system

Abstract

We provide the system. [Solution] A means by which sales explanations and questions are entered from the user's terminal, A means by which the server receives data sent from the user terminal, A means for sending a request to a generative AI, and for the generative AI to generate an answer based on the user's question, A means of returning the generated response to the user's terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional stores, sales explanations and mobile contract procedures largely depend on human skills, and there is a problem that the content of explanations and the quality of procedures vary depending on the person in charge. There is also a problem that OJT for new employees is necessary, and it takes time and cost for human resource development. Furthermore, users have to go to the store, and there is the hassle of procedures. The present invention aims to solve these problems.

Means for Solving the Problems

[0005] The present invention is a system that includes means for inputting sales explanations and questions from a user terminal, means for a server to receive data transmitted from the user terminal, means for sending a request to a generative AI, for the generative AI to generate an answer based on the user's question, and means for returning the generated answer to the user terminal. Furthermore, this system provides means for receiving contract information from the user terminal and verifying the necessary data, means for generating guidance for the next procedure using the generative AI, and means for displaying the generated guidance on the user terminal. This resolves the issues of personnel skills and on-the-job training in stores, and enables users to efficiently conduct sales explanations and mobile phone contract procedures remotely.

[0006] A "user terminal" refers to a computer device operated by a user, and includes internet-connected devices such as PCs, smartphones, and tablets.

[0007] A "server" refers to a central processing unit that receives requests from user terminals, processes data, and sends necessary information to the user terminals.

[0008] "Generative AI" refers to a system that uses artificial intelligence technology to generate appropriate answers based on user questions.

[0009] "Means of receiving data" refers to the functions and protocols for receiving data transmitted from a user's terminal.

[0010] "Means of sending requests" refers to the means of communication that a server uses to send data or inquiries to a generative AI.

[0011] "Means of generating answers" refers to the process by which a generative AI generates an appropriate answer based on the user's question.

[0012] "Guidance" refers to instructions and explanations provided to the user regarding the following procedures.

[0013] "Contract information" refers to personal information and contract plan information that users enter when applying for a mobile phone contract.

[0014] "Means of verification" refers to the process of confirming that the received data is accurate and complete.

[0015] "Means of display" refers to the function of visually displaying the generated responses and guidance on the user's terminal. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0030] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] ---

[0038] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. It also aims to improve the user experience by enabling mobile phone contract procedures to be completed online. This system consists of the following elements:

[0039] First, the user accesses the system's homepage using their device. The user enters their questions about sales or products into the inquiry form and sends the request by clicking the submit button. The user's device then sends this data to the server.

[0040] The server receives data sent by the user and analyzes its contents. The analyzed data is sent as a request to a generative AI, instructing the AI ​​to generate an answer to the question. The generative AI generates an answer based on the received question data and sends the result back to the server.

[0041] Next, the server receives the generated response and creates an HTML page to send back to the user. The user's device receives this HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[0042] Users wishing to enter into a mobile phone contract can proceed by entering the necessary information into a dedicated contract form and submitting it. The contract information sent from the user's terminal is received by the server and verified. Once verification is complete, the server instructs the generative AI to generate guidance for the next step. The server receives the guidance generated by the AI ​​and displays it on the user's terminal. Following this guidance, the user proceeds through each step of the procedure, and ultimately completes all procedures.

[0043] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user sends the question "I want to know about the features of the new product" from their terminal, the server sends the question to a generative AI. The AI ​​generates a response such as, "The new product is equipped with highly efficient energy functions and is easy to operate. For more details, please see the link below." This generated response is sent to the user's terminal via the server, and the user confirms it. The user can then continue to ask more detailed questions.

[0044] The above is a description of the operation of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on stores, thereby improving user convenience.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user accesses the system's homepage using a web browser.

[0048] Step 2:

[0049] The server receives the user's request and sends back the corresponding HTML file for the homepage.

[0050] Step 3:

[0051] The user's device receives an HTML file and displays the web page.

[0052] Step 4:

[0053] The user enters their sales explanation and questions into the inquiry form and presses the "Submit" button.

[0054] Step 5:

[0055] The user's device sends the form data (questionnaire content) to the server as an HTTP POST request.

[0056] Step 6:

[0057] The server parses the form data it receives and extracts the question content.

[0058] Step 7:

[0059] The server sends an HTTP request to the AI's API endpoint to send the question content as a request to the generation AI. This request includes the question content and other necessary information (e.g., user ID, session information, etc.).

[0060] Step 8:

[0061] The generative AI understands the questions received from the server and generates appropriate answers. The AI ​​understands the context of the questions and includes explanations of technical terms and detailed sales explanations as needed.

[0062] Step 9:

[0063] The generative AI sends the generated response back to the server. This is usually sent as an HTTP response.

[0064] Step 10:

[0065] The server receives the generated response and creates an HTML page to return to the user. This page contains the AI's response.

[0066] Step 11:

[0067] The server generates an HTML page which is then sent back to the user's device.

[0068] Step 12:

[0069] The user's device displays the HTML page received from the server in the browser.

[0070] Step 13:

[0071] Users can review the page and enter further questions if necessary.

[0072] Step 14:

[0073] When a user enters into a mobile phone contract, they fill in the required information on the contract form and submit it.

[0074] Step 15:

[0075] The user's device sends the contract information it received to the server.

[0076] Step 16:

[0077] The server analyzes the received contract information and checks the necessary fields (e.g., verifying required fields, validating input formats).

[0078] Step 17:

[0079] The server sends the verified data to the generative AI and requests it to generate guidance for the next procedure.

[0080] Step 18:

[0081] The generative AI generates the next steps the user should take based on the contract information it receives. For example, it generates guidance on the documents to be submitted and the input of additional information.

[0082] Step 19:

[0083] The generative AI generates guidance, which is then sent back to the server.

[0084] Step 20:

[0085] The server generates an HTML page based on the guidance received from the AI ​​and sends it to the user's device.

[0086] Step 21:

[0087] The user's device displays the HTML page received from the server in the browser.

[0088] Step 22:

[0089] The user follows the displayed guidance and performs the following steps.

[0090] (Example 1)

[0091] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] Traditional sales presentations and contract procedures were often conducted in person, resulting in low user convenience. Furthermore, there was a lack of systems to provide quick and appropriate responses to online inquiries. Additionally, contract procedures were complex, making it difficult for users to complete them smoothly.

[0093] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0094] In this invention, the server includes means for receiving data transmitted from a user terminal, means for analyzing the data received by the server, and means for sending a request to a generative AI, which then generates an answer based on the user's question. This makes it possible to answer user questions quickly and appropriately, and to efficiently conduct sales explanations and contract procedures online.

[0095] A "user terminal" refers to a computer device or mobile device such as a smartphone used by a user.

[0096] A "server" is a computer system that stores, processes, and provides data over a network.

[0097] "Means of receiving data" refers to functions for acquiring information transmitted from a user terminal.

[0098] "Means of data analysis" refer to methods and algorithms for understanding and analyzing received data.

[0099] "Generative AI" refers to systems and algorithms that use artificial intelligence technology to generate text and information.

[0100] "Means of sending requests" refers to functions for sending necessary data or requests to other systems (e.g., generative AI).

[0101] "Generated response" refers to information or text created by a generative AI based on a request.

[0102] "Means for generating HTML pages" refers to functions for creating HTML-formatted documents that can be displayed in a web browser.

[0103] "Contract information" refers to specific data and information related to the contract provided by the user.

[0104] "Means for verifying data" refers to functions for confirming whether the received data is accurate and appropriate.

[0105] "Guidance" refers to instructions or information that guides users through the next procedure or step.

[0106] The system of this invention receives sales explanations and questions entered from the user's terminal, generates appropriate answers using generative AI, and sends them back to the user. Furthermore, it aims to improve the user experience by enabling mobile phone contract procedures to be completed online.

[0107] 1. System Configuration

[0108] 1.1 Hardware and Software

[0109] The system of the present invention uses the following hardware and software.

[0110] User devices (e.g., personal computers, smartphones)

[0111] Servers (e.g., cloud infrastructure, on-premises servers)

[0112] Web browsers (e.g., Google Chrome®, Mozilla Firefox)

[0113] Data analysis software (e.g., Python, NLTK, MeCab)

[0114] Generative AI (e.g., OpenAI®, GPT-3®)

[0115] Database software (e.g., MySQL®, PostgreSQL)

[0116] Template engine (e.g., Jinja2)

[0117] 2. Operating Procedure

[0118] 2.1 Entering the Question

[0119] The user accesses the system's homepage using their own device. The user enters their questions about sales and products into the inquiry form and presses the submit button. For example, "I would like to know about the features of the new product."

[0120] 2.2 Data Reception and Analysis

[0121] The server receives data sent from the user's terminal. The server analyzes the received data to determine what information is required. Text analysis techniques such as morphological analysis are used in this process.

[0122] 2.3 Sending requests to generative AI

[0123] The server sends the analysis results as a request to the generative AI. The generative AI generates an answer based on the received question data. For example, an answer such as, "The new product is equipped with high-efficiency energy functions and is easy to operate."

[0124] 2.4 Receiving responses and generating HTML pages

[0125] The server receives the response from the generative AI and uses it to generate an HTML page to send back to the user. The HTML page is generated using a template engine.

[0126] 2.5 Display of answers

[0127] The user's device receives the HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[0128] 3. Mobile phone contract procedures

[0129] When a user wishes to proceed with a mobile phone contract, they fill in the required information on a dedicated contract form and submit it. The server receives the contract information sent from the user's device and verifies it. Once verification is complete, the server instructs the generative AI to generate guidance for the next procedure. The generated guidance is displayed on the user's device via the server. The user follows this guidance to proceed with the procedure and can ultimately complete all procedures online.

[0130] 4. Specific Examples

[0131] 4.1 Examples of Sales Presentations

[0132] Consider a scenario where a user asks about the features of a new product. The user sends a question from their device saying, "I want to know about the features of the new product." The server sends this question to a generative AI, which generates a response such as, "The new product features highly efficient energy functions and is easy to operate. For more details, please see the link below." The response is sent to the user's device via the server, and the user confirms it.

[0133] Examples of specific prompt messages:

[0134] User: I'd like to know about the features of the new product.

[0135] The above describes a specific embodiment of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on physical stores, thereby improving user convenience.

[0136] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0137] Step 1:

[0138] The user enters their question into the inquiry form and presses the submit button.

[0139] Input: The question text entered by the user in the form.

[0140] Specific action: The user opens the homepage in their browser, enters a message in the inquiry form, for example, "I would like to know about the features of the new product," and clicks the submit button.

[0141] Output: The HTTP request sent from the user's terminal to the server containing the question text.

[0142] Step 2:

[0143] The server receives data sent from the user's terminal.

[0144] Input: HTTP request data from the user's terminal.

[0145] Specific operation: The server receives an HTTP POST request and extracts the question text from the request body.

[0146] Output: Received question text.

[0147] Step 3:

[0148] The server analyzes the data it receives.

[0149] Input: Received question text.

[0150] Specific operation: The server executes a Python script to analyze the question text using a morphological analysis library (e.g., MeCab). It then extracts necessary themes and keywords from the analysis results.

[0151] Output: The subject and keywords of the analyzed question.

[0152] Step 4:

[0153] The server requests the analysis results from the generative AI.

[0154] Input: The subject or keywords of the analyzed question.

[0155] Specific operation: The server prepares the API request and sends the analysis results as a prompt to the generative AI (e.g., OpenAI's API).

[0156] Output: Request to the AI ​​model.

[0157] Step 5:

[0158] The generative AI generates an answer based on the question.

[0159] Input: Prompt sent from the server.

[0160] Specific operation: The generative AI generates an appropriate response based on the prompt. For example, it might generate a response such as, "The new product features high-efficiency energy functions and is easy to operate."

[0161] Output: Generated answer text.

[0162] Step 6:

[0163] The server receives the generated response.

[0164] Input: Text response from a generative AI.

[0165] Specific operation: The server receives the API response and extracts the response text.

[0166] Output: Received response text.

[0167] Step 7:

[0168] The server generates an HTML page containing the generated response.

[0169] Input: Received response text.

[0170] Specific operation: The server reads the HTML template and embeds the generated response into the template. A template engine (e.g., Jinja2) is used to generate the final HTML.

[0171] Output: The generated HTML page.

[0172] Step 8:

[0173] The user's device receives the generated HTML page and displays it in the browser.

[0174] Input: An HTML page sent from the server.

[0175] Specific operation: The server sends an HTML page to the user's terminal as an HTTP response. The user's terminal receives the HTML page and displays it in a web browser.

[0176] Output: The answer displayed in the browser.

[0177] The above outlines the specific processing flow of this system's program, as well as the specific actions and data processing performed at each step. This system allows users to receive quick and appropriate responses online, enabling efficient sales explanations and mobile phone contract procedures.

[0178] (Application Example 1)

[0179] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0180] With the increase in online purchasing and contract procedures, there is a need for support systems that can quickly and appropriately answer user questions and facilitate smooth transactions. However, current systems suffer from problems such as slow responses to user inquiries and complex, user-unfriendly procedures. This can lead to decreased user satisfaction and reduced purchasing intent.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0182] In this invention, the server includes means for receiving sales explanations and questions from a user terminal, means for receiving data transmitted from the user terminal, means for sending requests to a generating AI model, which in turn generates answers based on the user's questions, means for returning the generated answers to the user terminal, and means for providing guidance on the purchase procedure using the generating AI model. This enables a quick and appropriate response to user questions and facilitates smooth purchase and contract procedures.

[0183] "User terminal" refers to a computing device used by an end user, including smartphones, tablets, and personal computers.

[0184] "Sales explanation" refers to information that describes the features, benefits, and price of a product or service, providing users with the detailed explanations they need when considering a purchase or contract.

[0185] "Questions" refer to doubts or concerns that users have about a product or service, and appropriate answers to these questions are required.

[0186] A "server" is a computer system that receives requests from clients (user terminals) over a network, performs appropriate processing, and returns the results.

[0187] "Means of receiving data" refers to the functions and systems that allow a server to receive information (such as sales explanations, questions, and contract information) sent from a user's terminal.

[0188] A "generative AI model" is an artificial intelligence-based algorithm or system that generates appropriate answers in response to user questions.

[0189] "Means of sending requests" refers to functions or systems that send information received from the user's terminal to a generating AI model and process it to generate appropriate answers or guidance.

[0190] "Means of generating answers" refers to the functions and processes by which a generative AI model generates appropriate answers to a user's questions or concerns.

[0191] "Means of returning responses" refers to functions and systems that send responses generated by a generative AI model from a server to a user's terminal, allowing the user to review them.

[0192] "Means of providing guidance" refers to functions and systems that provide step-by-step instructions and guidance necessary for users to proceed with purchase or contract procedures.

[0193] "Means for verifying necessary data" refers to functions and systems that perform processing to confirm the accuracy and completeness of contract information and purchase information submitted by users.

[0194] "Purchase process" refers to the process of entering information, making payments, and confirming purchases of selected goods or services online.

[0195] The system for implementing this invention combines a user terminal, a server, and a generative AI model. First, the user terminal inputs information such as sales explanations, questions, and purchase procedures. This information is transmitted to the server via the internet. The server analyzes the received information and sends a request to the generative AI model. The generative AI model generates appropriate answers and guidance based on the request and sends the results back to the server. The server sends this back to the user terminal, and the user confirms the displayed content.

[0196] Hardware and software to be used

[0197] hardware

[0198] User devices (smartphones, tablets, PCs, etc.)

[0199] server

[0200] software

[0201] Flask (web framework)

[0202] Requests (HTTP Request Library)

[0203] OpenAI API (Generative AI Models)

[0204] Data processing and data calculation

[0205] 1. Data transmission

[0206] Users enter questions about products or services from their smartphones or computers and press the submit button. This data is sent to the server as an HTTP request.

[0207] 2. Data reception and analysis

[0208] The server analyzes the received data and extracts the content of the question. This analysis uses the Flask framework.

[0209] 3. Requests to the Generative AI Model

[0210] The server sends the analyzed data to the AI ​​model and requests it to generate an appropriate response. The OpenAI API is used for this process.

[0211] 4. Generating and sending responses

[0212] The generative AI model generates a response based on the request, and that response is sent back to the server. The server then generates the result as an HTML page and sends it back to the user's terminal.

[0213] Specific example

[0214] The user enters the question "Is this product waterproof?" from their device. The server receives the question and sends the following prompt to the AI ​​model.

