Text data formatted in HTTP request format is sent to the server.

[0981] Step 3:

[0982] The server formats the received text data into JSON format and sends it to the natural language processing means.

[0983] Input: Text data of the HTTP request sent from the terminal

[0984] How it works: A server-side program parses the data, converts it to JSON format, and, if necessary, retrieves past interaction history and related data from a database.

[0985] Output: The formatted JSON data is sent to the natural language processing means.

[0986] Step 4:

[0987] A natural language processing means analyzes the sentiment of the text data, extracts keywords, and generates a response message.

[0988] Input: JSON format text data sent from the server

[0989] Operation:

[0990] 1. Sentiment analysis: Classifying the sentiment of text as "positive," "negative," or "neutral."

[0991] 2. Keyword extraction: Extract important words and phrases such as "work" and "stress."

[0992] 3. Response generation: Use machine learning models or response templates to generate appropriate response messages (e.g., "You're working hard! It's important to take a break!").

[0993] Output: The generated response message is sent back to the server.

[0994] Step 5:

[0995] The server transmits the response message received from the natural language processing means to the terminal as an HTTP response.

[0996] Input: A response message generated by a natural language processing tool

[0997] How it works: A server-side program receives the response message and formats it as an HTTP response.

[0998] Output: The formatted response message is sent to the terminal.

[0999] Step 6:

[1000] The terminal displays the received response message on the user interface means.

[1001] Input: HTTP response message sent from the server

[1002] What it does: Uses HTML and JavaScript to display the received message in the appropriate location on the screen.

[1003] Output: A state is provided that allows the user to view the response message.

[1004] (Application example 1)

[1005] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1006] In modern factory environments, workers often feel stressed due to high workloads and mistakes. As a result, problems such as reduced work efficiency and increased employee turnover can occur. In response to this, a means is needed to provide workers with mental support quickly and at the right time, but conventional systems have been inadequate in this regard. Conventional systems have had difficulty providing appropriate support for the real-time stress and anxiety felt by workers.

[1007] 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.

[1008] In this invention, the server includes a user interface means for a user to input text data, a processing device means for analyzing the text data received from the user interface means, an automatic response generation means for generating an appropriate response from the text data analyzed by the processing device means, a means for displaying the response generated by the automatic response generation means on the user interface means, and a means for evaluating the emotional state of a worker during work and providing appropriate advice or encouragement. This makes it possible to provide quick and appropriate encouragement or advice in response to real-time stress or anxiety felt by the worker.

[1009] "User interface means" refers to means for providing a device or software screen that a user uses to input text data.

[1010] The "processor means" is a means for analyzing text data received from the user interface means and arranging it into an appropriate format.

[1011] The "automatic response generation means" is a means for generating an appropriate response based on the analyzed text data.

[1012] The "display means" is a means for displaying the generated response on the user interface means.

[1013] The "means for assessing the emotional state of a worker" is a means for analyzing emotions from text entered by a worker and assessing the worker's state.

[1014] The "means for providing an advice or encouragement response" is a means for generating and providing an appropriate advice or encouragement message based on the evaluated emotional state.

[1015] In the system that realizes this application example, a user uses a smartphone to input the stress or anxiety they feel while working, and the AI ​​responds by providing appropriate encouraging or advising messages. The system is composed of a user interface means, a processing device means, an automatic response generation means, a display means, a means for evaluating the emotional state of a worker, and a means for providing an advice or encouraging response.

[1016] System Components

[1017] Hardware

[1018] Smartphones: Used as a user interface means.

[1019] Server Hardware: Required for the operation of the Processing Unit, Auto-Response Generation Unit, and Database.

[1020] software

[1021] User interface means: Provides an application screen for workers to input their emotions.

[1022] Processing unit means: Using Python and Flask as a framework, it receives input text data, parses it, and formats it appropriately.

[1023] Automated response generation method: Hugging Face's Transformers are used to perform natural language processing using NLU models such as BERT.

[1024] Display means: The generated response message is displayed on the smartphone screen.

[1025] Sentiment analysis and response generation: Analyzes the sentiment of the input text data and generates an appropriate response based on the results.

[1026] System operation explanation

[1027] User Interface Means

[1028] Workers use a dedicated application on their smartphones to enter text about the stress or anxiety they feel while working, such as "I'm worried because I make a lot of mistakes while working."

[1029] Processing device means

[1030] The text entered is sent to the server using the Python and Flask frameworks, which then converts the received text data into JSON format and sends it to an automated response generator.

[1031] Auto-response generator

[1032] It uses Hugging Face's Transformers library for natural language processing, leveraging models such as BERT to analyze the sentiment of the text and generate appropriate responses, such as advice like, "Learning from mistakes is part of growing. Take a break and try again!"

[1033] Display means

[1034] The generated response message is sent from the server to the user interface means and displayed on the smartphone screen, allowing the worker to receive prompt and appropriate encouragement or advice.

[1035] Examples of concrete examples and prompts

[1036] Specific examples

[1037] User input: "I'm worried because I make a lot of mistakes while working."

[1038] The system's response: "Learning from your mistakes is part of growing. Take a break and try again!"

[1039] Prompt Sentence Examples

[1040] "You're inputting the anxiety that a worker feels while working. For example, if a worker says, 'I'm worried because I make a lot of mistakes while working,' you should respond with positive encouragement rather than dismissing it."

[1041] The system allows factory workers to reduce stress and anxiety in real time and improve their self-esteem.

[1042] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1043] Step 1:

[1044] The user enters text data

[1045] Users use a dedicated application on their smartphone to enter text about the stress or anxiety they feel at work, such as "I'm worried because I make a lot of mistakes at work," and then press the send button.

[1046] Input: Worker input text

[1047] Output: Request to send text data

[1048] Step 2:

[1049] The device sends text data to the server.

[1050] The terminal sends the text data entered by the user to the server as an HTTP request. The text data is formatted in JSON format and sent.

[1051] Input: Text data

[1052] Output: JSON format text data

[1053] Step 3:

[1054] The server receives and processes the text data.

[1055] The server analyzes the JSON-formatted text data received from the terminal and performs the necessary processing. Specifically, it prepares the data for transmission to the natural language processing means, including the process of formatting it into an appropriate format.

[1056] Input: JSON format text data

[1057] Output: Formatted text data

[1058] Step 4:

[1059] The server processes natural language

[1060] The server performs sentiment analysis on the formatted text data using Hugging Face's Transformers library, using BERT as the model to classify the sentiment of the input text as positive, negative, or neutral.

[1061] Input: Formatted text data

[1062] Output: Sentiment analysis results (positive, negative, neutral)

[1063] Step 5:

[1064] The server generates a response message

[1065] The server generates an appropriate response message based on the results of the sentiment analysis. For example, if a negative sentiment is detected, it generates a message like, "Learning from mistakes is part of growing. Take a break and try again!"

[1066] Input: Sentiment analysis results

[1067] Output: Response message

[1068] Step 6:

[1069] The server sends a response message to the terminal.

[1070] The server sends the generated response message to the terminal as an HTTP response. The message is sent in JSON format.

[1071] Input: Response message

[1072] Output: JSON formatted response message

[1073] Step 7:

[1074] The terminal displays a response message

[1075] The terminal displays the response message received from the server on the user interface means, and the worker checks the advice and encouragement message on the screen of his or her smartphone.

[1076] Input: JSON formatted response message

[1077] Output: Response message displayed on the smartphone screen

[1078] In this way, workers can receive appropriate encouragement and advice in real time to alleviate stress and anxiety, which can reduce the mental burden on them while they are working and improve their motivation.

[1079] 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.

[1080] The system of this invention is a chat AI platform designed to enhance users' self-esteem, and is composed of the following five main components:

[1081] 1. User Interface Means

[1082] 2. Server Means

[1083] 3. Natural Language Processing Methods

[1084] 4. Emotion Engine

[1085] 5. Database Means

[1086] User Interface Means

[1087] The user interface means provides a screen on which a user can input and send their worries and anxieties. The user accesses the platform using a smartphone or computer and inputs their worries and anxieties into a text box. When the user presses the send button, the text data is sent to the server means.

[1088] Server Means

[1089] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the text data into an appropriate format (e.g., JSON format) and sends it to the natural language processing means and emotion engine. At this time, it may also obtain the user's past dialogue history and related data from the database means and use them for analysis.

