System

Abstract

To provide a system.SOLUTION: A system includes: means for providing an idle talk tool by artificial intelligence for increasing human communication skills; means for producing an idea through the idle talk tool; means for releasing stress through the idle talk tool; means for increasing attachment to a partner through the idle talk tool; means for producing a new idea for improving production efficiency through the idle talk tool; means for recognizing feeling by artificial intelligence and providing interaction based on the feeling recognition results; and means for generating prompt text input to a generative AI by artificial intelligence and generating a response.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The COVID-19 pandemic has led to challenges such as a decline in people's communication skills, fewer ideas being generated, increased stress, and a weakening of attachment to others. [Means for solving the problem]

[0005] As a means to solve this problem, we propose a system that develops and provides AI-based chat tools. This system can improve human communication skills, generate ideas, relieve stress, and increase affection for others through AI-based chat. [Brief explanation of the drawings]

[0006] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, the coded communication I / F (Interface) refers to a communication processor and

[0013] The communication I / F is an interface that includes a network interface (NIC) and an antenna. The communication I / F controls communication between multiple computers. Examples of communication standards that can be applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0014] 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."

[0015] [First embodiment]

[0016] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0028] "Example 1"

[0029] One aspect of the present invention is a system for developing and providing AI-based chat tools. Specifically, an AI engine is used to enable chat with users. This AI engine uses natural language processing technology to understand input from users and generate appropriate responses. The AI ​​engine also has a learning function, and can learn the preferences and personality of users through chat with them, providing more personalized dialogue.

[0030] "Example 2"

[0031] This invention is a system that utilizes an AI chat tool in employee training. Specifically, as part of employee training, a program is implemented to improve communication skills through chatting with AI. For example, training can be conducted to draw out new perspectives and ideas through discussions with AI.

[0032] "Example 3"

[0033] This invention is a system that utilizes an AI chat tool for planning. Specifically, new ideas are generated through chatting with AI. For example, different perspectives and information provided by AI can be used to brainstorm plans.

[0034] The processing flow of each embodiment will be described below.

[0035] "Example 1"

[0036] Step 1: Launch the AI ​​engine, which uses natural language processing techniques to understand user input and generate appropriate responses.

[0037] Step 2: Receive input from the user. The input is in text format and contains the content of the conversation with the user.

[0038] Step 3: The AI ​​engine analyzes the user input and generates an appropriate response.

[0039] Step 4: The generated response is provided to the user, allowing them to chat with the AI.

[0040] "Example 2"

[0041] Step 1: As part of employee training, set up a program to improve communication skills through casual conversation with AI.

[0042] Step 2: The employee initiates a conversation with the AI, which analyzes the employee's input and generates an appropriate response.

[0043] Step 3: Improve employees' communication skills through casual conversation with AI.

[0044] "Example 3"

[0045] Step 1: Start brainstorming to come up with a plan, using casual conversation with AI.

[0046] Step 2: Utilize the different perspectives and information provided by AI to generate new ideas. Step 3: Based on the ideas obtained through casual conversation with AI, create a concrete plan.

[0047] Example 1

[0048] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0049] Conventional chat tools have a one-sided dialogue with users, making it difficult to provide personalized responses. They also lack the ability to learn users' preferences and personalities, which can lead to a decline in satisfaction over long-term interactions. Furthermore, it is difficult to release stress or generate ideas through chatting.

[0050] 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.

[0051] In this invention, the server includes means for a user to input to a terminal, means for the terminal to send the input to the server, means for the server to analyze the input, means for the server to pass a prompt sentence to the generative AI model, means for the generative AI model to generate a response, means for the server to return the response to the terminal, means for the terminal to display the response to the user, and means for the server to save the dialogue history and perform learning. This personalizes dialogue with the user, improves long-term satisfaction, and enables users to relieve stress and generate ideas through casual conversation.

[0052] The "means for a user to input information into a terminal" refers to a means for providing an interface for a user to input the content of a chat using a text box or input field.

[0053] The "means by which the terminal transmits input to the server" refers to a communication means for transmitting text data input by the user to the server via the network.

[0054] The "means by which the server analyzes the input" refers to the means by which the server analyzes the received user input text using natural language processing technology to understand the grammar and meaning.

[0055] "Means for the server to pass a prompt sentence to the generative AI model" refers to a means for passing the analyzed input text to the generative AI model as a prompt sentence.

[0056] "Means by which the generative AI model generates a response" refers to means by which the generative AI model generates an appropriate response based on a prompt sentence.

[0057] "Means for the server to return a response to the terminal" refers to the means for returning the response received from the generative AI model to the terminal via the network.

[0058] The "means for the terminal to display the response to the user" refers to a means for visually displaying the response received from the server to the user.

[0059] The "means for the server to store the dialogue history and perform learning" refers to a means for storing the dialogue history with the user in a database and learning the user's preferences and personality using a machine learning algorithm.

[0060] MODE FOR CARRYING OUT THE INVENTION

[0061] This invention is a system for providing personalized dialogue through casual conversation with a user. The system includes a series of processes in which a user inputs information into a terminal, the input is sent to a server, the server analyzes the input, passes a prompt to a generative AI model, the generative AI model generates a response, the server returns the response to the terminal, the terminal displays the response to the user, the server saves the dialogue history, and the server performs learning.

[0062] Hardware and software used

[0063] The server uses the following software:

[0064] Natural language processing libraries (e.g., NLTK, spaCy)

[0065] Generative AI models (e.g., OpenAI's GPT-3)

[0066] Machine learning algorithms (e.g., TENSORFLOW (registered trademark), PyTorch)

[0067] A terminal uses hardware and software to provide a user interface:

[0068] A web browser or mobile application to display text boxes and input fields

[0069] Internet connection for network communications

[0070] Specific operation of the system

[0071] 1. The user types into the terminal

[0072] The user enters the content of the conversation into the text box on the device. For example, they might enter "What's the weather like today?" When the user presses the send button, the conversation proceeds to the next step.

[0073] 2. The device sends the input to the server

[0074] The terminal sends the text data entered by the user to the server using an HTTP request. Specifically, the terminal converts the input data into JSON format and sends a POST request to the server's API endpoint.

[0075] 3. The server parses the input

[0076] The server analyzes the received user input. It uses a natural language processing library built using Python (e.g., NLTK or spaCy) to tokenize the input text and tag it with parts of speech. For example, it analyzes the input "What's the weather like today?" to understand the meaning and grammatical structure of each word.

[0077] 4. The server passes the prompt to the generative AI model

[0078] The server passes the analyzed input as a prompt to a generative AI model (e.g., OpenAI's GPT-3). Specifically, the prompt is sent to the generative AI model as an API request. For example, the prompt "What's the weather like today?" is passed to GPT-3.

[0079] 5. The generative AI model generates a response

[0080] The generative AI model generates an appropriate response based on the prompt it receives. GPT-3 has been trained with a large amount of data in advance and has advanced natural language generation capabilities. For example, it generates a response such as "It's sunny today."

[0081] 6. The server returns the response to the device

[0082] The server returns the response received from the generative AI model to the device. Specifically, it converts the response data into JSON format and sends it to the device as an HTTP response.

[0083] 7. The terminal displays the response to the user

[0084] The device displays the response received from the server to the user. Specifically, it parses the received JSON data and displays the response in a text box or chat window. For example, it displays the response "It's sunny today" to the user.

[0085] 8. The server stores the dialogue history and performs learning.

[0086] The server stores the user's interaction history in a database. It then uses machine learning algorithms (e.g., TensorFlow or PyTorch) to analyze the interaction data and learn the user's preferences and personality. This allows it to provide more personalized responses in subsequent interactions.

[0087] Specific examples

[0088] Example 1

[0089] The user types "What's the weather like today?" into the device and presses the send button. The device sends this input to the server. The server analyzes the input and passes the prompt "What's the weather like today?" to a generative AI model (e.g., GPT-3). The generative AI model generates a response, "It's sunny today," and returns it to the server. The server returns this response to the device, which displays it to the user.

[0090] Example 2

[0091] The user types "What movie do you recommend?" into the device and presses the send button. The device sends this input to the server. The server analyzes the input and passes the prompt "What movie do you recommend?" to a generative AI model (e.g., GPT-3). The generative AI model generates a response saying "The latest recommendation is 'Inception.'" and returns it to the server. The server returns this response to the device, which displays it to the user.

[0092] Prompt Sentence Examples

[0093] 1. "What's the weather like today?"

[0094] 2. "What movie do you recommend?"

[0095] 3. "Tell me about a book you read recently."

[0096] These prompts can be fed into a generative AI model to generate appropriate responses.

[0097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0098] Step 1:

[0099] The user types into the terminal.

[0100] The user enters the content of the conversation into the text box on the terminal. For example, they might enter "What's the weather like today?". When the user presses the send button, the input proceeds to the next step. The input is text data, and the output is the event of the send button being pressed.

[0101] Step 2:

[0102] The device sends input to the server.

[0103] The terminal sends the text data entered by the user to the server using an HTTP request. Specifically, the terminal converts the input data into JSON format and sends a POST request to the server's API endpoint. The input is the user's text data, and the output is the JSON data sent to the server.

[0104] Step 3:

[0105] The server parses the input.

[0106] The server parses the received user input. It uses a natural language processing library built using Python (e.g., NLTK or spaCy) to tokenize the input text and tag it with parts of speech. For example, it parses the input "What's the weather like today?" to understand the meaning and grammatical structure of each word. The input is text data in JSON format, and the output is the parsed text data.

[0107] Step 4:

[0108] The server passes the prompt to the generative AI model.

[0109] The server passes the analyzed input as a prompt to the generative AI model (e.g., OpenAI's GPT-3). Specifically, the prompt is sent to the generative AI model as an API request. For example, the prompt "What's the weather like today?" is passed to GPT-3. The input is the analyzed text data, and the output is the prompt sent to the generative AI model.

[0110] Step 5:

[0111] A generative AI model generates a response.

[0112] The generative AI model generates an appropriate response based on the received prompt. GPT-3 has been trained with a large amount of data in advance and has advanced natural language generation capabilities. For example, it generates the response "It's sunny today." The input is the prompt, and the output is the generated response text.

[0113] Step 6:

[0114] The server returns a response to the terminal.

[0115] The server returns the response received from the generative AI model to the terminal. Specifically, it converts the response data into JSON format and sends it to the terminal as an HTTP response. The input is the generated response text, and the output is the JSON data sent to the terminal.

[0116] Step 7:

[0117] The terminal displays the response to the user.

[0118] The terminal displays the response received from the server to the user. Specifically, it parses the received JSON data and displays the response in a text box or chat window. For example, it displays the response "It's sunny today" to the user. The input is the JSON data received from the server, and the output is the response text displayed to the user.

[0119] Step 8:

[0120] The server stores the dialogue history and performs learning.

[0121] The server stores the user's interaction history in a database. It then uses machine learning algorithms (e.g., TensorFlow or PyTorch) to analyze the interaction data and learn the user's preferences and personality. This allows it to provide more personalized responses in subsequent interactions. The input is the interaction history data, and the output is an updated learning model.

[0122] (Application example 1)

[0123] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0124] In today's brick-and-mortar stores, personalized service is required to improve the customer experience. However, traditional methods make it difficult to effectively utilize customer preferences and past purchase history, and in-store guidance and product suggestions are uniform, which can lead to a decline in customer satisfaction. Furthermore, there is a lack of ways to relieve stress or increase customer loyalty through dialogue with customers. To solve these issues, a new system utilizing AI technology is needed.

[0125] 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.

[0126] In this invention, the server includes means for developing and providing AI-based chat tools to improve human communication skills, means for generating ideas through AI-based chat, means for releasing stress through AI-based chat, means for increasing attachment to others through AI-based chat, means for improving the customer experience in a physical store through AI-based chat, means for making personalized product suggestions based on the customer's preferences and past purchase history through AI-based chat, and means for guiding customers within the store through AI-based chat. This makes it possible to provide personalized services according to the individual needs of each customer, thereby improving customer satisfaction and providing a pleasant shopping experience within the store.

[0127] "Human communication skills" are the ability to effectively exchange information with and understand others.

[0128] An "AI chat tool" is software or a system that uses artificial intelligence technology to engage in natural conversations with users.

[0129] A "development and delivery method" is a method or process for designing, building, and making available to users a particular technology or service.

[0130] "Idea generation methods" are methods or processes for generating creative ideas and new thoughts.

[0131] A "stress reliever" is a method or process for reducing mental tension and anxiety and for relaxing.

[0132] "Attachment strategies" are methods or processes for increasing familiarity and liking for others.

[0133] "Measures to improve the customer experience in physical stores" are methods and processes that increase the satisfaction and comfort that customers feel in physical stores.

[0134] "Customer preferences and past purchase history" refers to the customer's tendency to like products and services and records of products and services they have purchased in the past.

[0135] "Personalized product recommendations" is the method or process of recommending specific products or services to individual customers based on their preferences and needs.

[0136] "Means of providing guidance within the store" refers to the methods and processes of providing information and guidance to make it easier for customers to find the products and services they are looking for within the store.

[0137] To implement the present invention, the following system configuration and program are required.

[0138] System Configuration

[0139] 1. Hardware:

[0140] Smartphone: A device through which a user operates an application.

[0141] Server: A device that hosts the AI ​​engine and processes user interactions.

[0142] 2. Software:

[0143] AI Engine: Uses generative AI models such as OpenAI's GPT-3.

[0144] Natural Language Processing (NLP) libraries: Libraries for parsing user input and generating appropriate responses.

[0145] Database: A database for storing customer preferences and past purchase history.

[0146] Program processing explanation

[0147] The server receives text data entered by the user through the smartphone. The received data is analyzed using a natural language processing library. The analyzed data is sent to an AI engine, which generates an appropriate response. The generated response is then sent back to the smartphone and displayed to the user.

[0148] Specifically, the following process occurs:

[0149] 1. Receiving user input:

[0150] A user inputs text through an application on their smartphone, which is then sent to a server.

[0151] 2. Data Analysis:

[0152] The server uses natural language processing libraries to parse the user's input, which is then used to understand the user's intent and the question they are asking.

[0153] 3. Response generation by AI engine:

[0154] The analysis results are sent to an AI engine (e.g., OpenAI's GPT-3), which generates an appropriate response based on the user's input.

[0155] 4. Sending the response:

[0156] The generated response is sent from the server to the smartphone and displayed to the user.

[0157] Specific examples

[0158] For example, if a user types "I'm looking for new shoes, any recommendations?", the following prompt will be generated:

[0159] User: I'm looking for some new shoes, any recommendations?

[0160] AI:

[0161] Based on this prompt, the AI ​​engine generates an appropriate response, such as, "Could you please tell me the style and purpose of the shoes you are looking for? For example, are they for running or casual everyday wear?"

[0162] In this way, personalized product suggestions and in-store guidance can be provided through dialogue with the user, making it possible to provide services that meet the individual needs of each customer, improving customer satisfaction and providing a comfortable shopping experience in the store.

[0163] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0164] Step 1:

[0165] A user starts an application on their smartphone and enters a question or request in a text input field. The entered text is sent from the smartphone to a server. The input data is text data that includes the user's question or request.

[0166] Step 2:

[0167] The server analyzes the received text data using a natural language processing (NLP) library. As a result of the analysis, the user's intent and question are extracted. The input data is the user's text data, and the output data is the analyzed intent and question.

[0168] Step 3:

[0169] The server generates a prompt based on the analysis results. This prompt converts the user's input into an appropriate format in preparation for sending it to the AI ​​engine. The input data is the analyzed intent or question, and the output data is the generated prompt.

[0170] Step 4:

[0171] The server sends the generated prompt sentence to an AI engine (e.g., OpenAI's GPT-3). The AI ​​engine generates an appropriate response based on the prompt sentence. The input data is the prompt sentence, and the output data is the response generated by the AI ​​engine.

[0172] Step 5:

[0173] The server sends the response received from the AI ​​engine to the smartphone. The smartphone displays the received response to the user. The input data is the response from the AI ​​engine, and the output data is the text displayed to the user.

[0174] Step 6:

[0175] The user checks the response displayed on their smartphone and enters further questions or requests as needed. This process is repeated until the user is satisfied. The input data is the user's new question or request, and the output data is text data that is sent back to the server.

[0176] In this way, a continuous dialogue takes place between the user and the AI ​​engine, providing personalized product suggestions and in-store guidance.

[0177] Example 2

[0178] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0179] In today's business environment, employees are expected to improve their communication skills, generate new ideas, release stress, and develop affection for others. However, there is a lack of effective ways to improve these skills. In addition, there is a need for efficient and effective ways to develop these skills in employee training and planning.

[0180] 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. In this invention, the server includes a means for a user to input a prompt sentence to a terminal, a means for the terminal to send the prompt sentence to the server, a means for the server to input a prompt sentence to a generative AI model, a means for the generative AI model to generate a response, a means for the server to send the generated response to the terminal, a means for the terminal to display the response to the user, and a means for the user to input a prompt sentence again and continue the dialogue. This enables the user to hone their communication skills, generate new ideas, relieve stress, and increase affection for the other person through dialogue with AI.

[0181] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and perform tasks such as learning, reasoning, and problem-solving.

[0182] An "interactive tool" is software or a system that allows users and artificial intelligence to communicate in natural language.

[0183] A "prompt sentence" is text data such as a question or instruction that a user inputs into a dialogue tool.

[0184] A "terminal" is a device such as a computer, smartphone, or tablet that a user uses to access an interaction tool.

[0185] A "server" is a computer system that performs back-end processing for the interactive tool and executes the generative AI model.

[0186] A "generative AI model" is an artificial intelligence algorithm for generating appropriate responses based on a user's prompt.

[0187] A "response" is text data that a generative AI model generates in response to a prompt.

[0188] "Employee training" is an educational program implemented by a company with the aim of improving employees' skills and acquiring knowledge.

[0189] "Planning" is the process of planning a new project or business strategy and creating a specific implementation plan.

[0190] MODE FOR CARRYING OUT THE INVENTION

[0191] This invention is a system that uses dialogue tools based on artificial intelligence to improve communication skills in employee training and planning, generate new ideas, relieve stress, and increase affection for others. A specific embodiment of this system is shown below.

[0192] Hardware and software used

[0193] This system uses the following hardware and software:

[0194] Device: A device used by a user, such as a PC, smartphone, or tablet.

[0195] Server: A computer system that performs back-end processing for an interactive tool.

[0196] Generative AI model: For example, an artificial intelligence algorithm such as OpenAI's GPT-4®.

[0197] Data processing and calculation

[0198] In this system, the following data processing and calculation are performed.

[0199] 1. User input:

[0200] The user uses the terminal to enter a prompt sentence, for example, "I'd like to discuss ideas for a new project."

[0201] 2. Send the prompt:

[0202] The terminal sends the prompt text entered by the user to the server via an Internet connection.

[0203] 3. Input to the generative AI model:

[0204] The server inputs the received prompt sentence into the generative AI model, converts the prompt sentence into an appropriate format, and passes it to the AI ​​model.

[0205] 4. Generate response:

[0206] The generative AI model generates an appropriate response based on the input prompt, such as "What is the purpose of the project?"

[0207] 5. Sending the response:

[0208] The server sends the response received from the generative AI model to the device, converts the response data into an appropriate format, and sends it to the device.

[0209] 6. View the response:

[0210] The terminal displays the response received from the server to the user, who can then check the response from the AI ​​on the terminal screen.

[0211] 7. Continuing the dialogue:

[0212] The user continues the dialogue by inputting a prompt sentence based on the displayed response, for example, "The purpose of the project is to develop a new market." By repeating this process, the dialogue between the user and the AI ​​continues.

[0213] Specific examples

[0214] Below is an example of a specific dialogue.

[0215] Example prompt sentence:

[0216] The user types into the terminal, "I'd like to come up with some ideas for a new marketing strategy."

[0217] Dialogue flow:

[0218] 1. User: "I want to come up with ideas for a new marketing strategy."

[0219] 2. AI: “Who is the target audience for that marketing strategy?”

[0220] 3. User: "I'm a young person in my 20s or 30s."

[0221] 4. AI: “What media will you use to reach that target audience?”

[0222] 5. User: "I would like to use social media primarily."

[0223] In this way, users can hone their communication skills, generate new ideas, relieve stress, and increase affection for others through dialogue with AI.

[0224] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0225] Step 1:

[0226] The user inputs a prompt into the device. Using a device such as a PC or smartphone, the user inputs a prompt such as "I would like to discuss ideas for a new project" into the input field of the dialogue tool. The input prompt is temporarily stored in the device's memory.

[0227] Step 2:

[0228] The terminal sends the prompt text to the server. The terminal then sends the prompt text entered by the user to the server as an HTTP request. At this time, the prompt text is converted into a data format such as JSON and sent to the server via the Internet. The input is the prompt text, and the output is the request sent to the server.

[0229] Step 3:

[0230] The server inputs a prompt sentence to the generative AI model. The server generates an API request to pass the received prompt sentence to the generative AI model. For example, it sends the prompt sentence to the API endpoint of the generative AI model. The input is the prompt sentence, and the output is the request sent to the generative AI model.

[0231] Step 4:

[0232] The generative AI model generates a response. The generative AI model generates an appropriate response based on the input prompt. For example, it generates a response such as, "What is the purpose of the project?" The input is the prompt, and the output is the generated response.

[0233] Step 5:

[0234] The server sends the generated response to the terminal. The server sends the response received from the generative AI model to the terminal as an HTTP response. At this time, the response data is converted into a data format such as JSON and sent to the terminal via the Internet. The input is the generated response, and the output is the response sent to the terminal.

[0235] Step 6:

[0236] The terminal displays the response to the user. The terminal displays the response received from the server in the user interface. The user can check the response from the AI ​​on the terminal screen. The input is the generated response, and the output is the display in the user interface.

[0237] Step 7:

[0238] The user inputs a new prompt sentence and the dialogue continues. The user inputs a new prompt sentence based on the displayed response. For example, the user might input, "The purpose of the project is to develop new markets." By repeating this process, the dialogue between the user and the AI ​​continues. The input is a new prompt sentence, and the output is the continuation of the dialogue.

[0239] (Application example 2)

[0240] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0241] Traditional employee training and customer service training have made it difficult to simulate actual customer interactions, making it difficult to improve effective communication skills. Furthermore, opportunities for store employees to practice actual customer service scenarios are limited, so an effective method for improving the quality of customer service was needed.

[0242] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for developing and providing an AI-based chat tool to improve human communication skills, means for generating ideas through the AI-based chat, means for releasing stress through the AI-based chat, means for increasing attachment to others through the AI-based chat, means for store clerks in a physical store to wear smart glasses and improve their customer service skills through chat with the AI, and means for practicing customer service scenarios and customer interactions through the AI-based chat. This enables store clerks to practice actual customer service scenarios in real time and improve the quality of customer interaction.

[0243] "Human communication skills" refers to the ability of humans to effectively exchange information with and understand others.

[0244] An "AI chat tool" is software or a system that uses artificial intelligence to have natural conversations with humans.

[0245] "Development and provision" refers to the process of designing the technologies and methods necessary to achieve a specific purpose and providing them in a form that can actually be used.

[0246] "Means of generating ideas" are methods and techniques for creating new ideas and solutions.

[0247] "Stress relief" refers to methods and techniques for reducing mental tension and anxiety and for relaxation.

[0248] "Means to increase attachment to others" are methods and techniques for increasing familiarity and liking for others.

[0249] A "brick and mortar store" is a store that exists in a physical location and where customers can visit in person to purchase goods or services.

[0250] A "store clerk" is an employee who works in a physical store and provides products and services to customers.

[0251] "Smart glasses" are a wearable eyeglass-type device equipped with computer functions, and are capable of displaying information and accepting voice input.