[0215] Example of a prompt

[0216] text

[0217] I have a question about the product: Is this product waterproof?

[0218] Please answer:

[0219] The AI ​​model generates the response, "Yes, this product is waterproof up to 1 meter deep for a maximum of 30 minutes." The generated response is sent back to the user's terminal via the server, and the user can then verify its contents.

[0220] This system allows users to obtain real-time information about products and services online, enabling a smooth purchasing process. It also provides verification of contract information and guidance on the purchasing process, streamlining the overall process.

[0221] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0222] Step 1:

[0223] Users enter questions and purchase information from their smartphones or computers and press the submit button. The input includes text containing sales explanations and questions. This input data is sent to the server as an HTTP request. For example, a user might enter and submit a question such as, "Is this product waterproof?"

[0224] Step 2:

[0225] The server receives HTTP requests sent from the user's terminal and parses their contents. Specifically, it uses the Flask framework to extract the received data and extract the content of the question. The input is the question text mentioned earlier, and the output is the parsed question content.

[0226] Step 3:

[0227] The server sends the analyzed data to the generating AI model and requests it to generate an appropriate answer. Specifically, it uses the OpenAI API to send a prompt to the generating AI model. The input is the question text "Is this product waterproof?", and the output is the answer from the generating AI model. The prompt sent is "I have a question about the product: Is this product waterproof? Please answer:".

[0228] Step 4:

[0229] The generative AI model generates an answer based on the request, and that answer is sent back to the server. The input is a prompt sent from the server, and the output is the generated answer text. The generated answer is, "Yes, this product is waterproof up to 1 meter deep for a maximum of 30 minutes."

[0230] Step 5:

[0231] The server receives the generated response and creates an HTML page to display it. Specifically, it uses an HTML template to format the generated response text into a format that the user can view. The input is the response text returned from the generation AI model, and the output is HTML content to be sent to the user's terminal.

[0232] Step 6:

[0233] The server sends the generated HTML page back to the user's terminal, where the user can view it. The input is HTML content, and the output is a page displayed in the user's browser. The user can then view the answers to their questions on their smartphone or computer browser.

[0234] This processing flow allows users to obtain information about products and services online in real time, enabling a smooth purchasing process.

[0235] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0236] ---

[0237] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. Furthermore, by combining it with an emotion engine, it can also provide customized answers according to the user's emotional state. It also aims to improve the user experience by enabling mobile phone contract procedures to be carried out online. This system consists of the following elements.

[0238] First, the user accesses the system's homepage using their device. The user enters their questions about sales or products into the inquiry form and sends the request by clicking the submit button. The user's device then sends this data to the server.

[0239] The server receives data sent by the user and analyzes its contents. The analyzed data is sent as a request to a generative AI, instructing the AI ​​to generate an answer to the question. The generative AI generates an answer based on the received question data and sends the result back to the server.

[0240] Furthermore, this invention utilizes an emotion engine. The emotion engine analyzes the user's emotional state based on the user's facial expressions, tone of voice, input content, etc. It transmits the results of this analysis to a server. Based on the data from the emotion engine, the server instructs a generative AI to generate a customized response that corresponds to the emotion.

[0241] Next, the server receives the generated response and creates an HTML page to send back to the user. The user's device receives this HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[0242] Users wishing to enter into a mobile phone contract can proceed by entering the necessary information into a dedicated contract form and submitting it. The contract information sent from the user's terminal is received by the server and verified. Once verification is complete, the server instructs the generative AI to generate guidance for the next step. The server receives the guidance generated by the AI ​​and displays it on the user's terminal. Following this guidance, the user proceeds through each step of the procedure, and ultimately completes all procedures.

[0243] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user sends a question from their device, such as "I want to know about the features of the new product," the server sends that question to the generative AI. The emotion engine analyzes the user's emotional state, and if it determines, for example, that "the user is surprised," the generative AI generates a response that reflects that surprise, such as "This product is equipped with highly efficient energy functions and will perform beyond your expectations. For details, please see the link below." This generated response is sent to the user's device via the server, and the user confirms it. The user can then continue to ask more detailed questions.

[0244] The above is a description of the operation of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that previously relied on stores, thereby improving user convenience. Furthermore, by providing responses that respond to the user's emotions, it realizes a more personalized user experience.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user accesses the system's homepage using a web browser.

[0248] Step 2:

[0249] The server receives the user's request and sends back the corresponding HTML file for the homepage.

[0250] Step 3:

[0251] The user's device receives an HTML file and displays the web page.

[0252] Step 4:

[0253] Users enter their questions about sales explanations or products into the inquiry form and press the "Submit" button.

[0254] Step 5:

[0255] The user's device sends the form data (questionnaire content) to the server as an HTTP POST request.

[0256] Step 6:

[0257] The server parses the form data it receives and extracts the question content.

[0258] Step 7:

[0259] The server sends data to the emotion engine along with the question content, instructing it to analyze the user's emotional state.

[0260] Step 8:

[0261] The emotion engine analyzes the user's emotional state using their input, facial expressions, and tone of voice, and sends the results back to the server.

[0262] Step 9:

[0263] The server receives data from the emotion engine and sends the question content and emotion data as a request to the generative AI.

[0264] Step 10:

[0265] The generative AI generates answers based on the question content and sentiment data received from the server. For example, if the user expresses surprise, that emotion will be reflected in the answer.

[0266] Step 11:

[0267] The generative AI sends the generated response back to the server. This is usually sent as an HTTP response.

[0268] Step 12:

[0269] The server receives the generated response and creates an HTML page to return to the user. This page contains the AI's response.

[0270] Step 13:

[0271] The server generates an HTML page which is then sent back to the user's device.

[0272] Step 14:

[0273] The user's device displays the HTML page received from the server in the browser.

[0274] Step 15:

[0275] Users can review the page and enter further questions if necessary.

[0276] Step 16:

[0277] When a user enters into a mobile phone contract, they fill in the required information on the contract form and submit it.

[0278] Step 17:

[0279] The user's device sends the contract information it received to the server.

[0280] Step 18:

[0281] The server analyzes the received contract information and checks the necessary fields (e.g., confirmation of mandatory items, verification of input format).

[0282] Step 19:

[0283] The server sends the verified data to the generative AI and requests to generate guidance for the next procedure.

[0284] Step 20:

[0285] Based on the contract information and sentiment data received by the generative AI, it generates the next procedure for the user to perform. For example, it generates guidance on the documents to be submitted next and additional information input.

[0286] Step 21:

[0287] The generative AI returns the guidance it generated to the server.

[0288] Step 22:

[0289] The server generates an HTML page based on the guidance received from the AI and sends it to the user's terminal.

[0290] Step 23:

[0291] The user's terminal displays the HTML page received from the server in the browser.

[0292] Step 24:

[0293] The user executes the next step according to the displayed guidance.

[0294] The above describes the operating steps of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on stores, and by combining it with an emotion engine, it can provide a personalized experience that responds to the user's emotions.

[0295] (Example 2)

[0296] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0297] Traditional online sales explanation and contract system systems have a problem in that they provide uniform answers to user questions and guidance on procedures, making it difficult to customize them to the emotional state and needs of individual users. As a result, the user experience is uniform, and there is a lack of ability to respond to individual needs. Furthermore, the complexity of the contract procedure was stressful for users, and there was also the problem of slow progress in completing the procedure.

[0298] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving data transmitted from a user terminal, means for sending a request to a generative AI and for the generative AI to generate an answer based on the user's question, means for an emotion engine to analyze the user's emotional state, and means for causing the generative AI to generate a customized answer based on the emotions analyzed by the emotion engine. This enables customized answers according to individual emotional states, improves the user experience, reduces stress during contract procedures, and allows for quick completion.

[0299] A "user terminal" refers to a device that a user directly operates, such as a personal computer or smartphone, that can connect to the internet.

[0300] A "server" refers to a computer system that receives and processes data sent from a user terminal.

[0301] The "generative AI" is an artificial intelligence (AI) model that generates answers based on users' questions and is a technology for generating text data using natural language processing.

[0302] The "emotion engine" is a system for analyzing users' emotional states and refers to technologies for reading emotions from users' expressions, tones of voice, text inputs, etc.

[0303] The "request" refers to the request data transmitted from the user terminal to the server or the generative AI.

[0304] The "customized answer" refers to an answer specially adjusted by the generative AI according to the emotional state or individual desires of the user.

[0305] The "means for receiving data" refers to the functions or methods for the server to receive data transmitted from the user terminal.

[0306] The "means for generating an answer based on a question" refers to the methods or technologies for the generative AI to analyze the user's question and generate an answer thereto.

[0307] The "means for generating a customized answer for the generative AI based on emotions" refers to the methods or technologies for causing the generative AI to create a special answer according to the emotional state of the user analyzed by the emotion engine.

[0308] The system of the present invention receives business explanations and input of questions from the user terminal, generates appropriate answers using the generative AI, and returns them to the user. Furthermore, by combining the emotion engine, it is also possible to provide customized answers according to the emotional state of the user. Also, it aims to improve the user experience by enabling the implementation of mobile contract procedures online. This system includes the following components.

[0309] Use of the user terminal

[0310] Users access the system's homepage using devices such as smartphones or personal computers. They enter a question into the inquiry form, such as "I would like to know about the features of the new product," and press the submit button. The user's device sends this data to the server.

[0311] Server Functions

[0312] The server performs the following main processes:

[0313] Data reception: Data sent from the user's terminal is received as an HTTP request and its contents are parsed. The Node.js framework is used for this parsing.

[0314] Integration with generative AI: The received question data is converted into JSON format, and a request is sent to the generative AI (e.g., OpenAI's GPT-3 model). The prompt message might read, for example, "The user is asking about the features of a new product. Please provide a detailed answer."

[0315] Utilizing emotion analysis: The emotion engine analyzes the user's emotional state from facial expressions, tone of voice, input content, etc., and instructs the system to generate customized responses based on the results. The emotion engine uses the Python library OpenCV and the NLP library spaCy.

[0316] HTML page generation: An HTML page is generated to send back to the user based on the generated responses. The template engine Handlebars.js is used for this HTML page generation.

[0317] Using an Emotion Engine

[0318] The emotion engine is used to analyze the user's emotional state. Specifically, it identifies emotions such as surprise or delight based on data collected through the device's camera and microphone. For example, if a user asks, "I want to know the features of the new product," and the system determines that the user is "surprised," the generative AI will generate a response that reflects that surprise, such as, "This product features highly efficient energy functions and performs beyond your expectations. For more details, please see the link below."

[0319] Mobile phone contract procedure process

[0320] When a user wishes to proceed with a mobile phone contract, they enter the required information into a dedicated contract form and click the submit button. The user's device sends the contract information to the server, which receives and verifies the information. Once verification is complete, the server instructs a generative AI to generate guidance for the next step. Based on the generated guidance, the user proceeds through each step of the procedure, ultimately completing the contract process.

[0321] Examples of specific cases and prompt statements

[0322] As a concrete example of how it works, if a user asks a question about the features of a new product, it will operate as follows:

[0323] User question: "I want to know about the features of the new product."

[0324] Prompt for generative AI: "The user is asking about the features of a new product. Please provide a detailed answer."

[0325] Emotion detection: The emotion engine determines the user's emotional state to be "surprised".

[0326] Generated response: "This product features highly efficient energy-saving functions and delivers performance beyond your expectations. For more details, please see the link below."

[0327] This system provides real-time, personalized responses, improving the quality of the user experience and enabling the mobile contract process to be completed quickly and smoothly.

[0328] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0329] Step 1:

[0330] A user accesses the system's homepage using their own device (e.g., smartphone, computer). The user enters a question into the inquiry form and presses the "Submit" button. The data entered is, for example, the text "I would like to know about the features of the new product." The user's device sends this data to the server as an HTTP request.

[0331] Step 2:

[0332] The server receives an HTTP request sent from the user's terminal. The input data is text data containing the user's question. The server parses the received data and converts it to JSON format. The Node.js framework is used for this process. The converted JSON data is then sent to the generative AI.

[0333] Step 3:

[0334] The server sends a request to a generative AI (e.g., a GPT-3 model). The input data is in JSON format and contains the user's question. The prompt is written as, "The user is asking about the features of a new product. Please provide a detailed answer." The generative AI generates an answer based on this prompt and sends the output back to the server.

[0335] Step 4:

[0336] The emotion engine analyzes the user's emotional state. Input data includes the user's facial expressions, voice tone, and input content. The emotion engine uses the Python library OpenCV and the NLP library spaCy. The analysis result determines that "the user is surprised." This analysis result is then sent to the server.

[0337] Step 5:

[0338] The server receives the analysis results from the emotion engine and instructs the generative AI to generate a customized response that reflects the emotion. The input data consists of the emotion analysis results and the user's question. The generative AI generates a customized response such as, "This product features high-efficiency energy functions and delivers performance beyond your expectations. For more details, please see the link below," and sends the output back to the server.

[0339] Step 6:

[0340] The server receives the generated response and creates an HTML page to send back to the user. The input data is the response text from the generative AI. The template engine Handlebars.js is used to generate the HTML page. The generated HTML page is sent to the user's device.

[0341] Step 7:

[0342] The user's device displays the HTML page received from the server in its browser. The data entered is the HTML page. The user reviews the displayed page and can use the inquiry form again if they have further questions.

[0343] Step 8:

[0344] When a user wishes to proceed with a mobile phone contract, they enter the required information into a dedicated contract form and click the submit button. The user's device sends an HTTP request containing the contract information to the server. The data entered is the user's contract information.

[0345] Step 9:

[0346] The server receives contract information sent from the user terminal and verifies the data. The input data is contract information. Once verification is complete, the server instructs the generative AI to generate guidance for the next procedure. The generated guidance is then sent back to the server.

[0347] Step 10:

[0348] The server generates an HTML page based on the guidance received from the generative AI and sends it back to the user's terminal. The input data is the guidance text. The generated HTML page is sent to the user's terminal.

[0349] Step 11:

[0350] The user's device displays an HTML page received from the server in its browser, and the user proceeds with the process according to the guidance. The data entered is in the form of an HTML page. Finally, all contract procedures are completed.

[0351] The above describes the specific steps of the program processing of the present invention.

[0352] (Application Example 2)

[0353] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0354] Traditional systems failed to provide personalized conversational responses tailored to user emotions, and did not adequately streamline customer service in physical stores. Furthermore, they struggled to respond quickly and accurately to customer questions and concerns. This resulted in a poor user experience and difficulty in improving customer satisfaction.

[0355] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving sales explanations and questions from a user terminal, means for the server to receive data transmitted from the user terminal, means for sending a request to a generative AI, which then generates an answer based on the user's question, means for analyzing the user's emotional state using an emotion engine, means for the generative AI to generate a customized answer based on the emotion analysis results, means for returning the generated answer to the user terminal, and means for displaying the generated answer on a smart device. This enables personalized responses according to the user's emotional state, improving the efficiency of customer service in physical stores and enhancing the user experience.

[0356] A "user terminal" is a device used by a user, where they input questions and doubts, and where generated answers and guidance are displayed.

[0357] A "server" is a central processing unit that receives data sent from a user terminal, generates appropriate responses and guidance using generative AI and an emotion engine, and sends them back to the user terminal.

[0358] "Generative AI" refers to artificial intelligence that generates answers based on user question data, and is a system that performs the processes of analysis and generation.

[0359] An "emotion engine" is a system that analyzes a user's emotional state based on their facial expressions, tone of voice, input content, and other factors.

[0360] "Emotional state" refers to the feelings a user has in response to a question or inquiry, and includes surprise, anxiety, joy, and so on.

[0361] A "customized response" is a response generated by a generative AI that takes into account the user's emotional state, as analyzed by an emotion engine, and is tailored to the individual user's needs.

[0362] "Smart devices" refer to high-performance devices such as smartphones, smart glasses, and head-mounted displays, which are used to display responses from generative AI.

[0363] Modes for carrying out the invention

[0364] This invention is a system that streamlines customer service in physical stores and provides personalized conversational responses tailored to the user's emotional state. This system consists of the following main components:

[0365] First, the user receives service from a store employee wearing a smart device such as smart glasses. Questions and inquiries from the user are captured as voice input on the smart device. This voice input is converted into text data using a speech recognition API. Next, this text data is sent to an emotion engine, which analyzes the user's emotional state. The emotion engine uses Microsoft® Azure® Emotion API and other tools to determine the user's emotional state. The analyzed emotional state is then sent to a server.