[1090] Natural language processing tools

[1091] The natural language processing means receives the text data sent from the server means and generates a response in conjunction with the analysis results from the emotion engine. It extracts keywords and important phrases and understands the user's intent. Based on this information, it generates appropriate encouragement or advice using pre-prepared response templates and machine learning models. The generated response is returned to the server means.

[1092] Emotion Engine

[1093] The emotion engine receives the text data sent to the natural language processing means and performs emotion analysis. First, the emotion engine determines whether the text data contains positive, negative, or neutral emotions. It also extracts emotion-related keywords and important phrases from the text data. These results are passed to the natural language processing means, which uses them to generate an appropriate response.

[1094] Database Means

[1095] The database means stores user data, past dialogue history, response templates, etc. This allows the server means, natural language processing means, and emotion engine to quickly obtain the necessary information, accelerating analysis and response.

[1096] Specific examples

[1097] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[1098] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[1099] 2. The terminal sends this text data to the server means as an HTTP request.

[1100] 3. The server formats the received text data into JSON format and sends it to the natural language processing means and emotion engine.

[1101] 4. The emotion engine analyzes the received text data and determines it to be negative. It also extracts "work" and "stress" as emotion-related keywords.

[1102] 5. Based on the analysis results of the emotion engine and the text data, the natural language processing means generates a response message such as, "You're working hard. It's important to take a break!"

[1103] 6. The natural language processing means returns the generated response message to the server means.

[1104] 7. The server sends the received response message to the terminal as an HTTP response.

[1105] 8. The terminal displays the response message received from the server on the user interface means.

[1106] 9. The user reads the response message displayed on the device screen and receives encouragement and advice.

[1107] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[1108] The processing flow will be explained below.

[1109] Step 1:

[1110] The user uses the user interface means of the terminal to enter their worries and anxieties into the text box and presses the send button.

[1111] Step 2:

[1112] The terminal generates an HTTP request including the text data entered by the user and sends it to the server.

[1113] Step 3:

[1114] The server receives the HTTP request, analyzes it, extracts the text data, and formats it into JSON format.

[1115] Step 4:

[1116] The server sends the formatted JSON formatted text data to the natural language processing means and emotion engine.

[1117] Step 5:

[1118] The sentiment engine receives the text data and analyzes the sentiment of the text, first determining whether the text has a positive, negative, or neutral sentiment.

[1119] Step 6:

[1120] The emotion engine extracts emotion-related keywords and key phrases from the text data.

[1121] Step 7:

[1122] The emotion engine passes the analysis results to the natural language processing means, which include the emotion judgment result of the text and extracted keywords.

[1123] Step 8:

[1124] The natural language processing means generates an appropriate response message based on the analysis results from the emotion engine and the original text data, using pre-prepared templates and machine learning models.

[1125] Step 9:

[1126] The natural language processing means returns the generated response message to the server.

[1127] Step 10:

[1128] The server formats the response message received from the natural language processing means as an HTTP response.

[1129] Step 11:

[1130] The server sends an HTTP response to the device.

[1131] Step 12:

[1132] The terminal analyzes the HTTP response received from the server and displays the response message on the user interface means.

[1133] Step 13:

[1134] The user reads the response message displayed on the device screen and receives encouragement and advice.

[1135] Specific examples

[1136] Consider the example where a user types, "Work has been busy and stressful lately."

[1137] The specific process flow:

[1138] Step 1:

[1139] The user uses the user interface means of the terminal to input "I've been busy at work recently and I'm feeling stressed," and presses the send button.

[1140] Step 2:

[1141] The terminal generates an HTTP request including this text data and sends it to the server.

[1142] Step 3:

[1143] The server receives the HTTP request, analyzes it, extracts the text data "I've been busy at work lately and I'm feeling stressed," and formats it into JSON format.

[1144] Step 4:

[1145] The server sends the formatted JSON formatted text data to the natural language processing means and emotion engine.

[1146] Step 5:

[1147] The emotion engine receives the text "I've been busy at work lately and feeling stressed," performs emotion analysis, and determines that the emotion is negative.

[1148] Step 6:

[1149] The emotion engine extracts emotion-related keywords such as "work" and "stress" from the text data.

[1150] Step 7:

[1151] The emotion engine passes the emotion determination result and keywords of the text to the natural language processing means.

[1152] Step 8:

[1153] Based on the analysis results from the emotion engine and the original text data "I've been busy at work lately and feeling stressed," the natural language processing means generates a response message saying, "You're working really hard. It's important to take a short break!"

[1154] Step 9:

[1155] The natural language processing means returns the generated response message to the server.

[1156] Step 10:

[1157] The server formats the response message received from the natural language processing means as an HTTP response.

[1158] Step 11:

[1159] The server sends the HTTP response to the user's device.

[1160] Step 12:

[1161] The terminal analyzes the HTTP response received from the server and displays a response message on the user interface means saying, "You're working hard. It's important to take a short break!"

[1162] Step 13:

[1163] The user reads the response message displayed on the device screen and receives encouragement and advice.

[1164] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[1165] Example 2

[1166] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1167] Conventional chat systems have limitations in generating immediate and appropriate responses to user input, and it is particularly difficult to properly understand the user's emotions and provide responses that are in tune with those emotions. Furthermore, if the user interface is not intuitive, users may feel stressed when using the system, which may not lead to an improvement in self-esteem.

[1168] 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.

[1169] In this invention, the server includes user interface means for a user to input text data, server means for analyzing the text data received from the user interface means, natural language processing means for generating an appropriate response from the text data analyzed by the server means, an emotion engine for analyzing emotions based on the text data, and means for displaying the response generated by the natural language processing means and the emotion engine on the user interface means. This makes it possible to quickly and appropriately generate a response that is in line with the user's emotions and increase the user's sense of self-affirmation.

[1170] "User interface means" means a screen or device used by a user to input text data and transmit that data to the system.

[1171] The "server means" refers to a device or system that has the role of analyzing text data received from the user interface means and, if necessary, transmitting the data to the natural language processing means and the emotion engine.

[1172] "Natural language processing means" refers to the technology or method for analyzing text data provided by the server means and generating appropriate responses. This means is capable of extracting keywords and important phrases and understanding the user's intent.

[1173] An "emotion engine" is a technology or method that analyzes the emotions in text data and determines the emotional state, such as positive, negative, or neutral.

[1174] "Database means" refers to a system that stores user data, past dialogue history, response templates, etc., and can quickly retrieve them as needed.

[1175] A "response message" is a response to the user, such as encouragement or advice, generated by natural language processing means and an emotion engine.

[1176] "Text data" refers to character data such as worries or anxieties entered by the user.

[1177] "Sentiment analysis" is the process of determining a user's emotions from the content of text data and classifying that emotional state as positive, negative, neutral, etc.

[1178] A "response template" is a predefined example or format of a response used by a natural language processing tool.

[1179] A "generative AI model" is a model generated using machine learning technology for performing natural language processing, sentiment analysis, and other tasks.

[1180] A "prompt" is text or instructions that are input to a generative AI model.

[1181] The system of the present invention is a chat AI platform for enhancing users' self-esteem, and is composed of several main components: a user interface means, a server means, a natural language processing means, an emotion engine, and a database means.

[1182] Hardware and Software

[1183] To realize this system, the following hardware and software are required.

[1184] Hardware: smartphones, computers, servers

[1185] Software: Natural language processing libraries (e.g., NLTK, spaCy), sentiment analysis libraries (e.g., VADER, TextBlob), database systems (e.g., MySQL, MongoDB), web servers (e.g., Apache, Nginx)

[1186] User Interface

[1187] Users access the platform using a smartphone or computer. They enter their worries and anxieties into a text box via a browser or a dedicated application and press the send button. This data is sent as text data to a server.

[1188] Server Means

[1189] The server receives text data sent from the user interface means, converts the received text data into an appropriate format (e.g., JSON format), and sends it to the natural language processing means and emotion engine. It also retrieves the user's past dialogue history and related data from the database means as needed and uses them for analysis.

[1190] Natural language processing tools

[1191] The natural language processing means analyzes the text data sent from the server means. Specifically, it extracts keywords and important phrases and understands the user's intent. Based on this information, it uses a machine learning model (e.g., a generative AI model) to generate an appropriate response message of encouragement or advice. The generated response message is returned to the server means.