[0252] "Customer service skills" are the techniques and abilities required to respond appropriately to customers and increase their satisfaction.

[0253] A "customer service scenario" is a simulation that assumes specific situations and conversation flows when dealing with customers.

[0254] "Customer service practice" is training that simulates actual customer service, with the aim of improving skills.

[0255] This invention is a system in which store clerks wear smart glasses in brick-and-mortar stores and improve their customer service skills through casual conversations with AI. Specific embodiments for realizing this system are described below.

[0256] System Configuration

[0257] This system uses the following hardware and software:

[0258] Hardware: Smart glasses, microphone, speaker

[0259] Software: OpenAI API, Google® Speech Recognition API, gTTS (Google Text-to-Speech)

[0260] Program processing explanation

[0261] The server runs a program that uses the generative AI model to enable conversation between the store clerk and the AI. Specifically, it performs the following processes:

[0262] 1. Acquiring voice input:

[0263] The terminal (a microphone attached to smart glasses) captures the customer's question as voice.

[0264] The device converts the captured audio into text using the Google Speech Recognition API.

[0265] 2. AI-generated responses:

[0266] The server inputs the customer's textual question as a prompt into the generative AI model (OpenAI API).

[0267] The server takes the response from the generative AI model and generates an appropriate answer.

[0268] 3. Generate audio output:

[0269] The server converts the generated text response into speech using gTTS.

[0270] The device plays the converted audio through the speaker.

[0271] Specific examples

[0272] For example, if a store clerk asks, "How do you want to use this product?", the process will proceed as follows:

[0273] 1. Acquiring voice input:

[0274] The device captures the customer's voice question, "How do you use this product?", and converts it into text.

[0275] 2. AI-generated responses:

[0276] The server inputs the following prompt sentence into the generative AI model:

[0277] Customer Question: How do I use this product?

[0278] Clerk's response:

[0279] The server receives the response from the generative AI model: "First, turn on this product, then..."

[0280] 3. Generate audio output:

[0281] The server converts the received response into speech using gTTS, which the device plays through its speaker.

[0282] In this way, store associates can practice real-life customer interaction scenarios in real time and improve the quality of their customer interactions.

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

[0284] Step 1:

[0285] Acquiring voice input

[0286] The terminal (a microphone attached to smart glasses) captures the customer's question as voice.

[0287] Input: Customer voice question

[0288] Specific operation: The microphone captures voice data and sends it to the voice recognition module in the device.

[0289] Output: Audio data

[0290] Step 2:

[0291] Speech-to-text

[0292] The device converts the acquired voice data into text using the Google Speech Recognition API.

[0293] Input: Audio data

[0294] Specific operation: Sends audio data to the Google Speech Recognition API and receives text data.

[0295] Output: Text data (customer questions)

[0296] Step 3:

[0297] Generate prompt statement

[0298] The server generates a prompt based on the customer's textual question.

[0299] Input: Text data (customer question)

[0300] What it does: Inserts text data into a template to create a prompt suitable for the generative AI model.

[0301] Output: prompt statement

[0302] Customer Question: How do I use this product?

[0303] Clerk's response:

[0304] Step 4:

[0305] AI-powered response generation

[0306] The server inputs the generated prompt sentence into a generative AI model (OpenAI API) to generate an appropriate answer.

[0307] Input: prompt statement

[0308] Specific behavior: Sends a prompt to the OpenAI API and receives the generated text response.

[0309] Output: Text response (clerk's answer)

[0310] Step 5:

[0311] Generate audio output

[0312] The server converts the generated text response into speech using gTTS.

[0313] Input: Text response

[0314] What it does: Sends a text response to gTTS and generates an audio file.

[0315] Output: Audio data (clerk's response)

[0316] Step 6:

[0317] Playing audio

[0318] The terminal reproduces the generated audio data through a speaker.

[0319] Input: Voice data (clerk's response)

[0320] Specific operation: Sends audio data to the speaker and plays the audio.

[0321] Output: A voice response to the customer

[0322] Example 3

[0323] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0324] Conventional chat tools do not adequately support users in generating new ideas, and often respond unnaturally to user input. This makes it difficult for users to effectively plan and brainstorm. Furthermore, there is a lack of systems that can generate appropriate responses to prompts entered by users.

[0325] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0326] In this invention, the server includes means for a user to access the chat tool from a terminal, means for the user to input a prompt sentence, means for the terminal to send the input prompt sentence to the server, means for the server to send the prompt sentence to the generative AI model, means for the generative AI model to generate a response, means for the server to receive the generated response, means for the server to send the response to the terminal, and means for the terminal to display the response to the user. This allows users to generate new ideas through chatting with AI and effectively carry out planning and brainstorming.

[0327] "User" refers to a person who uses the system to chat with AI.

[0328] A "terminal" is a device used by a user to access the system, and includes a personal computer, a smartphone, etc.

[0329] A "chat tool" refers to software or an application that provides an interface for users to interact with AI.

[0330] A "prompt" is text that a user enters into a chat tool, including questions or requests for the AI.

[0331] "Server" refers to the computer system responsible for receiving user input, sending it to the generative AI model, and returning the generated response to the user.

[0332] "Generative AI model" refers to an artificial intelligence algorithm or software that generates appropriate responses based on a user's prompt.

[0333] "Response" refers to the text that a generative AI model generates in response to a user prompt.

[0334] "Preprocessing" refers to the process of converting the user's input text into a format that is easy for the generative AI model to understand.

[0335] "Post-processing" refers to the process of formatting the responses generated by the generative AI model into a format that is natural for the user.

[0336] This invention is a system that allows users to generate new ideas through casual conversations with AI, and to effectively carry out planning and brainstorming. Specific embodiments of this system are described below.

[0337] First, the user accesses the chat tool using a device such as a PC or smartphone. The chat tool is provided through a browser or a dedicated application. When the user accesses the chat tool's interface, a text box appears in which the user can enter a prompt.

[0338] For example, a user might input, "I want to think of a new product idea. Please tell me about recent trends." This prompt is sent from the device to the server. The server then preprocesses the received prompt to send it to a generative AI model (e.g., OpenAI's GPT-4).

[0339] The generative AI model generates an appropriate response based on the prompt it receives, for example, "What do you think of some trendy smart home appliances?" The generated response is then sent to a server, which then post-processes it and formats it in a way that is natural for the user.

[0340] The server then sends the processed response to the device, which then displays it on the chat tool's interface for the user to review. Through this process, the user can gain new ideas through chatting with the AI.

[0341] This system supports planning and brainstorming by using a generative AI model to generate appropriate responses to prompts entered by the user and providing them to the user. It also allows users to relieve stress and increase affection for the other party through dialogue with the AI.

[0342] The specific hardware includes the device used by the user (PC or smartphone) and the server, while the software includes the chat tool interface, the generative AI model (e.g., OpenAI's GPT-4), and the pre-processing and post-processing programs that run on the server.

[0343] This system allows users to obtain new perspectives and information through casual conversation with the AI, enabling them to carry out effective planning and brainstorming. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[0344] Step 1:

[0345] The user accesses the chat tool from the terminal.

[0346] Specifically, the user opens a browser on their computer or smartphone and accesses the chat tool's URL. This displays the chat tool's interface. The input is the user's access operation, and the output is the chat tool's interface display.

[0347] Step 2:

[0348] The user enters a prompt statement.

[0349] Specifically, the user inputs a prompt into a text box displayed on the chat tool's interface. For example, the user might type, "I'd like to come up with a new product idea. Please tell me about recent trends." The input is the user's text input, and the output is the input prompt.

[0350] Step 3:

[0351] The terminal sends the entered prompt text to the server.

[0352] Specifically, the terminal sends the prompt text entered by the user to the server as an HTTP request. The input is the prompt text entered by the user, and the output is the prompt text sent to the server.

[0353] Step 4:

[0354] The server sends the prompt to the generative AI model.

[0355] Specifically, the server preprocesses the received prompt text and converts it into a format suitable for the generative AI model. The converted text is then sent to the generative AI model. The input is the prompt text sent to the server, and the output is the preprocessed prompt text sent to the generative AI model.

[0356] Step 5:

[0357] A generative AI model generates a response.

[0358] Specifically, the generative AI model generates an appropriate response based on the received prompt, for example, "What do you think of smart home appliances that incorporate the latest trends?" The input is the preprocessed prompt, and the output is the generated response.

[0359] Step 6:

[0360] The server receives the generated response.

[0361] Specifically, the server receives the response generated by the generative AI model. The received response is post-processed to make it natural for the user. The input is the response from the generative AI model, and the output is the post-processed response.

[0362] Step 7:

[0363] The server sends a response to the terminal.

[0364] As a specific operation, the server sends the post-processed response to the terminal as an HTTP response. The input is the post-processed response, and the output is the response sent to the terminal.

[0365] Step 8:

[0366] The terminal displays the response to the user.

[0367] Specifically, the terminal displays the response received from the server on the interface of the chat tool. The input is the response from the server, and the output is the response displayed to the user.

[0368] (Application example 3)

[0369] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0370] Conventional AI chat tools have been useful in improving human communication skills, relieving stress, and increasing affection, but they lacked the functionality to provide ideas useful for improving the efficiency of factory production lines or developing new products. They also lacked the functionality to collect the opinions and ideas of workers and use them to propose new production methods or product improvements. This created the problem of being unable to fully improve factory productivity or the efficiency of new product development.

[0371] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0372] In this invention, the server includes means for developing and providing chat tools using AI to improve human communication skills, means for generating ideas through AI chat, means for releasing stress through AI chat, means for increasing affection for others through AI chat, means used to provide ideas useful for improving the efficiency of factory production lines and developing new products, and means for collecting opinions and ideas from workers and proposing new production methods and product improvements based on them, thereby enabling improved factory productivity and more efficient new product development.

[0373] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[0374] A "chat tool" is software or a system that uses artificial intelligence to have natural conversations with humans.

[0375] A "production line" is a series of work processes arranged in a factory to efficiently produce products.

[0376] "Efficiency" refers to processes and methods for achieving maximum results while minimizing the use of resources and time.

[0377] "New product development" is the process of designing, prototyping, and manufacturing new products in response to market needs and technological advances.

[0378] "Gathering opinions and ideas" is the activity of gathering feedback and suggestions from stakeholders.

[0379] A "production method" is a specific procedure or technique for manufacturing a product.

[0380] An "improvement" is a specific proposal or plan for improving an existing product or process.

[0381] As an embodiment of the present invention, a system is constructed that provides ideas that are useful for improving the efficiency of factory production lines and developing new products. Specific embodiments of this system are described below.

[0382] System Configuration

[0383] The system consists of the following main components:

[0384] 1. Server: Hosts the AI ​​model and performs back-end processing for the chat tool.

[0385] 2. Terminals: Devices used by factory workers (e.g., tablets, smartphones).

[0386] 3. Users: Factory workers and managers.

[0387] Hardware and software used

[0388] Hardware: Factory robots (e.g. ABB's IRB series), tablets, smartphones.

[0389] Software: OpenAI API, Python, database management system (e.g., MySQL®).

[0390] Data processing and calculation

[0391] The server processes the data in the following steps:

[0392] 1. Data collection: Collecting worker opinions and ideas from devices.

[0393] 2. Data analysis: The collected data is input into an artificial intelligence model, and new ideas are generated using a generative AI model.

[0394] 3. Providing results: The generated ideas are returned to the terminal and presented to the worker.

[0395] Specific examples

[0396] For example, when considering efficiency improvements on a factory production line, the following prompt sentences can be used:

[0397] Worker: Our production line has been inefficient lately. How can we improve it?

[0398] AI: That's an interesting question. For example, where do you feel you're losing efficiency?

[0399] Worker: The assembly process is particularly time-consuming.

[0400] AI: I see, which part of the assembly process takes the most time?

[0401] Worker: The screw tightening part.

[0402] AI: So, please suggest a new idea for automating the screw-driving process.

[0403] Based on this prompt, the server uses a generative AI model to generate new ideas and return them to the device, such as a specific proposal such as "introduce a specific robotic arm to automate the screw-driving process."

[0404] In this way, factory productivity can be improved and new product development can be made more efficient.

[0405] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0406] Step 1:

[0407] A user uses a terminal to input problems and opinions regarding the factory production line.

[0408] Input: User feedback or concerns (e.g., "Our production line has been inefficient lately. How can we improve it?")

[0409] Output: User input data

[0410] Specific operation: The user enters the problem in text format into the input form on the terminal and presses the submit button.

[0411] Step 2:

[0412] The terminal transmits the user's input data to the server.

[0413] Input: User-entered data

[0414] Output: Data sent to the server

[0415] Specific operation: The terminal sends the user's input data to the server as an HTTP request.

[0416] Step 3:

[0417] The server analyzes the received data and generates prompt sentences to input into the generative AI model.

[0418] Input: User-entered data

[0419] Output: Prompt sentence to be input to the generative AI model

[0420] Specific behavior: The server analyzes the user's input data and generates an appropriate prompt (e.g., "Our production line has been inefficient recently. How can we improve it?").

[0421] Step 4:

[0422] The server uses generative AI models to generate new ideas.

[0423] Input: prompt statement

[0424] Output: A new idea (e.g., "Introduce a specific robotic arm to automate the screw-driving process.")

[0425] Specific operation: The server inputs a prompt sentence into the generative AI model (e.g., OpenAI API) and obtains the generated ideas.

[0426] Step 5:

[0427] The server transmits the generated ideas to the terminal.

[0428] Input: New Idea

[0429] Output: Ideas sent to the terminal

[0430] Specific operation: The server sends the generated idea to the terminal as an HTTP response.

[0431] Step 6:

[0432] The terminal displays the received ideas to the user.

[0433] Input: Ideas received from the server

[0434] Output: The idea displayed to the user

[0435] Specific operation: The device displays the received ideas on the screen so that the user can review them.

[0436] 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.

[0437] "Example 1"

[0438] In the invention incorporating an emotion engine, an emotion engine is added to an AI engine to recognize the user's emotions and provide dialogue that corresponds to those emotions. Specifically, the engine analyzes emotional elements (e.g., joy, sadness, anger, surprise, etc.) contained in the text input from the user and generates an optimal response that corresponds to that emotion. In this form, the AI ​​engine can gain a deeper understanding of the user's emotions and provide a more human-like dialogue.

[0439] "Example 2"

[0440] An example of an invention incorporating an emotion engine being used in employee training is a program in which employees improve their communication skills through casual conversation with AI. In this program, the AI ​​engine recognizes the employee's emotions and provides dialogue that corresponds to those emotions. For example, if an employee is feeling stressed, the AI ​​engine will suggest topics to help them relax. In this form, the AI ​​engine can provide more effective communication training through dialogue that takes the employee's emotions into consideration.

[0441] "Example 3"

[0442] An example of how an invention incorporating an emotion engine can be used in planning is brainstorming, where new ideas are generated through casual conversation with AI. In this brainstorming session, the AI ​​engine recognizes the emotions of the participants and provides dialogue that reflects those emotions. For example, if a participant is confused, the AI ​​engine can provide new perspectives and information to stimulate ideas. In this form, the AI ​​engine can support more effective brainstorming through dialogue that takes into account the emotions of the participants.

[0443] The processing flow of each embodiment will be described below.

[0444] "Example 1"

[0445] Step 1: The user enters text into the AI ​​engine.

[0446] Step 2: The emotion engine analyzes the input text and recognizes the emotional elements it contains. Step 3: The AI ​​engine generates the optimal response based on the recognized emotions.

[0447] Step 4: The generated response is displayed to the user and the interaction progresses.

[0448] "Example 2"

[0449] Step 1: An employee starts chatting with the AI ​​engine.

[0450] Step 2: The emotion engine analyzes the employee's input text and recognizes the employee's emotions.

[0451] Step 3: The AI ​​engine generates optimal responses based on the emotions it recognizes, improving employees' communication skills.

[0452] "Example 3"

[0453] Step 1: Participants start brainstorming with the AI ​​engine.

[0454] Step 2: The emotion engine analyzes the participant's input text and recognizes their emotions. Step 3: Based on the emotions recognized by the AI ​​engine, it provides new perspectives and information to stimulate ideas.

[0455] Example 1

[0456] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0457] Conventional AI chat tools have the problem of being unable to fully understand the user's intentions and emotions, resulting in poor conversation quality. They also lack the ability to learn the user's preferences and personality, making it impossible to provide personalized conversations. Furthermore, these tools are often limited to specific uses, making them less versatile.

[0458] 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.

[0459] In this invention, the server includes means for receiving input text from a user, means for analyzing the user's intent using natural language processing technology, means for analyzing the user's emotions using emotion analysis technology, means for generating an appropriate response based on the analysis results, means for sending the generated response to the user, and means for saving a dialogue history with the user and using it as learning data. This makes it possible to accurately understand the user's intent and emotions and provide personalized dialogue. It also realizes a highly versatile system that can be used for a variety of purposes, such as employee training and planning.

[0460] The "means for receiving input text from the user" is a function for obtaining text data entered by the user and sending it to the system.

[0461] "Means of analyzing user intent using natural language processing technology" refers to technology that analyzes text data entered by the user and understands its context and meaning.

[0462] "Means for analyzing user emotions using emotion analysis technology" refers to technology for analyzing emotional elements contained in the user's input text and identifying the emotion.

[0463] The "means for generating an appropriate response based on the analysis results" is a function for generating an appropriate response to the user based on the results of natural language processing technology and sentiment analysis technology.

[0464] The "means for sending the generated response to the user" is a function for displaying or sending the generated response to the user.

[0465] "Means for saving dialogue history with the user and using it as learning data" refers to a function that records the content of past dialogues with the user, allows the system to learn from that data, and uses it for future dialogues.

[0466] This invention is a chat tool system that combines an AI engine and an emotion engine. Specifically, it works in cooperation with a server, terminals, and users.

[0467] System configuration

[0468] The server has the following functions:

[0469] 1. A means of receiving input text from the user

[0470] 2. Analyzing user intent using natural language processing technology

[0471] 3. Means of analyzing user emotions using emotion analysis technology

[0472] 4. A means of generating appropriate responses based on the analysis results

[0473] 5. A means of sending the generated response to the user

[0474] 6. A means to save user interaction history and use it as learning data

[0475] Hardware and software used

[0476] Hardware:

[0477] Server: Server equipment equipped with a high-performance processor and large-capacity memory

[0478] Devices: Personal computers, smartphones, tablets, etc. used by users

[0479] software:

[0480] Natural language processing technology: Python's NLTK library, Transformers library

[0481] Sentiment analysis technology: Python TextBlob, VADER Sentiment Analysis

[0482] Generative AI models: OpenAI's GPT-3 and GPT-4

[0483] Program processing

[0484] Receiving user input:

[0485] The device receives text data entered by the user and sends it to the server. For example, the user enters "I'm tired today."

[0486] Natural Language Processing:

[0487] The server sends the received text to the AI ​​engine, which uses Python's NLTK library to tokenize the text and perform grammar analysis, and the Transformers library to understand the context and analyze the user's intent.

[0488] Emotion analysis:

[0489] The server uses an emotion engine to extract emotions such as "fatigue" and "sadness" from the text "tired." The emotion engine calculates the emotion score of the text using TextBlob and evaluates the intensity of the emotion using VADER Sentiment Analysis.

[0490] Response generation:

[0491] The server generates a response such as "Please take a good rest today" based on the analysis results of the AI ​​engine and emotion engine. OpenAI's GPT-3 is used as the generative AI model, and the prompt is set as "If the user enters 'I'm tired today,' please generate an appropriate response."

[0492] Sending a response:

[0493] The server sends the generated response to the terminal, which then displays a message to the user saying, "Please take a good rest today."

[0494] Learning features:

[0495] The server stores the user's conversation history in a database. In subsequent conversations, the AI ​​engine refers to this history to learn the user's preferences and personality. For example, if the user frequently says "I'm tired," the AI ​​engine will learn that the user gets tired easily and provide more appropriate responses.

[0496] Specific examples

[0497] Explain what happens if the user types "I'm so happy today!"

[0498] 1. The device receives input from the user: "I'm so happy today!"

[0499] 2. The server uses an AI engine to analyze this text and understand the user's intent.

[0500] 3. The server uses an emotion engine to analyze the emotion "happy."

[0501] 4. The server generates a response based on the analysis: "That's great! What good happened?"

[0502] 5. The server generates a response and sends it to the terminal, which displays the response to the user.

[0503] 6. The server saves this dialogue history and uses it in subsequent dialogues.

[0504] In this way, the server, terminal, and user work together to create an AI-powered chat tool.

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

[0506] Step 1:

[0507] Receiving user input

[0508] A user inputs text into a terminal. For example, the user inputs "I'm tired today." The terminal sends this input text to a server. The input is the user's text data, and the output is the text data sent to the server.

[0509] Step 2:

[0510] Natural Language Processing

[0511] The server sends the received text data to the AI ​​engine, which uses Python's NLTK library to tokenize the text and perform grammatical analysis. It also uses the Transformers library to understand the context and analyze the user's intent. The input is the user's text data, and the output is the analyzed intent data.

[0512] Step 3:

[0513] Emotion analysis

[0514] The server uses an emotion engine to analyze the emotional elements contained in the user's input text. The emotion engine calculates the emotion score of the text using TextBlob and evaluates the intensity of the emotion using VADER Sentiment Analysis. For example, it extracts emotions such as "fatigue" and "sadness" from the text "tired." The input is the user's text data, and the output is the analyzed emotion data.

[0515] Step 4:

[0516] Response Generation

[0517] The server generates an appropriate response based on the analysis results of the AI ​​engine and emotion engine. OpenAI's GPT-3 is used as the generative AI model, and the prompt is set as "If the user enters 'I'm tired today,' please generate an appropriate response." For example, a response such as "Please take a good rest today" is generated. The input is the analyzed intent data and emotion data, and the output is the generated response data.

[0518] Step 5:

[0519] Sending a Response

[0520] The server sends the generated response to the terminal. The terminal displays the response to the user. For example, it displays "Please take a good rest today." The input is the generated response data, and the output is the response displayed to the user.

[0521] Step 6:

[0522] Learning Function

[0523] The server stores the user's interaction history in a database. In subsequent interactions, the AI ​​engine refers to this history to learn the user's preferences and personality. For example, if a user frequently says "I'm tired," the AI ​​engine will learn that the user gets tired easily and provide more appropriate responses. The input is the interaction history data, and the output is the learned user profile.

[0524] (Application example 1)

[0525] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0526] In traditional brick-and-mortar stores, it was difficult for store staff to instantly understand customers' emotions and preferences and provide appropriate product recommendations and customer service. Furthermore, service quality varied widely because it depended on the customer service skills and experience of the staff. Furthermore, there was a lack of effective ways to reduce customer stress and increase customer satisfaction.

[0527] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for developing and providing an AI-based chat tool to improve human communication skills, means for generating ideas through AI-based chat, means for releasing stress through AI-based chat, means for increasing attachment to others through AI-based chat, means for analyzing a user's emotions using emotion recognition technology and providing a dialogue based on those emotions, means for learning the user's preferences and personality and providing personalized dialogue, means for a sales clerk wearing smart glasses in a physical store to support conversations with customers, and means for providing appropriate product recommendations and customer service based on the customer's emotions and preferences. This enables sales clerks to instantly grasp the emotions and preferences of customers and provide appropriate product recommendations and customer service.

[0528] "Human communication skills" refers to the ability of humans to effectively exchange information with and understand others.

[0529] An "AI chat tool" is software or a system that uses artificial intelligence to engage in natural conversations with users.

[0530] A "development and delivery method" is a method or process for designing, implementing, and making available to users a particular technology or service.

[0531] "Idea generation methods" are methods and processes for generating creative ideas and new concepts.

[0532] A "stress reliever" is a method or process for reducing mental tension and anxiety and for relaxing.