[0366] The server receives user question data and emotional state data, and instructs the generative AI to generate answers based on this data. The generative AI utilizes advanced artificial intelligence models such as GPT-4® to generate customized answers that are tailored to the user's emotional state. The generated answers are sent to the smart device via the server and displayed. In this way, the store clerk can provide the user with appropriate answers through the smart device's display.

[0367] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user asks, "I want to know about the features of the new product," the smart device uses a speech recognition API to convert the question into text data. If the emotion engine analyzes the user's emotional state and determines that they are "surprised," the following prompt is passed to the generative AI:

[0368] Question: "I would like to know about the features of the new product."

[0369] Emotion: "Surprise"

[0370] Answer: "This product features highly efficient energy-saving functions and delivers performance beyond your expectations. For more details, please see the link below."

[0371] A generative AI model (such as GPT-4) generates a customized response based on this prompt and sends it back to the server. This response is then displayed on the smart device, and the store clerk uses this to provide information to the user.

[0372] The components of this system include:

[0373] Voice recognition (voice_recognition API)

[0374] Emotion analysis (Microsoft Azure Emotion API)

[0375] Generative AI (GPT-4, etc.)

[0376] Dedicated SDK for servers and smart devices

[0377] This enables personalized responses tailored to the user's emotional state, leading to increased efficiency in customer service at physical stores and an improved user experience.

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The user inputs the question by voice. The smart device captures the voice and converts it into text data using a speech recognition API (e.g., Google® Cloud Speech-to-Text API). In this step, the input is voice data and the output is text data.

[0381] Step 2:

[0382] The converted text data is sent to an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state. The emotion engine determines the emotion based on factors such as tone of voice and word choice. The input for this step is text data, and the output is the analyzed emotion data.

[0383] Step 3:

[0384] Emotional data and text data are sent to the server. The server receives this data and sends a request to the generative AI to generate a response based on the data. In this specific example, the input is emotional data and text data, and the output is the prompt text of the generative AI.

[0385] Step 4:

[0386] A generative AI (e.g., GPT-4) generates an answer based on the prompt text. This generated answer is customized to take into account the user's emotional state. The input for this step is the prompt text, and the output is the generated text answer.

[0387] Step 5:

[0388] The generated text response is sent back to the server. The server then sends the received response back to the smart device. In this step, the input is the generated text response, and the output is the response sent to the smart device.

[0389] Step 6:

[0390] The smart device displays the received response, and the store clerk uses this to provide an appropriate reply to the user. In this step, the input is the text response sent from the server, and the output is what is displayed on the smart device's screen.

[0391] As a concrete example of how this works, if a question is asked about the features of a new product, the process will be as follows:

[0392] Step 1: The user asks a voice message saying, "I want to know about the features of the new product."

[0393] Step 2: This audio is converted into text data, which reads, "I want to know about the features of the new product."

[0394] Step 3: The emotion engine analyzes the user's surprise and determines that "surprise" is present.

[0395] Step 4: The server sends this information to the generative AI and generates a prompt along with the emotion of "surprise".

[0396] Step 5: The generation AI generates a customized response: "This product features high-efficiency energy functions and delivers performance beyond your expectations. For more details, please see the link below."

[0397] Step 6: The server sends this response to the smart device, and it is displayed on the smart device's screen. The store clerk then provides the user with an appropriate explanation based on this display.

[0398] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0399] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0400] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0401] [Second Embodiment]

[0402] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0403] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0404] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0405] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0406] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0407] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0408] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0409] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0410] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0411] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0412] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0413] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0414] ---

[0415] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. It also aims to improve the user experience by enabling mobile phone contract procedures to be completed online. This system consists of the following elements:

[0416] First, the user accesses the system's homepage using their device. The user enters their questions about sales or products into the inquiry form and sends the request by clicking the submit button. The user's device then sends this data to the server.

[0417] The server receives data sent by the user and analyzes its contents. The analyzed data is sent as a request to a generative AI, instructing the AI ​​to generate an answer to the question. The generative AI generates an answer based on the received question data and sends the result back to the server.

[0418] Next, the server receives the generated response and creates an HTML page to send back to the user. The user's device receives this HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[0419] Users wishing to enter into a mobile phone contract can proceed by entering the necessary information into a dedicated contract form and submitting it. The contract information sent from the user's terminal is received by the server and verified. Once verification is complete, the server instructs the generative AI to generate guidance for the next step. The server receives the guidance generated by the AI ​​and displays it on the user's terminal. Following this guidance, the user proceeds through each step of the procedure, and ultimately completes all procedures.

[0420] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user sends the question "I want to know about the features of the new product" from their terminal, the server sends the question to a generative AI. The AI ​​generates a response such as, "The new product is equipped with highly efficient energy functions and is easy to operate. For more details, please see the link below." This generated response is sent to the user's terminal via the server, and the user confirms it. The user can then continue to ask more detailed questions.

[0421] The above is a description of the operation of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on stores, thereby improving user convenience.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The user accesses the system's homepage using a web browser.

[0425] Step 2:

[0426] The server receives the user's request and sends back the corresponding HTML file for the homepage.

[0427] Step 3:

[0428] The user's device receives an HTML file and displays the web page.

[0429] Step 4:

[0430] The user enters their sales explanation and questions into the inquiry form and presses the "Submit" button.

[0431] Step 5:

[0432] The user's device sends the form data (questionnaire content) to the server as an HTTP POST request.

[0433] Step 6:

[0434] The server parses the form data it receives and extracts the question content.

[0435] Step 7:

[0436] The server sends an HTTP request to the AI's API endpoint to send the question content as a request to the generation AI. This request includes the question content and other necessary information (e.g., user ID, session information, etc.).

[0437] Step 8:

[0438] The generative AI understands the questions received from the server and generates appropriate answers. The AI ​​understands the context of the questions and includes explanations of technical terms and detailed sales explanations as needed.

[0439] Step 9:

[0440] The generative AI sends the generated response back to the server. This is usually sent as an HTTP response.

[0441] Step 10:

[0442] The server receives the generated response and creates an HTML page to return to the user. This page contains the AI's response.

[0443] Step 11:

[0444] The server generates an HTML page which is then sent back to the user's device.

[0445] Step 12:

[0446] The user's device displays the HTML page received from the server in the browser.

[0447] Step 13:

[0448] Users can review the page and enter further questions if necessary.

[0449] Step 14:

[0450] When a user enters into a mobile phone contract, they fill in the required information on the contract form and submit it.

[0451] Step 15:

[0452] The user's device sends the contract information it received to the server.

[0453] Step 16:

[0454] The server analyzes the received contract information and checks the necessary fields (e.g., verifying required fields, validating input formats).

[0455] Step 17:

[0456] The server sends the verified data to the generative AI and requests it to generate guidance for the next procedure.

[0457] Step 18:

[0458] The generative AI generates the next steps the user should take based on the contract information it receives. For example, it generates guidance on the documents to be submitted and the input of additional information.

[0459] Step 19:

[0460] The generative AI generates guidance, which is then sent back to the server.

[0461] Step 20:

[0462] The server generates an HTML page based on the guidance received from the AI ​​and sends it to the user's device.

[0463] Step 21:

[0464] The user's device displays the HTML page received from the server in the browser.

[0465] Step 22:

[0466] The user follows the displayed guidance and performs the following steps.

[0467] (Example 1)

[0468] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0469] Traditional sales presentations and contract procedures were often conducted in person, resulting in low user convenience. Furthermore, there was a lack of systems to provide quick and appropriate responses to online inquiries. Additionally, contract procedures were complex, making it difficult for users to complete them smoothly.

[0470] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0471] In this invention, the server includes means for receiving data transmitted from a user terminal, means for analyzing the data received by the server, and means for sending a request to a generative AI, which then generates an answer based on the user's question. This makes it possible to answer user questions quickly and appropriately, and to efficiently conduct sales explanations and contract procedures online.

[0472] A "user terminal" refers to a computer device or mobile device such as a smartphone used by a user.

[0473] A "server" is a computer system that stores, processes, and provides data over a network.

[0474] "Means of receiving data" refers to functions for acquiring information transmitted from a user terminal.

[0475] "Means of data analysis" refer to methods and algorithms for understanding and analyzing received data.

[0476] "Generative AI" refers to systems and algorithms that use artificial intelligence technology to generate text and information.

[0477] "Means of sending requests" refers to functions for sending necessary data or requests to other systems (e.g., generative AI).

[0478] "Generated response" refers to information or text created by a generative AI based on a request.

[0479] "Means for generating HTML pages" refers to functions for creating HTML-formatted documents that can be displayed in a web browser.

[0480] "Contract information" refers to specific data and information related to the contract provided by the user.

[0481] "Means for verifying data" refers to functions for confirming whether the received data is accurate and appropriate.

[0482] "Guidance" refers to instructions or information that guides users through the next procedure or step.

[0483] The system of this invention receives sales explanations and questions entered from the user's terminal, generates appropriate answers using generative AI, and sends them back to the user. Furthermore, it aims to improve the user experience by enabling mobile phone contract procedures to be completed online.

[0484] 1. System Configuration

[0485] 1.1 Hardware and Software

[0486] The system of the present invention uses the following hardware and software.

[0487] User devices (e.g., personal computers, smartphones)

[0488] Servers (e.g., cloud infrastructure, on-premises servers)

[0489] Web browser (e.g., Google Chrome, Mozilla Firefox)

[0490] Data analysis software (e.g., Python, NLTK, MeCab)

[0491] Generative AI (e.g. OpenAI GPT-3)

[0492] Database software (e.g., MySQL, PostgreSQL)

[0493] Template engine (e.g., Jinja2)

[0494] 2. Operating Procedure

[0495] 2.1 Entering the Question

[0496] The user accesses the system's homepage using their own device. The user enters their questions about sales and products into the inquiry form and presses the submit button. For example, "I would like to know about the features of the new product."

[0497] 2.2 Data Reception and Analysis

[0498] The server receives data sent from the user's terminal. The server analyzes the received data to determine what information is required. Text analysis techniques such as morphological analysis are used in this process.

[0499] 2.3 Sending requests to generative AI

[0500] The server sends the analysis results as a request to the generative AI. The generative AI generates an answer based on the received question data. For example, an answer such as, "The new product is equipped with high-efficiency energy functions and is easy to operate."

[0501] 2.4 Receiving responses and generating HTML pages

[0502] The server receives the response from the generative AI and uses it to generate an HTML page to send back to the user. The HTML page is generated using a template engine.

[0503] 2.5 Display of answers

[0504] The user's device receives the HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[0505] 3. Mobile phone contract procedures

[0506] When a user wishes to proceed with a mobile phone contract, they fill in the required information on a dedicated contract form and submit it. The server receives the contract information sent from the user's device and verifies it. Once verification is complete, the server instructs the generative AI to generate guidance for the next procedure. The generated guidance is displayed on the user's device via the server. The user follows this guidance to proceed with the procedure and can ultimately complete all procedures online.

[0507] 4. Specific Examples

[0508] 4.1 Examples of Sales Presentations

[0509] Consider a scenario where a user asks about the features of a new product. The user sends a question from their device saying, "I want to know about the features of the new product." The server sends this question to a generative AI, which generates a response such as, "The new product features highly efficient energy functions and is easy to operate. For more details, please see the link below." The response is sent to the user's device via the server, and the user confirms it.

[0510] Examples of specific prompt messages:

[0511] User: I'd like to know about the features of the new product.

[0512] The above describes a specific embodiment of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on physical stores, thereby improving user convenience.

[0513] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0514] Step 1:

[0515] The user enters their question into the inquiry form and presses the submit button.

[0516] Input: The question text entered by the user in the form.

[0517] Specific action: The user opens the homepage in their browser, enters a message in the inquiry form, for example, "I would like to know about the features of the new product," and clicks the submit button.

[0518] Output: The HTTP request sent from the user's terminal to the server containing the question text.

[0519] Step 2:

[0520] The server receives data sent from the user's terminal.

[0521] Input: HTTP request data from the user's terminal.

[0522] Specific operation: The server receives an HTTP POST request and extracts the question text from the request body.

[0523] Output: Received question text.

[0524] Step 3:

[0525] The server analyzes the data it receives.

[0526] Input: Received question text.

[0527] Specific operation: The server executes a Python script to analyze the question text using a morphological analysis library (e.g., MeCab). It then extracts necessary themes and keywords from the analysis results.

[0528] Output: The subject and keywords of the analyzed question.

[0529] Step 4:

[0530] The server requests the analysis results from the generative AI.

[0531] Input: The subject or keywords of the analyzed question.

[0532] Specific operation: The server prepares the API request and sends the analysis results as a prompt to the generative AI (e.g., OpenAI's API).

[0533] Output: Request to the AI ​​model.

[0534] Step 5:

[0535] The generative AI generates an answer based on the question.

[0536] Input: Prompt sent from the server.

[0537] Specific operation: The generative AI generates an appropriate response based on the prompt. For example, it might generate a response such as, "The new product features high-efficiency energy functions and is easy to operate."

[0538] Output: Generated answer text.

[0539] Step 6:

[0540] The server receives the generated response.

[0541] Input: Text response from a generative AI.

[0542] Specific operation: The server receives the API response and extracts the response text.

[0543] Output: Received response text.

[0544] Step 7:

[0545] The server generates an HTML page containing the generated response.

[0546] Input: Received response text.

[0547] Specific operation: The server reads the HTML template and embeds the generated response into the template. A template engine (e.g., Jinja2) is used to generate the final HTML.

[0548] Output: The generated HTML page.

[0549] Step 8:

[0550] The user's device receives the generated HTML page and displays it in the browser.

[0551] Input: An HTML page sent from the server.

[0552] Specific operation: The server sends an HTML page to the user's terminal as an HTTP response. The user's terminal receives the HTML page and displays it in a web browser.

[0553] Output: The answer displayed in the browser.

[0554] The above outlines the specific processing flow of this system's program, as well as the specific actions and data processing performed at each step. This system allows users to receive quick and appropriate responses online, enabling efficient sales explanations and mobile phone contract procedures.

[0555] (Application Example 1)

[0556] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0557] With the increase in online purchasing and contract procedures, there is a need for support systems that can quickly and appropriately answer user questions and facilitate smooth transactions. However, current systems suffer from problems such as slow responses to user inquiries and complex, user-unfriendly procedures. This can lead to decreased user satisfaction and reduced purchasing intent.

[0558] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0559] In this invention, the server includes means for receiving sales explanations and questions from a user terminal, means for receiving data transmitted from the user terminal, means for sending requests to a generating AI model, which in turn generates answers based on the user's questions, means for returning the generated answers to the user terminal, and means for providing guidance on the purchase procedure using the generating AI model. This enables a quick and appropriate response to user questions and facilitates smooth purchase and contract procedures.

[0560] "User terminal" refers to a computing device used by an end user, including smartphones, tablets, and personal computers.

[0561] "Sales explanation" refers to information that describes the features, benefits, and price of a product or service, providing users with the detailed explanations they need when considering a purchase or contract.

[0562] "Questions" refer to doubts or concerns that users have about a product or service, and appropriate answers to these questions are required.

[0563] A "server" is a computer system that receives requests from clients (user terminals) over a network, performs appropriate processing, and returns the results.

[0564] "Means of receiving data" refers to the functions and systems that allow a server to receive information (such as sales explanations, questions, and contract information) sent from a user's terminal.

[0565] A "generative AI model" is an artificial intelligence-based algorithm or system that generates appropriate answers in response to user questions.

[0566] "Means of sending requests" refers to functions or systems that send information received from the user's terminal to a generating AI model and process it to generate appropriate answers or guidance.

[0567] "Means of generating answers" refers to the functions and processes by which a generative AI model generates appropriate answers to a user's questions or concerns.

[0568] "Means of returning responses" refers to functions and systems that send responses generated by a generative AI model from a server to a user's terminal, allowing the user to review them.

[0569] "Means of providing guidance" refers to functions and systems that provide step-by-step instructions and guidance necessary for users to proceed with purchase or contract procedures.

[0570] "Means for verifying necessary data" refers to functions and systems that perform processing to confirm the accuracy and completeness of contract information and purchase information submitted by users.

[0571] "Purchase process" refers to the process of entering information, making payments, and confirming purchases of selected goods or services online.