[1192] Emotion Engine

[1193] The emotion engine receives the text data and performs emotion analysis. The emotion engine uses an emotion analysis library to determine positive, negative, or neutral emotions from the content of the text data. It also extracts emotion-related keywords and provides the results to the natural language processing means.

[1194] Database Means

[1195] The database means stores user data, past dialogue history, response templates, etc. This allows the server means, natural language processing means, and emotion engine to quickly obtain the necessary information, accelerating analysis and response.

[1196] Specific examples

[1197] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[1198] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[1199] 2. The terminal sends this text data to the server means as an HTTP request.

[1200] 3. The server formats the received text data into JSON format and sends it to the natural language processing means and emotion engine.

[1201] 4. The emotion engine analyzes the received text data and determines it to be negative. It also extracts "work" and "stress" as emotion-related keywords.

[1202] 5. Based on the analysis results of the emotion engine and the text data, the natural language processing means generates a response message such as, "You're working hard. It's important to take a break!"

[1203] 6. The natural language processing means returns the generated response message to the server means.

[1204] 7. The server sends the received response message to the terminal as an HTTP response.

[1205] 8. The terminal displays the response message received from the server on the user interface means.

[1206] 9. The user reads the response message displayed on the device screen and receives encouragement and advice.

[1207] Prompt Sentence Examples

[1208] Below are some example prompts to input to a generative AI model:

[1209] "If a user types, 'I've been busy and stressed at work lately,' how should we respond?"

[1210] In this way, users can receive prompt and appropriate encouragement and advice, which can increase their self-esteem.

[1211] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1212] Step 1:

[1213] The user inputs text data into the user interface means of the terminal and presses the send button. The input text data is a sentence such as "I've been busy at work lately and I'm feeling stressed." The user interface means receives this input and captures the clicking of the send button as an event.

[1214] Step 2:

[1215] The terminal transmits the text data received from the user interface means to the server means. Specifically, the text data is transmitted using an HTTP POST request. For example, the text data is packed in JSON format and transmitted. The URL of this request is specified by the API endpoint of the server.

[1216] Step 3:

[1217] The server receives the HTTP POST request, extracts the text data, and formats it in a parsable form (e.g., JSON again). At this point, it performs error checking to remove any incomplete data, and adds metadata such as the user ID and timestamp. The formatted text data is then sent to the natural language processing tool and emotion engine.

[1218] Step 4:

[1219] The server retrieves the user's past interaction history and related data from the database means as needed. Specifically, it retrieves past interaction data associated with the user ID using SQL queries or NoSQL queries, and provides the data to the natural language processing means and emotion engine as additional information for analyzing the data.

[1220] Step 5:

[1221] The natural language processing (NLP) means analyzes the text data to extract keywords and important phrases. It uses a NLP library (e.g., NLTK or spaCy) to tokenize the text and tag it with parts of speech to extract keywords like "work" and "stress." The results of this analysis are also sent to the sentiment engine.

[1222] Step 6:

[1223] The sentiment engine receives the text data and performs sentiment analysis. For example, it uses a sentiment analysis library such as VADER or TextBlob to calculate a sentiment score for each word and classify it as positive, negative, or neutral. The classification results and sentiment-related keywords (e.g., "stress") are sent back to the natural language processing means.

[1224] Step 7:

[1225] The natural language processing means receives the results from the emotion engine and uses a generative AI model (e.g., GPT-3) to generate an appropriate response message, such as an encouraging message like, "You're working hard. It's important to take a break!" The generated response message is sent back to the server in JSON format.

[1226] Step 8:

[1227] The server sends the response message received from the natural language processing means to the terminal as an HTTP response. The response includes a status code (e.g., 200 OK) to indicate that the processing was completed successfully.

[1228] Step 9:

[1229] The device receives the HTTP response, analyzes the response message, and displays it in the user interface. Specifically, it uses HTML and JavaScript to display a message below the text box or in the conversation window: "You're working hard. It's important to take a break!"

[1230] Step 10:

[1231] Users can check the response messages displayed on the device screen and receive encouragement and advice, which is expected to increase their self-esteem and improve the quality of their daily lives.

[1232] (Application example 2)

[1233] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1234] Conventional chat AI platforms lack the accuracy and speed of emotion recognition to generate responses that enhance users' self-esteem. They also lack specific support measures to improve the user experience in virtual stores. This can result in users not receiving appropriate advice or encouragement, resulting in a poor quality shopping experience.

[1235] 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.

[1236] In this invention, the server includes a user interface means for a user to input text data, a server means for analyzing the text data received from the user interface means, an emotion engine means for analyzing the emotion of the text data analyzed by the server means, a natural language processing means including a generative AI model for generating an appropriate response from the text data analyzed by the server means, and a means for displaying the response generated by the natural language processing means on the user interface means. This makes it possible to quickly and appropriately provide encouragement or advice according to the user's emotion, thereby improving the quality of the shopping experience in the virtual store.

[1237] A "user interface means" is a method or device by which a user inputs and transmits textual data.

[1238] The "server means" is a central data processing device that processes and analyzes data received from the user interface means and performs necessary processing.

[1239] The "emotion engine means" is a device or method that analyzes the emotions of text data and determines emotions such as positive, negative, or neutral.

[1240] A "generative AI model" is a method or device that uses machine learning algorithms to automatically generate appropriate responses from input data.

[1241] A "natural language processing means" is a method or device for analyzing and understanding text data entered by a user and generating an appropriate response.

[1242] A "prompt sentence" is an input sentence that serves as the basis for a generative AI model to generate a response, and it affects the quality and appropriateness of the generated response.

[1243] The system of the present invention provides a chat AI assistant to enhance users' shopping experience and is composed of the following main means:

[1244] 1. User Interface Means

[1245] The user interface means provides a screen on which a user can input and send text data. The user can access the virtual store using, for example, a smartphone or a computer, and enter questions or concerns into the text box while shopping. When the user presses the send button, the input text data is sent to the server means.

[1246] 2. Server Means

[1247] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the received text data into an appropriate format (e.g., JSON format) and sends it to the emotion engine means and natural language processing means. It may also retrieve the user's past dialogue history and related data from a database and use them for analysis.

[1248] 3. Emotional Engine Means

[1249] The emotion engine means receives the text data sent from the server means and performs emotion analysis. This means determines positive, negative, or neutral emotions from the content of the text data. It also extracts emotion-related keywords and important phrases from the text data. These results are passed to the natural language processing means and are used to generate an appropriate response.

[1250] 4. Natural Language Processing Methods

[1251] The natural language processing means generates an appropriate response using a generative AI model based on data from the server means and the emotion engine means. This means generates a response including encouragement or advice using a prompt sentence according to the user's emotional state. For example, a prompt sentence such as "I think that's a good choice. Please take your time to choose." is used.

[1252] 5. Response Display Means

[1253] The generated response is sent to the user interface means via the server means and displayed on the user interface means, allowing the user to read the displayed message and receive appropriate advice or encouragement.

[1254] Hardware and software used

[1255] Server: Used as the central device for data processing and analysis. Uses Flask (a lightweight Python web application framework).

[1256] Sentiment Engine: For sentiment analysis, we use the pipeline (sentiment-analysis) model from the Transformers library.

[1257] Natural language processing means: For response generation, we use the pipeline (text-generation) model from the Transformers library.

[1258] Database: Use an appropriate data management solution (e.g., SQL or NoSQL database) to store user data and past interaction history.

[1259] Specific examples

[1260] A user types, "I want to find accessories that go well with this dress, but I'm not sure what to buy."

[1261] The server means receives this message, performs emotion analysis using the emotion engine means, and determines the emotion as "neutral."

[1262] Natural language processing generates an appropriate response based on the sentiment analysis results, for example, using a prompt such as "I think that's a good choice. Please take your time to choose."

[1263] The generated response is sent to the user through the server means and displayed in the user interface.

[1264] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1265] Step 1:

[1266] The user inputs "I want to buy accessories that go well with this dress, but I'm not sure what to buy" into the user interface means on the terminal and presses the send button.

[1267] Input: User's text data.

[1268] Output: Text data is ready to be sent.

[1269] Step 2:

[1270] The terminal transmits this text data to the server means as an HTTP request.

[1271] Input: User's text data.

[1272] Output: HTTP request with text data.