[0533] "Attachment strategies" are methods or processes for increasing familiarity and liking for others.

[0534] "Emotion recognition technology" is a technology that analyzes a user's facial expressions, tone of voice, etc. to identify their emotional state.

[0535] "Means for learning user preferences and characteristics" refers to methods or processes for analyzing user preferences and behavioral patterns and providing personalized services based on those.

[0536] "Personalized interaction" is interaction that is customized to the user's individual preferences and personality.

[0537] A "brick and mortar store" is a store that exists in a physical location and provides goods or services in person.

[0538] "Smart glasses" are glasses-type devices that have built-in cameras, displays, sensors, etc. and have the ability to display information and collect data.

[0539] A "store visitor" is a customer who visits a physical store.

[0540] "Means to support dialogue" are methods and processes for smoothly communicating with users.

[0541] "Product suggestion" is the act of recommending appropriate products based on the customer's needs and preferences.

[0542] "Customer service" refers to the service and response provided by store staff to customers.

[0543] As an embodiment of the present invention, a system is constructed in which store clerks wearing smart glasses in a brick-and-mortar store support conversations with customers. A specific embodiment of this system is described below.

[0544] System configuration

[0545] Hardware

[0546] Smart glasses: Devices with built-in cameras and microphones that capture customers' facial expressions and voices in real time.

[0547] Server: A computer system for processing data and running AI models.

[0548] Network: Infrastructure for data communication between the smart glasses and the server.

[0549] software

[0550] OpenCV: A library for performing face recognition.

[0551] Transformers: Generative AI models for emotion recognition.

[0552] Natural language processing engine: Software for generating dialogue with users.

[0553] Database: A system for storing user preferences and interaction history.

[0554] Data processing and calculation

[0555] Camera input and face recognition

[0556] The camera in the smart glasses captures the customer's image in real time and recognizes their face using OpenCV, and the recognized face data is sent to the server.

[0557] Emotion analysis

[0558] The server uses Transformers' emotion recognition model to analyze the customer's emotions from the facial data sent, outputting the results as emotional states such as joy, sadness, anger, and surprise.

[0559] Personalized interaction generation

[0560] The natural language processing engine generates appropriate dialogue content based on the results of emotion analysis and the customer's preferences and dialogue history stored in a database. The dialogue content is displayed on the smart glasses' display, and the store clerk then makes appropriate product suggestions and provides appropriate customer service.

[0561] Specific examples

[0562] When a customer enters a physical store and approaches a sales associate wearing smart glasses, the camera in the smart glasses captures the customer's facial expression. The server analyzes the captured image and provides feedback such as, "This customer seems a little tired." The sales associate then suggests, "You seem tired today. Would you like to find something to help you relax?"

[0563] Prompt Sentence Examples

[0564] Analyze customer facial expressions to recognize their emotions, then generate appropriate product suggestions based on those emotions.

[0565] In this way, the system can instantly grasp the emotions and preferences of customers and provide appropriate product suggestions and customer service, thereby increasing customer satisfaction and improving the quality of service in physical stores.

[0566] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0567] Step 1:

[0568] The camera in the smart glasses captures video of customers in real time. The input is the video data of the customers, and the output is the captured video data. The smart glasses use their built-in camera to acquire video including the customers' faces.

[0569] Step 2:

[0570] The smart glasses transmit the captured video data to a server. The input is the captured video data, and the output is the video data transmitted to the server. The smart glasses transfer the video data to the server via a network.

[0571] Step 3:

[0572] The server recognizes faces from video data using OpenCV. The input is the video data sent to the server, and the output is the location information of the recognized face. The server analyzes the video data and identifies the location of the face.

[0573] Step 4:

[0574] The server uses a Transformers emotion recognition model to analyze the emotions of the recognized faces. The input is the location information of the recognized faces, and the output is the emotion analysis results. The server runs the emotion recognition model based on the location information of the faces to identify emotional states such as joy, sadness, anger, and surprise.

[0575] Step 5:

[0576] The server uses a natural language processing engine to generate appropriate dialogue content based on the emotion analysis results and the customer's preferences and dialogue history stored in a database. The input is the emotion analysis results and the customer's preferences and dialogue history, and the output is the generated dialogue content. The server combines the emotion analysis results with past data to generate the optimal dialogue content for the customer.

[0577] Step 6:

[0578] The server sends the generated interaction content to the smart glasses. The input is the generated interaction content, and the output is the interaction content sent to the smart glasses. The server transfers the interaction content to the smart glasses via a network.

[0579] Step 7:

[0580] The smart glasses display the conversation content on a display, and the store clerk makes appropriate product suggestions and provides customer service. The input is the conversation content sent to the smart glasses, and the output is the product suggestions and customer service provided by the store clerk. The smart glasses display the conversation content on a display, and the store clerk responds to the customer based on that.

[0581] Example 2

[0582] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0583] Improving employees' communication skills is an important issue in today's business environment. However, traditional training methods make it difficult to provide appropriate feedback based on each employee's emotions and situation, making it difficult to expect effective improvement in communication skills. Furthermore, there is a lack of means to provide appropriate support when employees are feeling stressed or trying to generate new ideas. This can lead to a decline in employee motivation and productivity.

[0584] 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.

[0585] In this invention, the server includes a means for analyzing a user's emotions in real time using an emotion recognition engine, a means for generating appropriate dialogue content based on the emotion data using a generative AI model, a means for collecting dialogue log data and evaluating the improvement of the user's communication skills, and a means for providing feedback to the user based on the analysis results. This makes it possible to provide appropriate dialogue and feedback according to the emotions and situations of each employee, which is expected to improve effective communication skills. In addition, by providing appropriate support when employees are feeling stressed or when generating new ideas, employee motivation and productivity can be improved.

[0586] An "artificial intelligence dialogue tool" is an artificial intelligence system designed to engage in natural dialogue with humans.

[0587] An "emotion recognition engine" is software or hardware that analyzes a user's input text or voice data and identifies their emotional state in real time.

[0588] A "generative AI model" is an artificial intelligence model that generates appropriate responses and dialogue content based on input data.

[0589] "Dialogue log data" refers to recorded data of dialogue between a user and an AI, which is saved for later analysis.

[0590] "Feedback" refers to advice and evaluations provided to users based on dialogue log data and the analysis results of the emotion recognition engine.

[0591] "Communication skills" are the ability to effectively convey information and understand others.

[0592] "Employee training" is an educational program conducted to improve employees' skills and knowledge.

[0593] "Stress" is a state of mental or physical tension or strain.

[0594] An idea is a new concept or idea.

[0595] Attachment refers to the familiarity and affection one feels toward a particular object.

[0596] This invention is a system for improving the communication skills of employees by utilizing dialogue tools using artificial intelligence in employee training. Specific embodiments of this system will be described below.

[0597] System configuration

[0598] Hardware

[0599] Server: Use a server with high-performance computing power. The server has the processing power to run the emotion recognition engine and generative AI model.

[0600] Terminal: The terminal used by the user is a device such as a PC, tablet, or smartphone. These terminals are capable of connecting to the Internet and have an interface for communicating with the server.

[0601] software

[0602] Emotion recognition engine: Software that analyzes user input text and identifies emotions in real time. Specifically, it uses an engine that uses natural language processing technology (e.g., IBM Watson's Tone Analyzer).

[0603] Generative AI model: An artificial intelligence model that generates appropriate dialogue content based on user emotion data. Specifically, we use a generative AI model (e.g., OpenAI's GPT-4).

[0604] Data collection and analysis tools: Tools for collecting interaction log data and evaluating the improvement of users' communication skills, including databases and analysis algorithms.

[0605] System Operation

[0606] 1. User login:

[0607] A user logs into the system using a terminal. To log in, a user ID and password are required. The server checks the user's authentication information against the database and proceeds to the next step only if authentication is successful.

[0608] 2. Launch the emotion recognition engine:

[0609] The server starts an emotion recognition engine, which analyzes the text data entered by the user in real time and identifies the user's emotion. For example, if the user enters "I'm very tired today," the emotion recognition engine will detect "fatigue."

[0610] 3. Start the dialogue generation engine:

[0611] The server launches the generative AI model, which generates appropriate dialogue content based on the emotional data obtained from the emotion recognition engine. For example, if the emotion recognition engine detects "fatigue," the dialogue generation engine generates a response such as, "Shall we talk about ways to relax?"

[0612] 4. Execute the dialogue:

[0613] The user interacts with the AI ​​through the device. The text entered by the user is sent to the server, which uses an emotion recognition engine and a dialogue generation engine to generate an appropriate response and return it to the device. For example, if the user enters "I'm having trouble finding new hobbies," the server will respond with "What are you interested in to find a new hobby?"

[0614] 5. Data collection and analysis:

[0615] The server collects dialogue log data. This data includes the user's input text, the analysis results of the emotion recognition engine, and the responses of the dialogue generation engine. The server analyzes this data to evaluate the improvement of the user's communication skills. For example, it analyzes how stressed the user is and what dialogues were effective.

[0616] 6. Providing Feedback:

[0617] The server provides the user with feedback based on the analysis results. The feedback includes specific advice to help improve the user's communication skills. For example, the server may provide advice such as, "When you are feeling stressed, it would be good to choose a topic that helps you relax."

[0618] Examples of concrete examples and prompts

[0619] Examples:

[0620] A user logs into the system using a terminal and enters, "I've been busy at work lately and feeling stressed." The server uses an emotion recognition engine to detect "stress" and responds using a generative AI model, asking, "Shall we talk about something to help you relax?" If the user replies, "Yes, please," the server continues, "Would you like to talk about your recent hobbies or ways of relaxing?" The log data of this conversation is stored on the server and later analyzed. Based on the analysis results, the server provides feedback such as, "When you're feeling stressed, it would be a good idea to choose a topic to help you relax."

[0621] Example prompt sentence:

[0622] "If the user is feeling stressed, generate a conversation that offers a topic to help them relax."

[0623] In this way, the system enables effective communication training by linking the server, terminals, and users.

[0624] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0625] Step 1:

[0626] A user logs into the system using a terminal. The user enters a user ID and password, which the terminal sends to the server. The server authenticates the user by checking the database. If authentication is successful, the server returns a login success message to the user. If authentication fails, the server returns an error message.

[0627] Step 2:

[0628] The server starts the emotion recognition engine. The text data entered by the user through the device is sent to the server. The server passes this text data to the emotion recognition engine, which analyzes the emotion in real time. For example, if the user enters "I'm very tired today," the emotion recognition engine detects "fatigue" and returns the result to the server.

[0629] Step 3:

[0630] The server launches the generative AI model. The emotion data obtained from the emotion recognition engine is passed to the generative AI model as input. The generative AI model generates appropriate dialogue content based on this emotion data. For example, if the emotion recognition engine detects "fatigue," the generative AI model generates a response such as "Shall we talk about ways to relax?" and returns the result to the server.

[0631] Step 4:

[0632] The server sends the generated dialogue content to the user's device. The user then interacts with the AI ​​through the device. The text entered by the user is sent back to the server, which uses an emotion recognition engine and generative AI model to generate an appropriate response and returns it to the device. For example, if the user enters, "I'm having trouble finding new hobbies these days," the server will respond with, "What are you interested in to find a new hobby?"

[0633] Step 5:

[0634] The server collects dialogue log data, storing data such as the user's input text, the analysis results of the emotion recognition engine, and the responses of the generative AI model. The server analyzes this data to evaluate the improvement of the user's communication skills. For example, it analyzes how stressed the user is and what dialogues were effective.

[0635] Step 6:

[0636] The server provides the user with feedback based on the analysis results. The feedback includes specific advice to help improve the user's communication skills. For example, the server generates advice such as "When you are feeling stressed, it would be good to choose a topic that will help you relax," and sends it to the user's device.

[0637] (Application example 2)

[0638] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0639] In the past, communication within factories was often not smooth between workers or between workers and machines, which could affect work efficiency and safety. Furthermore, there was a lack of appropriate communication methods to reduce worker stress and fatigue. This has led to a demand for improvements in the work environment and increased worker motivation.

[0640] 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.

[0641] In this invention, the server includes means for developing and providing AI-based chat tools to improve human communication skills, means for generating ideas through AI-based chat, means for releasing stress through AI-based chat, means for increasing attachment to others through AI-based chat, means for analyzing a user's emotions using an emotion recognition engine and providing dialogue based on those emotions, means installed in a robot working in a factory to facilitate communication with workers, means for generating appropriate responses using a generative AI model, and means for generating prompt sentences and providing responses. This enables smooth communication between workers and robots, improving work efficiency and the work environment.

[0642] An "AI chat tool to improve human communication skills" is a tool that uses artificial intelligence to engage in casual conversation in order to improve a person's ability to communicate effectively with others.

[0643] "Means of generating ideas" is a method of using artificial intelligence to draw out new perspectives and ideas through casual conversation.

[0644] "Means to relieve stress" is a method that uses artificial intelligence to reduce users' stress through casual conversation.

[0645] "Means to increase attachment to others" refers to a method that uses artificial intelligence to make users feel closer to and more attached to others through casual conversation.

[0646] An "emotion recognition engine" is an engine that analyzes a user's emotions and provides appropriate dialogue based on those emotions.

[0647] A "factory robot" is an automated mechanical device designed to perform tasks in a factory.

[0648] A "generative AI model" is a model that uses artificial intelligence to generate appropriate responses based on user input.

[0649] A "prompt" is a command used to cause an artificial intelligence to generate a particular response.

[0650] As an embodiment of the present invention, an AI chat tool is installed in a robot working in a factory, and a system is constructed to facilitate communication with workers. A specific embodiment of this system is shown below.

[0651] 1. System Program

[0652] This system uses the following hardware and software:

[0653] Hardware: Factory robots, microphones, speakers

[0654] Software: OpenAI API, Emotion Recognition API

[0655] 2. Program processing explanation

[0656] When a worker speaks to the robot, the server first captures the voice with a microphone and converts it into text data using speech recognition technology. This text data undergoes emotion analysis using the Emotion Recognition API. Based on the results of the emotion analysis, an appropriate prompt is generated.

[0657] The generated prompt is sent to the OpenAI API, which uses a generative AI model to generate an appropriate response, which is then converted back to voice and transmitted to the worker via a speaker.

[0658] 3. Specific Examples

[0659] For example, if a worker says, "I'm tired today," the emotion recognition engine will detect this and generate a prompt like this:

[0660] Prompt Sentence Examples

[0661] Give them something to talk about to relax.

[0662] This prompt is sent to the OpenAI API, and the generative AI model generates an appropriate response, such as a relaxing topic like, "What fun things did you do today?"

[0663] In this way, factory robots can facilitate communication with workers, improving work efficiency and the working environment.

[0664] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0665] Step 1:

[0666] The user talks to the robot.

[0667] Input: User's voice

[0668] Output: Audio data

[0669] Specific operation: When the user says, "I feel tired today," the robot's microphone picks up the voice.

[0670] Step 2:

[0671] The terminal uses voice recognition technology to convert the voice data into text data.

[0672] Input: Audio data

[0673] Output: Text data

[0674] Specific operation: The captured voice data is analyzed by voice recognition software and converted into text data such as "I'm tired today."

[0675] Step 3:

[0676] The server performs sentiment analysis on the text data using the Emotion Recognition API.

[0677] Input: Text data

[0678] Output: Emotion data (e.g., fatigue)

[0679] Specific operation: Send the text data "I'm tired today" to the Emotion Recognition API for emotion analysis. The result is the emotion data "fatigue."

[0680] Step 4:

[0681] The server generates a prompt sentence based on the emotion data.

[0682] Input: Emotion data

[0683] Output: prompt statement

[0684] Specific operation: Based on the emotional data "fatigue," generate a prompt sentence such as "Please give me a topic to talk about to help me relax."

[0685] Step 5:

[0686] The server uses the OpenAI API to generate a response based on the prompt.

[0687] Input: prompt statement

[0688] Output: Response text

[0689] Specific behavior: The generated prompt sentence is sent to the OpenAI API, and the generative AI model generates a response text such as "What fun things did you do today?"

[0690] Step 6:

[0691] The terminal converts the response text into audio data.

[0692] Input: Response text

[0693] Output: Audio data

[0694] Specific operation: The response text "What fun things did you have today?" is analyzed by speech synthesis software and converted into voice data.

[0695] Step 7:

[0696] The terminal transmits the audio data to the user through a speaker.

[0697] Input: Audio data

[0698] Output: A spoken response to the user

[0699] Specific operation: The converted voice data is transmitted to the user through the speaker, and the user receives a voice response saying, "What fun things did you have today?"

[0700] Example 3

[0701] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0702] Conventional chat tools provide dialogue without considering the user's feelings, making it difficult to brainstorm effectively or relieve stress according to the user's needs. Furthermore, when users generate new ideas, they are unable to provide the appropriate perspectives or information, which reduces the efficiency of planning.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0704] In this invention, the server includes a means for a user to access the system through a terminal, a means for the server to receive user input and analyze emotions using an emotion analysis engine, a means for the server to send prompt sentences to a generative AI model and generate a dialogue, a means for the server to return the generated dialogue to the user, and a means for the user to obtain new ideas and enter input again. This allows for dialogue based on the user's emotions, enabling effective brainstorming and stress relief. Furthermore, by allowing users to obtain new perspectives and information, the efficiency of planning can be improved.

[0705] "User" refers to a person who uses the system.

[0706] "Terminal" refers to an electronic device such as a computer or smartphone that a user uses to access the system.

[0707] A "server" is a computer that is the central part of a system and is a device that receives and processes user input.

[0708] An "emotion analysis engine" refers to software that analyzes emotions from user input and identifies those emotions.

[0709] A "generative AI model" refers to an artificial intelligence model that generates dialogue based on user input and prompts.

[0710] A "prompt" is a piece of text containing instructions or questions that a generative AI model uses to generate a dialogue.

[0711] "Dialogue" refers to the exchange of messages between the user and the system (AI).

[0712] "Brainstorming" refers to a method for freely sharing multiple ideas and generating new ideas.

[0713] "Planning" refers to the process of planning a new project or product.

[0714] This invention is a system that supports effective brainstorming and stress relief by allowing users to access the system through their terminals and having the server generate dialogue using an emotion analysis engine and a generative AI model.

[0715] The server provides a web application that allows users to access the system through their terminals. Users use terminals such as PCs or smartphones to access the system's web application and log in to the system by entering authentication information on the login screen.

[0716] When a user types a message into the chat box in the system, the server receives the message and sends it to a sentiment analysis engine. For example, IBM Watson's Tone Analyzer can be used as the sentiment analysis engine. The sentiment analysis engine analyzes the user's message and identifies its emotion. For example, if a user types, "I'm worried about whether this idea will work," the sentiment analysis engine will detect "anxiety."

[0717] Next, the server creates a prompt sentence based on the emotional information obtained from the emotion analysis engine and sends it to the generative AI model. OpenAI's GPT-4, for example, can be used as the generative AI model. The prompt sentence is text containing instructions and questions for generating a dialogue that corresponds to the user's emotions. For example, a prompt sentence such as "The user is feeling anxious. Please generate a dialogue that will alleviate the user's anxiety by providing new perspectives and information" is sent to the generative AI model. The generative AI model generates a dialogue based on this prompt sentence.

[0718] The server receives the dialogue returned by the generative AI model and sends it to the user's device. For example, a message is displayed to the user saying, "Let's think about it from a new perspective. For example, what if we applied this idea to a different market?"

[0719] The user then comes up with a new idea based on the AI's suggestions and inputs it back into the system. For example, they might input, "The idea of ​​applying it to a different market is good. Which market would that be specifically?" This triggers a repeat of the dialogue generation process using the sentiment analysis engine and generative AI model.

[0720] Examples:

[0721] Suppose a user is planning a new smartphone app. The user uses the system as follows:

[0722] A user types into the chat box, "I can't think of any unique features for this app." The server sends this message to a sentiment analysis engine, which detects "confused."

[0723] The server sends the generative AI model a prompt saying, "The user is confused. Please generate a dialogue that will alleviate the user's confusion by providing new perspectives and information." The generative AI model then generates a dialogue saying, "For example, how about adding a function to monitor the user's health condition in real time?"

[0724] The server returns the generated dialogue to the user and displays it on the user's device. The user then enters, "The health monitoring feature is great. How should I implement it specifically?", and the dialogue generation process using the emotion analysis engine and generative AI model is repeated.

[0725] In this way, a dialogue that corresponds to the user's feelings can be provided, and effective brainstorming and stress relief can be supported. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0726] Step 1:

[0727] A user accesses the system through a terminal.

[0728] Specific operation: A user accesses the system's web application using a device such as a PC or smartphone. The user enters authentication information on the login screen and logs in to the system.

[0729] Input: User authentication information (username, password)

[0730] Output: User logged into the system

[0731] Step 2:

[0732] The user types a message into a chat box within the system.

[0733] What happens: The user types a message into the chat box, such as "I'm not sure if this idea will work."

[0734] Input: User's message

[0735] Output: The server receives the user's message

[0736] Step 3:

[0737] The server receives the user's input and analyzes the emotion using an emotion analysis engine.

[0738] Specific operation: The server sends the user's message to a sentiment analysis engine (e.g., IBM Watson's Tone Analyzer), which analyzes the message and identifies the user's sentiment.

[0739] Input: User's message

[0740] Output: Emotion information from the emotion analysis engine (e.g., anxiety)

[0741] Step 4:

[0742] The server sends prompts to the generative AI model to generate a dialogue.

[0743] Specific operation: Based on the emotional information obtained from the emotion analysis engine, the server creates and sends a prompt to the generative AI model (for example, OpenAI's GPT-4). The prompt might be something like, "The user is feeling anxious. Please provide new perspectives and information to generate a dialogue that will alleviate the user's anxiety." The generative AI model then generates a dialogue based on this prompt.

[0744] Input: Emotion information, prompt sentence

[0745] Output: A dialogue message from the generative AI model (e.g., "Let's think about it from a new perspective. For example, what if we applied this idea to a different market?")

[0746] Step 5:

[0747] The server returns the generated interaction to the user.

[0748] Specific operation: The server receives the dialogue returned by the generative AI model and sends it to the user's device. The dialogue message is displayed in the user's chat box.

[0749] Input: Dialogue message from the generative AI model

[0750] Output: The dialogue message displayed in the user's chat box.

[0751] Step 6:

[0752] The user gets a new idea and inputs it again.

[0753] Specific operation: The user comes up with a new idea based on the AI's suggestions and inputs it into the system again. For example, the user might input, "The idea of ​​applying it to a different market is good. Which market would be good specifically?" This will cause the process to repeat from step 3.

[0754] Input: User's new message

[0755] Output: The server receives the user's new message.

[0756] In this way, the user, terminal, and server work together to process each step, providing dialogue that responds to the user's emotions, making this a system that supports effective brainstorming and stress relief.

[0757] (Application example 3)

[0758] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0759] Conventional AI chat tools have focused primarily on improving communication skills, relieving stress, and forming attachments, but they have not adequately addressed the improvement of production efficiency or the generation of new ideas. Furthermore, they lacked the ability to recognize emotions and provide dialogue based on those emotions, making it difficult to generate appropriate responses according to the user's emotions. This has hindered effective brainstorming based on the emotions of workers, particularly in production sites such as factories, and has hindered the improvement of production efficiency.

[0760] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0761] In this invention, the server includes means for developing and providing chat tools using AI, means for generating ideas through chat using AI, means for releasing stress through chat using AI, means for increasing attachment to others through chat using AI, means for generating new ideas for improving productivity through chat using AI, means for AI to recognize emotions and provide dialogue based on the recognized emotions, and means for AI to generate responses based on prompt sentences using a generative AI model. This makes it possible to generate new ideas and improve productivity through appropriate dialogue according to the user's emotions.