[0572] The system for implementing this invention combines a user terminal, a server, and a generative AI model. First, the user terminal inputs information such as sales explanations, questions, and purchase procedures. This information is transmitted to the server via the internet. The server analyzes the received information and sends a request to the generative AI model. The generative AI model generates appropriate answers and guidance based on the request and sends the results back to the server. The server sends this back to the user terminal, and the user confirms the displayed content.

[0573] Hardware and software to be used

[0574] hardware

[0575] User devices (smartphones, tablets, PCs, etc.)

[0576] server

[0577] software

[0578] Flask (web framework)

[0579] Requests (HTTP Request Library)

[0580] OpenAI API (Generative AI Models)

[0581] Data processing and data calculation

[0582] 1. Data transmission

[0583] Users enter questions about products or services from their smartphones or computers and press the submit button. This data is sent to the server as an HTTP request.

[0584] 2. Data reception and analysis

[0585] The server analyzes the received data and extracts the content of the question. This analysis uses the Flask framework.

[0586] 3. Requests to the Generative AI Model

[0587] The server sends the analyzed data to the AI ​​model and requests it to generate an appropriate response. The OpenAI API is used for this process.

[0588] 4. Generating and sending responses

[0589] The generative AI model generates a response based on the request, and that response is sent back to the server. The server then generates the result as an HTML page and sends it back to the user's terminal.

[0590] Specific example

[0591] The user enters the question "Is this product waterproof?" from their device. The server receives the question and sends the following prompt to the AI ​​model.

[0592] Example of a prompt

[0593] text

[0594] I have a question about the product: Is this product waterproof?

[0595] Please answer:

[0596] The AI ​​model generates the response, "Yes, this product is waterproof up to 1 meter deep for a maximum of 30 minutes." The generated response is sent back to the user's terminal via the server, and the user can then verify its contents.

[0597] This system allows users to obtain real-time information about products and services online, enabling a smooth purchasing process. It also provides verification of contract information and guidance on the purchasing process, streamlining the overall process.

[0598] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0599] Step 1:

[0600] Users enter questions and purchase information from their smartphones or computers and press the submit button. The input includes text containing sales explanations and questions. This input data is sent to the server as an HTTP request. For example, a user might enter and submit a question such as, "Is this product waterproof?"

[0601] Step 2:

[0602] The server receives HTTP requests sent from the user's terminal and parses their contents. Specifically, it uses the Flask framework to extract the received data and extract the content of the question. The input is the question text mentioned earlier, and the output is the parsed question content.

[0603] Step 3:

[0604] The server sends the analyzed data to the generating AI model and requests it to generate an appropriate answer. Specifically, it uses the OpenAI API to send a prompt to the generating AI model. The input is the question text "Is this product waterproof?", and the output is the answer from the generating AI model. The prompt sent is "I have a question about the product: Is this product waterproof? Please answer:".

[0605] Step 4:

[0606] The generative AI model generates an answer based on the request, and that answer is sent back to the server. The input is a prompt sent from the server, and the output is the generated answer text. The generated answer is, "Yes, this product is waterproof up to 1 meter deep for a maximum of 30 minutes."

[0607] Step 5:

[0608] The server receives the generated response and creates an HTML page to display it. Specifically, it uses an HTML template to format the generated response text into a format that the user can view. The input is the response text returned from the generation AI model, and the output is HTML content to be sent to the user's terminal.

[0609] Step 6:

[0610] The server sends the generated HTML page back to the user's terminal, where the user can view it. The input is HTML content, and the output is a page displayed in the user's browser. The user can then view the answers to their questions on their smartphone or computer browser.

[0611] This processing flow allows users to obtain information about products and services online in real time, enabling a smooth purchasing process.

[0612] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0613] ---

[0614] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. Furthermore, by combining it with an emotion engine, it can also provide customized answers according to the user's emotional state. It also aims to improve the user experience by enabling mobile phone contract procedures to be carried out online. This system consists of the following elements.

[0615] First, the user accesses the system's homepage using their device. The user enters their questions about sales or products into the inquiry form and sends the request by clicking the submit button. The user's device then sends this data to the server.

[0616] The server receives data sent by the user and analyzes its contents. The analyzed data is sent as a request to a generative AI, instructing the AI ​​to generate an answer to the question. The generative AI generates an answer based on the received question data and sends the result back to the server.

[0617] Furthermore, this invention utilizes an emotion engine. The emotion engine analyzes the user's emotional state based on the user's facial expressions, tone of voice, input content, etc. It transmits the results of this analysis to a server. Based on the data from the emotion engine, the server instructs a generative AI to generate a customized response that corresponds to the emotion.

[0618] Next, the server receives the generated response and creates an HTML page to send back to the user. The user's device receives this HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[0619] Users wishing to enter into a mobile phone contract can proceed by entering the necessary information into a dedicated contract form and submitting it. The contract information sent from the user's terminal is received by the server and verified. Once verification is complete, the server instructs the generative AI to generate guidance for the next step. The server receives the guidance generated by the AI ​​and displays it on the user's terminal. Following this guidance, the user proceeds through each step of the procedure, and ultimately completes all procedures.

[0620] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user sends a question from their device, such as "I want to know about the features of the new product," the server sends that question to the generative AI. The emotion engine analyzes the user's emotional state, and if it determines, for example, that "the user is surprised," the generative AI generates a response that reflects that surprise, such as "This product is equipped with highly efficient energy functions and will perform beyond your expectations. For details, please see the link below." This generated response is sent to the user's device via the server, and the user confirms it. The user can then continue to ask more detailed questions.

[0621] The above is a description of the operation of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that previously relied on stores, thereby improving user convenience. Furthermore, by providing responses that respond to the user's emotions, it realizes a more personalized user experience.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The user accesses the system's homepage using a web browser.

[0625] Step 2:

[0626] The server receives the user's request and sends back the corresponding HTML file for the homepage.

[0627] Step 3:

[0628] The user's device receives an HTML file and displays the web page.

[0629] Step 4:

[0630] Users enter their questions about sales explanations or products into the inquiry form and press the "Submit" button.

[0631] Step 5:

[0632] The user's device sends the form data (questionnaire content) to the server as an HTTP POST request.

[0633] Step 6:

[0634] The server parses the form data it receives and extracts the question content.

[0635] Step 7:

[0636] The server sends data to the emotion engine along with the question content, instructing it to analyze the user's emotional state.

[0637] Step 8:

[0638] The emotion engine analyzes the user's emotional state using their input, facial expressions, and tone of voice, and sends the results back to the server.

[0639] Step 9:

[0640] The server receives data from the emotion engine and sends the question content and emotion data as a request to the generative AI.

[0641] Step 10:

[0642] The generative AI generates answers based on the question content and sentiment data received from the server. For example, if the user expresses surprise, that emotion will be reflected in the answer.

[0643] Step 11:

[0644] The generative AI sends the generated response back to the server. This is usually sent as an HTTP response.

[0645] Step 12:

[0646] The server receives the generated response and creates an HTML page to return to the user. This page contains the AI's response.

[0647] Step 13:

[0648] The server generates an HTML page which is then sent back to the user's device.

[0649] Step 14:

[0650] The user's device displays the HTML page received from the server in the browser.

[0651] Step 15:

[0652] Users can review the page and enter further questions if necessary.

[0653] Step 16:

[0654] When a user enters into a mobile phone contract, they fill in the required information on the contract form and submit it.

[0655] Step 17:

[0656] The user's device sends the contract information it received to the server.

[0657] Step 18:

[0658] The server analyzes the received contract information and checks the necessary fields (e.g., verifying required fields, validating input formats).

[0659] Step 19:

[0660] The server sends the verified data to the generative AI and requests it to generate guidance for the next procedure.

[0661] Step 20:

[0662] Based on the contract information and sentiment data it receives, the generative AI generates the next steps the user should take. For example, it generates guidance on the documents to be submitted or the input of additional information.

[0663] Step 21:

[0664] The generative AI generates guidance, which is then sent back to the server.

[0665] Step 22:

[0666] The server generates an HTML page based on the guidance received from the AI ​​and sends it to the user's device.

[0667] Step 23:

[0668] The user's device displays the HTML page received from the server in the browser.

[0669] Step 24:

[0670] The user follows the displayed guidance and performs the following steps.

[0671] The above describes the operating steps of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on stores, and by combining it with an emotion engine, it can provide a personalized experience that responds to the user's emotions.

[0672] (Example 2)

[0673] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0674] Traditional online sales explanation and contract system systems have a problem in that they provide uniform answers to user questions and guidance on procedures, making it difficult to customize them to the emotional state and needs of individual users. As a result, the user experience is uniform, and there is a lack of ability to respond to individual needs. Furthermore, the complexity of the contract procedure was stressful for users, and there was also the problem of slow progress in completing the procedure.

[0675] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving data transmitted from a user terminal, means for sending a request to a generative AI and for the generative AI to generate an answer based on the user's question, means for an emotion engine to analyze the user's emotional state, and means for causing the generative AI to generate a customized answer based on the emotions analyzed by the emotion engine. This enables customized answers according to individual emotional states, improves the user experience, reduces stress during contract procedures, and allows for quick completion.

[0676] A "user terminal" refers to a device that a user directly operates, such as a personal computer or smartphone, that can connect to the internet.

[0677] A "server" refers to a computer system that receives and processes data sent from a user terminal.

[0678] "Generative AI" refers to artificial intelligence (AI) models that generate answers based on user questions, and it is a technology that generates text data using natural language processing.

[0679] An "emotion engine" is a system for analyzing a user's emotional state, and refers to technology that reads emotions from the user's facial expressions, tone of voice, text input, etc.

[0680] A "request" refers to request data sent from a user's terminal to a server or generative AI.

[0681] A "customized response" refers to a response that a generative AI has specially adjusted according to the user's emotional state and individual requests.

[0682] "Means of receiving data" refers to the functions and methods used by the server to receive data sent from a user terminal.

[0683] "Means of generating answers based on questions" refers to methods and technologies for generative AI to analyze a user's question and generate an answer to it.

[0684] "Means of generating customized responses from generative AI based on emotions" refers to methods and technologies for causing generative AI to create special responses according to the user's emotional state, as analyzed by the emotion engine.

[0685] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. Furthermore, by combining it with an emotion engine, it is possible to provide customized answers according to the user's emotional state. It also aims to improve the user experience by enabling mobile phone contract procedures to be carried out online. This system includes the following components.

[0686] User terminal usage

[0687] Users access the system's homepage using devices such as smartphones or personal computers. They enter a question into the inquiry form, such as "I would like to know about the features of the new product," and press the submit button. The user's device sends this data to the server.

[0688] Server Functions

[0689] The server performs the following main processes:

[0690] Data reception: Data sent from the user's terminal is received as an HTTP request and its contents are parsed. The Node.js framework is used for this parsing.

[0691] Integration with generative AI: The received question data is converted into JSON format, and a request is sent to the generative AI (e.g., OpenAI's GPT-3 model). The prompt message might read, for example, "The user is asking about the features of a new product. Please provide a detailed answer."

[0692] Utilizing emotion analysis: The emotion engine analyzes the user's emotional state from facial expressions, tone of voice, input content, etc., and instructs the system to generate customized responses based on the results. The emotion engine uses the Python library OpenCV and the NLP library spaCy.

[0693] HTML page generation: An HTML page is generated to send back to the user based on the generated responses. The template engine Handlebars.js is used for this HTML page generation.

[0694] Using an Emotion Engine

[0695] The emotion engine is used to analyze the user's emotional state. Specifically, it identifies emotions such as surprise or delight based on data collected through the device's camera and microphone. For example, if a user asks, "I want to know the features of the new product," and the system determines that the user is "surprised," the generative AI will generate a response that reflects that surprise, such as, "This product features highly efficient energy functions and performs beyond your expectations. For more details, please see the link below."

[0696] Mobile phone contract procedure process

[0697] When a user wishes to proceed with a mobile phone contract, they enter the required information into a dedicated contract form and click the submit button. The user's device sends the contract information to the server, which receives and verifies the information. Once verification is complete, the server instructs a generative AI to generate guidance for the next step. Based on the generated guidance, the user proceeds through each step of the procedure, ultimately completing the contract process.

[0698] Examples of specific cases and prompt statements

[0699] As a concrete example of how it works, if a user asks a question about the features of a new product, it will operate as follows:

[0700] User question: "I want to know about the features of the new product."

[0701] Prompt for generative AI: "The user is asking about the features of a new product. Please provide a detailed answer."

[0702] Emotion detection: The emotion engine determines the user's emotional state to be "surprised".

[0703] Generated response: "This product features highly efficient energy-saving functions and delivers performance beyond your expectations. For more details, please see the link below."

[0704] This system provides real-time, personalized responses, improving the quality of the user experience and enabling the mobile contract process to be completed quickly and smoothly.

[0705] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0706] Step 1:

[0707] A user accesses the system's homepage using their own device (e.g., smartphone, computer). The user enters a question into the inquiry form and presses the "Submit" button. The data entered is, for example, the text "I would like to know about the features of the new product." The user's device sends this data to the server as an HTTP request.

[0708] Step 2:

[0709] The server receives an HTTP request sent from the user's terminal. The input data is text data containing the user's question. The server parses the received data and converts it to JSON format. The Node.js framework is used for this process. The converted JSON data is then sent to the generative AI.

[0710] Step 3:

[0711] The server sends a request to a generative AI (e.g., a GPT-3 model). The input data is in JSON format and contains the user's question. The prompt is written as, "The user is asking about the features of a new product. Please provide a detailed answer." The generative AI generates an answer based on this prompt and sends the output back to the server.

[0712] Step 4:

[0713] The emotion engine analyzes the user's emotional state. Input data includes the user's facial expressions, voice tone, and input content. The emotion engine uses the Python library OpenCV and the NLP library spaCy. The analysis result determines that "the user is surprised." This analysis result is then sent to the server.

[0714] Step 5:

[0715] The server receives the analysis results from the emotion engine and instructs the generative AI to generate a customized response that reflects the emotion. The input data consists of the emotion analysis results and the user's question. The generative AI generates a customized response such as, "This product features high-efficiency energy functions and delivers performance beyond your expectations. For more details, please see the link below," and sends the output back to the server.

[0716] Step 6:

[0717] The server receives the generated response and creates an HTML page to send back to the user. The input data is the response text from the generative AI. The template engine Handlebars.js is used to generate the HTML page. The generated HTML page is sent to the user's device.

[0718] Step 7:

[0719] The user's device displays the HTML page received from the server in its browser. The data entered is the HTML page. The user reviews the displayed page and can use the inquiry form again if they have further questions.

[0720] Step 8:

[0721] When a user wishes to proceed with a mobile phone contract, they enter the required information into a dedicated contract form and click the submit button. The user's device sends an HTTP request containing the contract information to the server. The data entered is the user's contract information.

[0722] Step 9:

[0723] The server receives contract information sent from the user terminal and verifies the data. The input data is contract information. Once verification is complete, the server instructs the generative AI to generate guidance for the next procedure. The generated guidance is then sent back to the server.

[0724] Step 10:

[0725] The server generates an HTML page based on the guidance received from the generative AI and sends it back to the user's terminal. The input data is the guidance text. The generated HTML page is sent to the user's terminal.

[0726] Step 11:

[0727] The user's device displays an HTML page received from the server in its browser, and the user proceeds with the process according to the guidance. The data entered is in the form of an HTML page. Finally, all contract procedures are completed.

[0728] The above describes the specific steps of the program processing of the present invention.

[0729] (Application Example 2)

[0730] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0731] Traditional systems failed to provide personalized conversational responses tailored to user emotions, and did not adequately streamline customer service in physical stores. Furthermore, they struggled to respond quickly and accurately to customer questions and concerns. This resulted in a poor user experience and difficulty in improving customer satisfaction.

[0732] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving sales explanations and questions from a user terminal, means for the server to receive data transmitted from the user terminal, means for sending a request to a generative AI, which then generates an answer based on the user's question, means for analyzing the user's emotional state using an emotion engine, means for the generative AI to generate a customized answer based on the emotion analysis results, means for returning the generated answer to the user terminal, and means for displaying the generated answer on a smart device. This enables personalized responses according to the user's emotional state, improving the efficiency of customer service in physical stores and enhancing the user experience.

[0733] A "user terminal" is a device used by a user, where they input questions and doubts, and where generated answers and guidance are displayed.

[0734] A "server" is a central processing unit that receives data sent from a user terminal, generates appropriate responses and guidance using generative AI and an emotion engine, and sends them back to the user terminal.