[1273] Step 3:

[1274] The server formats the received text data into JSON format and sends it to the emotion engine means and natural language processing means.

[1275] Input: HTTP request containing the user's text data.

[1276] Output: Data in JSON format that is sent to the sentiment engine means and natural language processing means.

[1277] Step 4:

[1278] The emotion engine means performs emotion analysis on the received text data to determine whether the emotion is positive, negative, or neutral, and extracts important keywords.

[1279] Input: Text data in JSON format.

[1280] Output: Sentiment analysis results and keywords.

[1281] Step 5:

[1282] The natural language processing means receives the analysis results from the emotion engine means and uses the generative AI model to generate an appropriate response based on the prompt sentence. In this case, the prompt sentence used is, "I think that's a good choice. Please take your time to choose."

[1283] Input: Sentiment analysis results and keywords.

[1284] Output: The generated response message.

[1285] Step 6:

[1286] The server transmits the response message received from the natural language processing means to the terminal as an HTTP response.

[1287] Input: The generated response message.

[1288] Output: The HTTP response containing the response message.

[1289] Step 7:

[1290] The terminal displays the response message received from the server on the user interface means, and the user can read the displayed message and receive appropriate advice or encouragement.

[1291] Input: The HTTP response containing the response message.

[1292] Output: The response message displayed in the user interface.

[1293] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1294] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1295] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1296] [Fourth embodiment]

[1297] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1298] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1299] 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).

[1300] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1301] 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.

[1302] 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).

[1303] 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.

[1304] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1305] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1306] 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.

[1307] 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.

[1308] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1309] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1310] The system of this invention is a chat AI platform for increasing users' self-esteem, and consists of the following four main components:

[1311] 1. User Interface Means

[1312] 2. Server Means

[1313] 3. Natural Language Processing Methods

[1314] 4. Database Means

[1315] User Interface Means

[1316] The user interface means provides a screen on which a user can input and send their worries and anxieties. The user accesses the platform using a smartphone or computer and inputs their worries and anxieties into a text box. When the user presses the send button, the text data is sent to the server means.

[1317] Server Means

[1318] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the text data into an appropriate format (e.g., JSON format) and sends it to the natural language processing means. At this time, it may also obtain the user's past dialogue history and related data from the database means and use them for analysis.

[1319] Natural language processing tools

[1320] The natural language processing means receives the text data sent from the server means, analyzes it, and generates a response. First, it performs sentiment analysis on the text data and determines whether it is positive, negative, or neutral. Next, it extracts keywords and important phrases to understand the user's intent. Based on this information, it uses pre-prepared response templates and machine learning models to generate appropriate encouragement or advice. Once generated, the response is returned to the server means.

[1321] Database Means

[1322] The database means stores user data, past dialogue history, response templates, etc. This allows the server means and natural language processing means to quickly obtain the necessary information, accelerating analysis and response.

[1323] Specific examples

[1324] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[1325] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[1326] 2. The terminal sends this text data to the server means as an HTTP request.

[1327] 3. The server formats the received text data into JSON format and sends it to the natural language processing means.

[1328] 4. The natural language processing means performs sentiment analysis on the received text data, determining it as negative, and extracting the keywords "work" and "stress."

[1329] 5. Based on the analysis results, the natural language processing means generates a response message saying, "You're working hard. It's important to take a short break!" and sends it back to the server means.

[1330] 6. The server sends the received response message to the terminal as an HTTP response.

[1331] 7. The terminal displays the received response message on the user interface means, and the user reads the message.

[1332] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[1333] The processing flow will be explained below.

[1334] Step 1:

[1335] The user uses the user interface means of the terminal to enter their worries and anxieties into the text box and presses the send button.

[1336] Step 2:

[1337] The terminal generates an HTTP request including the text data entered by the user and sends it to the server.

[1338] Step 3:

[1339] The server analyzes the HTTP request received, extracts the text data, and formats the extracted text data in JSON format.

[1340] Step 4:

[1341] The server transmits the formatted JSON format text data to the natural language processing means.

[1342] Step 5:

[1343] A natural language processing means analyzes the received text data and performs a sentiment analysis, which determines whether the text is positive, negative, or neutral.

[1344] Step 6:

[1345] Natural language processing means extract keywords and important phrases from the text data.

[1346] Step 7:

[1347] Natural language processing tools use the analysis results to generate appropriate responses, which are generated using pre-defined templates and machine learning models.

[1348] Step 8:

[1349] The natural language processing means returns the generated response to the server.

[1350] Step 9:

[1351] The server receives the response sent back from the natural language processing means and formats it as an HTTP response.

[1352] Step 10:

[1353] The server sends an HTTP response to the device.

[1354] Step 11:

[1355] The terminal analyzes the HTTP response received from the server and displays the response message on the user interface means.

[1356] Step 12:

[1357] The user reads the response message displayed on the device screen and receives encouragement and advice.

[1358] Example 1

[1359] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1360] In modern society, many people experience stress from work and life, but there is a lack of appropriate support to reduce stress and improve self-esteem. Conventional systems have found it difficult to accurately analyze users' emotions and intentions and provide appropriate encouragement and advice in a timely manner. There is a need to solve this problem and provide an efficient and effective method to improve users' self-esteem.

[1361] 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.

[1362] In this invention, the server includes means for formatting text data entered by a user into an appropriate format and sending it to natural language processing means, natural language processing means for analyzing emotions in the text data, extracting keywords, and generating an appropriate response, and means for generating a response using a machine learning model or a response template, thereby making it possible to accurately analyze user emotions and quickly provide an appropriate response.

[1363] "User interface means" refers to an interface device that allows a user to input and transmit text data, and specifically operates on a smartphone or computer application or web browser.

[1364] The "server means" is a device or program that receives text data sent from the user interface means, formats it into an appropriate format, and performs the necessary analysis processing.

[1365] The "natural language processing means" is a device or program that performs a series of processes, such as analyzing text data received from the server means, performing sentiment analysis and keyword extraction, and generating an appropriate response message.

[1366] "Sentiment analysis" is an analytical process that determines the emotions in text data as positive, negative, or neutral.

[1367] "Keyword extraction" is the process of extracting important words and phrases from text data.

[1368] A "response template" is a template of a response message prepared in advance, which is used by the natural language processing means when generating an appropriate response.

[1369] A "machine learning model" is a model that learns from large amounts of data and performs tasks such as prediction and classification, and is used in this system to generate appropriate response messages.

[1370] An "HTTP request" is a protocol used by a client to send data to a server.

[1371] "HTTP response" is a protocol used by a server to send response data to a client.

[1372] The "database means" is a device or program that stores information such as user data, past dialogue history, and response templates, and enables the server or natural language processing means to quickly obtain the information required.

[1373] The system of this invention is a chat AI platform that enhances users' self-esteem and is composed of the following four major components: 1. user interface means, 2. server means, 3. natural language processing means, and 4. database means.

[1374] User Interface Means

[1375] Users access the system's user interface from a smartphone or computer browser. This interface provides a text box and a send button where users can enter and submit their worries and anxieties. The text data entered by the user is sent to the system by pressing the send button.

[1376] Server Means

[1377] The server receives text data sent from the user interface means and formats it into an appropriate format (e.g., JSON format). If necessary, it retrieves the user's past interaction history and other related data from the database means and uses them for analysis. The formatted data is then sent to the natural language processing means. On the server side, this data is sent and received using HTTP requests and responses.

[1378] Natural language processing tools

[1379] The natural language processing means performs sentiment analysis on the text data sent from the server means and determines whether it is positive, negative, or neutral. It also extracts keywords and important phrases from the text data. Based on this information, it uses pre-prepared response templates and machine learning models to generate appropriate messages of encouragement or advice. For example, it generates a response message such as, "You're working hard. It's important to take a short break!"

[1380] Database Means

[1381] The database stores user data, past interaction history, response templates, etc. This allows the server means and natural language processing means to quickly retrieve the required information, facilitating analysis and response. To manage this storage process, a relational database management system (RDBMS) or a non-relational database (such as NoSQL) can be used.

[1382] Specific examples

[1383] As a concrete example, if the user enters "I've been busy at work lately and I'm stressed," the following will be processed:

[1384] 1. The user inputs "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[1385] 2. The terminal sends this text data to the server means as an HTTP request.

[1386] 3. The server formats the received text data into JSON format and sends it to the natural language processing means.