[0762] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[0763] A "chat tool" is software or a system that allows natural conversation with users.

[0764] "Emotion recognition" is a technology that analyzes and identifies emotions from a user's facial expressions, tone of voice, etc.

[0765] A "generative AI model" is an artificial intelligence model that generates new text or responses based on input data.

[0766] A "prompt" is text data input to a generative AI model that contains instructions or questions that the model uses to generate a response.

[0767] "Production efficiency" is the ability to achieve maximum production results using a given amount of resources and time.

[0768] Brainstorming is a method for multiple people to freely exchange ideas and find new ideas and solutions.

[0769] "Dialogue" is the act of two or more people exchanging words and information and opinions.

[0770] The system for implementing this invention uses an AI chat tool to generate new ideas through dialogue that responds to the user's emotions, thereby improving production efficiency. A specific embodiment of this system is described below.

[0771] System Configuration

[0772] 1. Hardware:

[0773] Camera: Used to capture the user's facial expressions. An example is a webcam, such as the Logitech C920.

[0774] Microphone: Used to capture the user's voice. An example is a USB microphone such as the Blue Yeti.

[0775] Computer: Used to process data and run artificial intelligence models. This can be a regular PC or a server.

[0776] 2. Software:

[0777] Emotion recognition software: Recognizes emotions by analyzing the user's facial expressions and tone of voice. An example is the EmotionRecognizer library.

[0778] Generative AI models: Generate responses based on user input and perceived emotions. An example is OpenAI's GPT-3 model.

[0779] Data processing and calculation

[0780] 1. Emotion recognition:

[0781] The camera and microphone are used to capture the user's facial expressions and voice.

[0782] The acquired data is fed into emotion recognition software to identify the user's emotions.

[0783] 2. Dialogue generation:

[0784] The user's input (text) and the recognized emotion are input as prompts into the generative AI model.

[0785] A generative AI model generates an appropriate response based on the prompt.

[0786] Specific examples

[0787] For example, if a factory worker types, "I don't know how to improve the efficiency of the production line," the system will act as follows:

[0788] 1. Capture the facial expressions and voices of workers using a camera and microphone.

[0789] 2. Emotion recognition software recognizes the worker's confused emotions.

[0790] 3. The worker's input and the recognized emotion are fed into the generative AI model as prompt sentences.

[0791] 4. The generative AI model generates a response like this:

[0792] "Why not consider introducing new machinery or reviewing work procedures?"

[0793] Prompt Sentence Examples

[0794] User Emotion: Confused

[0795] User Input: I don't know how to improve the efficiency of my production line.

[0796] AI response: Why not consider introducing new machinery or revising work procedures?

[0797] In this way, the system can support the creation of new ideas and the improvement of production efficiency through appropriate dialogue that responds to the user's emotions.

[0798] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0799] Step 1:

[0800] The user accesses the system through a camera and microphone.

[0801] Input: User's facial expressions and voice

[0802] Output: Image and audio data obtained from the camera and microphone

[0803] Specific operation: A user stands in front of the camera and speaks into the microphone. The camera captures the user's facial expressions, and the microphone records the user's voice.

[0804] Step 2:

[0805] The image data and audio data acquired by the device are input into emotion recognition software.

[0806] Input: Image and audio data obtained from the camera and microphone

[0807] Output: User's emotion (e.g., confusion, joy, sadness, etc.)

[0808] Specific operation: The device uses the EmotionRecognizer library to analyze image and audio data and identify the user's emotions.

[0809] Step 3:

[0810] The user enters text into the terminal.

[0811] Input: User text input (e.g., "I don't know how to improve the efficiency of our production line.")

[0812] Output: User's text input data

[0813] Specific behavior: The user enters a question or comment into the device using a keyboard or voice input.

[0814] Step 4:

[0815] The device inputs the user's emotions and text input as prompts into the generative AI model.

[0816] Input: User sentiment and text input data

[0817] Output: Prompt statement (e.g., "User emotion: Confused. User input: I don't know how to improve the efficiency of the production line.")

[0818] Specific operation: The device combines the user's emotions and text input to create a prompt sentence suitable for the generative AI model.

[0819] Step 5:

[0820] The server uses a generative AI model to generate a response based on the prompt.

[0821] Input: prompt statement

[0822] Output: The AI's response (e.g., "Maybe we should consider introducing new machinery or revising our work procedures?")

[0823] What it does: The server uses OpenAI's GPT-3 model to generate an appropriate response based on the prompt.

[0824] Step 6:

[0825] The terminal displays or speaks the generated response to the user.

[0826] Input: AI response

[0827] Output: The text or audio response that is displayed to the user

[0828] Specific behavior: The device displays the generated response on the screen or uses speech synthesis to communicate it to the user.

[0829] 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.

[0830] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0831] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0832] 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.

[0833] [Second embodiment]

[0834] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0835] 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.

[0836] 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).

[0837] 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.

[0838] 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.

[0839] 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).

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] In the smart glasses 214, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The processor 46 executes the reception output process in accordance with the storage 50.

[0845] The processor 46 reads the reception output program 60 from the RAM 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.

[0846] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0847] "Example 1"

[0848] One aspect of the present invention is a system for developing and providing AI-based chat tools. Specifically, an AI engine is used to enable chat with users. This AI engine uses natural language processing technology to understand input from users and generate appropriate responses. The AI ​​engine also has a learning function, and can learn the preferences and personality of users through chat with them, providing more personalized dialogue.

[0849] "Example 2"

[0850] This invention is a system that utilizes an AI chat tool in employee training. Specifically, as part of employee training, a program is implemented to improve communication skills through chatting with AI. For example, training can be conducted to draw out new perspectives and ideas through discussions with AI.

[0851] "Example 3"

[0852] This invention is a system that utilizes an AI chat tool for planning. Specifically, new ideas are generated through chatting with AI. For example, different perspectives and information provided by AI can be used to brainstorm plans.

[0853] The processing flow of each embodiment will be described below.

[0854] "Example 1"

[0855] Step 1: Launch the AI ​​engine, which uses natural language processing techniques to understand user input and generate appropriate responses.

[0856] Step 2: Receive input from the user. The input is in text format and contains the content of the conversation with the user.

[0857] Step 3: The AI ​​engine analyzes the user input and generates an appropriate response.

[0858] Step 4: The generated response is provided to the user, allowing them to chat with the AI.

[0859] "Example 2"

[0860] Step 1: As part of employee training, set up a program to improve communication skills through casual conversation with AI.

[0861] Step 2: The employee initiates a conversation with the AI, which analyzes the employee's input and generates an appropriate response.

[0862] Step 3: Improve employees' communication skills through casual conversation with AI.

[0863] "Example 3"

[0864] Step 1: Start brainstorming to come up with a plan, utilizing casual conversation with AI.

[0865] Step 2: Utilize the different perspectives and information provided by AI to generate new ideas. Step 3: Based on the ideas obtained through casual conversation with AI, create a concrete plan.

[0866] Example 1

[0867] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0868] Conventional chat tools have a one-sided dialogue with users, making it difficult to provide personalized responses. They also lack the ability to learn users' preferences and personalities, which can lead to a decline in satisfaction over long-term interactions. Furthermore, it is difficult to release stress or generate ideas through chatting.

[0869] 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.

[0870] In this invention, the server includes means for a user to input to a terminal, means for the terminal to send the input to the server, means for the server to analyze the input, means for the server to pass a prompt sentence to the generative AI model, means for the generative AI model to generate a response, means for the server to return the response to the terminal, means for the terminal to display the response to the user, and means for the server to save the dialogue history and perform learning. This personalizes dialogue with the user, improves long-term satisfaction, and enables users to relieve stress and generate ideas through casual conversation.

[0871] The "means for a user to input information into a terminal" refers to a means for providing an interface for a user to input the content of a chat using a text box or input field.

[0872] The "means by which the terminal transmits input to the server" refers to a communication means for transmitting text data input by the user to the server via the network.

[0873] The "means by which the server analyzes the input" refers to the means by which the server analyzes the received user input text using natural language processing technology to understand the grammar and meaning.

[0874] "Means for the server to pass a prompt sentence to the generative AI model" refers to a means for passing the analyzed input text to the generative AI model as a prompt sentence.

[0875] "Means by which the generative AI model generates a response" refers to means by which the generative AI model generates an appropriate response based on a prompt sentence.

[0876] "Means for the server to return a response to the terminal" refers to the means for returning the response received from the generative AI model to the terminal via the network.

[0877] The "means for the terminal to display the response to the user" refers to a means for visually displaying the response received from the server to the user.

[0878] The "means for the server to store the dialogue history and perform learning" refers to a means for storing the dialogue history with the user in a database and learning the user's preferences and personality using a machine learning algorithm.

[0879] MODE FOR CARRYING OUT THE INVENTION

[0880] This invention is a system for providing personalized dialogue through casual conversation with a user. The system includes a series of processes in which a user inputs information into a terminal, the input is sent to a server, the server analyzes the input, passes a prompt to a generative AI model, the generative AI model generates a response, the server returns the response to the terminal, the terminal displays the response to the user, the server saves the dialogue history, and the server performs learning.

[0881] Hardware and software used

[0882] The server uses the following software:

[0883] Natural language processing libraries (e.g., NLTK, spaCy)

[0884] Generative AI models (e.g., OpenAI's GPT-3)

[0885] Machine learning algorithms (e.g. TensorFlow, PyTorch)

[0886] A terminal uses hardware and software to provide a user interface:

[0887] A web browser or mobile application to display text boxes and input fields

[0888] Internet connection for network communications

[0889] Specific operation of the system

[0890] 1. The user types into the terminal

[0891] The user enters the content of the conversation into the text box on the device. For example, they might enter "What's the weather like today?" When the user presses the send button, the conversation proceeds to the next step.

[0892] 2. The device sends the input to the server

[0893] The terminal sends the text data entered by the user to the server using an HTTP request. Specifically, the terminal converts the input data into JSON format and sends a POST request to the server's API endpoint.

[0894] 3. The server parses the input

[0895] The server analyzes the received user input. It uses a natural language processing library built using Python (e.g., NLTK or spaCy) to tokenize the input text and tag it with parts of speech. For example, it analyzes the input "What's the weather like today?" to understand the meaning and grammatical structure of each word.

[0896] 4. The server passes the prompt to the generative AI model

[0897] The server passes the analyzed input as a prompt to a generative AI model (e.g., OpenAI's GPT-3). Specifically, the prompt is sent to the generative AI model as an API request. For example, the prompt "What's the weather like today?" is passed to GPT-3.

[0898] 5. The generative AI model generates a response

[0899] The generative AI model generates an appropriate response based on the prompt it receives. GPT-3 has been trained with a large amount of data in advance and has advanced natural language generation capabilities. For example, it generates a response such as "It's sunny today."

[0900] 6. The server returns the response to the device

[0901] The server returns the response received from the generative AI model to the device. Specifically, it converts the response data into JSON format and sends it to the device as an HTTP response.

[0902] 7. The terminal displays the response to the user

[0903] The device displays the response received from the server to the user. Specifically, it parses the received JSON data and displays the response in a text box or chat window. For example, it displays the response "It's sunny today" to the user.

[0904] 8. The server stores the dialogue history and performs learning.

[0905] The server stores the user's interaction history in a database. It then uses machine learning algorithms (e.g., TensorFlow or PyTorch) to analyze the interaction data and learn the user's preferences and personality. This allows it to provide more personalized responses in subsequent interactions.

[0906] Specific examples

[0907] Example 1

[0908] The user types "What's the weather like today?" into the device and presses the send button. The device sends this input to the server. The server analyzes the input and passes the prompt "What's the weather like today?" to a generative AI model (e.g., GPT-3). The generative AI model generates a response, "It's sunny today," and returns it to the server. The server returns this response to the device, which displays it to the user.

[0909] Example 2

[0910] The user types "What movie do you recommend?" into the device and presses the send button. The device sends this input to the server. The server analyzes the input and passes the prompt "What movie do you recommend?" to a generative AI model (e.g., GPT-3). The generative AI model generates a response saying "The latest recommendation is 'Inception.'" and returns it to the server. The server returns this response to the device, which displays it to the user.

[0911] Prompt Sentence Examples

[0912] 1. "What's the weather like today?"

[0913] 2. "What movie do you recommend?"

[0914] 3. "Tell me about a book you read recently."

[0915] These prompts can be fed into a generative AI model to generate appropriate responses.

[0916] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0917] Step 1:

[0918] The user types into the terminal.

[0919] The user enters the content of the conversation into the text box on the terminal. For example, they might enter "What's the weather like today?". When the user presses the send button, the input proceeds to the next step. The input is text data, and the output is the event of the send button being pressed.

[0920] Step 2:

[0921] The device sends input to the server.

[0922] The terminal sends the text data entered by the user to the server using an HTTP request. Specifically, the terminal converts the input data into JSON format and sends a POST request to the server's API endpoint. The input is the user's text data, and the output is the JSON data sent to the server.

[0923] Step 3:

[0924] The server parses the input.

[0925] The server parses the received user input. It uses a natural language processing library built using Python (e.g., NLTK or spaCy) to tokenize the input text and tag it with parts of speech. For example, it parses the input "What's the weather like today?" to understand the meaning and grammatical structure of each word. The input is text data in JSON format, and the output is the parsed text data.

[0926] Step 4:

[0927] The server passes the prompt to the generative AI model.

[0928] The server passes the analyzed input as a prompt to the generative AI model (e.g., OpenAI's GPT-3). Specifically, the prompt is sent to the generative AI model as an API request. For example, the prompt "What's the weather like today?" is passed to GPT-3. The input is the analyzed text data, and the output is the prompt sent to the generative AI model.

[0929] Step 5:

[0930] A generative AI model generates a response.

[0931] The generative AI model generates an appropriate response based on the received prompt. GPT-3 has been trained with a large amount of data in advance and has advanced natural language generation capabilities. For example, it generates the response "It's sunny today." The input is the prompt, and the output is the generated response text.

[0932] Step 6:

[0933] The server returns a response to the terminal.

[0934] The server returns the response received from the generative AI model to the terminal. Specifically, it converts the response data into JSON format and sends it to the terminal as an HTTP response. The input is the generated response text, and the output is the JSON data sent to the terminal.

[0935] Step 7:

[0936] The terminal displays the response to the user.

[0937] The terminal displays the response received from the server to the user. Specifically, it parses the received JSON data and displays the response in a text box or chat window. For example, it displays the response "It's sunny today" to the user. The input is the JSON data received from the server, and the output is the response text displayed to the user.

[0938] Step 8:

[0939] The server stores the dialogue history and performs learning.

[0940] The server stores the user's interaction history in a database. It then uses machine learning algorithms (e.g., TensorFlow or PyTorch) to analyze the interaction data and learn the user's preferences and personality. This allows it to provide more personalized responses in subsequent interactions. The input is the interaction history data, and the output is an updated learning model.

[0941] (Application example 1)

[0942] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0943] In today's brick-and-mortar stores, personalized service is required to improve the customer experience. However, traditional methods make it difficult to effectively utilize customer preferences and past purchase history, and in-store guidance and product suggestions are uniform, which can lead to a decline in customer satisfaction. Furthermore, there is a lack of ways to relieve stress or increase customer loyalty through dialogue with customers. To solve these issues, a new system utilizing AI technology is needed.

[0944] 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.

[0945] In this invention, the server includes means for developing and providing AI-based chat tools to improve human communication skills, means for generating ideas through AI-based chat, means for releasing stress through AI-based chat, means for increasing attachment to others through AI-based chat, means for improving the customer experience in a physical store through AI-based chat, means for making personalized product suggestions based on the customer's preferences and past purchase history through AI-based chat, and means for guiding customers within the store through AI-based chat. This makes it possible to provide personalized services according to the individual needs of each customer, thereby improving customer satisfaction and providing a pleasant shopping experience within the store.

[0946] "Human communication skills" are the ability to effectively exchange information with and understand others.

[0947] An "AI chat tool" is software or a system that uses artificial intelligence technology to engage in natural conversations with users.

[0948] A "development and delivery method" is a method or process for designing, building, and making available to users a particular technology or service.

[0949] "Idea generation methods" are methods or processes for generating creative ideas and new thoughts.

[0950] A "stress reliever" is a method or process for reducing mental tension and anxiety and for relaxing.

[0951] "Attachment strategies" are methods or processes for increasing familiarity and liking for others.

[0952] "Measures to improve the customer experience in physical stores" are methods and processes that increase the satisfaction and comfort that customers feel in physical stores.

[0953] "Customer preferences and past purchase history" refers to the customer's tendency to like products and services and records of products and services they have purchased in the past.

[0954] "Personalized product recommendations" is the method or process of recommending specific products or services to individual customers based on their preferences and needs.

[0955] "Means of providing guidance within the store" refers to the methods and processes of providing information and guidance to make it easier for customers to find the products and services they are looking for within the store.

[0956] To implement the present invention, the following system configuration and program are required.

[0957] System Configuration

[0958] 1. Hardware:

[0959] Smartphone: A device through which a user operates an application.

[0960] Server: A device that hosts the AI ​​engine and processes user interactions.

[0961] 2. Software:

[0962] AI Engine: Uses generative AI models such as OpenAI's GPT-3.

[0963] Natural Language Processing (NLP) libraries: Libraries for parsing user input and generating appropriate responses.

[0964] Database: A database for storing customer preferences and past purchase history.

[0965] Program processing explanation

[0966] The server receives text data entered by the user through the smartphone. The received data is analyzed using a natural language processing library. The analyzed data is sent to an AI engine, which generates an appropriate response. The generated response is then sent back to the smartphone and displayed to the user.

[0967] Specifically, the following process occurs:

[0968] 1. Receiving user input:

[0969] A user inputs text through an application on their smartphone, which is then sent to a server.

[0970] 2. Data Analysis:

[0971] The server uses natural language processing libraries to parse the user's input, which is then used to understand the user's intent and the question they are asking.

[0972] 3. Response generation by AI engine:

[0973] The analysis results are sent to an AI engine (e.g., OpenAI's GPT-3), which generates an appropriate response based on the user's input.

[0974] 4. Sending the response:

[0975] The generated response is sent from the server to the smartphone and displayed to the user.

[0976] Specific examples

[0977] For example, if a user types "I'm looking for new shoes, any recommendations?", the following prompt will be generated:

[0978] User: I'm looking for some new shoes, any recommendations?

[0979] AI:

[0980] Based on this prompt, the AI ​​engine generates an appropriate response, such as, "Could you please tell me the style and purpose of the shoes you are looking for? For example, are they for running or casual everyday wear?"

[0981] In this way, personalized product suggestions and in-store guidance can be provided through dialogue with the user, making it possible to provide services that meet the individual needs of each customer, improving customer satisfaction and providing a comfortable shopping experience in the store.

[0982] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0983] Step 1:

[0984] A user starts an application on their smartphone and enters a question or request in a text input field. The entered text is sent from the smartphone to a server. The input data is text data that includes the user's question or request.

[0985] Step 2:

[0986] The server analyzes the received text data using a natural language processing (NLP) library. As a result of the analysis, the user's intent and question are extracted. The input data is the user's text data, and the output data is the analyzed intent and question.

[0987] Step 3:

[0988] The server generates a prompt based on the analysis results. This prompt converts the user's input into an appropriate format in preparation for sending it to the AI ​​engine. The input data is the analyzed intent or question, and the output data is the generated prompt.

[0989] Step 4:

[0990] The server sends the generated prompt sentence to an AI engine (e.g., OpenAI's GPT-3). The AI ​​engine generates an appropriate response based on the prompt sentence. The input data is the prompt sentence, and the output data is the response generated by the AI ​​engine.

[0991] Step 5:

[0992] The server sends the response received from the AI ​​engine to the smartphone. The smartphone displays the received response to the user. The input data is the response from the AI ​​engine, and the output data is the text displayed to the user.

[0993] Step 6:

[0994] The user checks the response displayed on their smartphone and enters further questions or requests as needed. This process is repeated until the user is satisfied. The input data is the user's new question or request, and the output data is text data that is sent back to the server.

[0995] In this way, a continuous dialogue takes place between the user and the AI ​​engine, providing personalized product suggestions and in-store guidance.

[0996] Example 2

[0997] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0998] In today's business environment, employees are expected to improve their communication skills, generate new ideas, release stress, and develop affection for others. However, there is a lack of effective ways to improve these skills. In addition, there is a need for efficient and effective ways to develop these skills in employee training and planning.

[0999] 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. In this invention, the server includes a means for a user to input a prompt sentence to a terminal, a means for the terminal to send the prompt sentence to the server, a means for the server to input a prompt sentence to a generative AI model, a means for the generative AI model to generate a response, a means for the server to send the generated response to the terminal, a means for the terminal to display the response to the user, and a means for the user to input a prompt sentence again and continue the dialogue. This enables the user to hone their communication skills, generate new ideas, relieve stress, and increase affection for the other person through dialogue with AI.

[1000] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and perform tasks such as learning, reasoning, and problem-solving.

[1001] An "interactive tool" is software or a system that allows users and artificial intelligence to communicate in natural language.

[1002] A "prompt sentence" is text data such as a question or instruction that a user inputs into a dialogue tool.

[1003] A "terminal" is a device such as a computer, smartphone, or tablet that a user uses to access an interaction tool.

[1004] A "server" is a computer system that performs back-end processing for the interactive tool and executes the generative AI model.

[1005] A "generative AI model" is an artificial intelligence algorithm for generating appropriate responses based on a user's prompt.

[1006] A "response" is text data that a generative AI model generates in response to a prompt.

[1007] "Employee training" is an educational program implemented by a company with the aim of improving employees' skills and acquiring knowledge.

[1008] "Planning" is the process of planning a new project or business strategy and creating a specific implementation plan.

[1009] MODE FOR CARRYING OUT THE INVENTION

[1010] This invention is a system that uses dialogue tools based on artificial intelligence to improve communication skills in employee training and planning, generate new ideas, relieve stress, and increase affection for others. A specific embodiment of this system is shown below.

[1011] Hardware and software used

[1012] This system uses the following hardware and software:

[1013] Device: A device used by a user, such as a PC, smartphone, or tablet.

[1014] Server: A computer system that performs back-end processing for an interactive tool.

[1015] Generative AI models: Artificial intelligence algorithms such as OpenAI's GPT-4.

[1016] Data processing and calculation

[1017] In this system, the following data processing and calculation are performed.

[1018] 1. User input:

[1019] The user uses the terminal to enter a prompt sentence, for example, "I'd like to discuss ideas for a new project."

[1020] 2. Send the prompt:

[1021] The terminal sends the prompt text entered by the user to the server via an Internet connection.

[1022] 3. Input to the generative AI model:

[1023] The server inputs the received prompt sentence into the generative AI model, converts the prompt sentence into an appropriate format, and passes it to the AI ​​model.

[1024] 4. Generate response:

[1025] The generative AI model generates an appropriate response based on the input prompt, such as "What is the purpose of the project?"

[1026] 5. Sending the response:

[1027] The server sends the response received from the generative AI model to the device, converts the response data into an appropriate format, and sends it to the device.

[1028] 6. View the response:

[1029] The terminal displays the response received from the server to the user, who can then check the response from the AI ​​on the terminal screen.

[1030] 7. Continuing the dialogue:

[1031] The user continues the dialogue by inputting a prompt sentence based on the displayed response, for example, "The purpose of the project is to develop a new market." By repeating this process, the dialogue between the user and the AI ​​continues.

[1032] Specific examples

[1033] A specific example of dialogue is shown below.

[1034] Example prompt sentence:

[1035] The user types into the terminal, "I'd like to come up with some ideas for a new marketing strategy."

[1036] Dialogue flow:

[1037] 1. User: "I want to come up with ideas for a new marketing strategy."

[1038] 2. AI: “Who is the target audience for that marketing strategy?”