[0735] "Generative AI" refers to artificial intelligence that generates answers based on user question data, and is a system that performs the processes of analysis and generation.

[0736] An "emotion engine" is a system that analyzes a user's emotional state based on their facial expressions, tone of voice, input content, and other factors.

[0737] "Emotional state" refers to the feelings a user has in response to a question or inquiry, and includes surprise, anxiety, joy, and so on.

[0738] A "customized response" is a response generated by a generative AI that takes into account the user's emotional state, as analyzed by an emotion engine, and is tailored to the individual user's needs.

[0739] "Smart devices" refer to high-performance devices such as smartphones, smart glasses, and head-mounted displays, which are used to display responses from generative AI.

[0740] Modes for carrying out the invention

[0741] This invention is a system that streamlines customer service in physical stores and provides personalized conversational responses tailored to the user's emotional state. This system consists of the following main components:

[0742] First, the user receives service from a store employee wearing a smart device such as smart glasses. Questions and inquiries from the user are captured as voice input on the smart device. This voice input is converted into text data using a speech recognition API. Next, this text data is sent to an emotion engine, which analyzes the user's emotional state. The emotion engine uses the Microsoft Azure Emotion API, among others, to determine the user's emotional state. The analyzed emotional state is then sent to a server.

[0743] The server receives user question data and emotional state data, and instructs a generative AI to generate answers based on this data. The generative AI utilizes advanced artificial intelligence models such as GPT-4 to generate customized answers tailored to the user's emotional state. The generated answers are sent to the smart device via the server and displayed. In this way, the store clerk can provide the user with appropriate answers through the smart device's display.

[0744] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user asks, "I want to know about the features of the new product," the smart device uses a speech recognition API to convert the question into text data. If the emotion engine analyzes the user's emotional state and determines that they are "surprised," the following prompt is passed to the generative AI:

[0745] Question: "I would like to know about the features of the new product."

[0746] Emotion: "Surprise"

[0747] Answer: "This product features highly efficient energy-saving functions and delivers performance beyond your expectations. For more details, please see the link below."

[0748] A generative AI model (such as GPT-4) generates a customized response based on this prompt and sends it back to the server. This response is then displayed on the smart device, and the store clerk uses this to provide information to the user.

[0749] The components of this system include:

[0750] Voice recognition (voice_recognition API)

[0751] Emotion analysis (Microsoft Azure Emotion API)

[0752] Generative AI (GPT-4, etc.)

[0753] Dedicated SDK for servers and smart devices

[0754] This enables personalized responses tailored to the user's emotional state, leading to increased efficiency in customer service at physical stores and an improved user experience.

[0755] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0756] Step 1:

[0757] The user inputs the question by voice. The smart device captures the voice and converts it into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text API). In this step, the input is voice data and the output is text data.

[0758] Step 2:

[0759] The converted text data is sent to an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state. The emotion engine determines the emotion based on factors such as tone of voice and word choice. The input for this step is text data, and the output is the analyzed emotion data.

[0760] Step 3:

[0761] Emotional data and text data are sent to the server. The server receives this data and sends a request to the generative AI to generate a response based on the data. In this specific example, the input is emotional data and text data, and the output is the prompt text of the generative AI.

[0762] Step 4:

[0763] A generative AI (e.g., GPT-4) generates an answer based on the prompt text. This generated answer is customized to take into account the user's emotional state. The input for this step is the prompt text, and the output is the generated text answer.

[0764] Step 5:

[0765] The generated text response is sent back to the server. The server then sends the received response back to the smart device. In this step, the input is the generated text response, and the output is the response sent to the smart device.

[0766] Step 6:

[0767] The smart device displays the received response, and the store clerk uses this to provide an appropriate reply to the user. In this step, the input is the text response sent from the server, and the output is what is displayed on the smart device's screen.

[0768] As a concrete example of how this works, if a question is asked about the features of a new product, the process will be as follows:

[0769] Step 1: The user asks a voice message saying, "I want to know about the features of the new product."

[0770] Step 2: This audio is converted into text data, which reads, "I want to know about the features of the new product."

[0771] Step 3: The emotion engine analyzes the user's surprise and determines that "surprise" is present.

[0772] Step 4: The server sends this information to the generative AI and generates a prompt along with the emotion of "surprise".

[0773] Step 5: The generation AI generates a customized response: "This product features high-efficiency energy functions and delivers performance beyond your expectations. For more details, please see the link below."

[0774] Step 6: The server sends this response to the smart device, and it is displayed on the smart device's screen. The store clerk then provides the user with an appropriate explanation based on this display.

[0775] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0776] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0777] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0778] [Third Embodiment]

[0779] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0780] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0781] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0782] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0783] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0784] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0785] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0786] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0787] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0788] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0789] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0790] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0791] ---

[0792] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. It also aims to improve the user experience by enabling mobile phone contract procedures to be completed online. This system consists of the following elements:

[0793] First, the user accesses the system's homepage using their device. The user enters their questions about sales or products into the inquiry form and sends the request by clicking the submit button. The user's device then sends this data to the server.

[0794] The server receives data sent by the user and analyzes its contents. The analyzed data is sent as a request to a generative AI, instructing the AI ​​to generate an answer to the question. The generative AI generates an answer based on the received question data and sends the result back to the server.

[0795] Next, the server receives the generated response and creates an HTML page to send back to the user. The user's device receives this HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[0796] Users wishing to enter into a mobile phone contract can proceed by entering the necessary information into a dedicated contract form and submitting it. The contract information sent from the user's terminal is received by the server and verified. Once verification is complete, the server instructs the generative AI to generate guidance for the next step. The server receives the guidance generated by the AI ​​and displays it on the user's terminal. Following this guidance, the user proceeds through each step of the procedure, and ultimately completes all procedures.

[0797] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user sends the question "I want to know about the features of the new product" from their terminal, the server sends the question to a generative AI. The AI ​​generates a response such as, "The new product is equipped with highly efficient energy functions and is easy to operate. For more details, please see the link below." This generated response is sent to the user's terminal via the server, and the user confirms it. The user can then continue to ask more detailed questions.

[0798] The above is a description of the operation of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on stores, thereby improving user convenience.

[0799] The following describes the processing flow.

[0800] Step 1:

[0801] The user accesses the system's homepage using a web browser.

[0802] Step 2:

[0803] The server receives the user's request and sends back the corresponding HTML file for the homepage.

[0804] Step 3:

[0805] The user's device receives an HTML file and displays the web page.

[0806] Step 4:

[0807] The user enters their sales explanation and questions into the inquiry form and presses the "Submit" button.

[0808] Step 5:

[0809] The user's device sends the form data (questionnaire content) to the server as an HTTP POST request.

[0810] Step 6:

[0811] The server parses the form data it receives and extracts the question content.

[0812] Step 7:

[0813] The server sends an HTTP request to the AI's API endpoint to send the question content as a request to the generation AI. This request includes the question content and other necessary information (e.g., user ID, session information, etc.).

[0814] Step 8:

[0815] The generative AI understands the questions received from the server and generates appropriate answers. The AI ​​understands the context of the questions and includes explanations of technical terms and detailed sales explanations as needed.

[0816] Step 9:

[0817] The generative AI sends the generated response back to the server. This is usually sent as an HTTP response.

[0818] Step 10:

[0819] The server receives the generated response and creates an HTML page to return to the user. This page contains the AI's response.

[0820] Step 11:

[0821] The server generates an HTML page which is then sent back to the user's device.

[0822] Step 12:

[0823] The user's device displays the HTML page received from the server in the browser.

[0824] Step 13:

[0825] Users can review the page and enter further questions if necessary.

[0826] Step 14:

[0827] When a user enters into a mobile phone contract, they fill in the required information on the contract form and submit it.

[0828] Step 15:

[0829] The user's device sends the contract information it received to the server.

[0830] Step 16:

[0831] The server analyzes the received contract information and checks the necessary fields (e.g., verifying required fields, validating input formats).

[0832] Step 17:

[0833] The server sends the verified data to the generative AI and requests it to generate guidance for the next procedure.

[0834] Step 18:

[0835] The generative AI generates the next steps the user should take based on the contract information it receives. For example, it generates guidance on the documents to be submitted and the input of additional information.

[0836] Step 19:

[0837] The generative AI generates guidance, which is then sent back to the server.

[0838] Step 20:

[0839] The server generates an HTML page based on the guidance received from the AI ​​and sends it to the user's device.

[0840] Step 21:

[0841] The user's device displays the HTML page received from the server in the browser.

[0842] Step 22:

[0843] The user follows the displayed guidance and performs the following steps.

[0844] (Example 1)

[0845] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0846] Traditional sales presentations and contract procedures were often conducted in person, resulting in low user convenience. Furthermore, there was a lack of systems to provide quick and appropriate responses to online inquiries. Additionally, contract procedures were complex, making it difficult for users to complete them smoothly.

[0847] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0848] In this invention, the server includes means for receiving data transmitted from a user terminal, means for analyzing the data received by the server, and means for sending a request to a generative AI, which then generates an answer based on the user's question. This makes it possible to answer user questions quickly and appropriately, and to efficiently conduct sales explanations and contract procedures online.

[0849] A "user terminal" refers to a computer device or mobile device such as a smartphone used by a user.

[0850] A "server" is a computer system that stores, processes, and provides data over a network.

[0851] "Means of receiving data" refers to functions for acquiring information transmitted from a user terminal.

[0852] "Means of data analysis" refer to methods and algorithms for understanding and analyzing received data.

[0853] "Generative AI" refers to systems and algorithms that use artificial intelligence technology to generate text and information.

[0854] "Means of sending requests" refers to functions for sending necessary data or requests to other systems (e.g., generative AI).

[0855] "Generated response" refers to information or text created by a generative AI based on a request.

[0856] "Means for generating HTML pages" refers to functions for creating HTML-formatted documents that can be displayed in a web browser.

[0857] "Contract information" refers to specific data and information related to the contract provided by the user.

[0858] "Means for verifying data" refers to functions for confirming whether the received data is accurate and appropriate.

[0859] "Guidance" refers to instructions or information that guides users through the next procedure or step.

[0860] The system of this invention receives sales explanations and questions entered from the user's terminal, generates appropriate answers using generative AI, and sends them back to the user. Furthermore, it aims to improve the user experience by enabling mobile phone contract procedures to be completed online.

[0861] 1. System Configuration

[0862] 1.1 Hardware and Software

[0863] The system of the present invention uses the following hardware and software.

[0864] User devices (e.g., personal computers, smartphones)

[0865] Servers (e.g., cloud infrastructure, on-premises servers)

[0866] Web browser (e.g., Google Chrome, Mozilla Firefox)

[0867] Data analysis software (e.g., Python, NLTK, MeCab)

[0868] Generative AI (e.g. OpenAI GPT-3)

[0869] Database software (e.g., MySQL, PostgreSQL)

[0870] Template engine (e.g., Jinja2)

[0871] 2. Operating Procedure

[0872] 2.1 Entering the Question

[0873] The user accesses the system's homepage using their own device. The user enters their questions about sales and products into the inquiry form and presses the submit button. For example, "I would like to know about the features of the new product."

[0874] 2.2 Data Reception and Analysis

[0875] The server receives data sent from the user's terminal. The server analyzes the received data to determine what information is required. Text analysis techniques such as morphological analysis are used in this process.

[0876] 2.3 Sending requests to generative AI

[0877] The server sends the analysis results as a request to the generative AI. The generative AI generates an answer based on the received question data. For example, an answer such as, "The new product is equipped with high-efficiency energy functions and is easy to operate."

[0878] 2.4 Receiving responses and generating HTML pages

[0879] The server receives the response from the generative AI and uses it to generate an HTML page to send back to the user. The HTML page is generated using a template engine.

[0880] 2.5 Display of answers

[0881] The user's device receives the HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[0882] 3. Mobile phone contract procedures

[0883] When a user wishes to proceed with a mobile phone contract, they fill in the required information on a dedicated contract form and submit it. The server receives the contract information sent from the user's device and verifies it. Once verification is complete, the server instructs the generative AI to generate guidance for the next procedure. The generated guidance is displayed on the user's device via the server. The user follows this guidance to proceed with the procedure and can ultimately complete all procedures online.

[0884] 4. Specific Examples

[0885] 4.1 Examples of Sales Presentations

[0886] Consider a scenario where a user asks about the features of a new product. The user sends a question from their device saying, "I want to know about the features of the new product." The server sends this question to a generative AI, which generates a response such as, "The new product features highly efficient energy functions and is easy to operate. For more details, please see the link below." The response is sent to the user's device via the server, and the user confirms it.

[0887] Examples of specific prompt messages:

[0888] User: I'd like to know about the features of the new product.

[0889] The above describes a specific embodiment of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on physical stores, thereby improving user convenience.

[0890] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0891] Step 1:

[0892] The user enters their question into the inquiry form and presses the submit button.

[0893] Input: The question text entered by the user in the form.

[0894] Specific action: The user opens the homepage in their browser, enters a message in the inquiry form, for example, "I would like to know about the features of the new product," and clicks the submit button.

[0895] Output: The HTTP request sent from the user's terminal to the server containing the question text.

[0896] Step 2:

[0897] The server receives data sent from the user's terminal.

[0898] Input: HTTP request data from the user's terminal.

[0899] Specific operation: The server receives an HTTP POST request and extracts the question text from the request body.

[0900] Output: Received question text.

[0901] Step 3:

[0902] The server analyzes the data it receives.

[0903] Input: Received question text.

[0904] Specific operation: The server executes a Python script to analyze the question text using a morphological analysis library (e.g., MeCab). It then extracts necessary themes and keywords from the analysis results.

[0905] Output: The subject and keywords of the analyzed question.

[0906] Step 4:

[0907] The server requests the analysis results from the generative AI.

[0908] Input: The subject or keywords of the analyzed question.

[0909] Specific operation: The server prepares the API request and sends the analysis results as a prompt to the generative AI (e.g., OpenAI's API).

[0910] Output: Request to the AI ​​model.

[0911] Step 5:

[0912] The generative AI generates an answer based on the question.

[0913] Input: Prompt sent from the server.

[0914] Specific operation: The generative AI generates an appropriate response based on the prompt. For example, it might generate a response such as, "The new product features high-efficiency energy functions and is easy to operate."

[0915] Output: Generated answer text.

[0916] Step 6:

[0917] The server receives the generated response.

[0918] Input: Text response from a generative AI.

[0919] Specific operation: The server receives the API response and extracts the response text.

[0920] Output: Received response text.

[0921] Step 7:

[0922] The server generates an HTML page containing the generated response.

[0923] Input: Received response text.

[0924] Specific operation: The server reads the HTML template and embeds the generated response into the template. A template engine (e.g., Jinja2) is used to generate the final HTML.

[0925] Output: The generated HTML page.

[0926] Step 8:

[0927] The user's device receives the generated HTML page and displays it in the browser.

[0928] Input: An HTML page sent from the server.

[0929] Specific operation: The server sends an HTML page to the user's terminal as an HTTP response. The user's terminal receives the HTML page and displays it in a web browser.

[0930] Output: The answer displayed in the browser.

[0931] The above outlines the specific processing flow of this system's program, as well as the specific actions and data processing performed at each step. This system allows users to receive quick and appropriate responses online, enabling efficient sales explanations and mobile phone contract procedures.

[0932] (Application Example 1)

[0933] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0934] With the increase in online purchasing and contract procedures, there is a need for support systems that can quickly and appropriately answer user questions and facilitate smooth transactions. However, current systems suffer from problems such as slow responses to user inquiries and complex, user-unfriendly procedures. This can lead to decreased user satisfaction and reduced purchasing intent.

[0935] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0936] In this invention, the server includes means for receiving sales explanations and questions from a user terminal, means for receiving data transmitted from the user terminal, means for sending requests to a generating AI model, which in turn generates answers based on the user's questions, means for returning the generated answers to the user terminal, and means for providing guidance on the purchase procedure using the generating AI model. This enables a quick and appropriate response to user questions and facilitates smooth purchase and contract procedures.

[0937] "User terminal" refers to a computing device used by an end user, including smartphones, tablets, and personal computers.

[0938] "Sales explanation" refers to information that describes the features, benefits, and price of a product or service, providing users with the detailed explanations they need when considering a purchase or contract.

[0939] "Questions" refer to doubts or concerns that users have about a product or service, and appropriate answers to these questions are required.