[1387] 4. The natural language processing means performs sentiment analysis on the received text data, determining it as negative, and extracting the keywords "work" and "stress."

[1388] 5. Based on the analysis results, the natural language processing means generates a response message saying, "You're working hard. It's important to take a short break!" and sends it back to the server means.

[1389] 6. The server sends the received response message to the terminal as an HTTP response.

[1390] 7. The terminal displays the received response message on the user interface means, and the user reads the message.

[1391] Prompt Sentence Examples

[1392] Here are some example input prompts for a generative AI model:

[1393] User: I've been busy at work lately and feeling stressed.

[1394] Generative AI model: You work hard, it's important to take a break!

[1395] In this way, users can receive appropriate encouragement and advice to boost their self-esteem and improve the quality of their daily lives.

[1396] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1397] Step 1:

[1398] The user inputs text data into the user interface means on the terminal and presses the send button.

[1399] Input: User's worries or concerns (e.g., "I've been busy at work lately and I'm feeling stressed.")

[1400] How it works: A user enters a problem into a text box in a browser or application and clicks the submit button.

[1401] Output: The entered text data is passed to the terminal by the send action.

[1402] Step 2:

[1403] The terminal transmits the input text data to the server means as an HTTP request.

[1404] Input: Text data when the send button is pressed

[1405] How it works: Using JavaScript etc., it collects the entered text data and sends it to the server as an Ajax request.

[1406] Output: Text data formatted in HTTP request format is sent to the server.

[1407] Step 3:

[1408] The server formats the received text data into JSON format and sends it to the natural language processing means.

[1409] Input: Text data of the HTTP request sent from the terminal

[1410] How it works: A server-side program parses the data, converts it to JSON format, and, if necessary, retrieves past interaction history and related data from a database.

[1411] Output: The formatted JSON data is sent to the natural language processing means.

[1412] Step 4:

[1413] A natural language processing means analyzes the sentiment of the text data, extracts keywords, and generates a response message.

[1414] Input: JSON format text data sent from the server

[1415] Operation:

[1416] 1. Sentiment analysis: Classifying the sentiment of text as "positive," "negative," or "neutral."

[1417] 2. Keyword extraction: Extract important words and phrases such as "work" and "stress."

[1418] 3. Response generation: Use machine learning models or response templates to generate appropriate response messages (e.g., "You're working hard! It's important to take a break!").

[1419] Output: The generated response message is sent back to the server.

[1420] Step 5:

[1421] The server transmits the response message received from the natural language processing means to the terminal as an HTTP response.

[1422] Input: A response message generated by a natural language processing tool

[1423] How it works: A server-side program receives the response message and formats it as an HTTP response.

[1424] Output: The formatted response message is sent to the terminal.

[1425] Step 6:

[1426] The terminal displays the received response message on the user interface means.

[1427] Input: HTTP response message sent from the server

[1428] What it does: Uses HTML and JavaScript to display the received message in the appropriate location on the screen.

[1429] Output: A state is provided that allows the user to view the response message.

[1430] (Application example 1)

[1431] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1432] In modern factory environments, workers often feel stressed due to high workloads and mistakes. As a result, problems such as reduced work efficiency and increased employee turnover can occur. In response to this, a means is needed to provide workers with mental support quickly and at the right time, but conventional systems have been inadequate in this regard. Conventional systems have had difficulty providing appropriate support for the real-time stress and anxiety felt by workers.

[1433] 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.

[1434] In this invention, the server includes a user interface means for a user to input text data, a processing device means for analyzing the text data received from the user interface means, an automatic response generation means for generating an appropriate response from the text data analyzed by the processing device means, a means for displaying the response generated by the automatic response generation means on the user interface means, and a means for evaluating the emotional state of a worker during work and providing appropriate advice or encouragement. This makes it possible to provide quick and appropriate encouragement or advice in response to real-time stress or anxiety felt by the worker.

[1435] "User interface means" refers to means for providing a device or software screen that a user uses to input text data.

[1436] The "processor means" is a means for analyzing text data received from the user interface means and arranging it into an appropriate format.

[1437] The "automatic response generation means" is a means for generating an appropriate response based on the analyzed text data.

[1438] The "display means" is a means for displaying the generated response on the user interface means.

[1439] The "means for assessing the emotional state of a worker" is a means for analyzing emotions from text entered by a worker and assessing the worker's state.

[1440] The "means for providing an advice or encouragement response" is a means for generating and providing an appropriate advice or encouragement message based on the evaluated emotional state.

[1441] In the system that realizes this application example, a user uses a smartphone to input the stress or anxiety they feel while working, and the AI ​​responds by providing appropriate encouraging or advising messages. The system is composed of a user interface means, a processing device means, an automatic response generation means, a display means, a means for evaluating the emotional state of a worker, and a means for providing an advice or encouraging response.

[1442] System Components

[1443] Hardware

[1444] Smartphones: Used as a user interface means.

[1445] Server Hardware: Required for the operation of the Processing Unit, Auto-Response Generation Unit, and Database.

[1446] software

[1447] User interface means: Provides an application screen for workers to input their emotions.

[1448] Processing unit means: Using Python and Flask as a framework, it receives input text data, parses it, and formats it appropriately.

[1449] Automated response generation method: Hugging Face's Transformers are used to perform natural language processing using NLU models such as BERT.

[1450] Display means: The generated response message is displayed on the smartphone screen.

[1451] Sentiment analysis and response generation: Analyzes the sentiment of the input text data and generates an appropriate response based on the results.

[1452] System operation explanation

[1453] User Interface Means

[1454] Workers use a dedicated application on their smartphones to enter text about the stress or anxiety they feel while working, such as "I'm worried because I make a lot of mistakes while working."

[1455] Processing device means

[1456] The text entered is sent to the server using the Python and Flask frameworks, which then converts the received text data into JSON format and sends it to an automated response generator.

[1457] Auto-response generator

[1458] It uses Hugging Face's Transformers library for natural language processing, leveraging models such as BERT to analyze the sentiment of the text and generate appropriate responses, such as advice like, "Learning from mistakes is part of growing. Take a break and try again!"

[1459] Display means

[1460] The generated response message is sent from the server to the user interface means and displayed on the smartphone screen, allowing the worker to receive prompt and appropriate encouragement or advice.

[1461] Examples of concrete examples and prompts

[1462] Specific examples

[1463] User input: "I'm worried because I make a lot of mistakes while working."

[1464] The system's response: "Learning from your mistakes is part of growing. Take a break and try again!"

[1465] Prompt Sentence Examples

[1466] "You're inputting the anxiety that a worker feels while working. For example, if a worker says, 'I'm worried because I make a lot of mistakes while working,' you should respond with positive encouragement rather than dismissing it."

[1467] The system allows factory workers to reduce stress and anxiety in real time and improve their self-esteem.

[1468] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1469] Step 1:

[1470] The user enters text data

[1471] Users use a dedicated application on their smartphone to enter text about the stress or anxiety they feel at work, such as "I'm worried because I make a lot of mistakes at work," and then press the send button.

[1472] Input: Worker input text

[1473] Output: Request to send text data

[1474] Step 2:

[1475] The device sends text data to the server.

[1476] The terminal sends the text data entered by the user to the server as an HTTP request. The text data is formatted in JSON format and sent.

[1477] Input: Text data

[1478] Output: JSON format text data

[1479] Step 3:

[1480] The server receives and processes the text data.

[1481] The server analyzes the JSON-formatted text data received from the terminal and performs the necessary processing. Specifically, it prepares the data for transmission to the natural language processing means, including the process of formatting it into an appropriate format.

[1482] Input: JSON format text data

[1483] Output: Formatted text data

[1484] Step 4:

[1485] The server processes natural language

[1486] The server performs sentiment analysis on the formatted text data using Hugging Face's Transformers library, using BERT as the model to classify the sentiment of the input text as positive, negative, or neutral.

[1487] Input: Formatted text data

[1488] Output: Sentiment analysis results (positive, negative, neutral)

[1489] Step 5:

[1490] The server generates a response message

[1491] The server generates an appropriate response message based on the results of the sentiment analysis. For example, if a negative sentiment is detected, it generates a message like, "Learning from mistakes is part of growing. Take a break and try again!"

[1492] Input: Sentiment analysis results

[1493] Output: Response message

[1494] Step 6:

[1495] The server sends a response message to the terminal.