[1039] 3. User: "I'm a young person in my 20s or 30s."

[1040] 4. AI: “What media will you use to reach that target audience?”

[1041] 5. User: "I would like to use social media primarily."

[1042] In this way, users can hone their communication skills, generate new ideas, relieve stress, and increase affection for others through dialogue with AI.

[1043] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1044] Step 1:

[1045] The user inputs a prompt into the device. Using a device such as a PC or smartphone, the user inputs a prompt such as "I would like to discuss ideas for a new project" into the input field of the dialogue tool. The input prompt is temporarily stored in the device's memory.

[1046] Step 2:

[1047] The terminal sends the prompt text to the server. The terminal then sends the prompt text entered by the user to the server as an HTTP request. At this time, the prompt text is converted into a data format such as JSON and sent to the server via the Internet. The input is the prompt text, and the output is the request sent to the server.

[1048] Step 3:

[1049] The server inputs a prompt sentence to the generative AI model. The server generates an API request to pass the received prompt sentence to the generative AI model. For example, it sends the prompt sentence to the API endpoint of the generative AI model. The input is the prompt sentence, and the output is the request sent to the generative AI model.

[1050] Step 4:

[1051] The generative AI model generates a response. The generative AI model generates an appropriate response based on the input prompt. For example, it generates a response such as, "What is the purpose of the project?" The input is the prompt, and the output is the generated response.

[1052] Step 5:

[1053] The server sends the generated response to the terminal. The server sends the response received from the generative AI model to the terminal as an HTTP response. At this time, the response data is converted into a data format such as JSON and sent to the terminal via the Internet. The input is the generated response, and the output is the response sent to the terminal.

[1054] Step 6:

[1055] The terminal displays the response to the user. The terminal displays the response received from the server in the user interface. The user can check the response from the AI ​​on the terminal screen. The input is the generated response, and the output is the display in the user interface.

[1056] Step 7:

[1057] The user inputs a new prompt sentence and the dialogue continues. The user inputs a new prompt sentence based on the displayed response. For example, the user might input, "The purpose of the project is to develop new markets." By repeating this process, the dialogue between the user and the AI ​​continues. The input is a new prompt sentence, and the output is the continuation of the dialogue.

[1058] (Application example 2)

[1059] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1060] Traditional employee training and customer service training have made it difficult to simulate actual customer interactions, making it difficult to improve effective communication skills. Furthermore, opportunities for store employees to practice actual customer service scenarios are limited, so an effective method for improving the quality of customer service was needed.

[1061] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for developing and providing an AI-based chat tool to improve human communication skills, means for generating ideas through the AI-based chat, means for releasing stress through the AI-based chat, means for increasing attachment to others through the AI-based chat, means for store clerks in a physical store to wear smart glasses and improve their customer service skills through chat with the AI, and means for practicing customer service scenarios and customer interactions through the AI-based chat. This enables store clerks to practice actual customer service scenarios in real time and improve the quality of customer interaction.

[1062] "Human communication skills" refers to the ability of humans to effectively exchange information with and understand others.

[1063] An "AI chat tool" is software or a system that uses artificial intelligence to have natural conversations with humans.

[1064] "Development and provision" refers to the process of designing the technologies and methods necessary to achieve a specific purpose and providing them in a form that can actually be used.

[1065] "Means of generating ideas" are methods and techniques for creating new ideas and solutions.

[1066] "Stress relief" refers to methods and techniques for reducing mental tension and anxiety and for relaxation.

[1067] "Means to increase attachment to others" are methods and techniques for increasing familiarity and liking for others.

[1068] A "brick and mortar store" is a store that exists in a physical location and where customers can visit in person to purchase goods or services.

[1069] A "store clerk" is an employee who works in a physical store and provides products and services to customers.

[1070] "Smart glasses" are a wearable eyeglass-type device equipped with computer functions, and are capable of displaying information and accepting voice input.

[1071] "Customer service skills" are the techniques and abilities required to respond appropriately to customers and increase their satisfaction.

[1072] A "customer service scenario" is a simulation that assumes specific situations and conversation flows when dealing with customers.

[1073] "Customer service practice" is training that simulates actual customer service, with the aim of improving skills.

[1074] This invention is a system in which store clerks wear smart glasses in brick-and-mortar stores and improve their customer service skills through casual conversations with AI. Specific embodiments for realizing this system are described below.

[1075] System Configuration

[1076] This system uses the following hardware and software:

[1077] Hardware: Smart glasses, microphone, speaker

[1078] Software: OpenAI API, Google Speech Recognition API, gTTS (Google Text-to-Speech)

[1079] Program processing explanation

[1080] The server runs a program that uses the generative AI model to enable conversation between the store clerk and the AI. Specifically, it performs the following processes:

[1081] 1. Acquiring voice input:

[1082] The terminal (a microphone attached to smart glasses) captures the customer's question as voice.

[1083] The device converts the captured audio into text using the Google Speech Recognition API.

[1084] 2. AI-generated responses:

[1085] The server inputs the customer's textual question as a prompt into the generative AI model (OpenAI API).

[1086] The server takes the response from the generative AI model and generates an appropriate answer.

[1087] 3. Generate audio output:

[1088] The server converts the generated text response into speech using gTTS.

[1089] The device plays the converted audio through the speaker.

[1090] Specific examples

[1091] For example, if a store clerk asks, "How do you want to use this product?", the process will proceed as follows:

[1092] 1. Acquiring voice input:

[1093] The device captures the customer's voice question, "How do you use this product?", and converts it into text.

[1094] 2. AI-generated responses:

[1095] The server inputs the following prompt sentence into the generative AI model:

[1096] Customer Question: How do I use this product?

[1097] Clerk's response:

[1098] The server receives the response from the generative AI model: "First, turn on this product, then..."

[1099] 3. Generate audio output:

[1100] The server converts the received response into speech using gTTS, which the device plays through its speaker.

[1101] In this way, store associates can practice real-life customer interaction scenarios in real time and improve the quality of their customer interactions.

[1102] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1103] Step 1:

[1104] Acquiring voice input

[1105] The terminal (a microphone attached to smart glasses) captures the customer's question as voice.

[1106] Input: Customer voice question

[1107] Specific operation: The microphone captures voice data and sends it to the voice recognition module in the device.

[1108] Output: Audio data

[1109] Step 2:

[1110] Speech-to-text

[1111] The device converts the acquired voice data into text using the Google Speech Recognition API.

[1112] Input: Audio data

[1113] Specific operation: Sends audio data to the Google Speech Recognition API and receives text data.

[1114] Output: Text data (customer questions)

[1115] Step 3:

[1116] Generate prompt statement

[1117] The server generates a prompt based on the customer's textual question.

[1118] Input: Text data (customer question)

[1119] What it does: Inserts text data into a template to create a prompt suitable for the generative AI model.

[1120] Output: prompt statement

[1121] Customer Question: How do I use this product?

[1122] Store clerk's response:

[1123] Step 4:

[1124] AI-powered response generation

[1125] The server inputs the generated prompt sentence into a generative AI model (OpenAI API) to generate an appropriate answer.

[1126] Input: prompt statement

[1127] Specific behavior: Sends a prompt to the OpenAI API and receives the generated text response.

[1128] Output: Text response (clerk's answer)

[1129] Step 5:

[1130] Generate audio output

[1131] The server converts the generated text response into speech using gTTS.

[1132] Input: Text response

[1133] What it does: Sends a text response to gTTS and generates an audio file.

[1134] Output: Audio data (clerk's response)

[1135] Step 6:

[1136] Playing audio

[1137] The terminal reproduces the generated audio data through a speaker.

[1138] Input: Voice data (clerk's response)

[1139] Specific operation: Sends audio data to the speaker and plays the audio.

[1140] Output: A voice response to the customer

[1141] Example 3

[1142] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1143] Conventional chat tools do not adequately support users in generating new ideas, and often respond unnaturally to user input. This makes it difficult for users to effectively plan and brainstorm. Furthermore, there is a lack of systems that can generate appropriate responses to prompts entered by users.

[1144] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1145] In this invention, the server includes means for a user to access the chat tool from a terminal, means for the user to input a prompt sentence, means for the terminal to send the input prompt sentence to the server, means for the server to send the prompt sentence to the generative AI model, means for the generative AI model to generate a response, means for the server to receive the generated response, means for the server to send the response to the terminal, and means for the terminal to display the response to the user. This allows users to generate new ideas through chatting with AI and effectively carry out planning and brainstorming.

[1146] "User" refers to a person who uses the system to chat with AI.

[1147] A "terminal" is a device used by a user to access the system, and includes a personal computer, a smartphone, etc.

[1148] A "chat tool" refers to software or an application that provides an interface for users to interact with AI.

[1149] A "prompt" is text that a user enters into a chat tool, including questions or requests for the AI.

[1150] "Server" refers to the computer system responsible for receiving user input, sending it to the generative AI model, and returning the generated response to the user.

[1151] "Generative AI model" refers to an artificial intelligence algorithm or software that generates appropriate responses based on a user prompt.

[1152] "Response" refers to the text that a generative AI model generates in response to a user prompt.

[1153] "Preprocessing" refers to the process of converting the user's input text into a format that is easy for the generative AI model to understand.

[1154] "Post-processing" refers to the process of formatting the responses generated by the generative AI model into a format that is natural for the user.

[1155] This invention is a system that allows users to generate new ideas through casual conversations with AI, and to effectively carry out planning and brainstorming. Specific embodiments of this system are described below.

[1156] First, the user accesses the chat tool using a device such as a PC or smartphone. The chat tool is provided through a browser or a dedicated application. When the user accesses the chat tool's interface, a text box appears in which the user can enter a prompt.

[1157] For example, a user might input, "I want to think of a new product idea. Please tell me about recent trends." This prompt is sent from the device to the server. The server then preprocesses the received prompt to send it to a generative AI model (e.g., OpenAI's GPT-4).

[1158] The generative AI model generates an appropriate response based on the prompt it receives, for example, "What do you think of some trendy smart home appliances?" The generated response is then sent to a server, which then post-processes it and formats it in a way that is natural for the user.

[1159] The server then sends the processed response to the device, which then displays it on the chat tool's interface for the user to review. Through this process, the user can gain new ideas through chatting with the AI.

[1160] This system supports planning and brainstorming by using a generative AI model to generate appropriate responses to prompts entered by the user and providing them to the user. It also allows users to relieve stress and increase affection for the other party through dialogue with the AI.

[1161] The specific hardware includes the device used by the user (PC or smartphone) and the server, while the software includes the chat tool interface, the generative AI model (e.g., OpenAI's GPT-4), and the pre-processing and post-processing programs that run on the server.

[1162] This system allows users to obtain new perspectives and information through casual conversation with the AI, enabling them to carry out effective planning and brainstorming. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1163] Step 1:

[1164] The user accesses the chat tool from the terminal.

[1165] Specifically, the user opens a browser on their computer or smartphone and accesses the chat tool's URL. This displays the chat tool's interface. The input is the user's access operation, and the output is the chat tool's interface display.

[1166] Step 2:

[1167] The user enters a prompt statement.

[1168] Specifically, the user inputs a prompt into a text box displayed on the chat tool's interface. For example, the user might type, "I'd like to come up with a new product idea. Please tell me about recent trends." The input is the user's text input, and the output is the input prompt.

[1169] Step 3:

[1170] The terminal sends the entered prompt text to the server.

[1171] Specifically, the terminal sends the prompt text entered by the user to the server as an HTTP request. The input is the prompt text entered by the user, and the output is the prompt text sent to the server.

[1172] Step 4:

[1173] The server sends the prompt to the generative AI model.

[1174] Specifically, the server preprocesses the received prompt text and converts it into a format suitable for the generative AI model. The converted text is then sent to the generative AI model. The input is the prompt text sent to the server, and the output is the preprocessed prompt text sent to the generative AI model.

[1175] Step 5:

[1176] A generative AI model generates a response.

[1177] Specifically, the generative AI model generates an appropriate response based on the received prompt, for example, "What do you think of smart home appliances that incorporate the latest trends?" The input is the preprocessed prompt, and the output is the generated response.

[1178] Step 6:

[1179] The server receives the generated response.

[1180] Specifically, the server receives the response generated by the generative AI model. The received response is post-processed to make it natural for the user. The input is the response from the generative AI model, and the output is the post-processed response.

[1181] Step 7:

[1182] The server sends a response to the terminal.

[1183] As a specific operation, the server sends the post-processed response to the terminal as an HTTP response. The input is the post-processed response, and the output is the response sent to the terminal.

[1184] Step 8:

[1185] The terminal displays the response to the user.

[1186] Specifically, the terminal displays the response received from the server on the interface of the chat tool. The input is the response from the server, and the output is the response displayed to the user.

[1187] (Application example 3)

[1188] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1189] Conventional AI chat tools have been useful in improving human communication skills, relieving stress, and increasing affection, but they lacked the functionality to provide ideas useful for improving the efficiency of factory production lines or developing new products. They also lacked the functionality to collect the opinions and ideas of workers and use them to propose new production methods or product improvements. This created the problem of being unable to fully improve factory productivity or the efficiency of new product development.

[1190] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1191] In this invention, the server includes means for developing and providing chat tools using AI to improve human communication skills, means for generating ideas through AI chat, means for releasing stress through AI chat, means for increasing affection for others through AI chat, means used to provide ideas useful for improving the efficiency of factory production lines and developing new products, and means for collecting opinions and ideas from workers and proposing new production methods and product improvements based on them, thereby enabling improved factory productivity and more efficient new product development.

[1192] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[1193] A "chat tool" is software or a system that uses artificial intelligence to have natural conversations with humans.

[1194] A "production line" is a series of work processes arranged in a factory to efficiently produce products.

[1195] "Efficiency" refers to processes and methods for achieving maximum results while minimizing the use of resources and time.

[1196] "New product development" is the process of designing, prototyping, and manufacturing new products in response to market needs and technological advances.

[1197] "Gathering opinions and ideas" is the activity of gathering feedback and suggestions from stakeholders.

[1198] A "production method" is a specific procedure or technique for manufacturing a product.

[1199] An "improvement" is a specific proposal or plan for improving an existing product or process.

[1200] As an embodiment of the present invention, a system is constructed that provides ideas that are useful for improving the efficiency of factory production lines and developing new products. Specific embodiments of this system are described below.

[1201] System Configuration

[1202] The system consists of the following main components:

[1203] 1. Server: Hosts the AI ​​model and performs back-end processing for the chat tool.

[1204] 2. Terminals: Devices used by factory workers (e.g., tablets, smartphones).

[1205] 3. Users: Factory workers and managers.

[1206] Hardware and software used

[1207] Hardware: Factory robots (e.g. ABB's IRB series), tablets, smartphones.

[1208] Software: OpenAI API, Python, database management system (e.g., MySQL).

[1209] Data processing and calculation

[1210] The server processes the data in the following steps:

[1211] 1. Data collection: Collecting worker opinions and ideas from devices.

[1212] 2. Data analysis: The collected data is input into an artificial intelligence model, and new ideas are generated using a generative AI model.

[1213] 3. Providing results: The generated ideas are returned to the terminal and presented to the worker.

[1214] Specific examples

[1215] For example, when considering efficiency improvements on a factory production line, the following prompt sentences can be used:

[1216] Worker: Our production line has been inefficient lately. How can we improve it?

[1217] AI: That's an interesting question. For example, where do you feel you're losing efficiency?

[1218] Worker: The assembly process is particularly time-consuming.

[1219] AI: I see, which part of the assembly process takes the most time?

[1220] Worker: The screw tightening part.

[1221] AI: So, please suggest a new idea for automating the screw-driving process.

[1222] Based on this prompt, the server uses a generative AI model to generate new ideas and return them to the device, such as a specific proposal such as "introduce a specific robotic arm to automate the screw-driving process."

[1223] In this way, factory productivity can be improved and new product development can be made more efficient.

[1224] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1225] Step 1:

[1226] A user uses a terminal to input problems and opinions regarding the factory production line.

[1227] Input: User feedback or concerns (e.g., "Our production line has been inefficient lately. How can we improve it?")

[1228] Output: User input data

[1229] Specific operation: The user enters the problem in text format into the input form on the terminal and presses the submit button.

[1230] Step 2:

[1231] The terminal transmits the user's input data to the server.

[1232] Input: User-entered data

[1233] Output: Data sent to the server

[1234] Specific operation: The terminal sends the user's input data to the server as an HTTP request.

[1235] Step 3:

[1236] The server analyzes the received data and generates prompt sentences to input into the generative AI model.

[1237] Input: User-entered data

[1238] Output: Prompt sentence to be input to the generative AI model

[1239] Specific behavior: The server analyzes the user's input data and generates an appropriate prompt (e.g., "Our production line has been inefficient recently. How can we improve it?").

[1240] Step 4:

[1241] The server uses generative AI models to generate new ideas.

[1242] Input: prompt statement

[1243] Output: A new idea (e.g., "Introduce a specific robotic arm to automate the screw-driving process.")

[1244] Specific operation: The server inputs a prompt sentence into the generative AI model (e.g., OpenAI API) and obtains the generated ideas.

[1245] Step 5:

[1246] The server transmits the generated ideas to the terminal.

[1247] Input: New Idea

[1248] Output: Ideas sent to the terminal

[1249] Specific operation: The server sends the generated idea to the terminal as an HTTP response.

[1250] Step 6:

[1251] The terminal displays the received ideas to the user.

[1252] Input: Ideas received from the server

[1253] Output: The idea displayed to the user

[1254] Specific operation: The device displays the received ideas on the screen so that the user can review them.

[1255] 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.

[1256] "Example 1"

[1257] The invention form that combines an emotion engine is to add an emotion engine to an AI engine.

[1258] It recognizes the user's emotions and provides dialogue that reflects those emotions. Specifically, it analyzes the emotional elements (e.g., joy, sadness, anger, surprise, etc.) contained in the text input from the user and generates the optimal response that corresponds to that emotion. In this form, the AI ​​engine can gain a deeper understanding of the user's emotions and provide a more human-like dialogue.

[1259] "Example 2"

[1260] An example of an invention incorporating an emotion engine being used in employee training is a program in which employees improve their communication skills through casual conversation with AI. In this program, the AI ​​engine recognizes the employee's emotions and provides dialogue that corresponds to those emotions. For example, if an employee is feeling stressed, the AI ​​engine will suggest topics to help them relax. In this form, the AI ​​engine can provide more effective communication training through dialogue that takes the employee's emotions into consideration.

[1261] "Example 3"

[1262] An example of how an invention incorporating an emotion engine can be used in planning is brainstorming, where new ideas are generated through casual conversation with AI. In this brainstorming session, the AI ​​engine recognizes the emotions of the participants and provides dialogue that reflects those emotions. For example, if a participant is confused, the AI ​​engine can provide new perspectives and information to stimulate ideas. In this form, the AI ​​engine can support more effective brainstorming through dialogue that takes into account the emotions of the participants.

[1263] The processing flow of each embodiment will be described below.

[1264] "Example 1"

[1265] Step 1: The user enters text into the AI ​​engine.

[1266] Step 2: The emotion engine analyzes the input text and recognizes the emotional elements it contains. Step 3: The AI ​​engine generates the optimal response based on the recognized emotions.

[1267] Step 4: The generated response is displayed to the user and the interaction progresses.

[1268] "Example 2"

[1269] Step 1: An employee starts chatting with the AI ​​engine.

[1270] Step 2: The emotion engine analyzes the employee's input text and recognizes the employee's emotions.

[1271] Step 3: The AI ​​engine generates optimal responses based on the emotions it recognizes, improving employees' communication skills.

[1272] "Example 3"

[1273] Step 1: Participants start brainstorming with the AI ​​engine.

[1274] Step 2: The emotion engine analyzes the participant's input text and recognizes their emotions. Step 3: Based on the emotions recognized by the AI ​​engine, it provides new perspectives and information to stimulate ideas.

[1275] Example 1

[1276] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1277] Conventional AI chat tools have the problem of being unable to fully understand the user's intentions and emotions, resulting in poor conversation quality. They also lack the ability to learn the user's preferences and personality, making it impossible to provide personalized conversations. Furthermore, these tools are often limited to specific uses, making them less versatile.

[1278] 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.

[1279] In this invention, the server includes means for receiving input text from a user, means for analyzing the user's intent using natural language processing technology, means for analyzing the user's emotions using emotion analysis technology, means for generating an appropriate response based on the analysis results, means for sending the generated response to the user, and means for saving a dialogue history with the user and using it as learning data. This makes it possible to accurately understand the user's intent and emotions and provide personalized dialogue. It also realizes a highly versatile system that can be used for a variety of purposes, such as employee training and planning.

[1280] The "means for receiving input text from the user" is a function for obtaining text data entered by the user and sending it to the system.

[1281] "Means of analyzing user intent using natural language processing technology" refers to technology that analyzes text data entered by the user and understands its context and meaning.

[1282] "Means for analyzing user emotions using emotion analysis technology" refers to technology for analyzing emotional elements contained in the user's input text and identifying the emotion.

[1283] The "means for generating an appropriate response based on the analysis results" is a function for generating an appropriate response to the user based on the results of natural language processing technology and sentiment analysis technology.

[1284] The "means for sending the generated response to the user" is a function for displaying or sending the generated response to the user.

[1285] "Means for saving dialogue history with the user and using it as learning data" refers to a function that records the content of past dialogues with the user, allows the system to learn from that data, and uses it for future dialogues.

[1286] This invention is a chat tool system that combines an AI engine and an emotion engine. Specifically, it works in cooperation with a server, terminals, and users.

[1287] System configuration

[1288] The server has the following functions:

[1289] 1. A means of receiving input text from the user

[1290] 2. Analyzing user intent using natural language processing technology

[1291] 3. Means of analyzing user emotions using emotion analysis technology

[1292] 4. A means of generating appropriate responses based on the analysis results

[1293] 5. A means of sending the generated response to the user

[1294] 6. A means to save user interaction history and use it as learning data

[1295] Hardware and software used

[1296] Hardware:

[1297] Server: Server equipment equipped with a high-performance processor and large-capacity memory

[1298] Devices: Personal computers, smartphones, tablets, etc. used by users

[1299] software:

[1300] Natural language processing technology: Python's NLTK library, Transformers library

[1301] Sentiment analysis technology: Python TextBlob, VADER Sentiment Analysis

[1302] Generative AI models: OpenAI's GPT-3 and GPT-4

[1303] Program processing

[1304] Receiving user input:

[1305] The device receives text data entered by the user and sends it to the server. For example, the user enters "I'm tired today."

[1306] Natural Language Processing:

[1307] The server sends the received text to the AI ​​engine, which uses Python's NLTK library to tokenize the text and perform grammar analysis, and the Transformers library to understand the context and analyze the user's intent.

[1308] Emotion analysis:

[1309] The server uses an emotion engine to extract emotions such as "fatigue" and "sadness" from the text "tired." The emotion engine calculates the emotion score of the text using TextBlob and evaluates the intensity of the emotion using VADER Sentiment Analysis.

[1310] Response generation:

[1311] The server generates a response such as "Please take a good rest today" based on the analysis results of the AI ​​engine and emotion engine. OpenAI's GPT-3 is used as the generative AI model, and the prompt is set as "If the user enters 'I'm tired today,' please generate an appropriate response."

[1312] Sending a response:

[1313] The server sends the generated response to the terminal, which then displays a message to the user saying, "Please take a good rest today."

[1314] Learning features:

[1315] The server stores the user's conversation history in a database. In subsequent conversations, the AI ​​engine refers to this history to learn the user's preferences and personality. For example, if the user frequently says "I'm tired," the AI ​​engine will learn that the user gets tired easily and provide more appropriate responses.

[1316] Specific examples

[1317] Explain what happens if the user types "I'm so happy today!"