[0940] A "server" is a computer system that receives requests from clients (user terminals) over a network, performs appropriate processing, and returns the results.

[0941] "Means of receiving data" refers to the functions and systems that allow a server to receive information (such as sales explanations, questions, and contract information) sent from a user's terminal.

[0942] A "generative AI model" is an artificial intelligence-based algorithm or system that generates appropriate answers in response to user questions.

[0943] "Means of sending requests" refers to functions or systems that send information received from the user's terminal to a generating AI model and process it to generate appropriate answers or guidance.

[0944] "Means of generating answers" refers to the functions and processes by which a generative AI model generates appropriate answers to a user's questions or concerns.

[0945] "Means of returning responses" refers to functions and systems that send responses generated by a generative AI model from a server to a user's terminal, allowing the user to review them.

[0946] "Means of providing guidance" refers to functions and systems that provide step-by-step instructions and guidance necessary for users to proceed with purchase or contract procedures.

[0947] "Means for verifying necessary data" refers to functions and systems that perform processing to confirm the accuracy and completeness of contract information and purchase information submitted by users.

[0948] "Purchase process" refers to the process of entering information, making payments, and confirming purchases of selected goods or services online.

[0949] The system for implementing this invention combines a user terminal, a server, and a generative AI model. First, the user terminal inputs information such as sales explanations, questions, and purchase procedures. This information is transmitted to the server via the internet. The server analyzes the received information and sends a request to the generative AI model. The generative AI model generates appropriate answers and guidance based on the request and sends the results back to the server. The server sends this back to the user terminal, and the user confirms the displayed content.

[0950] Hardware and software to be used

[0951] hardware

[0952] User devices (smartphones, tablets, PCs, etc.)

[0953] server

[0954] software

[0955] Flask (web framework)

[0956] Requests (HTTP Request Library)

[0957] OpenAI API (Generative AI Models)

[0958] Data processing and data calculation

[0959] 1. Data transmission

[0960] Users enter questions about products or services from their smartphones or computers and press the submit button. This data is sent to the server as an HTTP request.

[0961] 2. Data reception and analysis

[0962] The server analyzes the received data and extracts the content of the question. This analysis uses the Flask framework.

[0963] 3. Requests to the Generative AI Model

[0964] The server sends the analyzed data to the AI ​​model and requests it to generate an appropriate response. The OpenAI API is used for this process.

[0965] 4. Generating and sending responses

[0966] The generative AI model generates a response based on the request, and that response is sent back to the server. The server then generates the result as an HTML page and sends it back to the user's terminal.

[0967] Specific example

[0968] The user enters the question "Is this product waterproof?" from their device. The server receives the question and sends the following prompt to the AI ​​model.

[0969] Example of a prompt

[0970] text

[0971] I have a question about the product: Is this product waterproof?

[0972] Please answer:

[0973] The AI ​​model generates the response, "Yes, this product is waterproof up to 1 meter deep for a maximum of 30 minutes." The generated response is sent back to the user's terminal via the server, and the user can then verify its contents.

[0974] This system allows users to obtain real-time information about products and services online, enabling a smooth purchasing process. It also provides verification of contract information and guidance on the purchasing process, streamlining the overall process.

[0975] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0976] Step 1:

[0977] Users enter questions and purchase information from their smartphones or computers and press the submit button. The input includes text containing sales explanations and questions. This input data is sent to the server as an HTTP request. For example, a user might enter and submit a question such as, "Is this product waterproof?"

[0978] Step 2:

[0979] The server receives HTTP requests sent from the user's terminal and parses their contents. Specifically, it uses the Flask framework to extract the received data and extract the content of the question. The input is the question text mentioned earlier, and the output is the parsed question content.

[0980] Step 3:

[0981] The server sends the analyzed data to the generating AI model and requests it to generate an appropriate answer. Specifically, it uses the OpenAI API to send a prompt to the generating AI model. The input is the question text "Is this product waterproof?", and the output is the answer from the generating AI model. The prompt sent is "I have a question about the product: Is this product waterproof? Please answer:".

[0982] Step 4:

[0983] The generative AI model generates an answer based on the request, and that answer is sent back to the server. The input is a prompt sent from the server, and the output is the generated answer text. The generated answer is, "Yes, this product is waterproof up to 1 meter deep for a maximum of 30 minutes."

[0984] Step 5:

[0985] The server receives the generated response and creates an HTML page to display it. Specifically, it uses an HTML template to format the generated response text into a format that the user can view. The input is the response text returned from the generation AI model, and the output is HTML content to be sent to the user's terminal.

[0986] Step 6:

[0987] The server sends the generated HTML page back to the user's terminal, where the user can view it. The input is HTML content, and the output is a page displayed in the user's browser. The user can then view the answers to their questions on their smartphone or computer browser.

[0988] This processing flow allows users to obtain information about products and services online in real time, enabling a smooth purchasing process.

[0989] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0990] ---

[0991] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. Furthermore, by combining it with an emotion engine, it can also provide customized answers according to the user's emotional state. It also aims to improve the user experience by enabling mobile phone contract procedures to be carried out online. This system consists of the following elements.

[0992] First, the user accesses the system's homepage using their device. The user enters their questions about sales or products into the inquiry form and sends the request by clicking the submit button. The user's device then sends this data to the server.

[0993] The server receives data sent by the user and analyzes its contents. The analyzed data is sent as a request to a generative AI, instructing the AI ​​to generate an answer to the question. The generative AI generates an answer based on the received question data and sends the result back to the server.

[0994] Furthermore, this invention utilizes an emotion engine. The emotion engine analyzes the user's emotional state based on the user's facial expressions, tone of voice, input content, etc. It transmits the results of this analysis to a server. Based on the data from the emotion engine, the server instructs a generative AI to generate a customized response that corresponds to the emotion.

[0995] Next, the server receives the generated response and creates an HTML page to send back to the user. The user's device receives this HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[0996] Users wishing to enter into a mobile phone contract can proceed by entering the necessary information into a dedicated contract form and submitting it. The contract information sent from the user's terminal is received by the server and verified. Once verification is complete, the server instructs the generative AI to generate guidance for the next step. The server receives the guidance generated by the AI ​​and displays it on the user's terminal. Following this guidance, the user proceeds through each step of the procedure, and ultimately completes all procedures.

[0997] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user sends a question from their device, such as "I want to know about the features of the new product," the server sends that question to the generative AI. The emotion engine analyzes the user's emotional state, and if it determines, for example, that "the user is surprised," the generative AI generates a response that reflects that surprise, such as "This product is equipped with highly efficient energy functions and will perform beyond your expectations. For details, please see the link below." This generated response is sent to the user's device via the server, and the user confirms it. The user can then continue to ask more detailed questions.

[0998] The above is a description of the operation of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that previously relied on stores, thereby improving user convenience. Furthermore, by providing responses that respond to the user's emotions, it realizes a more personalized user experience.

[0999] The following describes the processing flow.

[1000] Step 1:

[1001] The user accesses the system's homepage using a web browser.

[1002] Step 2:

[1003] The server receives the user's request and sends back the corresponding HTML file for the homepage.

[1004] Step 3:

[1005] The user's device receives an HTML file and displays the web page.

[1006] Step 4:

[1007] Users enter their questions about sales explanations or products into the inquiry form and press the "Submit" button.

[1008] Step 5:

[1009] The user's device sends the form data (questionnaire content) to the server as an HTTP POST request.

[1010] Step 6:

[1011] The server parses the form data it receives and extracts the question content.

[1012] Step 7:

[1013] The server sends data to the emotion engine along with the question content, instructing it to analyze the user's emotional state.

[1014] Step 8:

[1015] The emotion engine analyzes the user's emotional state using their input, facial expressions, and tone of voice, and sends the results back to the server.

[1016] Step 9:

[1017] The server receives data from the emotion engine and sends the question content and emotion data as a request to the generative AI.

[1018] Step 10:

[1019] The generative AI generates answers based on the question content and sentiment data received from the server. For example, if the user expresses surprise, that emotion will be reflected in the answer.

[1020] Step 11:

[1021] The generative AI sends the generated response back to the server. This is usually sent as an HTTP response.

[1022] Step 12:

[1023] The server receives the generated response and creates an HTML page to return to the user. This page contains the AI's response.

[1024] Step 13:

[1025] The server generates an HTML page which is then sent back to the user's device.

[1026] Step 14:

[1027] The user's device displays the HTML page received from the server in the browser.

[1028] Step 15:

[1029] Users can review the page and enter further questions if necessary.

[1030] Step 16:

[1031] When a user enters into a mobile phone contract, they fill in the required information on the contract form and submit it.

[1032] Step 17:

[1033] The user's device sends the contract information it received to the server.

[1034] Step 18:

[1035] The server analyzes the received contract information and checks the necessary fields (e.g., verifying required fields, validating input formats).

[1036] Step 19:

[1037] The server sends the verified data to the generative AI and requests it to generate guidance for the next procedure.

[1038] Step 20:

[1039] Based on the contract information and sentiment data it receives, the generative AI generates the next steps the user should take. For example, it generates guidance on the documents to be submitted or the input of additional information.

[1040] Step 21:

[1041] The generative AI generates guidance, which is then sent back to the server.

[1042] Step 22:

[1043] The server generates an HTML page based on the guidance received from the AI ​​and sends it to the user's device.

[1044] Step 23:

[1045] The user's device displays the HTML page received from the server in the browser.

[1046] Step 24:

[1047] The user follows the displayed guidance and performs the following steps.

[1048] The above describes the operating steps of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on stores, and by combining it with an emotion engine, it can provide a personalized experience that responds to the user's emotions.

[1049] (Example 2)

[1050] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1051] Traditional online sales explanation and contract system systems have a problem in that they provide uniform answers to user questions and guidance on procedures, making it difficult to customize them to the emotional state and needs of individual users. As a result, the user experience is uniform, and there is a lack of ability to respond to individual needs. Furthermore, the complexity of the contract procedure was stressful for users, and there was also the problem of slow progress in completing the procedure.

[1052] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving data transmitted from a user terminal, means for sending a request to a generative AI and for the generative AI to generate an answer based on the user's question, means for an emotion engine to analyze the user's emotional state, and means for causing the generative AI to generate a customized answer based on the emotions analyzed by the emotion engine. This enables customized answers according to individual emotional states, improves the user experience, reduces stress during contract procedures, and allows for quick completion.

[1053] A "user terminal" refers to a device that a user directly operates, such as a personal computer or smartphone, that can connect to the internet.

[1054] A "server" refers to a computer system that receives and processes data sent from a user terminal.

[1055] "Generative AI" refers to artificial intelligence (AI) models that generate answers based on user questions, and it is a technology that generates text data using natural language processing.

[1056] An "emotion engine" is a system for analyzing a user's emotional state, and refers to technology that reads emotions from the user's facial expressions, tone of voice, text input, etc.

[1057] A "request" refers to request data sent from a user's terminal to a server or generative AI.

[1058] A "customized response" refers to a response that a generative AI has specially adjusted according to the user's emotional state and individual requests.

[1059] "Means of receiving data" refers to the functions and methods used by the server to receive data sent from a user terminal.

[1060] "Means of generating answers based on questions" refers to methods and technologies for generative AI to analyze a user's question and generate an answer to it.

[1061] "Means of generating customized responses from generative AI based on emotions" refers to methods and technologies for causing generative AI to create special responses according to the user's emotional state, as analyzed by the emotion engine.

[1062] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. Furthermore, by combining it with an emotion engine, it is possible to provide customized answers according to the user's emotional state. It also aims to improve the user experience by enabling mobile phone contract procedures to be carried out online. This system includes the following components.

[1063] User terminal usage

[1064] Users access the system's homepage using devices such as smartphones or personal computers. They enter a question into the inquiry form, such as "I would like to know about the features of the new product," and press the submit button. The user's device sends this data to the server.

[1065] Server Functions

[1066] The server performs the following main processes:

[1067] Data reception: Data sent from the user's terminal is received as an HTTP request and its contents are parsed. The Node.js framework is used for this parsing.

[1068] Integration with generative AI: The received question data is converted into JSON format, and a request is sent to the generative AI (e.g., OpenAI's GPT-3 model). The prompt message might read, for example, "The user is asking about the features of a new product. Please provide a detailed answer."

[1069] Utilizing emotion analysis: The emotion engine analyzes the user's emotional state from facial expressions, tone of voice, input content, etc., and instructs the system to generate customized responses based on the results. The emotion engine uses the Python library OpenCV and the NLP library spaCy.

[1070] HTML page generation: An HTML page is generated to send back to the user based on the generated responses. The template engine Handlebars.js is used for this HTML page generation.

[1071] Using an Emotion Engine

[1072] The emotion engine is used to analyze the user's emotional state. Specifically, it identifies emotions such as surprise or delight based on data collected through the device's camera and microphone. For example, if a user asks, "I want to know the features of the new product," and the system determines that the user is "surprised," the generative AI will generate a response that reflects that surprise, such as, "This product features highly efficient energy functions and performs beyond your expectations. For more details, please see the link below."

[1073] Mobile phone contract procedure process

[1074] When a user wishes to proceed with a mobile phone contract, they enter the required information into a dedicated contract form and click the submit button. The user's device sends the contract information to the server, which receives and verifies the information. Once verification is complete, the server instructs a generative AI to generate guidance for the next step. Based on the generated guidance, the user proceeds through each step of the procedure, ultimately completing the contract process.

[1075] Examples of specific cases and prompt statements

[1076] As a concrete example of how it works, if a user asks a question about the features of a new product, it will operate as follows:

[1077] User question: "I want to know about the features of the new product."

[1078] Prompt for generative AI: "The user is asking about the features of a new product. Please provide a detailed answer."

[1079] Emotion detection: The emotion engine determines the user's emotional state to be "surprised".

[1080] Generated response: "This product features highly efficient energy-saving functions and delivers performance beyond your expectations. For more details, please see the link below."

[1081] This system provides real-time, personalized responses, improving the quality of the user experience and enabling the mobile contract process to be completed quickly and smoothly.

[1082] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1083] Step 1:

[1084] A user accesses the system's homepage using their own device (e.g., smartphone, computer). The user enters a question into the inquiry form and presses the "Submit" button. The data entered is, for example, the text "I would like to know about the features of the new product." The user's device sends this data to the server as an HTTP request.

[1085] Step 2:

[1086] The server receives an HTTP request sent from the user's terminal. The input data is text data containing the user's question. The server parses the received data and converts it to JSON format. The Node.js framework is used for this process. The converted JSON data is then sent to the generative AI.

[1087] Step 3:

[1088] The server sends a request to a generative AI (e.g., a GPT-3 model). The input data is in JSON format and contains the user's question. The prompt is written as, "The user is asking about the features of a new product. Please provide a detailed answer." The generative AI generates an answer based on this prompt and sends the output back to the server.

[1089] Step 4:

[1090] The emotion engine analyzes the user's emotional state. Input data includes the user's facial expressions, voice tone, and input content. The emotion engine uses the Python library OpenCV and the NLP library spaCy. The analysis result determines that "the user is surprised." This analysis result is then sent to the server.

[1091] Step 5:

[1092] The server receives the analysis results from the emotion engine and instructs the generative AI to generate a customized response that reflects the emotion. The input data consists of the emotion analysis results and the user's question. The generative AI generates a customized response such as, "This product features high-efficiency energy functions and delivers performance beyond your expectations. For more details, please see the link below," and sends the output back to the server.

[1093] Step 6:

[1094] The server receives the generated response and creates an HTML page to send back to the user. The input data is the response text from the generative AI. The template engine Handlebars.js is used to generate the HTML page. The generated HTML page is sent to the user's device.

[1095] Step 7:

[1096] The user's device displays the HTML page received from the server in its browser. The data entered is the HTML page. The user reviews the displayed page and can use the inquiry form again if they have further questions.

[1097] Step 8:

[1098] When a user wishes to proceed with a mobile phone contract, they enter the required information into a dedicated contract form and click the submit button. The user's device sends an HTTP request containing the contract information to the server. The data entered is the user's contract information.

[1099] Step 9:

[1100] The server receives contract information sent from the user terminal and verifies the data. The input data is contract information. Once verification is complete, the server instructs the generative AI to generate guidance for the next procedure. The generated guidance is then sent back to the server.

[1101] Step 10:

[1102] The server generates an HTML page based on the guidance received from the generative AI and sends it back to the user's terminal. The input data is the guidance text. The generated HTML page is sent to the user's terminal.