[1496] The server sends the generated response message to the terminal as an HTTP response. The message is sent in JSON format.

[1497] Input: Response message

[1498] Output: JSON formatted response message

[1499] Step 7:

[1500] The terminal displays a response message

[1501] The terminal displays the response message received from the server on the user interface means, and the worker checks the advice and encouragement message on the screen of his or her smartphone.

[1502] Input: JSON formatted response message

[1503] Output: Response message displayed on the smartphone screen

[1504] In this way, workers can receive appropriate encouragement and advice in real time to alleviate stress and anxiety, which can reduce the mental burden on them while they are working and improve their motivation.

[1505] 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.

[1506] The system of this invention is a chat AI platform designed to enhance users' self-esteem, and is composed of the following five main components:

[1507] 1. User Interface Means

[1508] 2. Server Means

[1509] 3. Natural Language Processing Methods

[1510] 4. Emotion Engine

[1511] 5. Database Means

[1512] User Interface Means

[1513] The user interface means provides a screen on which a user can input and send their worries and anxieties. The user accesses the platform using a smartphone or computer and inputs their worries and anxieties into a text box. When the user presses the send button, the text data is sent to the server means.

[1514] Server Means

[1515] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the text data into an appropriate format (e.g., JSON format) and sends it to the natural language processing means and emotion engine. At this time, it may also obtain the user's past dialogue history and related data from the database means and use them for analysis.

[1516] Natural language processing tools

[1517] The natural language processing means receives the text data sent from the server means and generates a response in conjunction with the analysis results from the emotion engine. It extracts keywords and important phrases and understands the user's intent. Based on this information, it generates appropriate encouragement or advice using pre-prepared response templates and machine learning models. The generated response is returned to the server means.

[1518] Emotion Engine

[1519] The emotion engine receives the text data sent to the natural language processing means and performs emotion analysis. First, the emotion engine determines whether the text data contains positive, negative, or neutral emotions. It also extracts emotion-related keywords and important phrases from the text data. These results are passed to the natural language processing means, which uses them to generate an appropriate response.

[1520] Database Means

[1521] The database means stores user data, past dialogue history, response templates, etc. This allows the server means, natural language processing means, and emotion engine to quickly obtain the necessary information, accelerating analysis and response.

[1522] Specific examples

[1523] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[1524] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[1525] 2. The terminal sends this text data to the server means as an HTTP request.

[1526] 3. The server formats the received text data into JSON format and sends it to the natural language processing means and emotion engine.

[1527] 4. The emotion engine analyzes the received text data and determines it to be negative. It also extracts "work" and "stress" as emotion-related keywords.

[1528] 5. Based on the analysis results of the emotion engine and the text data, the natural language processing means generates a response message such as, "You're working hard. It's important to take a break!"

[1529] 6. The natural language processing means returns the generated response message to the server means.

[1530] 7. The server sends the received response message to the terminal as an HTTP response.

[1531] 8. The terminal displays the response message received from the server on the user interface means.

[1532] 9. The user reads the response message displayed on the device screen and receives encouragement and advice.

[1533] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[1534] The processing flow will be explained below.

[1535] Step 1:

[1536] The user uses the user interface means of the terminal to enter their worries and anxieties into the text box and presses the send button.

[1537] Step 2:

[1538] The terminal generates an HTTP request including the text data entered by the user and sends it to the server.

[1539] Step 3:

[1540] The server receives the HTTP request, analyzes it, extracts the text data, and formats it into JSON format.

[1541] Step 4:

[1542] The server sends the formatted JSON formatted text data to the natural language processing means and emotion engine.

[1543] Step 5:

[1544] The sentiment engine receives the text data and analyzes the sentiment of the text, first determining whether the text has a positive, negative, or neutral sentiment.

[1545] Step 6:

[1546] The emotion engine extracts emotion-related keywords and key phrases from the text data.

[1547] Step 7:

[1548] The emotion engine passes the analysis results to the natural language processing means, which include the emotion judgment result of the text and extracted keywords.

[1549] Step 8:

[1550] The natural language processing means generates an appropriate response message based on the analysis results from the emotion engine and the original text data, using pre-prepared templates and machine learning models.

[1551] Step 9:

[1552] The natural language processing means returns the generated response message to the server.

[1553] Step 10:

[1554] The server formats the response message received from the natural language processing means as an HTTP response.

[1555] Step 11:

[1556] The server sends an HTTP response to the device.

[1557] Step 12:

[1558] The terminal analyzes the HTTP response received from the server and displays the response message on the user interface means.

[1559] Step 13:

[1560] The user reads the response message displayed on the device screen and receives encouragement and advice.

[1561] Specific examples

[1562] Consider the example where a user types, "Work has been busy and stressful lately."

[1563] The specific process flow:

[1564] Step 1:

[1565] The user uses the user interface means of the terminal to input "I've been busy at work recently and I'm feeling stressed," and presses the send button.

[1566] Step 2:

[1567] The terminal generates an HTTP request including this text data and sends it to the server.

[1568] Step 3:

[1569] The server receives the HTTP request, analyzes it, extracts the text data "I've been busy at work lately and I'm feeling stressed," and formats it into JSON format.

[1570] Step 4:

[1571] The server sends the formatted JSON formatted text data to the natural language processing means and emotion engine.

[1572] Step 5:

[1573] The emotion engine receives the text "I've been busy at work lately and feeling stressed," performs emotion analysis, and determines that the emotion is negative.

[1574] Step 6:

[1575] The emotion engine extracts emotion-related keywords such as "work" and "stress" from the text data.

[1576] Step 7:

[1577] The emotion engine passes the emotion determination result and keywords of the text to the natural language processing means.

[1578] Step 8:

[1579] Based on the analysis results from the emotion engine and the original text data "I've been busy at work lately and feeling stressed," the natural language processing means generates a response message saying, "You're working really hard. It's important to take a short break!"

[1580] Step 9:

[1581] The natural language processing means returns the generated response message to the server.

[1582] Step 10:

[1583] The server formats the response message received from the natural language processing means as an HTTP response.

[1584] Step 11:

[1585] The server sends the HTTP response to the user's device.

[1586] Step 12:

[1587] The terminal analyzes the HTTP response received from the server and displays a response message on the user interface means saying, "You're working hard. It's important to take a short break!"

[1588] Step 13:

[1589] The user reads the response message displayed on the device screen and receives encouragement and advice.

[1590] In this way, users can receive prompt and appropriate encouragement and advice, which can boost their self-esteem and improve the quality of their daily lives.

[1591] Example 2

[1592] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1593] Conventional chat systems have limitations in generating immediate and appropriate responses to user input, and it is particularly difficult to properly understand the user's emotions and provide responses that are in tune with those emotions. Furthermore, if the user interface is not intuitive, users may feel stressed when using the system, which may not lead to an improvement in self-esteem.

[1594] 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.

[1595] In this invention, the server includes user interface means for a user to input text data, server means for analyzing the text data received from the user interface means, natural language processing means for generating an appropriate response from the text data analyzed by the server means, an emotion engine for analyzing emotions based on the text data, and means for displaying the response generated by the natural language processing means and the emotion engine on the user interface means. This makes it possible to quickly and appropriately generate a response that is in line with the user's emotions and increase the user's sense of self-affirmation.

[1596] "User interface means" means a screen or device used by a user to input text data and transmit that data to the system.

[1597] The "server means" refers to a device or system that has the role of analyzing text data received from the user interface means and, if necessary, transmitting the data to the natural language processing means and the emotion engine.

[1598] "Natural language processing means" refers to the technology or method for analyzing text data provided by the server means and generating appropriate responses. This means is capable of extracting keywords and important phrases and understanding the user's intent.

[1599] An "emotion engine" is a technology or method that analyzes the emotions in text data and determines the emotional state, such as positive, negative, or neutral.

[1600] "Database means" refers to a system that stores user data, past dialogue history, response templates, etc., and can quickly retrieve them as needed.

[1601] A "response message" is a response to the user, such as encouragement or advice, generated by natural language processing means and an emotion engine.

[1602] "Text data" refers to character data such as worries or anxieties entered by the user.

[1603] "Sentiment analysis" is the process of determining a user's emotions from the content of text data and classifying that emotional state as positive, negative, neutral, etc.