[1318] 1. The device receives input from the user: "I'm so happy today!"

[1319] 2. The server uses an AI engine to analyze this text and understand the user's intent.

[1320] 3. The server uses an emotion engine to analyze the emotion "happy."

[1321] 4. The server generates a response based on the analysis: "That's great! What good happened?"

[1322] 5. The server generates a response and sends it to the terminal, which displays the response to the user.

[1323] 6. The server saves this dialogue history and uses it in subsequent dialogues.

[1324] In this way, the server, terminal, and user work together to create an AI-powered chat tool.

[1325] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1326] Step 1:

[1327] Receiving user input

[1328] A user inputs text into a terminal. For example, the user inputs "I'm tired today." The terminal sends this input text to a server. The input is the user's text data, and the output is the text data sent to the server.

[1329] Step 2:

[1330] Natural Language Processing

[1331] The server sends the received text data to the AI ​​engine, which uses Python's NLTK library to tokenize the text and perform grammatical analysis. It also uses the Transformers library to understand the context and analyze the user's intent. The input is the user's text data, and the output is the analyzed intent data.

[1332] Step 3:

[1333] Emotion analysis

[1334] The server uses an emotion engine to analyze the emotional elements contained in the user's input text. The emotion engine calculates the emotion score of the text using TextBlob and evaluates the intensity of the emotion using VADER Sentiment Analysis. For example, it extracts emotions such as "fatigue" and "sadness" from the text "tired." The input is the user's text data, and the output is the analyzed emotion data.

[1335] Step 4:

[1336] Response Generation

[1337] The server generates an appropriate response based on the analysis results of the AI ​​engine and emotion engine. OpenAI's GPT-3 is used as the generative AI model, and the prompt is set as "If the user enters 'I'm tired today,' please generate an appropriate response." For example, a response such as "Please take a good rest today" is generated. The input is the analyzed intent data and emotion data, and the output is the generated response data.

[1338] Step 5:

[1339] Sending a Response

[1340] The server sends the generated response to the terminal. The terminal displays the response to the user. For example, it displays "Please take a good rest today." The input is the generated response data, and the output is the response displayed to the user.

[1341] Step 6:

[1342] Learning Function

[1343] The server stores the user's interaction history in a database. In subsequent interactions, the AI ​​engine refers to this history to learn the user's preferences and personality. For example, if a user frequently says "I'm tired," the AI ​​engine will learn that the user gets tired easily and provide more appropriate responses. The input is the interaction history data, and the output is the learned user profile.

[1344] (Application example 1)

[1345] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1346] In traditional brick-and-mortar stores, it was difficult for store staff to instantly understand customers' emotions and preferences and provide appropriate product recommendations and customer service. Furthermore, service quality varied widely because it depended on the customer service skills and experience of the staff. Furthermore, there was a lack of effective ways to reduce customer stress and increase customer satisfaction.

[1347] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for developing and providing an AI-based chat tool to improve human communication skills, means for generating ideas through AI-based chat, means for releasing stress through AI-based chat, means for increasing attachment to others through AI-based chat, means for analyzing a user's emotions using emotion recognition technology and providing a dialogue based on those emotions, means for learning the user's preferences and personality and providing personalized dialogue, means for a sales clerk wearing smart glasses in a physical store to support conversations with customers, and means for providing appropriate product recommendations and customer service based on the customer's emotions and preferences. This enables sales clerks to instantly grasp the emotions and preferences of customers and provide appropriate product recommendations and customer service.

[1348] "Human communication skills" refers to the ability of humans to effectively exchange information with and understand others.

[1349] An "AI chat tool" is software or a system that uses artificial intelligence to engage in natural conversations with users.

[1350] A "development and delivery method" is a method or process for designing, implementing, and making available to users a particular technology or service.

[1351] "Idea generation methods" are methods and processes for generating creative ideas and new concepts.

[1352] A "stress reliever" is a method or process for reducing mental tension and anxiety and for relaxing.

[1353] "Attachment strategies" are methods or processes for increasing familiarity and liking for others.

[1354] "Emotion recognition technology" is a technology that analyzes a user's facial expressions, tone of voice, etc. to identify their emotional state.

[1355] "Means for learning user preferences and characteristics" refers to methods or processes for analyzing user preferences and behavioral patterns and providing personalized services based on those.

[1356] "Personalized interaction" is interaction that is customized to the user's individual preferences and personality.

[1357] A "brick and mortar store" is a store that exists in a physical location and provides goods or services in person.

[1358] "Smart glasses" are glasses-type devices that have built-in cameras, displays, sensors, etc. and have the ability to display information and collect data.

[1359] A "store visitor" is a customer who visits a physical store.

[1360] "Means to support dialogue" are methods and processes for smoothly communicating with users.

[1361] "Product suggestion" is the act of recommending appropriate products based on the customer's needs and preferences.

[1362] "Customer service" refers to the service and response provided by store staff to customers.

[1363] As an embodiment of the present invention, a system is constructed in which store clerks wearing smart glasses in a brick-and-mortar store support conversations with customers. A specific embodiment of this system is described below.

[1364] System configuration

[1365] Hardware

[1366] Smart glasses: Devices with built-in cameras and microphones that capture customers' facial expressions and voices in real time.

[1367] Server: A computer system for processing data and running AI models.

[1368] Network: Infrastructure for data communication between the smart glasses and the server.

[1369] software

[1370] OpenCV: A library for performing face recognition.

[1371] Transformers: Generative AI models for emotion recognition.

[1372] Natural language processing engine: Software for generating dialogue with users.

[1373] Database: A system for storing user preferences and interaction history.

[1374] Data processing and calculation

[1375] Camera input and face recognition

[1376] The camera in the smart glasses captures the customer's image in real time and recognizes their face using OpenCV, and the recognized face data is sent to the server.

[1377] Emotion analysis

[1378] The server uses Transformers' emotion recognition model to analyze the customer's emotions from the facial data sent, outputting the results as emotional states such as joy, sadness, anger, and surprise.

[1379] Personalized interaction generation

[1380] The natural language processing engine generates appropriate dialogue content based on the results of emotion analysis and the customer's preferences and dialogue history stored in a database. The dialogue content is displayed on the smart glasses' display, and the store clerk then makes appropriate product suggestions and provides appropriate customer service.

[1381] Specific examples

[1382] When a customer enters a physical store and approaches a sales associate wearing smart glasses, the camera in the smart glasses captures the customer's facial expression. The server analyzes the captured image and provides feedback such as, "This customer seems a little tired." The sales associate then suggests, "You seem tired today. Would you like to find something to help you relax?"

[1383] Prompt Sentence Examples

[1384] Analyze customer facial expressions to recognize their emotions, then generate appropriate product suggestions based on those emotions.

[1385] In this way, the system can instantly grasp the emotions and preferences of customers and provide appropriate product suggestions and customer service, thereby increasing customer satisfaction and improving the quality of service in physical stores.

[1386] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1387] Step 1:

[1388] The camera in the smart glasses captures video of customers in real time. The input is the video data of the customers, and the output is the captured video data. The smart glasses use their built-in camera to acquire video including the customers' faces.

[1389] Step 2:

[1390] The smart glasses transmit the captured video data to a server. The input is the captured video data, and the output is the video data transmitted to the server. The smart glasses transfer the video data to the server via a network.

[1391] Step 3:

[1392] The server recognizes faces from video data using OpenCV. The input is the video data sent to the server, and the output is the location information of the recognized face. The server analyzes the video data and identifies the location of the face.

[1393] Step 4:

[1394] The server uses a Transformers emotion recognition model to analyze the emotions of the recognized faces. The input is the location information of the recognized faces, and the output is the emotion analysis results. The server runs the emotion recognition model based on the location information of the faces to identify emotional states such as joy, sadness, anger, and surprise.

[1395] Step 5:

[1396] The server uses a natural language processing engine to generate appropriate dialogue content based on the emotion analysis results and the customer's preferences and dialogue history stored in a database. The input is the emotion analysis results and the customer's preferences and dialogue history, and the output is the generated dialogue content. The server combines the emotion analysis results with past data to generate the optimal dialogue content for the customer.

[1397] Step 6:

[1398] The server sends the generated interaction content to the smart glasses. The input is the generated interaction content, and the output is the interaction content sent to the smart glasses. The server transfers the interaction content to the smart glasses via a network.

[1399] Step 7:

[1400] The smart glasses display the conversation content on a display, and the store clerk makes appropriate product suggestions and provides customer service. The input is the conversation content sent to the smart glasses, and the output is the product suggestions and customer service provided by the store clerk. The smart glasses display the conversation content on a display, and the store clerk responds to the customer based on that.

[1401] Example 2

[1402] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1403] Improving employees' communication skills is an important issue in today's business environment. However, traditional training methods make it difficult to provide appropriate feedback based on each employee's emotions and situation, making it difficult to expect effective improvement in communication skills. Furthermore, there is a lack of means to provide appropriate support when employees are feeling stressed or trying to generate new ideas. This can lead to a decline in employee motivation and productivity.

[1404] 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.

[1405] In this invention, the server includes a means for analyzing a user's emotions in real time using an emotion recognition engine, a means for generating appropriate dialogue content based on the emotion data using a generative AI model, a means for collecting dialogue log data and evaluating the improvement of the user's communication skills, and a means for providing feedback to the user based on the analysis results. This makes it possible to provide appropriate dialogue and feedback according to the emotions and situations of each employee, which is expected to improve effective communication skills. In addition, by providing appropriate support when employees are feeling stressed or when generating new ideas, employee motivation and productivity can be improved.

[1406] An "artificial intelligence dialogue tool" is an artificial intelligence system designed to engage in natural dialogue with humans.

[1407] An "emotion recognition engine" is software or hardware that analyzes a user's input text or voice data and identifies their emotional state in real time.

[1408] A "generative AI model" is an artificial intelligence model that generates appropriate responses and dialogue content based on input data.

[1409] "Dialogue log data" refers to recorded data of dialogue between a user and an AI, which is saved for later analysis.

[1410] "Feedback" refers to advice and evaluations provided to users based on dialogue log data and the analysis results of the emotion recognition engine.

[1411] "Communication skills" are the ability to effectively convey information and understand others.

[1412] "Employee training" is an educational program conducted to improve employees' skills and knowledge.

[1413] "Stress" is a state of mental or physical tension or strain.

[1414] An idea is a new concept or idea.

[1415] Attachment refers to the familiarity and affection one feels toward a particular object.

[1416] This invention is a system for improving the communication skills of employees by utilizing dialogue tools using artificial intelligence in employee training. Specific embodiments of this system will be described below.

[1417] System configuration

[1418] Hardware

[1419] Server: Use a server with high-performance computing power. The server has the processing power to run the emotion recognition engine and generative AI model.

[1420] Terminal: The terminal used by the user is a device such as a PC, tablet, or smartphone. These terminals are capable of connecting to the Internet and have an interface for communicating with the server.

[1421] software

[1422] Emotion recognition engine: Software that analyzes user input text and identifies emotions in real time. Specifically, it uses engines that use natural language processing technology (e.g., IBM Watson's Tone Analyzer).

[1423] Generative AI model: An artificial intelligence model that generates appropriate dialogue content based on user emotion data. Specifically, we use a generative AI model (e.g., OpenAI's GPT-4).

[1424] Data collection and analysis tools: Tools for collecting interaction log data and evaluating the improvement of users' communication skills, including databases and analysis algorithms.

[1425] System Operation

[1426] 1. User login:

[1427] A user logs into the system using a terminal. To log in, a user ID and password are required. The server checks the user's authentication information against the database and proceeds to the next step only if authentication is successful.

[1428] 2. Launch the emotion recognition engine:

[1429] The server starts an emotion recognition engine, which analyzes the text data entered by the user in real time and identifies the user's emotion. For example, if the user enters "I'm very tired today," the emotion recognition engine will detect "fatigue."

[1430] 3. Start the dialogue generation engine:

[1431] The server launches the generative AI model, which generates appropriate dialogue content based on the emotional data obtained from the emotion recognition engine. For example, if the emotion recognition engine detects "fatigue," the dialogue generation engine generates a response such as, "Shall we talk about ways to relax?"

[1432] 4. Execute the dialogue:

[1433] The user interacts with the AI ​​through the device. The text entered by the user is sent to the server, which uses an emotion recognition engine and a dialogue generation engine to generate an appropriate response and return it to the device. For example, if the user enters "I'm having trouble finding new hobbies," the server will respond with "What are you interested in to find a new hobby?"

[1434] 5. Data collection and analysis:

[1435] The server collects dialogue log data. This data includes the user's input text, the analysis results of the emotion recognition engine, and the responses of the dialogue generation engine. The server analyzes this data to evaluate the improvement of the user's communication skills. For example, it analyzes how stressed the user is and what dialogues were effective.

[1436] 6. Providing Feedback:

[1437] The server provides the user with feedback based on the analysis results. The feedback includes specific advice to help improve the user's communication skills. For example, the server may provide advice such as, "When you are feeling stressed, it would be good to choose a topic that helps you relax."

[1438] Examples of concrete examples and prompts

[1439] Examples:

[1440] A user logs into the system using a terminal and enters, "I've been busy at work lately and feeling stressed." The server uses an emotion recognition engine to detect "stress" and responds using a generative AI model, asking, "Shall we talk about something to help you relax?" If the user replies, "Yes, please," the server continues, "Would you like to talk about your recent hobbies or ways of relaxing?" The log data of this conversation is stored on the server and later analyzed. Based on the analysis results, the server provides feedback such as, "When you're feeling stressed, it would be a good idea to choose a topic to help you relax."

[1441] Example prompt sentence:

[1442] "If the user is feeling stressed, generate a conversation that offers a topic to help them relax."

[1443] In this way, the system enables effective communication training by linking the server, terminals, and users.

[1444] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1445] Step 1:

[1446] A user logs into the system using a terminal. The user enters a user ID and password, which the terminal sends to the server. The server authenticates the user by checking the database. If authentication is successful, the server returns a login success message to the user. If authentication fails, the server returns an error message.

[1447] Step 2:

[1448] The server starts the emotion recognition engine. The text data entered by the user through the device is sent to the server. The server passes this text data to the emotion recognition engine, which analyzes the emotion in real time. For example, if the user enters "I'm very tired today," the emotion recognition engine detects "fatigue" and returns the result to the server.

[1449] Step 3:

[1450] The server launches the generative AI model. The emotion data obtained from the emotion recognition engine is passed to the generative AI model as input. The generative AI model generates appropriate dialogue content based on this emotion data. For example, if the emotion recognition engine detects "fatigue," the generative AI model generates a response such as "Shall we talk about ways to relax?" and returns the result to the server.

[1451] Step 4:

[1452] The server sends the generated dialogue content to the user's device. The user then interacts with the AI ​​through the device. The text entered by the user is sent back to the server, which uses an emotion recognition engine and generative AI model to generate an appropriate response and returns it to the device. For example, if the user enters, "I'm having trouble finding new hobbies these days," the server will respond with, "What are you interested in to find a new hobby?"

[1453] Step 5:

[1454] The server collects dialogue log data, storing data such as the user's input text, the analysis results of the emotion recognition engine, and the responses of the generative AI model. The server analyzes this data to evaluate the improvement of the user's communication skills. For example, it analyzes how stressed the user is and what dialogues were effective.

[1455] Step 6:

[1456] The server provides the user with feedback based on the analysis results. The feedback includes specific advice to help improve the user's communication skills. For example, the server generates advice such as "When you are feeling stressed, it would be good to choose a topic that will help you relax," and sends it to the user's device.

[1457] (Application example 2)

[1458] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1459] In the past, communication within factories was often not smooth between workers or between workers and machines, which could affect work efficiency and safety. Furthermore, there was a lack of appropriate communication methods to reduce worker stress and fatigue. This has led to a demand for improvements in the work environment and increased worker motivation.

[1460] 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.

[1461] In this invention, the server includes means for developing and providing AI-based chat tools to improve human communication skills, means for generating ideas through AI-based chat, means for releasing stress through AI-based chat, means for increasing attachment to others through AI-based chat, means for analyzing a user's emotions using an emotion recognition engine and providing dialogue based on those emotions, means installed in a robot working in a factory to facilitate communication with workers, means for generating appropriate responses using a generative AI model, and means for generating prompt sentences and providing responses. This enables smooth communication between workers and robots, improving work efficiency and the work environment.

[1462] An "AI chat tool to improve human communication skills" is a tool that uses artificial intelligence to engage in casual conversation in order to improve a person's ability to communicate effectively with others.

[1463] "Means of generating ideas" is a method of using artificial intelligence to draw out new perspectives and ideas through casual conversation.

[1464] "Means to relieve stress" is a method that uses artificial intelligence to reduce users' stress through casual conversation.

[1465] "Means to increase attachment to others" refers to a method that uses artificial intelligence to make users feel closer to and more attached to others through casual conversation.

[1466] An "emotion recognition engine" is an engine that analyzes a user's emotions and provides appropriate dialogue based on those emotions.

[1467] A "factory robot" is an automated mechanical device designed to perform tasks in a factory.

[1468] A "generative AI model" is a model that uses artificial intelligence to generate appropriate responses based on user input.

[1469] A "prompt" is a command used to cause an artificial intelligence to generate a particular response.

[1470] As an embodiment of the present invention, an AI chat tool is installed in a robot working in a factory, and a system is constructed to facilitate communication with workers. A specific embodiment of this system is shown below.

[1471] 1. System Program

[1472] This system uses the following hardware and software:

[1473] Hardware: Factory robots, microphones, speakers

[1474] Software: OpenAI API, Emotion Recognition API

[1475] 2. Program processing explanation

[1476] When a worker speaks to the robot, the server first captures the voice with a microphone and converts it into text data using speech recognition technology. This text data undergoes emotion analysis using the Emotion Recognition API. Based on the results of the emotion analysis, an appropriate prompt is generated.

[1477] The generated prompt is sent to the OpenAI API, which uses a generative AI model to generate an appropriate response, which is then converted back to voice and transmitted to the worker via a speaker.

[1478] 3. Specific Examples

[1479] For example, if a worker says, "I'm tired today," the emotion recognition engine will detect this and generate a prompt like this:

[1480] Prompt Sentence Examples

[1481] Give them something to talk about to relax.

[1482] This prompt is sent to the OpenAI API, and the generative AI model generates an appropriate response, such as a relaxing topic like, "What fun things did you do today?"

[1483] In this way, factory robots can facilitate communication with workers, improving work efficiency and the working environment.

[1484] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1485] Step 1:

[1486] The user talks to the robot.

[1487] Input: User's voice

[1488] Output: Audio data

[1489] Specific operation: When the user says, "I feel tired today," the robot's microphone picks up the voice.

[1490] Step 2:

[1491] The terminal uses voice recognition technology to convert the voice data into text data.

[1492] Input: Audio data

[1493] Output: Text data

[1494] Specific operation: The captured voice data is analyzed by voice recognition software and converted into text data such as "I'm tired today."

[1495] Step 3:

[1496] The server performs sentiment analysis on the text data using the Emotion Recognition API.

[1497] Input: Text data

[1498] Output: Emotion data (e.g., fatigue)

[1499] Specific operation: Send the text data "I'm tired today" to the Emotion Recognition API for emotion analysis. The result is the emotion data "fatigue."

[1500] Step 4:

[1501] The server generates a prompt sentence based on the emotion data.

[1502] Input: Emotion data

[1503] Output: prompt statement

[1504] Specific operation: Based on the emotional data "fatigue," generate a prompt sentence such as "Please give me a topic to talk about to help me relax."

[1505] Step 5:

[1506] The server uses the OpenAI API to generate a response based on the prompt.

[1507] Input: prompt statement

[1508] Output: Response text

[1509] Specific behavior: The generated prompt sentence is sent to the OpenAI API, and the generative AI model generates a response text such as "What fun things did you do today?"

[1510] Step 6:

[1511] The terminal converts the response text into audio data.

[1512] Input: Response text

[1513] Output: Audio data

[1514] Specific operation: The response text "What fun things did you have today?" is analyzed by speech synthesis software and converted into voice data.

[1515] Step 7:

[1516] The terminal transmits the audio data to the user through a speaker.

[1517] Input: Audio data

[1518] Output: A spoken response to the user

[1519] Specific operation: The converted voice data is transmitted to the user through the speaker, and the user receives a voice response saying, "What fun things did you have today?"

[1520] Example 3

[1521] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1522] Conventional chat tools provide dialogue without considering the user's feelings, making it difficult to brainstorm effectively or relieve stress according to the user's needs. Furthermore, when users generate new ideas, they are unable to provide the appropriate perspectives or information, which reduces the efficiency of planning.

[1523] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1524] In this invention, the server includes a means for a user to access the system through a terminal, a means for the server to receive user input and analyze emotions using an emotion analysis engine, a means for the server to send prompt sentences to a generative AI model and generate a dialogue, a means for the server to return the generated dialogue to the user, and a means for the user to obtain new ideas and enter input again. This allows for dialogue based on the user's emotions, enabling effective brainstorming and stress relief. Furthermore, by allowing users to obtain new perspectives and information, the efficiency of planning can be improved.

[1525] "User" refers to a person who uses the system.

[1526] "Terminal" refers to an electronic device such as a computer or smartphone that a user uses to access the system.

[1527] A "server" is a computer that is the central part of a system and is a device that receives and processes user input.

[1528] An "emotion analysis engine" refers to software that analyzes emotions from user input and identifies those emotions.

[1529] A "generative AI model" refers to an artificial intelligence model that generates dialogue based on user input and prompts.

[1530] A "prompt" is a piece of text containing instructions or questions that a generative AI model uses to generate a dialogue.

[1531] "Dialogue" refers to the exchange of messages between the user and the system (AI).

[1532] "Brainstorming" refers to a method for freely sharing multiple ideas and generating new ideas.

[1533] "Planning" refers to the process of creating plans for a new project or product.

[1534] This invention is a system that supports effective brainstorming and stress relief by allowing users to access the system through their terminals and having the server generate dialogue using an emotion analysis engine and a generative AI model.

[1535] The server provides a web application that allows users to access the system through their terminals. Users use terminals such as PCs or smartphones to access the system's web application and log in to the system by entering authentication information on the login screen.

[1536] When a user types a message into the chat box in the system, the server receives the message and sends it to a sentiment analysis engine. For example, IBM Watson's Tone Analyzer can be used as the sentiment analysis engine. The sentiment analysis engine analyzes the user's message and identifies its emotion. For example, if a user types, "I'm worried about whether this idea will work," the sentiment analysis engine will detect "anxiety."

[1537] Next, the server creates a prompt sentence based on the emotional information obtained from the emotion analysis engine and sends it to the generative AI model. OpenAI's GPT-4, for example, can be used as the generative AI model. The prompt sentence is text containing instructions and questions for generating a dialogue that corresponds to the user's emotions. For example, a prompt sentence such as "The user is feeling anxious. Please generate a dialogue that will alleviate the user's anxiety by providing new perspectives and information" is sent to the generative AI model. The generative AI model generates a dialogue based on this prompt sentence.

[1538] The server receives the dialogue returned by the generative AI model and sends it to the user's device. For example, a message is displayed to the user saying, "Let's think about it from a new perspective. For example, what if we applied this idea to a different market?"

[1539] The user then comes up with a new idea based on the AI's suggestions and inputs it back into the system. For example, they might input, "The idea of ​​applying it to a different market is good. Which market would that be specifically?" This triggers a repeat of the dialogue generation process using the sentiment analysis engine and generative AI model.

[1540] Examples:

[1541] Suppose a user is planning a new smartphone app. The user uses the system as follows:

[1542] A user types into the chat box, "I can't think of any unique features for this app." The server sends this message to a sentiment analysis engine, which detects "confused."