[1103] Step 11:

[1104] The user's device displays an HTML page received from the server in its browser, and the user proceeds with the process according to the guidance. The data entered is in the form of an HTML page. Finally, all contract procedures are completed.

[1105] The above describes the specific steps of the program processing of the present invention.

[1106] (Application Example 2)

[1107] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1108] Traditional systems failed to provide personalized conversational responses tailored to user emotions, and did not adequately streamline customer service in physical stores. Furthermore, they struggled to respond quickly and accurately to customer questions and concerns. This resulted in a poor user experience and difficulty in improving customer satisfaction.

[1109] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving sales explanations and questions from a user terminal, means for the server to receive data transmitted from the user terminal, means for sending a request to a generative AI, which then generates an answer based on the user's question, means for analyzing the user's emotional state using an emotion engine, means for the generative AI to generate a customized answer based on the emotion analysis results, means for returning the generated answer to the user terminal, and means for displaying the generated answer on a smart device. This enables personalized responses according to the user's emotional state, improving the efficiency of customer service in physical stores and enhancing the user experience.

[1110] A "user terminal" is a device used by a user, where they input questions and doubts, and where generated answers and guidance are displayed.

[1111] A "server" is a central processing unit that receives data sent from a user terminal, generates appropriate responses and guidance using generative AI and an emotion engine, and sends them back to the user terminal.

[1112] "Generative AI" refers to artificial intelligence that generates answers based on user question data, and is a system that performs the processes of analysis and generation.

[1113] An "emotion engine" is a system that analyzes a user's emotional state based on their facial expressions, tone of voice, input content, and other factors.

[1114] "Emotional state" refers to the feelings a user has in response to a question or inquiry, and includes surprise, anxiety, joy, and so on.

[1115] A "customized response" is a response generated by a generative AI that takes into account the user's emotional state, as analyzed by an emotion engine, and is tailored to the individual user's needs.

[1116] "Smart devices" refer to high-performance devices such as smartphones, smart glasses, and head-mounted displays, which are used to display responses from generative AI.

[1117] Modes for carrying out the invention

[1118] This invention is a system that streamlines customer service in physical stores and provides personalized conversational responses tailored to the user's emotional state. This system consists of the following main components:

[1119] First, the user receives service from a store employee wearing a smart device such as smart glasses. Questions and inquiries from the user are captured as voice input on the smart device. This voice input is converted into text data using a speech recognition API. Next, this text data is sent to an emotion engine, which analyzes the user's emotional state. The emotion engine uses the Microsoft Azure Emotion API, among others, to determine the user's emotional state. The analyzed emotional state is then sent to a server.

[1120] The server receives user question data and emotional state data, and instructs a generative AI to generate answers based on this data. The generative AI utilizes advanced artificial intelligence models such as GPT-4 to generate customized answers tailored to the user's emotional state. The generated answers are sent to the smart device via the server and displayed. In this way, the store clerk can provide the user with appropriate answers through the smart device's display.

[1121] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user asks, "I want to know about the features of the new product," the smart device uses a speech recognition API to convert the question into text data. If the emotion engine analyzes the user's emotional state and determines that they are "surprised," the following prompt is passed to the generative AI:

[1122] Question: "I would like to know about the features of the new product."

[1123] Emotion: "Surprise"

[1124] Answer: "This product features highly efficient energy-saving functions and delivers performance beyond your expectations. For more details, please see the link below."

[1125] A generative AI model (such as GPT-4) generates a customized response based on this prompt and sends it back to the server. This response is then displayed on the smart device, and the store clerk uses this to provide information to the user.

[1126] The components of this system include:

[1127] Voice recognition (voice_recognition API)

[1128] Emotion analysis (Microsoft Azure Emotion API)

[1129] Generative AI (GPT-4, etc.)

[1130] Dedicated SDK for servers and smart devices

[1131] This enables personalized responses tailored to the user's emotional state, leading to increased efficiency in customer service at physical stores and an improved user experience.

[1132] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1133] Step 1:

[1134] The user inputs the question by voice. The smart device captures the voice and converts it into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text API). In this step, the input is voice data and the output is text data.

[1135] Step 2:

[1136] The converted text data is sent to an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state. The emotion engine determines the emotion based on factors such as tone of voice and word choice. The input for this step is text data, and the output is the analyzed emotion data.

[1137] Step 3:

[1138] Emotional data and text data are sent to the server. The server receives this data and sends a request to the generative AI to generate a response based on the data. In this specific example, the input is emotional data and text data, and the output is the prompt text of the generative AI.

[1139] Step 4:

[1140] A generative AI (e.g., GPT-4) generates an answer based on the prompt text. This generated answer is customized to take into account the user's emotional state. The input for this step is the prompt text, and the output is the generated text answer.

[1141] Step 5:

[1142] The generated text response is sent back to the server. The server then sends the received response back to the smart device. In this step, the input is the generated text response, and the output is the response sent to the smart device.

[1143] Step 6:

[1144] The smart device displays the received response, and the store clerk uses this to provide an appropriate reply to the user. In this step, the input is the text response sent from the server, and the output is what is displayed on the smart device's screen.

[1145] As a concrete example of how this works, if a question is asked about the features of a new product, the process will be as follows:

[1146] Step 1: The user asks a voice message saying, "I want to know about the features of the new product."

[1147] Step 2: This audio is converted into text data, which reads, "I want to know about the features of the new product."

[1148] Step 3: The emotion engine analyzes the user's surprise and determines that "surprise" is present.

[1149] Step 4: The server sends this information to the generative AI and generates a prompt along with the emotion of "surprise".

[1150] Step 5: The generation AI generates a customized response: "This product features high-efficiency energy functions and delivers performance beyond your expectations. For more details, please see the link below."

[1151] Step 6: The server sends this response to the smart device, and it is displayed on the smart device's screen. The store clerk then provides the user with an appropriate explanation based on this display.

[1152] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1153] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1154] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1155] [Fourth Embodiment]

[1156] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1157] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1160] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1163] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1165] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1166] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1167] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1168] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1169] ---

[1170] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. It also aims to improve the user experience by enabling mobile phone contract procedures to be completed online. This system consists of the following elements:

[1171] First, the user accesses the system's homepage using their device. The user enters their questions about sales or products into the inquiry form and sends the request by clicking the submit button. The user's device then sends this data to the server.

[1172] The server receives data sent by the user and analyzes its contents. The analyzed data is sent as a request to a generative AI, instructing the AI ​​to generate an answer to the question. The generative AI generates an answer based on the received question data and sends the result back to the server.

[1173] Next, the server receives the generated response and creates an HTML page to send back to the user. The user's device receives this HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[1174] Users wishing to enter into a mobile phone contract can proceed by entering the necessary information into a dedicated contract form and submitting it. The contract information sent from the user's terminal is received by the server and verified. Once verification is complete, the server instructs the generative AI to generate guidance for the next step. The server receives the guidance generated by the AI ​​and displays it on the user's terminal. Following this guidance, the user proceeds through each step of the procedure, and ultimately completes all procedures.

[1175] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user sends the question "I want to know about the features of the new product" from their terminal, the server sends the question to a generative AI. The AI ​​generates a response such as, "The new product is equipped with highly efficient energy functions and is easy to operate. For more details, please see the link below." This generated response is sent to the user's terminal via the server, and the user confirms it. The user can then continue to ask more detailed questions.

[1176] The above is a description of the operation of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on stores, thereby improving user convenience.

[1177] The following describes the processing flow.

[1178] Step 1:

[1179] The user accesses the system's homepage using a web browser.

[1180] Step 2:

[1181] The server receives the user's request and sends back the corresponding HTML file for the homepage.

[1182] Step 3:

[1183] The user's device receives an HTML file and displays the web page.

[1184] Step 4:

[1185] The user enters their sales explanation and questions into the inquiry form and presses the "Submit" button.

[1186] Step 5:

[1187] The user's device sends the form data (questionnaire content) to the server as an HTTP POST request.

[1188] Step 6:

[1189] The server parses the form data it receives and extracts the question content.

[1190] Step 7:

[1191] The server sends an HTTP request to the AI's API endpoint to send the question content as a request to the generation AI. This request includes the question content and other necessary information (e.g., user ID, session information, etc.).

[1192] Step 8:

[1193] The generative AI understands the questions received from the server and generates appropriate answers. The AI ​​understands the context of the questions and includes explanations of technical terms and detailed sales explanations as needed.

[1194] Step 9:

[1195] The generative AI sends the generated response back to the server. This is usually sent as an HTTP response.

[1196] Step 10:

[1197] The server receives the generated response and creates an HTML page to return to the user. This page contains the AI's response.

[1198] Step 11:

[1199] The server generates an HTML page which is then sent back to the user's device.

[1200] Step 12:

[1201] The user's device displays the HTML page received from the server in the browser.

[1202] Step 13:

[1203] Users can review the page and enter further questions if necessary.

[1204] Step 14:

[1205] When a user enters into a mobile phone contract, they fill in the required information on the contract form and submit it.

[1206] Step 15:

[1207] The user's device sends the contract information it received to the server.

[1208] Step 16:

[1209] The server analyzes the received contract information and checks the necessary fields (e.g., verifying required fields, validating input formats).

[1210] Step 17:

[1211] The server sends the verified data to the generative AI and requests it to generate guidance for the next procedure.

[1212] Step 18:

[1213] The generative AI generates the next steps the user should take based on the contract information it receives. For example, it generates guidance on the documents to be submitted and the input of additional information.

[1214] Step 19:

[1215] The generative AI generates guidance, which is then sent back to the server.

[1216] Step 20:

[1217] The server generates an HTML page based on the guidance received from the AI ​​and sends it to the user's device.

[1218] Step 21:

[1219] The user's device displays the HTML page received from the server in the browser.

[1220] Step 22:

[1221] The user follows the displayed guidance and performs the following steps.

[1222] (Example 1)

[1223] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1224] Traditional sales presentations and contract procedures were often conducted in person, resulting in low user convenience. Furthermore, there was a lack of systems to provide quick and appropriate responses to online inquiries. Additionally, contract procedures were complex, making it difficult for users to complete them smoothly.

[1225] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1226] In this invention, the server includes means for receiving data transmitted from a user terminal, means for analyzing the data received by the server, and means for sending a request to a generative AI, which then generates an answer based on the user's question. This makes it possible to answer user questions quickly and appropriately, and to efficiently conduct sales explanations and contract procedures online.

[1227] A "user terminal" refers to a computer device or mobile device such as a smartphone used by a user.

[1228] A "server" is a computer system that stores, processes, and provides data over a network.

[1229] "Means of receiving data" refers to functions for acquiring information transmitted from a user terminal.

[1230] "Means of data analysis" refer to methods and algorithms for understanding and analyzing received data.

[1231] "Generative AI" refers to systems and algorithms that use artificial intelligence technology to generate text and information.

[1232] "Means of sending requests" refers to functions for sending necessary data or requests to other systems (e.g., generative AI).

[1233] "Generated response" refers to information or text created by a generative AI based on a request.

[1234] "Means for generating HTML pages" refers to functions for creating HTML-formatted documents that can be displayed in a web browser.

[1235] "Contract information" refers to specific data and information related to the contract provided by the user.

[1236] "Means for verifying data" refers to functions for confirming whether the received data is accurate and appropriate.

[1237] "Guidance" refers to instructions or information that guides users through the next procedure or step.

[1238] The system of this invention receives sales explanations and questions entered from the user's terminal, generates appropriate answers using generative AI, and sends them back to the user. Furthermore, it aims to improve the user experience by enabling mobile phone contract procedures to be completed online.

[1239] 1. System Configuration

[1240] 1.1 Hardware and Software

[1241] The system of the present invention uses the following hardware and software.

[1242] User devices (e.g., personal computers, smartphones)

[1243] Servers (e.g., cloud infrastructure, on-premises servers)

[1244] Web browser (e.g., Google Chrome, Mozilla Firefox)

[1245] Data analysis software (e.g., Python, NLTK, MeCab)

[1246] Generative AI (e.g. OpenAI GPT-3)

[1247] Database software (e.g., MySQL, PostgreSQL)

[1248] Template engine (e.g., Jinja2)

[1249] 2. Operating Procedure

[1250] 2.1 Entering the Question

[1251] The user accesses the system's homepage using their own device. The user enters their questions about sales and products into the inquiry form and presses the submit button. For example, "I would like to know about the features of the new product."

[1252] 2.2 Data Reception and Analysis

[1253] The server receives data sent from the user's terminal. The server analyzes the received data to determine what information is required. Text analysis techniques such as morphological analysis are used in this process.

[1254] 2.3 Sending requests to generative AI

[1255] The server sends the analysis results as a request to the generative AI. The generative AI generates an answer based on the received question data. For example, an answer such as, "The new product is equipped with high-efficiency energy functions and is easy to operate."

[1256] 2.4 Receiving responses and generating HTML pages

[1257] The server receives the response from the generative AI and uses it to generate an HTML page to send back to the user. The HTML page is generated using a template engine.

[1258] 2.5 Display of answers

[1259] The user's device receives the HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[1260] 3. Mobile phone contract procedures

[1261] When a user wishes to proceed with a mobile phone contract, they fill in the required information on a dedicated contract form and submit it. The server receives the contract information sent from the user's device and verifies it. Once verification is complete, the server instructs the generative AI to generate guidance for the next procedure. The generated guidance is displayed on the user's device via the server. The user follows this guidance to proceed with the procedure and can ultimately complete all procedures online.

[1262] 4. Specific Examples

[1263] 4.1 Examples of Sales Presentations

[1264] Consider a scenario where a user asks about the features of a new product. The user sends a question from their device saying, "I want to know about the features of the new product." The server sends this question to a generative AI, which generates a response such as, "The new product features highly efficient energy functions and is easy to operate. For more details, please see the link below." The response is sent to the user's device via the server, and the user confirms it.

[1265] Examples of specific prompt messages:

[1266] User: I'd like to know about the features of the new product.

[1267] The above describes a specific embodiment of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on physical stores, thereby improving user convenience.

[1268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1269] Step 1:

[1270] The user enters their question into the inquiry form and presses the submit button.

[1271] Input: The question text entered by the user in the form.

[1272] Specific action: The user opens the homepage in their browser, enters a message in the inquiry form, for example, "I would like to know about the features of the new product," and clicks the submit button.

[1273] Output: The HTTP request sent from the user's terminal to the server containing the question text.

[1274] Step 2:

[1275] The server receives data sent from the user's terminal.

[1276] Input: HTTP request data from the user's terminal.

[1277] Specific operation: The server receives an HTTP POST request and extracts the question text from the request body.

[1278] Output: Received question text.

[1279] Step 3:

[1280] The server analyzes the data it receives.

[1281] Input: Received question text.

[1282] Specific operation: The server executes a Python script to analyze the question text using a morphological analysis library (e.g., MeCab). It then extracts necessary themes and keywords from the analysis results.

[1283] Output: The subject and keywords of the analyzed question.

[1284] Step 4:

[1285] The server requests the analysis results from the generative AI.

[1286] Input: The subject or keywords of the analyzed question.

[1287] Specific operation: The server prepares the API request and sends the analysis results as a prompt to the generative AI (e.g., OpenAI's API).

[1288] Output: Request to the AI ​​model.

[1289] Step 5:

[1290] The generative AI generates an answer based on the question.

[1291] Input: Prompt sent from the server.

[1292] Specific operation: The generative AI generates an appropriate response based on the prompt. For example, it might generate a response such as, "The new product features high-efficiency energy functions and is easy to operate."

[1293] Output: Generated answer text.

[1294] Step 6:

[1295] The server receives the generated response.

[1296] Input: Text response from a generative AI.

[1297] Specific operation: The server receives the API response and extracts the response text.

[1298] Output: Received response text.

[1299] Step 7:

[1300] The server generates an HTML page containing the generated response.

[1301] Input: Received response text.

[1302] Specific operation: The server reads the HTML template and embeds the generated response into the template. A template engine (e.g., Jinja2) is used to generate the final HTML.

[1303] Output: The generated HTML page.

[1304] Step 8:

[1305] The user's device receives the generated HTML page and displays it in the browser.

[1306] Input: An HTML page sent from the server.

[1307] Specific operation: The server sends an HTML page to the user's terminal as an HTTP response. The user's terminal receives the HTML page and displays it in a web browser.

[1308] Output: The answer displayed in the browser.