[1604] A "response template" is a predefined example or format of a response used by a natural language processing tool.

[1605] A "generative AI model" is a model generated using machine learning technology for performing natural language processing, sentiment analysis, and other tasks.

[1606] A "prompt" is text or instructions that are input to a generative AI model.

[1607] The system of the present invention is a chat AI platform for enhancing users' self-esteem, and is composed of several main components: a user interface means, a server means, a natural language processing means, an emotion engine, and a database means.

[1608] Hardware and Software

[1609] To realize this system, the following hardware and software are required.

[1610] Hardware: smartphones, computers, servers

[1611] Software: Natural language processing libraries (e.g., NLTK, spaCy), sentiment analysis libraries (e.g., VADER, TextBlob), database systems (e.g., MySQL, MongoDB), web servers (e.g., Apache, Nginx)

[1612] User Interface

[1613] Users access the platform using a smartphone or computer. They enter their worries and anxieties into a text box via a browser or a dedicated application and press the send button. This data is sent as text data to a server.

[1614] Server Means

[1615] The server receives text data sent from the user interface means, converts the received text data into an appropriate format (e.g., JSON format), and sends it to the natural language processing means and emotion engine. It also retrieves the user's past dialogue history and related data from the database means as needed and uses them for analysis.

[1616] Natural language processing tools

[1617] The natural language processing means analyzes the text data sent from the server means. Specifically, it extracts keywords and important phrases and understands the user's intent. Based on this information, it uses a machine learning model (e.g., a generative AI model) to generate an appropriate response message of encouragement or advice. The generated response message is returned to the server means.

[1618] Emotion Engine

[1619] The emotion engine receives the text data and performs emotion analysis. The emotion engine uses an emotion analysis library to determine positive, negative, or neutral emotions from the content of the text data. It also extracts emotion-related keywords and provides the results to the natural language processing means.

[1620] Database Means

[1621] The database means stores user data, past dialogue history, response templates, etc. This allows the server means, natural language processing means, and emotion engine to quickly obtain the necessary information, accelerating analysis and response.

[1622] Specific examples

[1623] As a specific example, the processing when the user inputs "I've been busy at work recently and I'm feeling stressed" will be explained.

[1624] 1. The user enters "I've been busy at work lately and I'm feeling stressed" into the user interface means on the terminal and presses the send button.

[1625] 2. The terminal sends this text data to the server means as an HTTP request.

[1626] 3. The server formats the received text data into JSON format and sends it to the natural language processing means and emotion engine.

[1627] 4. The emotion engine analyzes the received text data and determines it to be negative. It also extracts "work" and "stress" as emotion-related keywords.

[1628] 5. Based on the analysis results of the emotion engine and the text data, the natural language processing means generates a response message such as, "You're working hard. It's important to take a break!"

[1629] 6. The natural language processing means returns the generated response message to the server means.

[1630] 7. The server sends the received response message to the terminal as an HTTP response.

[1631] 8. The terminal displays the response message received from the server on the user interface means.

[1632] 9. The user reads the response message displayed on the device screen and receives encouragement and advice.

[1633] Prompt Sentence Examples

[1634] Below are some example prompts to input to a generative AI model:

[1635] "If a user types, 'I've been busy and stressed at work lately,' how should we respond?"

[1636] In this way, users can receive prompt and appropriate encouragement and advice, which can increase their self-esteem.

[1637] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1638] Step 1:

[1639] The user inputs text data into the user interface means of the terminal and presses the send button. The input text data is a sentence such as "I've been busy at work lately and I'm feeling stressed." The user interface means receives this input and captures the clicking of the send button as an event.

[1640] Step 2:

[1641] The terminal transmits the text data received from the user interface means to the server means. Specifically, the text data is transmitted using an HTTP POST request. For example, the text data is packed in JSON format and transmitted. The URL of this request is specified by the API endpoint of the server.

[1642] Step 3:

[1643] The server receives the HTTP POST request, extracts the text data, and formats it in a parsable form (e.g., JSON again). At this point, it performs error checking to remove any incomplete data, and adds metadata such as the user ID and timestamp. The formatted text data is then sent to the natural language processing tool and emotion engine.

[1644] Step 4:

[1645] The server retrieves the user's past interaction history and related data from the database means as needed. Specifically, it retrieves past interaction data associated with the user ID using SQL queries or NoSQL queries, and provides the data to the natural language processing means and emotion engine as additional information for analyzing the data.

[1646] Step 5:

[1647] The natural language processing (NLP) means analyzes the text data to extract keywords and important phrases. It uses a NLP library (e.g., NLTK or spaCy) to tokenize the text and tag it with parts of speech to extract keywords like "work" and "stress." The results of this analysis are also sent to the sentiment engine.

[1648] Step 6:

[1649] The sentiment engine receives the text data and performs sentiment analysis. For example, it uses a sentiment analysis library such as VADER or TextBlob to calculate a sentiment score for each word and classify it as positive, negative, or neutral. The classification results and sentiment-related keywords (e.g., "stress") are sent back to the natural language processing means.

[1650] Step 7:

[1651] The natural language processing means receives the results from the emotion engine and uses a generative AI model (e.g., GPT-3) to generate an appropriate response message, such as an encouraging message like, "You're working hard. It's important to take a break!" The generated response message is sent back to the server in JSON format.

[1652] Step 8:

[1653] The server sends the response message received from the natural language processing means to the terminal as an HTTP response. The response includes a status code (e.g., 200 OK) to indicate that the processing was completed successfully.

[1654] Step 9:

[1655] The device receives the HTTP response, analyzes the response message, and displays it in the user interface. Specifically, it uses HTML and JavaScript to display a message below the text box or in the conversation window: "You're working hard. It's important to take a break!"

[1656] Step 10:

[1657] Users can check the response messages displayed on the device screen and receive encouragement and advice, which is expected to increase their self-esteem and improve the quality of their daily lives.

[1658] (Application example 2)

[1659] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1660] Conventional chat AI platforms lack the accuracy and speed of emotion recognition to generate responses that enhance users' self-esteem. They also lack specific support measures to improve the user experience in virtual stores. This can result in users not receiving appropriate advice or encouragement, resulting in a poor quality shopping experience.

[1661] 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.

[1662] In this invention, the server includes a user interface means for a user to input text data, a server means for analyzing the text data received from the user interface means, an emotion engine means for analyzing the emotion of the text data analyzed by the server means, a natural language processing means including a generative AI model for generating an appropriate response from the text data analyzed by the server means, and a means for displaying the response generated by the natural language processing means on the user interface means. This makes it possible to quickly and appropriately provide encouragement or advice according to the user's emotion, thereby improving the quality of the shopping experience in the virtual store.

[1663] A "user interface means" is a method or device by which a user inputs and transmits textual data.

[1664] The "server means" is a central data processing device that processes and analyzes data received from the user interface means and performs necessary processing.

[1665] The "emotion engine means" is a device or method that analyzes the emotions of text data and determines emotions such as positive, negative, or neutral.

[1666] A "generative AI model" is a method or device that uses machine learning algorithms to automatically generate appropriate responses from input data.

[1667] A "natural language processing means" is a method or device for analyzing and understanding text data entered by a user and generating an appropriate response.

[1668] A "prompt sentence" is an input sentence that serves as the basis for a generative AI model to generate a response, and it affects the quality and appropriateness of the generated response.

[1669] The system of the present invention provides a chat AI assistant to enhance users' shopping experience and is composed of the following main means:

[1670] 1. User Interface Means

[1671] The user interface means provides a screen on which a user can input and send text data. The user can access the virtual store using, for example, a smartphone or a computer, and enter questions or concerns into the text box while shopping. When the user presses the send button, the input text data is sent to the server means.

[1672] 2. Server Means

[1673] The server means receives and analyzes the text data sent from the user interface means. Specifically, it formats the received text data into an appropriate format (e.g., JSON format) and sends it to the emotion engine means and natural language processing means. It may also retrieve the user's past dialogue history and related data from a database and use them for analysis.

[1674] 3. Emotional Engine Means

[1675] The emotion engine means receives the text data sent from the server means and performs emotion analysis. This means determines positive, negative, or neutral emotions from the content of the text data. It also extracts emotion-related keywords and important phrases from the text data. These results are passed to the natural language processing means and are used to generate an appropriate response.