[1543] The server sends the generative AI model a prompt saying, "The user is confused. Please generate a dialogue that will alleviate the user's confusion by providing new perspectives and information." The generative AI model then generates a dialogue saying, "For example, how about adding a function to monitor the user's health condition in real time?"

[1544] The server returns the generated dialogue to the user and displays it on the user's device. The user then enters, "The health monitoring feature is great. How should I implement it specifically?", and the dialogue generation process using the emotion analysis engine and generative AI model is repeated.

[1545] In this way, a dialogue that corresponds to the user's feelings can be provided, and effective brainstorming and stress relief can be supported. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1546] Step 1:

[1547] A user accesses the system through a terminal.

[1548] Specific operation: A user accesses the system's web application using a device such as a PC or smartphone. The user enters authentication information on the login screen and logs in to the system.

[1549] Input: User authentication information (username, password)

[1550] Output: User logged into the system

[1551] Step 2:

[1552] The user types a message into a chat box within the system.

[1553] What happens: The user types a message into the chat box, such as "I'm not sure if this idea will work."

[1554] Input: User's message

[1555] Output: The server receives the user's message

[1556] Step 3:

[1557] The server receives the user's input and analyzes the emotion using an emotion analysis engine.

[1558] Specific operation: The server sends the user's message to a sentiment analysis engine (e.g., IBM Watson's Tone Analyzer), which analyzes the message and identifies the user's sentiment.

[1559] Input: User's message

[1560] Output: Emotion information from the emotion analysis engine (e.g., anxiety)

[1561] Step 4:

[1562] The server sends prompts to the generative AI model to generate a dialogue.

[1563] Specific operation: Based on the emotional information obtained from the emotion analysis engine, the server creates and sends a prompt to the generative AI model (for example, OpenAI's GPT-4). The prompt might be something like, "The user is feeling anxious. Please provide new perspectives and information to generate a dialogue that will alleviate the user's anxiety." The generative AI model then generates a dialogue based on this prompt.

[1564] Input: Emotion information, prompt sentence

[1565] Output: A dialogue message from the generative AI model (e.g., "Let's think about it from a new perspective. For example, what if we applied this idea to a different market?")

[1566] Step 5:

[1567] The server returns the generated interaction to the user.

[1568] Specific operation: The server receives the dialogue returned by the generative AI model and sends it to the user's device. The dialogue message is displayed in the user's chat box.

[1569] Input: Dialogue message from the generative AI model

[1570] Output: The dialogue message displayed in the user's chat box.

[1571] Step 6:

[1572] The user gets a new idea and inputs it again.

[1573] Specific operation: The user comes up with a new idea based on the AI's suggestions and inputs it into the system again. For example, the user might input, "The idea of ​​applying it to a different market is good. Which market would be good specifically?" This will cause the process to repeat from step 3.

[1574] Input: User's new message

[1575] Output: The server receives the user's new message.

[1576] In this way, the user, terminal, and server work together to process each step, providing dialogue that responds to the user's emotions, making this a system that supports effective brainstorming and stress relief.

[1577] (Application example 3)

[1578] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1579] Conventional AI chat tools have focused primarily on improving communication skills, relieving stress, and forming attachments, but they have not adequately addressed the improvement of production efficiency or the generation of new ideas. Furthermore, they lacked the ability to recognize emotions and provide dialogue based on those emotions, making it difficult to generate appropriate responses according to the user's emotions. This has hindered effective brainstorming based on the emotions of workers, particularly in production sites such as factories, and has hindered the improvement of production efficiency.

[1580] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1581] In this invention, the server includes means for developing and providing chat tools using AI, means for generating ideas through chat using AI, means for releasing stress through chat using AI, means for increasing attachment to others through chat using AI, means for generating new ideas for improving productivity through chat using AI, means for AI to recognize emotions and provide dialogue based on the recognized emotions, and means for AI to generate responses based on prompt sentences using a generative AI model. This makes it possible to generate new ideas and improve productivity through appropriate dialogue according to the user's emotions.

[1582] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[1583] A "chat tool" is software or a system that allows natural conversation with users.

[1584] "Emotion recognition" is a technology that analyzes and identifies emotions from a user's facial expressions, tone of voice, etc.

[1585] A "generative AI model" is an artificial intelligence model that generates new text or responses based on input data.

[1586] A "prompt" is text data input to a generative AI model that contains instructions or questions that the model uses to generate a response.

[1587] "Production efficiency" is the ability to achieve maximum production results using a given amount of resources and time.

[1588] Brainstorming is a method for multiple people to freely exchange ideas and find new ideas and solutions.

[1589] "Dialogue" is the act of two or more people exchanging words and information and opinions.

[1590] The system for implementing this invention uses an AI chat tool to generate new ideas through dialogue that responds to the user's emotions, thereby improving production efficiency. A specific embodiment of this system is described below.

[1591] System Configuration

[1592] 1. Hardware:

[1593] Camera: Used to capture the user's facial expressions. An example is a webcam, such as the Logitech C920.

[1594] Microphone: Used to capture the user's voice. An example is a USB microphone such as the Blue Yeti.

[1595] Computer: Used to process data and run artificial intelligence models. This can be a regular PC or a server.

[1596] 2. Software:

[1597] Emotion recognition software: Recognizes emotions by analyzing the user's facial expressions and tone of voice. An example is the EmotionRecognizer library.

[1598] Generative AI models: Generate responses based on user input and perceived emotions. An example is OpenAI's GPT-3 model.

[1599] Data processing and calculation

[1600] 1. Emotion recognition:

[1601] The camera and microphone are used to capture the user's facial expressions and voice.

[1602] The acquired data is fed into emotion recognition software to identify the user's emotions.

[1603] 2. Dialogue generation:

[1604] The user's input (text) and the recognized emotion are input as prompts into the generative AI model.

[1605] A generative AI model generates an appropriate response based on the prompt.

[1606] Specific examples

[1607] For example, if a factory worker types, "I don't know how to improve the efficiency of the production line," the system will act as follows:

[1608] 1. Capture the facial expressions and voices of workers using a camera and microphone.

[1609] 2. Emotion recognition software recognizes the worker's confused emotions.

[1610] 3. The worker's input and the recognized emotion are fed into the generative AI model as prompt sentences.

[1611] 4. The generative AI model generates a response like this:

[1612] "Why not consider introducing new machinery or reviewing work procedures?"

[1613] Prompt Sentence Examples

[1614] User Emotion: Confused

[1615] User Input: I don't know how to improve the efficiency of my production line.

[1616] AI response: Why not consider introducing new machinery or revising work procedures?

[1617] In this way, the system can support the creation of new ideas and the improvement of production efficiency through appropriate dialogue that responds to the user's emotions.

[1618] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1619] Step 1:

[1620] The user accesses the system through a camera and microphone.

[1621] Input: User's facial expressions and voice

[1622] Output: Image and audio data obtained from the camera and microphone

[1623] Specific operation: A user stands in front of the camera and speaks into the microphone. The camera captures the user's facial expressions, and the microphone records the user's voice.

[1624] Step 2:

[1625] The image data and voice data acquired by the device are input into emotion recognition software.

[1626] Input: Image and audio data obtained from the camera and microphone

[1627] Output: User's emotion (e.g., confusion, joy, sadness, etc.)

[1628] Specific operation: The device uses the EmotionRecognizer library to analyze image and audio data and identify the user's emotions.

[1629] Step 3:

[1630] The user enters text into the terminal.

[1631] Input: User text input (e.g., "I don't know how to improve the efficiency of our production line.")

[1632] Output: User's text input data

[1633] Specific behavior: The user enters a question or comment into the device using a keyboard or voice input.

[1634] Step 4:

[1635] The device inputs the user's emotions and text input as prompts into the generative AI model.

[1636] Input: User sentiment and text input data

[1637] Output: Prompt statement (e.g., "User emotion: Confused. User input: I don't know how to improve the efficiency of the production line.")

[1638] Specific operation: The device combines the user's emotions and text input to create a prompt sentence suitable for the generative AI model.

[1639] Step 5:

[1640] The server uses a generative AI model to generate a response based on the prompt.

[1641] Input: prompt statement

[1642] Output: The AI's response (e.g., "Maybe we should consider introducing new machinery or revising our work procedures?")

[1643] What it does: The server uses OpenAI's GPT-3 model to generate an appropriate response based on the prompt.

[1644] Step 6:

[1645] The terminal displays or speaks the generated response to the user.

[1646] Input: AI response

[1647] Output: The text or audio response that is displayed to the user

[1648] Specific behavior: The device displays the generated response on the screen or uses speech synthesis to communicate it to the user.

[1649] 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.

[1650] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by 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 the data format of voice data, text data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1651] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1652] 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.

[1653] [Third embodiment]

[1654] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1655] 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.

[1656] 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).

[1657] 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.

[1658] 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.

[1659] 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).

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1666] "Example 1"

[1667] One aspect of the present invention is a system for developing and providing AI-based chat tools. Specifically, an AI engine is used to enable chat with users. This AI engine uses natural language processing technology to understand input from users and generate appropriate responses. The AI ​​engine also has a learning function, and can learn the preferences and personality of users through chat with them, providing more personalized dialogue.

[1668] "Example 2"

[1669] This invention is a system that utilizes an AI chat tool in employee training. Specifically, as part of employee training, a program is implemented to improve communication skills through chatting with AI. For example, training can be conducted to draw out new perspectives and ideas through discussions with AI.

[1670] "Example 3"

[1671] This invention is a system that utilizes an AI chat tool for planning. Specifically, new ideas are generated through chatting with AI. For example, different perspectives and information provided by AI can be used to brainstorm plans.

[1672] The processing flow of each embodiment will be described below.

[1673] "Example 1"

[1674] Step 1: Launch the AI ​​engine, which uses natural language processing techniques to understand user input and generate appropriate responses.

[1675] Step 2: Receive input from the user. The input is in text format and contains the content of the conversation with the user.

[1676] Step 3: The AI ​​engine analyzes the user input and generates an appropriate response.

[1677] Step 4: The generated response is provided to the user, allowing them to chat with the AI.

[1678] "Example 2"

[1679] Step 1: As part of employee training, set up a program to improve communication skills through casual conversation with AI.

[1680] Step 2: The employee initiates a conversation with the AI, which analyzes the employee's input and generates an appropriate response.

[1681] Step 3: Improve employees' communication skills through casual conversation with AI.

[1682] "Example 3"

[1683] Step 1: Start brainstorming to come up with a plan, using casual conversation with AI.

[1684] Step 2: Utilize the different perspectives and information provided by AI to generate new ideas. Step 3: Based on the ideas obtained through casual conversation with AI, create a concrete plan.

[1685] Example 1

[1686] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1687] Conventional chat tools have a one-sided dialogue with users, making it difficult to provide personalized responses. They also lack the ability to learn users' preferences and personalities, which can lead to a decline in satisfaction over long-term interactions. Furthermore, it is difficult to release stress or generate ideas through chatting.

[1688] 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.

[1689] In this invention, the server includes means for a user to input to a terminal, means for the terminal to send the input to the server, means for the server to analyze the input, means for the server to pass a prompt sentence to the generative AI model, means for the generative AI model to generate a response, means for the server to return the response to the terminal, means for the terminal to display the response to the user, and means for the server to save the dialogue history and perform learning. This personalizes dialogue with the user, improves long-term satisfaction, and enables users to relieve stress and generate ideas through casual conversation.

[1690] The "means for a user to input information into a terminal" refers to a means for providing an interface for a user to input the content of a chat using a text box or input field.

[1691] The "means by which the terminal transmits input to the server" refers to a communication means for transmitting text data input by the user to the server via the network.

[1692] The "means by which the server analyzes the input" refers to the means by which the server analyzes the received user input text using natural language processing technology to understand the grammar and meaning.

[1693] "Means for the server to pass a prompt sentence to the generative AI model" refers to a means for passing the analyzed input text to the generative AI model as a prompt sentence.

[1694] "Means by which the generative AI model generates a response" refers to means by which the generative AI model generates an appropriate response based on a prompt sentence.

[1695] "Means for the server to return a response to the terminal" refers to the means for returning the response received from the generative AI model to the terminal via the network.

[1696] The "means for the terminal to display the response to the user" refers to a means for visually displaying the response received from the server to the user.

[1697] The "means for the server to store the dialogue history and perform learning" refers to a means for storing the dialogue history with the user in a database and learning the user's preferences and personality using a machine learning algorithm.

[1698] MODE FOR CARRYING OUT THE INVENTION

[1699] This invention is a system for providing personalized dialogue through casual conversation with a user. The system includes a series of processes in which a user inputs information into a terminal, the input is sent to a server, the server analyzes the input, passes a prompt to a generative AI model, the generative AI model generates a response, the server returns the response to the terminal, the terminal displays the response to the user, the server saves the dialogue history, and the server performs learning.

[1700] Hardware and software used

[1701] The server uses the following software:

[1702] Natural language processing libraries (e.g., NLTK, spaCy)

[1703] Generative AI models (e.g., OpenAI's GPT-3)

[1704] Machine learning algorithms (e.g. TensorFlow, PyTorch)

[1705] A terminal uses hardware and software to provide a user interface:

[1706] A web browser or mobile application to display text boxes and input fields

[1707] Internet connection for network communications

[1708] Specific operation of the system

[1709] 1. The user types into the terminal

[1710] The user enters the content of the conversation into the text box on the device. For example, they might enter "What's the weather like today?" When the user presses the send button, the conversation proceeds to the next step.

[1711] 2. The device sends the input to the server

[1712] The terminal sends the text data entered by the user to the server using an HTTP request. Specifically, the terminal converts the input data into JSON format and sends a POST request to the server's API endpoint.

[1713] 3. The server parses the input

[1714] The server analyzes the received user input. It uses a natural language processing library built using Python (e.g., NLTK or spaCy) to tokenize the input text and tag it with parts of speech. For example, it analyzes the input "What's the weather like today?" to understand the meaning and grammatical structure of each word.

[1715] 4. The server passes the prompt to the generative AI model

[1716] The server passes the analyzed input as a prompt to a generative AI model (e.g., OpenAI's GPT-3). Specifically, the prompt is sent to the generative AI model as an API request. For example, the prompt "What's the weather like today?" is passed to GPT-3.

[1717] 5. The generative AI model generates a response

[1718] The generative AI model generates an appropriate response based on the prompt it receives. GPT-3 has been trained with a large amount of data in advance and has advanced natural language generation capabilities. For example, it generates a response such as "It's sunny today."

[1719] 6. The server returns the response to the device

[1720] The server returns the response received from the generative AI model to the device. Specifically, it converts the response data into JSON format and sends it to the device as an HTTP response.

[1721] 7. The terminal displays the response to the user

[1722] The device displays the response received from the server to the user. Specifically, it parses the received JSON data and displays the response in a text box or chat window. For example, it displays the response "It's sunny today" to the user.

[1723] 8. The server stores the dialogue history and performs learning.

[1724] The server stores the user's interaction history in a database. It then uses machine learning algorithms (e.g., TensorFlow or PyTorch) to analyze the interaction data and learn the user's preferences and personality. This allows it to provide more personalized responses in subsequent interactions.

[1725] Specific examples

[1726] Example 1

[1727] The user types "What's the weather like today?" into the device and presses the send button. The device sends this input to the server. The server analyzes the input and passes the prompt "What's the weather like today?" to a generative AI model (e.g., GPT-3). The generative AI model generates a response, "It's sunny today," and returns it to the server. The server returns this response to the device, which displays it to the user.

[1728] Example 2

[1729] The user types "What movie do you recommend?" into the device and presses the send button. The device sends this input to the server. The server analyzes the input and passes the prompt "What movie do you recommend?" to a generative AI model (e.g., GPT-3). The generative AI model generates a response saying "The latest recommendation is 'Inception.'" and returns it to the server. The server returns this response to the device, which displays it to the user.

[1730] Prompt Sentence Examples

[1731] 1. "What's the weather like today?"

[1732] 2. "What movie do you recommend?"

[1733] 3. "Tell me about a book you read recently."

[1734] These prompts can be fed into a generative AI model to generate appropriate responses.

[1735] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1736] Step 1:

[1737] The user types into the terminal.

[1738] The user enters the content of the conversation into the text box on the terminal. For example, they might enter "What's the weather like today?". When the user presses the send button, the input proceeds to the next step. The input is text data, and the output is the event of the send button being pressed.

[1739] Step 2:

[1740] The device sends input to the server.

[1741] The terminal sends the text data entered by the user to the server using an HTTP request. Specifically, the terminal converts the input data into JSON format and sends a POST request to the server's API endpoint. The input is the user's text data, and the output is the JSON data sent to the server.

[1742] Step 3:

[1743] The server parses the input.

[1744] The server parses the received user input. It uses a natural language processing library built using Python (e.g., NLTK or spaCy) to tokenize the input text and tag it with parts of speech. For example, it parses the input "What's the weather like today?" to understand the meaning and grammatical structure of each word. The input is text data in JSON format, and the output is the parsed text data.

[1745] Step 4:

[1746] The server passes the prompt to the generative AI model.

[1747] The server passes the analyzed input as a prompt to the generative AI model (e.g., OpenAI's GPT-3). Specifically, the prompt is sent to the generative AI model as an API request. For example, the prompt "What's the weather like today?" is passed to GPT-3. The input is the analyzed text data, and the output is the prompt sent to the generative AI model.

[1748] Step 5:

[1749] A generative AI model generates a response.

[1750] The generative AI model generates an appropriate response based on the received prompt. GPT-3 has been trained with a large amount of data in advance and has advanced natural language generation capabilities. For example, it generates the response "It's sunny today." The input is the prompt, and the output is the generated response text.

[1751] Step 6:

[1752] The server returns a response to the terminal.

[1753] The server returns the response received from the generative AI model to the terminal. Specifically, it converts the response data into JSON format and sends it to the terminal as an HTTP response. The input is the generated response text, and the output is the JSON data sent to the terminal.

[1754] Step 7:

[1755] The terminal displays the response to the user.

[1756] The terminal displays the response received from the server to the user. Specifically, it parses the received JSON data and displays the response in a text box or chat window. For example, it displays the response "It's sunny today" to the user. The input is the JSON data received from the server, and the output is the response text displayed to the user.

[1757] Step 8:

[1758] The server stores the dialogue history and performs learning.

[1759] The server stores the user's interaction history in a database. It then uses machine learning algorithms (e.g., TensorFlow or PyTorch) to analyze the interaction data and learn the user's preferences and personality. This allows it to provide more personalized responses in subsequent interactions. The input is the interaction history data, and the output is an updated learning model.

[1760] (Application example 1)

[1761] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1762] In today's brick-and-mortar stores, personalized service is required to improve the customer experience. However, traditional methods make it difficult to effectively utilize customer preferences and past purchase history, and in-store guidance and product suggestions are uniform, which can lead to a decline in customer satisfaction. Furthermore, there is a lack of ways to relieve stress or increase customer loyalty through dialogue with customers. To solve these issues, a new system utilizing AI technology is needed.

[1763] 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.

[1764] In this invention, the server includes means for developing and providing AI-based chat tools to improve human communication skills, means for generating ideas through AI-based chat, means for releasing stress through AI-based chat, means for increasing attachment to others through AI-based chat, means for improving the customer experience in a physical store through AI-based chat, means for making personalized product suggestions based on the customer's preferences and past purchase history through AI-based chat, and means for guiding customers within the store through AI-based chat. This makes it possible to provide personalized services according to the individual needs of each customer, thereby improving customer satisfaction and providing a pleasant shopping experience within the store.

[1765] "Human communication skills" are the ability to effectively exchange information with and understand others.

[1766] An "AI chat tool" is software or a system that uses artificial intelligence technology to engage in natural conversations with users.

[1767] A "development and delivery method" is a method or process for designing, building, and making available to users a particular technology or service.

[1768] "Idea generation methods" are methods or processes for generating creative ideas and new thoughts.

[1769] A "stress reliever" is a method or process for reducing mental tension and anxiety and for relaxing.

[1770] "Attachment strategies" are methods or processes for increasing familiarity and liking for others.

[1771] "Measures to improve the customer experience in physical stores" are methods and processes that increase the satisfaction and comfort that customers feel in physical stores.

[1772] "Customer preferences and past purchase history" refers to the customer's tendency to like products and services and records of products and services they have purchased in the past.

[1773] "Personalized product recommendations" is the method or process of recommending specific products or services to individual customers based on their preferences and needs.

[1774] "Means of providing guidance within the store" refers to the methods and processes of providing information and guidance to make it easier for customers to find the products and services they are looking for within the store.

[1775] To implement the present invention, the following system configuration and program are required.

[1776] System Configuration

[1777] 1. Hardware:

[1778] Smartphone: A device through which a user operates an application.

[1779] Server: A device that hosts the AI ​​engine and processes user interactions.

[1780] 2. Software:

[1781] AI Engine: Uses generative AI models such as OpenAI's GPT-3.

[1782] Natural Language Processing (NLP) libraries: Libraries for parsing user input and generating appropriate responses.

[1783] Database: A database for storing customer preferences and past purchase history.

[1784] Program processing explanation

[1785] The server receives text data entered by the user through the smartphone. The received data is analyzed using a natural language processing library. The analyzed data is sent to an AI engine, which generates an appropriate response. The generated response is then sent back to the smartphone and displayed to the user.

[1786] Specifically, the following process occurs:

[1787] 1. Receiving user input:

[1788] A user inputs text through an application on their smartphone, which is then sent to a server.

[1789] 2. Data Analysis:

[1790] The server uses natural language processing libraries to parse the user's input, which is then used to understand the user's intent and the question they are asking.

[1791] 3. Response generation by AI engine:

[1792] The analysis results are sent to an AI engine (e.g., OpenAI's GPT-3), which generates an appropriate response based on the user's input.

[1793] 4. Sending the response:

[1794] The generated response is sent from the server to the smartphone and displayed to the user.

[1795] Specific examples

[1796] For example, if a user types "I'm looking for new shoes, any recommendations?", the following prompt will be generated:

[1797] User: I'm looking for some new shoes, any recommendations?

[1798] AI:

[1799] Based on this prompt, the AI ​​engine generates an appropriate response, such as, "Could you please tell me the style and purpose of the shoes you are looking for? For example, are they for running or casual everyday wear?"

[1800] In this way, personalized product suggestions and in-store guidance can be provided through dialogue with the user, making it possible to provide services that meet the individual needs of each customer, improving customer satisfaction and providing a comfortable shopping experience in the store.

[1801] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1802] Step 1:

[1803] A user starts an application on their smartphone and enters a question or request in a text input field. The entered text is sent from the smartphone to a server. The input data is text data that includes the user's question or request.

[1804] Step 2:

[1805] The server analyzes the received text data using a natural language processing (NLP) library. As a result of the analysis, the user's intent and question are extracted. The input data is the user's text data, and the output data is the analyzed intent and question.

[1806] Step 3:

[1807] The server generates a prompt based on the analysis results. This prompt converts the user's input into an appropriate format in preparation for sending it to the AI ​​engine. The input data is the analyzed intent or question, and the output data is the generated prompt.

[1808] Step 4:

[1809] The server sends the generated prompt sentence to an AI engine (e.g., OpenAI's GPT-3). The AI ​​engine generates an appropriate response based on the prompt sentence. The input data is the prompt sentence, and the output data is the response generated by the AI ​​engine.

[1810] Step 5:

[1811] The server sends the response received from the AI ​​engine to the smartphone. The smartphone displays the received response to the user. The input data is the response from the AI ​​engine, and the output data is the text displayed to the user.

[1812] Step 6:

[1813] The user checks the response displayed on their smartphone and enters further questions or requests as needed. This process is repeated until the user is satisfied. The input data is the user's new question or request, and the output data is text data that is sent back to the server.

[1814] In this way, a continuous dialogue takes place between the user and the AI ​​engine, providing personalized product suggestions and in-store guidance.

[1815] Example 2

[1816] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1817] In today's business environment, employees are expected to improve their communication skills, generate new ideas, release stress, and develop affection for others. However, there is a lack of effective ways to improve these skills. In addition, there is a need for efficient and effective ways to develop these skills in employee training and planning.

[1818] 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. In this invention, the server includes a means for a user to input a prompt sentence to a terminal, a means for the terminal to send the prompt sentence to the server, a means for the server to input a prompt sentence to a generative AI model, a means for the generative AI model to generate a response, a means for the server to send the generated response to the terminal, a means for the terminal to display the response to the user, and a means for the user to input a prompt sentence again and continue the dialogue. This enables the user to hone their communication skills, generate new ideas, relieve stress, and increase affection for the other person through dialogue with AI.

[1819] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and perform tasks such as learning, reasoning, and problem-solving.

[1820] An "interactive tool" is software or a system that allows users and artificial intelligence to communicate in natural language.

[1821] A "prompt sentence" is text data such as a question or instruction that a user inputs into a dialogue tool.

[1822] A "terminal" is a device such as a computer, smartphone, or tablet that a user uses to access an interaction tool.

[1823] A "server" is a computer system that performs back-end processing for the interactive tool and executes the generative AI model.

[1824] A "generative AI model" is an artificial intelligence algorithm for generating appropriate responses based on a user's prompt.

[1825] A "response" is text data that a generative AI model generates in response to a prompt.

[1826] "Employee training" is an educational program implemented by a company with the aim of improving employees' skills and acquiring knowledge.

[1827] "Planning" is the process of planning a new project or business strategy and creating a specific implementation plan.

[1828] MODE FOR CARRYING OUT THE INVENTION

[1829] This invention is a system that uses dialogue tools based on artificial intelligence to improve communication skills in employee training and planning, generate new ideas, relieve stress, and increase affection for others. A specific embodiment of this system is shown below.

[1830] Hardware and software used

[1831] This system uses the following hardware and software:

[1832] Device: A device used by a user, such as a PC, smartphone, or tablet.

[1833] Server: A computer system that performs back-end processing for an interactive tool.

[1834] Generative AI models: Artificial intelligence algorithms such as OpenAI's GPT-4.

[1835] Data processing and calculation

[1836] In this system, the following data processing and calculation are performed.

[1837] 1. User input:

[1838] The user uses the terminal to enter a prompt sentence, for example, "I'd like to discuss ideas for a new project."

[1839] 2. Send the prompt:

[1840] The terminal sends the prompt text entered by the user to the server via an Internet connection.

[1841] 3. Input to the generative AI model:

[1842] The server inputs the received prompt sentence into the generative AI model, converts the prompt sentence into an appropriate format, and passes it to the AI ​​model.

[1843] 4. Generate response:

[1844] The generative AI model generates an appropriate response based on the input prompt, such as "What is the purpose of the project?"

[1845] 5. Sending the response:

[1846] The server sends the response received from the generative AI model to the device, converts the response data into an appropriate format, and sends it to the device.

[1847] 6. View the response:

[1848] The terminal displays the response received from the server to the user, who can then check the response from the AI ​​on the terminal screen.

[1849] 7. Continuing the dialogue:

[1850] The user continues the dialogue by inputting a prompt sentence based on the displayed response, for example, "The purpose of the project is to develop a new market." By repeating this process, the dialogue between the user and the AI ​​continues.

[1851] Specific examples

[1852] A specific example of dialogue is shown below.

[1853] Example prompt sentence:

[1854] The user types into the terminal, "I'd like to come up with some ideas for a new marketing strategy."

[1855] Dialogue flow:

[1856] 1. User: "I want to come up with ideas for a new marketing strategy."

[1857] 2. AI: “Who is the target audience for that marketing strategy?”

[1858] 3. User: "I'm a young person in my 20s or 30s."

[1859] 4. AI: “What media will you use to reach that target audience?”

[1860] 5. User: "I would like to use social media primarily."

[1861] In this way, users can hone their communication skills, generate new ideas, relieve stress, and increase affection for others through dialogue with AI.

[1862] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1863] Step 1:

[1864] The user inputs a prompt into the device. Using a device such as a PC or smartphone, the user inputs a prompt such as "I would like to discuss ideas for a new project" into the input field of the dialogue tool. The input prompt is temporarily stored in the device's memory.

[1865] Step 2:

[1866] The terminal sends the prompt text to the server. The terminal then sends the prompt text entered by the user to the server as an HTTP request. At this time, the prompt text is converted into a data format such as JSON and sent to the server via the Internet. The input is the prompt text, and the output is the request sent to the server.

[1867] Step 3:

[1868] The server inputs a prompt sentence to the generative AI model. The server generates an API request to pass the received prompt sentence to the generative AI model. For example, it sends the prompt sentence to the API endpoint of the generative AI model. The input is the prompt sentence, and the output is the request sent to the generative AI model.

[1869] Step 4:

[1870] The generative AI model generates a response. The generative AI model generates an appropriate response based on the input prompt. For example, it generates a response such as, "What is the purpose of the project?" The input is the prompt, and the output is the generated response.

[1871] Step 5:

[1872] The server sends the generated response to the terminal. The server sends the response received from the generative AI model to the terminal as an HTTP response. At this time, the response data is converted into a data format such as JSON and sent to the terminal via the Internet. The input is the generated response, and the output is the response sent to the terminal.

[1873] Step 6:

[1874] The terminal displays the response to the user. The terminal displays the response received from the server in the user interface. The user can check the response from the AI ​​on the terminal screen. The input is the generated response, and the output is the display in the user interface.

[1875] Step 7:

[1876] The user inputs a new prompt sentence and the dialogue continues. The user inputs a new prompt sentence based on the displayed response. For example, the user might input, "The purpose of the project is to develop new markets." By repeating this process, the dialogue between the user and the AI ​​continues. The input is a new prompt sentence, and the output is the continuation of the dialogue.

[1877] (Application example 2)

[1878] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1879] Traditional employee training and customer service training have made it difficult to simulate actual customer interactions, making it difficult to improve effective communication skills. Furthermore, opportunities for store employees to practice actual customer service scenarios are limited, so an effective method for improving the quality of customer service was needed.

[1880] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for developing and providing an AI-based chat tool to improve human communication skills, means for generating ideas through the AI-based chat, means for releasing stress through the AI-based chat, means for increasing attachment to others through the AI-based chat, means for store clerks in a physical store to wear smart glasses and improve their customer service skills through chat with the AI, and means for practicing customer service scenarios and customer interactions through the AI-based chat. This enables store clerks to practice actual customer service scenarios in real time and improve the quality of customer interaction.

[1881] "Human communication skills" refers to the ability of humans to effectively exchange information with and understand others.

[1882] An "AI chat tool" is software or a system that uses artificial intelligence to have natural conversations with humans.

[1883] "Development and provision" refers to the process of designing the technologies and methods necessary to achieve a specific purpose and providing them in a form that can actually be used.

[1884] "Means of generating ideas" are methods and techniques for creating new ideas and solutions.

[1885] "Stress relief" refers to methods and techniques for reducing mental tension and anxiety and for relaxation.

[1886] "Means to increase attachment to others" are methods and techniques for increasing familiarity and liking for others.

[1887] A "brick and mortar store" is a store that exists in a physical location and where customers can visit in person to purchase goods or services.

[1888] A "store clerk" is an employee who works in a physical store and provides products and services to customers.

[1889] "Smart glasses" are a wearable eyeglass-type device equipped with computer functions, and are capable of displaying information and accepting voice input.

[1890] "Customer service skills" are the techniques and abilities required to respond appropriately to customers and increase their satisfaction.

[1891] A "customer service scenario" is a simulation that assumes specific situations and conversation flows when dealing with customers.

[1892] "Customer service practice" is training that simulates actual customer service, with the aim of improving skills.

[1893] This invention is a system in which store clerks wear smart glasses in brick-and-mortar stores and improve their customer service skills through casual conversations with AI. Specific embodiments for realizing this system are described below.

[1894] System Configuration

[1895] This system uses the following hardware and software:

[1896] Hardware: Smart glasses, microphone, speaker

[1897] Software: OpenAI API, Google Speech Recognition API, gTTS (Google Text-to-Speech)

[1898] Program processing explanation

[1899] The server runs a program that uses the generative AI model to enable conversation between the store clerk and the AI. Specifically, it performs the following processes:

[1900] 1. Acquiring voice input:

[1901] The terminal (a microphone attached to smart glasses) captures the customer's question as voice.

[1902] The device converts the captured audio into text using the Google Speech Recognition API.

[1903] 2. AI-generated responses:

[1904] The server inputs the customer's textual question as a prompt into the generative AI model (OpenAI API).

[1905] The server takes the response from the generative AI model and generates an appropriate answer.

[1906] 3. Generate audio output:

[1907] The server converts the generated text response into speech using gTTS.

[1908] The device plays the converted audio through the speaker.

[1909] Specific examples

[1910] For example, if a store clerk asks, "How do you want to use this product?", the process will proceed as follows:

[1911] 1. Acquiring voice input:

[1912] The device captures the customer's voice question, "How do you use this product?", and converts it into text.

[1913] 2. AI-generated responses:

[1914] The server inputs the following prompt sentence into the generative AI model:

[1915] Customer Question: How do I use this product?

[1916] Clerk's response:

[1917] The server receives the response from the generative AI model: "First, turn on this product, then..."

[1918] 3. Generate audio output:

[1919] The server converts the received response into speech using gTTS, which the device plays through its speaker.

[1920] In this way, store associates can practice real-life customer interaction scenarios in real time and improve the quality of their customer interactions.

[1921] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1922] Step 1:

[1923] Acquiring voice input

[1924] The terminal (a microphone attached to smart glasses) captures the customer's question as voice.

[1925] Input: Customer voice question

[1926] Specific operation: The microphone captures voice data and sends it to the voice recognition module in the device.

[1927] Output: Audio data

[1928] Step 2:

[1929] Speech-to-text

[1930] The device converts the acquired voice data into text using the Google Speech Recognition API.

[1931] Input: Audio data

[1932] Specific operation: Sends audio data to the Google Speech Recognition API and receives text data.

[1933] Output: Text data (customer questions)

[1934] Step 3:

[1935] Generate prompt statement

[1936] The server generates a prompt based on the customer's textual question.

[1937] Input: Text data (customer question)

[1938] What it does: Inserts text data into a template to create a prompt suitable for the generative AI model.

[1939] Output: prompt statement

[1940] Customer Question: How do I use this product?

[1941] Store clerk's response:

[1942] Step 4:

[1943] AI-powered response generation

[1944] The server inputs the generated prompt sentence into a generative AI model (OpenAI API) to generate an appropriate answer.

[1945] Input: prompt statement

[1946] Specific behavior: Sends a prompt to the OpenAI API and receives the generated text response.

[1947] Output: Text response (clerk's answer)

[1948] Step 5:

[1949] Generate audio output

[1950] The server converts the generated text response into speech using gTTS.

[1951] Input: Text response

[1952] What it does: Sends a text response to gTTS and generates an audio file.

[1953] Output: Audio data (clerk's response)

[1954] Step 6:

[1955] Playing audio

[1956] The terminal reproduces the generated audio data through a speaker.

[1957] Input: Voice data (clerk's response)

[1958] Specific operation: Sends audio data to the speaker and plays the audio.

[1959] Output: A voice response to the customer

[1960] Example 3

[1961] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1962] Conventional chat tools do not adequately support users in generating new ideas, and often respond unnaturally to user input. This makes it difficult for users to effectively plan and brainstorm. Furthermore, there is a lack of systems that can generate appropriate responses to prompts entered by users.

[1963] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1964] In this invention, the server includes means for a user to access the chat tool from a terminal, means for the user to input a prompt sentence, means for the terminal to send the input prompt sentence to the server, means for the server to send the prompt sentence to the generative AI model, means for the generative AI model to generate a response, means for the server to receive the generated response, means for the server to send the response to the terminal, and means for the terminal to display the response to the user. This allows users to generate new ideas through chatting with AI and effectively carry out planning and brainstorming.

[1965] "User" refers to a person who uses the system to chat with AI.

[1966] A "terminal" is a device used by a user to access the system, and includes a personal computer, a smartphone, etc.

[1967] A "chat tool" refers to software or an application that provides an interface for users to interact with AI.

[1968] A "prompt" is text that a user enters into a chat tool, including questions or requests for the AI.

[1969] "Server" refers to the computer system responsible for receiving user input, sending it to the generative AI model, and returning the generated response to the user.

[1970] "Generative AI model" refers to an artificial intelligence algorithm or software that generates appropriate responses based on a user prompt.

[1971] "Response" refers to the text that a generative AI model generates in response to a user prompt.

[1972] "Preprocessing" refers to the process of converting the user's input text into a format that is easy for the generative AI model to understand.

[1973] "Post-processing" refers to the process of formatting the responses generated by the generative AI model into a format that is natural for the user.

[1974] This invention is a system that allows users to generate new ideas through casual conversations with AI, and to effectively carry out planning and brainstorming. Specific embodiments of this system are described below.

[1975] First, the user accesses the chat tool using a device such as a PC or smartphone. The chat tool is provided through a browser or a dedicated application. When the user accesses the chat tool's interface, a text box appears in which the user can enter a prompt.

[1976] For example, a user might input, "I want to think of a new product idea. Please tell me about recent trends." This prompt is sent from the device to the server. The server then preprocesses the received prompt to send it to a generative AI model (e.g., OpenAI's GPT-4).

[1977] The generative AI model generates an appropriate response based on the prompt it receives, for example, "What do you think of some trendy smart home appliances?" The generated response is then sent to a server, which then post-processes it and formats it in a way that is natural for the user.

[1978] The server then sends the processed response to the device, which then displays it on the chat tool's interface for the user to review. Through this process, the user can gain new ideas through chatting with the AI.

[1979] This system supports planning and brainstorming by using a generative AI model to generate appropriate responses to prompts entered by the user and providing them to the user. It also allows users to relieve stress and increase affection for the other party through dialogue with the AI.

[1980] The specific hardware includes the device used by the user (PC or smartphone) and the server, while the software includes the chat tool interface, the generative AI model (e.g., OpenAI's GPT-4), and the pre-processing and post-processing programs that run on the server.

[1981] This system allows users to obtain new perspectives and information through casual conversation with the AI, enabling them to carry out effective planning and brainstorming. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1982] Step 1:

[1983] The user accesses the chat tool from the terminal.

[1984] Specifically, the user opens a browser on their computer or smartphone and accesses the chat tool's URL. This displays the chat tool's interface. The input is the user's access operation, and the output is the chat tool's interface display.

[1985] Step 2:

[1986] The user enters a prompt statement.

[1987] Specifically, the user inputs a prompt into a text box displayed on the chat tool's interface. For example, the user might type, "I'd like to come up with a new product idea. Please tell me about recent trends." The input is the user's text input, and the output is the input prompt.

[1988] Step 3:

[1989] The terminal sends the entered prompt text to the server.

[1990] Specifically, the terminal sends the prompt text entered by the user to the server as an HTTP request. The input is the prompt text entered by the user, and the output is the prompt text sent to the server.

[1991] Step 4:

[1992] The server sends the prompt to the generative AI model.

[1993] Specifically, the server preprocesses the received prompt text and converts it into a format suitable for the generative AI model. The converted text is then sent to the generative AI model. The input is the prompt text sent to the server, and the output is the preprocessed prompt text sent to the generative AI model.

[1994] Step 5:

[1995] A generative AI model generates a response.

[1996] Specifically, the generative AI model generates an appropriate response based on the received prompt, for example, "What do you think of smart home appliances that incorporate the latest trends?" The input is the preprocessed prompt, and the output is the generated response.

[1997] Step 6:

[1998] The server receives the generated response.

[1999] Specifically, the server receives the response generated by the generative AI model. The received response is post-processed to make it natural for the user. The input is the response from the generative AI model, and the output is the post-processed response.

[2000] Step 7:

[2001] The server sends a response to the terminal.

[2002] As a specific operation, the server sends the post-processed response to the terminal as an HTTP response. The input is the post-processed response, and the output is the response sent to the terminal.

[2003] Step 8:

[2004] The terminal displays the response to the user.

[2005] Specifically, the terminal displays the response received from the server on the interface of the chat tool. The input is the response from the server, and the output is the response displayed to the user.

[2006] (Application example 3)

[2007] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2008] Conventional AI chat tools have been useful in improving human communication skills, relieving stress, and increasing affection, but they lacked the functionality to provide ideas useful for improving the efficiency of factory production lines or developing new products. They also lacked the functionality to collect the opinions and ideas of workers and use them to propose new production methods or product improvements. This created the problem of being unable to fully improve factory productivity or the efficiency of new product development.

[2009] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[2010] In this invention, the server includes means for developing and providing chat tools using AI to improve human communication skills, means for generating ideas through AI chat, means for releasing stress through AI chat, means for increasing affection for others through AI chat, means used to provide ideas useful for improving the efficiency of factory production lines and developing new products, and means for collecting opinions and ideas from workers and proposing new production methods and product improvements based on them, thereby enabling improved factory productivity and more efficient new product development.

[2011] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[2012] A "chat tool" is software or a system that uses artificial intelligence to have natural conversations with humans.

[2013] A "production line" is a series of work processes arranged in a factory to efficiently produce products.

[2014] "Efficiency" refers to processes and methods for achieving maximum results while minimizing resources and time.

[2015] "New product development" is the process of designing, prototyping, and manufacturing new products in response to market needs and technological advances.

[2016] "Gathering opinions and ideas" is the activity of gathering feedback and suggestions from stakeholders.

[2017] A "production method" is a specific procedure or technique for manufacturing a product.

[2018] An "improvement" is a specific proposal or plan for improving an existing product or process.

[2019] As an embodiment of the present invention, a system is constructed that provides ideas that are useful for improving the efficiency of factory production lines and developing new products. Specific embodiments of this system are described below.

[2020] System Configuration

[2021] The system consists of the following main components:

[2022] 1. Server: Hosts the AI ​​model and performs back-end processing for the chat tool.

[2023] 2. Terminals: Devices used by factory workers (e.g., tablets, smartphones).

[2024] 3. Users: Factory workers and managers.

[2025] Hardware and software used

[2026] Hardware: Factory robots (e.g. ABB's IRB series), tablets, smartphones.

[2027] Software: OpenAI API, Python, database management system (e.g., MySQL).

[2028] Data processing and calculation

[2029] The server processes the data in the following steps:

[2030] 1. Data collection: Collecting worker opinions and ideas from devices.

[2031] 2. Data analysis: The collected data is input into an artificial intelligence model, and new ideas are generated using a generative AI model.

[2032] 3. Providing results: The generated ideas are returned to the terminal and presented to the worker.

[2033] Specific examples

[2034] For example, when considering efficiency improvements on a factory production line, the following prompt sentences can be used:

[2035] Worker: Our production line has been inefficient lately. How can we improve it?

[2036] AI: That's an interesting question. For example, where do you feel you're losing efficiency?

[2037] Worker: The assembly process is particularly time-consuming.

[2038] AI: I see, which part of the assembly process takes the most time?

[2039] Worker: The screw tightening part.

[2040] AI: So, please suggest a new idea for automating the screw-driving process.

[2041] Based on this prompt, the server uses a generative AI model to generate new ideas and return them to the device, such as a specific proposal such as "introduce a specific robotic arm to automate the screw-driving process."

[2042] In this way, factory productivity can be improved and new product development can be made more efficient.

[2043] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[2044] Step 1:

[2045] A user uses a terminal to input problems and opinions regarding the factory production line.

[2046] Input: User feedback or concerns (e.g., "Our production line has been inefficient lately. How can we improve it?")

[2047] Output: User input data

[2048] Specific operation: The user enters the problem in text format into the input form on the terminal and presses the submit button.

[2049] Step 2:

[2050] The terminal transmits the user's input data to the server.

[2051] Input: User-entered data

[2052] Output: Data sent to the server

[2053] Specific operation: The terminal sends the user's input data to the server as an HTTP request.

[2054] Step 3:

[2055] The server analyzes the received data and generates prompt sentences to input into the generative AI model.

[2056] Input: User-entered data

[2057] Output: Prompt sentence to be input to the generative AI model

[2058] Specific behavior: The server analyzes the user's input data and generates an appropriate prompt (e.g., "Our production line has been inefficient recently. How can we improve it?").

[2059] Step 4:

[2060] The server uses generative AI models to generate new ideas.

[2061] Input: prompt statement

[2062] Output: A new idea (e.g., "Introduce a specific robotic arm to automate the screw-driving process.")

[2063] Specific operation: The server inputs a prompt sentence into the generative AI model (e.g., OpenAI API) and obtains the generated ideas.

[2064] Step 5:

[2065] The server transmits the generated ideas to the terminal.

[2066] Input: New Idea

[2067] Output: Ideas sent to the terminal

[2068] Specific operation: The server sends the generated idea to the terminal as an HTTP response.

[2069] Step 6:

[2070] The terminal displays the received ideas to the user.

[2071] Input: Ideas received from the server

[2072] Output: The idea displayed to the user

[2073] Specific operation: The device displays the received ideas on the screen so that the user can review them.

[2074] 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.

[2075] "Example 1"

[2076] In the invention incorporating an emotion engine, an emotion engine is added to an AI engine to recognize the user's emotions and provide dialogue that corresponds to those emotions. Specifically, the engine analyzes emotional elements (e.g., joy, sadness, anger, surprise, etc.) contained in the text input from the user and generates an optimal response that corresponds to that emotion. In this form, the AI ​​engine can gain a deeper understanding of the user's emotions and provide a more human-like dialogue.

[2077] "Example 2"

[2078] An example of an invention incorporating an emotion engine being used in employee training is a program in which employees improve their communication skills through casual conversation with AI. In this program, the AI ​​engine recognizes the employee's emotions and provides dialogue that corresponds to those emotions. For example, if an employee is feeling stressed, the AI ​​engine will suggest topics to help them relax. In this form, the AI ​​engine can provide more effective communication training through dialogue that takes the employee's emotions into consideration.

[2079] "Example 3"

[2080] An example of how an invention incorporating an emotion engine can be used in planning is brainstorming, where new ideas are generated through casual conversation with AI. In this brainstorming session, the AI ​​engine recognizes the emotions of the participants and provides dialogue that reflects those emotions. For example, if a participant is confused, the AI ​​engine can provide new perspectives and information to stimulate ideas. In this form, the AI ​​engine can support more effective brainstorming through dialogue that takes into account the emotions of the participants.

[2081] The processing flow of each embodiment will be described below.

[2082] "Example 1"

[2083] Step 1: The user enters text into the AI ​​engine.

[2084] Step 2: The emotion engine analyzes the input text and recognizes the emotional elements it contains. Step 3: The AI ​​engine generates the optimal response based on the recognized emotions.

[2085] Step 4: The generated response is displayed to the user and the interaction progresses.

[2086] "Example 2"

[2087] Step 1: An employee starts chatting with the AI ​​engine.

[2088] Step 2: The emotion engine analyzes the employee's input text and recognizes the employee's emotions.

[2089] ...

Claims

1. A means of providing AI-based chat tools to improve human communication skills; A means for generating ideas through said chat tool; A means for releasing stress through said chat tool; A means for increasing affection for a person through the chat tool; A means for generating new ideas for improving production efficiency through the chat tool; A means for recognizing emotions using the artificial intelligence and providing a dialogue based on the emotion recognition result; A means for generating a prompt sentence to be input to the generation AI by the artificial intelligence and generating a response; A system including:

2. The system according to claim 1 , wherein the means for developing and providing chat tools using artificial intelligence is utilized for employee training.

3. The system according to claim 1 , wherein the means for developing and providing chat tools using artificial intelligence is utilized for planning purposes.

Images