[1676] 4. Natural Language Processing Methods

[1677] The natural language processing means generates an appropriate response using a generative AI model based on data from the server means and the emotion engine means. This means generates a response including encouragement or advice using a prompt sentence according to the user's emotional state. For example, a prompt sentence such as "I think that's a good choice. Please take your time to choose." is used.

[1678] 5. Response Display Means

[1679] The generated response is sent to the user interface means via the server means and displayed on the user interface means, allowing the user to read the displayed message and receive appropriate advice or encouragement.

[1680] Hardware and software used

[1681] Server: Used as the central device for data processing and analysis. Uses Flask (a lightweight Python web application framework).

[1682] Sentiment Engine: For sentiment analysis, we use the pipeline (sentiment-analysis) model from the Transformers library.

[1683] Natural language processing means: For response generation, we use the pipeline (text-generation) model from the Transformers library.

[1684] Database: Use an appropriate data management solution (e.g., SQL or NoSQL database) to store user data and past interaction history.

[1685] Specific examples

[1686] A user types, "I want to find accessories that go well with this dress, but I'm not sure what to buy."

[1687] The server means receives this message, performs emotion analysis using the emotion engine means, and determines the emotion as "neutral."

[1688] Natural language processing generates an appropriate response based on the sentiment analysis results, for example, using a prompt such as "I think that's a good choice. Please take your time to choose."

[1689] The generated response is sent to the user through the server means and displayed in the user interface.

[1690] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1691] Step 1:

[1692] The user inputs "I want to buy accessories that go well with this dress, but I'm not sure what to buy" into the user interface means on the terminal and presses the send button.

[1693] Input: User's text data.

[1694] Output: Text data is ready to be sent.

[1695] Step 2:

[1696] The terminal transmits this text data to the server means as an HTTP request.

[1697] Input: User's text data.

[1698] Output: HTTP request with text data.

[1699] Step 3:

[1700] The server formats the received text data into JSON format and sends it to the emotion engine means and natural language processing means.

[1701] Input: HTTP request containing the user's text data.

[1702] Output: Data in JSON format that is sent to the sentiment engine means and natural language processing means.

[1703] Step 4:

[1704] The emotion engine means performs emotion analysis on the received text data to determine whether the emotion is positive, negative, or neutral, and extracts important keywords.

[1705] Input: Text data in JSON format.

[1706] Output: Sentiment analysis results and keywords.

[1707] Step 5:

[1708] The natural language processing means receives the analysis results from the emotion engine means and uses the generative AI model to generate an appropriate response based on the prompt sentence. In this case, the prompt sentence used is, "I think that's a good choice. Please take your time to choose."

[1709] Input: Sentiment analysis results and keywords.

[1710] Output: The generated response message.

[1711] Step 6:

[1712] The server transmits the response message received from the natural language processing means to the terminal as an HTTP response.

[1713] Input: The generated response message.

[1714] Output: The HTTP response containing the response message.

[1715] Step 7:

[1716] The terminal displays the response message received from the server on the user interface means, and the user can read the displayed message and receive appropriate advice or encouragement.

[1717] Input: The HTTP response containing the response message.

[1718] Output: The response message displayed in the user interface.

[1719] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1720] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1721] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1722] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1723] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1724] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1725] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1726] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1727] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1728] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1729] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1730] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1731] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1732] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1733] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1734] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1735] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1736] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1737] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1738] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1739] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1740] The following is further disclosed regarding the above embodiment.

[1741] (Claim 1)

[1742] user interface means for a user to input text data;

[1743] a server means for analyzing the text data received from the user interface means;

[1744] natural language processing means for generating an appropriate response from the text data analyzed by the server means;

[1745] means for displaying a response generated by said natural language processing means on said user interface means;

[1746] A system including:

[1747] (Claim 2)

[1748] the server means includes means for formatting the text data and transmitting the formatted text data to the natural language processing means;

[1749] 10. The system of claim 1.

[1750] (Claim 3)

[1751] the natural language processing means includes means for analyzing sentiment in the text data and generating an appropriate response;

[1752] 10. The system of claim 1.

[1753] "Example 1"

[1754] (Claim 1)

[1755] user interface means for a user to input text data;

[1756] a server means for analyzing the text data received from the user interface means;

[1757] natural language processing means for generating an appropriate response from the text data analyzed by the server means;

[1758] means for displaying a response generated by said natural language processing means on said user interface means;

[1759] A system including:

[1760] (Claim 2)

[1761] the server means includes means for formatting the text data and transmitting the formatted text data to the natural language processing means;

[1762] 10. The system of claim 1.

[1763] (Claim 3)

[1764] the natural language processing means includes means for analyzing sentiment in the text data, extracting keywords, and generating an appropriate response;

[1765] 10. The system of claim 1.

[1766] (Claim 4)

[1767] the natural language processing means includes means for generating responses using a machine learning model or a response template;

[1768] 10. The system of claim 1.

[1769] (Claim 5)

[1770] The server means includes means for acquiring the user's past interaction history from a database and using the history for analysis.

[1771] 10. The system of claim 1.

[1772] (Claim 6)

[1773] the user interface means includes means for transmitting text data input by a user to a server means as an HTTP request;

[1774] 10. The system of claim 1.

[1775] (Claim 7)

[1776] the server means includes means for transmitting a response message received from the natural language processing means to the user interface means as an HTTP response;

[1777] 10. The system of claim 1.

[1778] (Claim 8)

[1779] the user interface means including means for displaying the received response message;

[1780] 10. The system of claim 1.

[1781] "Application Example 1"

[1782] (Claim 1)

[1783] user interface means for a user to input text data;

[1784] processor means for analyzing the text data received from the user interface means;

[1785] an automatic response generator for generating an appropriate response from the text data analyzed by the processor;

[1786] means for displaying the response generated by the automatic response generating means on the user interface means;

[1787] A means of assessing the worker's emotional state at work and providing appropriate advice and encouragement responses;

[1788] A system including:

[1789] (Claim 2)

[1790] the processing device means includes means for formatting the text data and transmitting the formatted text data to the automatic response generation means;

[1791] 10. The system of claim 1.

[1792] (Claim 3)

[1793] The automatic response generating means includes means for analyzing emotions in the text data and generating an appropriate response.

[1794] 10. The system of claim 1.

[1795] "Example 2: Combining Emotion Engines"

[1796] (Claim 1)

[1797] user interface means for a user to input text data;

[1798] a server means for analyzing the text data received from the user interface means;

[1799] natural language processing means for generating an appropriate response from the text data analyzed by the server means;

[1800] an emotion engine that analyzes emotions based on the text data;

[1801] means for displaying a response generated by said natural language processing means and emotion engine on said user interface means;

[1802] A system including:

[1803] (Claim 2)

[1804] the server means includes means for formatting the text data and transmitting the formatted text data to the natural language processing means and the emotion engine;

[1805] 10. The system of claim 1.

[1806] (Claim 3)

[1807] the natural language processing means includes means for generating an appropriate response based on the emotion analysis result by the emotion engine;

[1808] 10. The system of claim 1.

[1809] "Application example 2 when combining emotion engines"

[1810] (Claim 1)

[1811] user interface means for a user to input text data;

[1812] a server means for analyzing the text data received from the user interface means;

[1813] emotion engine means for analyzing emotions in the text data analyzed by the server means;

[1814] a natural language processing means including a generative AI model that generates an appropriate response from the text data analyzed by the server means;

[1815] means for displaying a response generated by said natural language processing means on said user interface means;

[1816] A system including:

[1817] (Claim 2)

[1818] the server means includes means for formatting the text data and transmitting the formatted text data to the natural language processing means as a prompt sentence;

[1819] 10. The system of claim 1.

[1820] (Claim 3)

[1821] the natural language processing means includes means for analyzing the sentiment of the text data and generating an appropriate response using a prompt sentence based on the sentiment;

[1822] 10. The system of claim 1. [Explanation of symbols]

[1823] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. user interface means for a user to input text data; a server means for analyzing the text data received from the user interface means; natural language processing means for generating an appropriate response from the text data analyzed by the server means; means for displaying a response generated by said natural language processing means on said user interface means; A system including:

2. the server means includes means for formatting the text data and transmitting the formatted text data to the natural language processing means; The system of claim 1 .

3. the natural language processing means includes means for analyzing sentiment in the text data and generating an appropriate response; The system of claim 1 .

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