[1309] The above outlines the specific processing flow of this system's program, as well as the specific actions and data processing performed at each step. This system allows users to receive quick and appropriate responses online, enabling efficient sales explanations and mobile phone contract procedures.

[1310] (Application Example 1)

[1311] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1312] With the increase in online purchasing and contract procedures, there is a need for support systems that can quickly and appropriately answer user questions and facilitate smooth transactions. However, current systems suffer from problems such as slow responses to user inquiries and complex, user-unfriendly procedures. This can lead to decreased user satisfaction and reduced purchasing intent.

[1313] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1314] In this invention, the server includes means for receiving sales explanations and questions from a user terminal, means for receiving data transmitted from the user terminal, means for sending requests to a generating AI model, which in turn generates answers based on the user's questions, means for returning the generated answers to the user terminal, and means for providing guidance on the purchase procedure using the generating AI model. This enables a quick and appropriate response to user questions and facilitates smooth purchase and contract procedures.

[1315] "User terminal" refers to a computing device used by an end user, including smartphones, tablets, and personal computers.

[1316] "Sales explanation" refers to information that describes the features, benefits, and price of a product or service, providing users with the detailed explanations they need when considering a purchase or contract.

[1317] "Questions" refer to doubts or concerns that users have about a product or service, and appropriate answers to these questions are required.

[1318] A "server" is a computer system that receives requests from clients (user terminals) over a network, performs appropriate processing, and returns the results.

[1319] "Means of receiving data" refers to the functions and systems that allow a server to receive information (such as sales explanations, questions, and contract information) sent from a user's terminal.

[1320] A "generative AI model" is an artificial intelligence-based algorithm or system that generates appropriate answers in response to user questions.

[1321] "Means of sending requests" refers to functions or systems that send information received from the user's terminal to a generating AI model and process it to generate appropriate answers or guidance.

[1322] "Means of generating answers" refers to the functions and processes by which a generative AI model generates appropriate answers to a user's questions or concerns.

[1323] "Means of returning responses" refers to functions and systems that send responses generated by a generative AI model from a server to a user's terminal, allowing the user to review them.

[1324] "Means of providing guidance" refers to functions and systems that provide step-by-step instructions and guidance necessary for users to proceed with purchase or contract procedures.

[1325] "Means for verifying necessary data" refers to functions and systems that perform processing to confirm the accuracy and completeness of contract information and purchase information submitted by users.

[1326] "Purchase process" refers to the process of entering information, making payments, and confirming purchases of selected goods or services online.

[1327] The system for implementing this invention combines a user terminal, a server, and a generative AI model. First, the user terminal inputs information such as sales explanations, questions, and purchase procedures. This information is transmitted to the server via the internet. The server analyzes the received information and sends a request to the generative AI model. The generative AI model generates appropriate answers and guidance based on the request and sends the results back to the server. The server sends this back to the user terminal, and the user confirms the displayed content.

[1328] Hardware and software to be used

[1329] hardware

[1330] User devices (smartphones, tablets, PCs, etc.)

[1331] server

[1332] software

[1333] Flask (web framework)

[1334] Requests (HTTP Request Library)

[1335] OpenAI API (Generative AI Models)

[1336] Data processing and data calculation

[1337] 1. Data transmission

[1338] Users enter questions about products or services from their smartphones or computers and press the submit button. This data is sent to the server as an HTTP request.

[1339] 2. Data reception and analysis

[1340] The server analyzes the received data and extracts the content of the question. This analysis uses the Flask framework.

[1341] 3. Requests to the Generative AI Model

[1342] The server sends the analyzed data to the AI ​​model and requests it to generate an appropriate response. The OpenAI API is used for this process.

[1343] 4. Generating and sending responses

[1344] The generative AI model generates a response based on the request, and that response is sent back to the server. The server then generates the result as an HTML page and sends it back to the user's terminal.

[1345] Specific example

[1346] The user enters the question "Is this product waterproof?" from their device. The server receives the question and sends the following prompt to the AI ​​model.

[1347] Example of a prompt

[1348] text

[1349] I have a question about the product: Is this product waterproof?

[1350] Please answer:

[1351] The AI ​​model generates the response, "Yes, this product is waterproof up to 1 meter deep for a maximum of 30 minutes." The generated response is sent back to the user's terminal via the server, and the user can then verify its contents.

[1352] This system allows users to obtain real-time information about products and services online, enabling a smooth purchasing process. It also provides verification of contract information and guidance on the purchasing process, streamlining the overall process.

[1353] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1354] Step 1:

[1355] Users enter questions and purchase information from their smartphones or computers and press the submit button. The input includes text containing sales explanations and questions. This input data is sent to the server as an HTTP request. For example, a user might enter and submit a question such as, "Is this product waterproof?"

[1356] Step 2:

[1357] The server receives HTTP requests sent from the user's terminal and parses their contents. Specifically, it uses the Flask framework to extract the received data and extract the content of the question. The input is the question text mentioned earlier, and the output is the parsed question content.

[1358] Step 3:

[1359] The server sends the analyzed data to the generating AI model and requests it to generate an appropriate answer. Specifically, it uses the OpenAI API to send a prompt to the generating AI model. The input is the question text "Is this product waterproof?", and the output is the answer from the generating AI model. The prompt sent is "I have a question about the product: Is this product waterproof? Please answer:".

[1360] Step 4:

[1361] The generative AI model generates an answer based on the request, and that answer is sent back to the server. The input is a prompt sent from the server, and the output is the generated answer text. The generated answer is, "Yes, this product is waterproof up to 1 meter deep for a maximum of 30 minutes."

[1362] Step 5:

[1363] The server receives the generated response and creates an HTML page to display it. Specifically, it uses an HTML template to format the generated response text into a format that the user can view. The input is the response text returned from the generation AI model, and the output is HTML content to be sent to the user's terminal.

[1364] Step 6:

[1365] The server sends the generated HTML page back to the user's terminal, where the user can view it. The input is HTML content, and the output is a page displayed in the user's browser. The user can then view the answers to their questions on their smartphone or computer browser.

[1366] This processing flow allows users to obtain information about products and services online in real time, enabling a smooth purchasing process.

[1367] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1368] ---

[1369] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. Furthermore, by combining it with an emotion engine, it can also provide customized answers according to the user's emotional state. It also aims to improve the user experience by enabling mobile phone contract procedures to be carried out online. This system consists of the following elements.

[1370] First, the user accesses the system's homepage using their device. The user enters their questions about sales or products into the inquiry form and sends the request by clicking the submit button. The user's device then sends this data to the server.

[1371] The server receives data sent by the user and analyzes its contents. The analyzed data is sent as a request to a generative AI, instructing the AI ​​to generate an answer to the question. The generative AI generates an answer based on the received question data and sends the result back to the server.

[1372] Furthermore, this invention utilizes an emotion engine. The emotion engine analyzes the user's emotional state based on the user's facial expressions, tone of voice, input content, etc. It transmits the results of this analysis to a server. Based on the data from the emotion engine, the server instructs a generative AI to generate a customized response that corresponds to the emotion.

[1373] Next, the server receives the generated response and creates an HTML page to send back to the user. The user's device receives this HTML page and displays it in the browser. The user reviews it and can use the form again if they have further questions.

[1374] Users wishing to enter into a mobile phone contract can proceed by entering the necessary information into a dedicated contract form and submitting it. The contract information sent from the user's terminal is received by the server and verified. Once verification is complete, the server instructs the generative AI to generate guidance for the next step. The server receives the guidance generated by the AI ​​and displays it on the user's terminal. Following this guidance, the user proceeds through each step of the procedure, and ultimately completes all procedures.

[1375] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user sends a question from their device, such as "I want to know about the features of the new product," the server sends that question to the generative AI. The emotion engine analyzes the user's emotional state, and if it determines, for example, that "the user is surprised," the generative AI generates a response that reflects that surprise, such as "This product is equipped with highly efficient energy functions and will perform beyond your expectations. For details, please see the link below." This generated response is sent to the user's device via the server, and the user confirms it. The user can then continue to ask more detailed questions.

[1376] The above is a description of the operation of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that previously relied on stores, thereby improving user convenience. Furthermore, by providing responses that respond to the user's emotions, it realizes a more personalized user experience.

[1377] The following describes the processing flow.

[1378] Step 1:

[1379] The user accesses the system's homepage using a web browser.

[1380] Step 2:

[1381] The server receives the user's request and sends back the corresponding HTML file for the homepage.

[1382] Step 3:

[1383] The user's device receives an HTML file and displays the web page.

[1384] Step 4:

[1385] Users enter their questions about sales explanations or products into the inquiry form and press the "Submit" button.

[1386] Step 5:

[1387] The user's device sends the form data (questionnaire content) to the server as an HTTP POST request.

[1388] Step 6:

[1389] The server parses the form data it receives and extracts the question content.

[1390] Step 7:

[1391] The server sends data to the emotion engine along with the question content, instructing it to analyze the user's emotional state.

[1392] Step 8:

[1393] The emotion engine analyzes the user's emotional state using their input, facial expressions, and tone of voice, and sends the results back to the server.

[1394] Step 9:

[1395] The server receives data from the emotion engine and sends the question content and emotion data as a request to the generative AI.

[1396] Step 10:

[1397] The generative AI generates answers based on the question content and sentiment data received from the server. For example, if the user expresses surprise, that emotion will be reflected in the answer.

[1398] Step 11:

[1399] The generative AI sends the generated response back to the server. This is usually sent as an HTTP response.

[1400] Step 12:

[1401] The server receives the generated response and creates an HTML page to return to the user. This page contains the AI's response.

[1402] Step 13:

[1403] The server generates an HTML page which is then sent back to the user's device.

[1404] Step 14:

[1405] The user's device displays the HTML page received from the server in the browser.

[1406] Step 15:

[1407] Users can review the page and enter further questions if necessary.

[1408] Step 16:

[1409] When a user enters into a mobile phone contract, they fill in the required information on the contract form and submit it.

[1410] Step 17:

[1411] The user's device sends the contract information it received to the server.

[1412] Step 18:

[1413] The server analyzes the received contract information and checks the necessary fields (e.g., verifying required fields, validating input formats).

[1414] Step 19:

[1415] The server sends the verified data to the generative AI and requests it to generate guidance for the next procedure.

[1416] Step 20:

[1417] Based on the contract information and sentiment data it receives, the generative AI generates the next steps the user should take. For example, it generates guidance on the documents to be submitted or the input of additional information.

[1418] Step 21:

[1419] The generative AI generates guidance, which is then sent back to the server.

[1420] Step 22:

[1421] The server generates an HTML page based on the guidance received from the AI ​​and sends it to the user's device.

[1422] Step 23:

[1423] The user's device displays the HTML page received from the server in the browser.

[1424] Step 24:

[1425] The user follows the displayed guidance and performs the following steps.

[1426] The above describes the operating steps of the system of the present invention. This system significantly streamlines sales explanations and mobile phone contract procedures that traditionally depend on stores, and by combining it with an emotion engine, it can provide a personalized experience that responds to the user's emotions.

[1427] (Example 2)

[1428] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1429] Traditional online sales explanation and contract system systems have a problem in that they provide uniform answers to user questions and guidance on procedures, making it difficult to customize them to the emotional state and needs of individual users. As a result, the user experience is uniform, and there is a lack of ability to respond to individual needs. Furthermore, the complexity of the contract procedure was stressful for users, and there was also the problem of slow progress in completing the procedure.

[1430] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving data transmitted from a user terminal, means for sending a request to a generative AI and for the generative AI to generate an answer based on the user's question, means for an emotion engine to analyze the user's emotional state, and means for causing the generative AI to generate a customized answer based on the emotions analyzed by the emotion engine. This enables customized answers according to individual emotional states, improves the user experience, reduces stress during contract procedures, and allows for quick completion.

[1431] A "user terminal" refers to a device that a user directly operates, such as a personal computer or smartphone, that can connect to the internet.

[1432] A "server" refers to a computer system that receives and processes data sent from a user terminal.

[1433] "Generative AI" refers to artificial intelligence (AI) models that generate answers based on user questions, and it is a technology that generates text data using natural language processing.

[1434] An "emotion engine" is a system for analyzing a user's emotional state, and refers to technology that reads emotions from the user's facial expressions, tone of voice, text input, etc.

[1435] A "request" refers to request data sent from a user's terminal to a server or generative AI.

[1436] A "customized response" refers to a response that a generative AI has specially adjusted according to the user's emotional state and individual requests.

[1437] "Means of receiving data" refers to the functions and methods used by the server to receive data sent from a user terminal.

[1438] "Means of generating answers based on questions" refers to methods and technologies for generative AI to analyze a user's question and generate an answer to it.

[1439] "Means of generating customized responses from generative AI based on emotions" refers to methods and technologies for causing generative AI to create special responses according to the user's emotional state, as analyzed by the emotion engine.

[1440] The system of this invention receives sales explanations and questions from a user terminal, generates appropriate answers using generative AI, and responds to the user. Furthermore, by combining it with an emotion engine, it is possible to provide customized answers according to the user's emotional state. It also aims to improve the user experience by enabling mobile phone contract procedures to be carried out online. This system includes the following components.

[1441] User terminal usage

[1442] Users access the system's homepage using devices such as smartphones or personal computers. They enter a question into the inquiry form, such as "I would like to know about the features of the new product," and press the submit button. The user's device sends this data to the server.

[1443] Server Functions

[1444] The server performs the following main processes:

[1445] Data reception: Data sent from the user's terminal is received as an HTTP request and its contents are parsed. The Node.js framework is used for this parsing.

[1446] Integration with generative AI: The received question data is converted into JSON format, and a request is sent to the generative AI (e.g., OpenAI's GPT-3 model). The prompt message might read, for example, "The user is asking about the features of a new product. Please provide a detailed answer."

[1447] Utilizing emotion analysis: The emotion engine analyzes the user's emotional state from facial expressions, tone of voice, input content, etc., and instructs the system to generate customized responses based on the results. The emotion engine uses the Python library OpenCV and the NLP library spaCy.

[1448] HTML page generation: An HTML page is generated to send back to the user based on the generated responses. The template engine Handlebars.js is used for this HTML page generation.

[1449] Using an Emotion Engine

[1450] The emotion engine is used to analyze the user's emotional state. Specifically, it identifies emotions such as surprise or delight based on data collected through the device's camera and microphone. For example, if a user asks, "I want to know the features of the new product," and the system determines that the user is "surprised," the generative AI will generate a response that reflects that surprise, such as, "This product features highly efficient energy functions and performs beyond your expectations. For more details, please see the link below."

[1451] Mobile phone contract procedure process

[1452] When a user wishes to proceed with a mobile phone contract, they enter the required information into a dedicated contract form and click the submit button. The user's device sends the contract information to the server, which receives and verifies the information. Once verification is complete, the server instructs a generative AI to generate guidance for the next step. Based on the generated guidance, the user proceeds through each step of the procedure, ultimately completing the contract process.

[1453] Examples of specific cases and prompt statements

[1454] As a concrete example of how it works, if a user asks a question about the features of a new product, it will operate as follows:

[1455] User question: "I want to know about the features of the new product."

[1456] Prompt for generative AI: "The user is asking about the features of a new product. Please provide a detailed answer."

[1457] Emotion detection: The emotion engine determines the user's emotional state to be "surprised".

[1458] Generated response: "This product features highly efficient energy-saving functions and delivers performance beyond your expectations. For more details, please see the link below."

[1459] This system provides real-time, personalized responses, improving the quality of the user experience and enabling the mobile contract process to be completed quickly and smoothly.

[1460] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1461] Step 1:

[1462] A user accesses the system's homepage using their own device (e.g., smartphone, computer). The user enters a question into the inquiry form and presses the "Submit" button. The data entered is, for example, the text "I would like to know about the features of the new product." The user's device sends this data to the server as an HTTP request.

[1463] Step 2:

[1464] The server receives an HTTP request sent from the user's terminal. The input data is text data containing the user's question. The server parses the received data and converts it to JSON format. The Node.js framework is used for this process. The converted JSON data is then sent to the generative AI.

[1465] Step 3:

[1466] The server sends a request to a generative AI (e.g., a GPT-3 model). The input data is in JSON format and contains the user's question. The prompt is written as, "The user is asking about the features of a new product. Please provide a detailed answer." The generative AI generates an answer based on this prompt and sends the output back to the server.

[1467] Step 4:

[1468] The emotion engine analyzes the user's emotional state. Input data includes the user's facial expressions, voice tone, and input content. The emotion engine uses the Python library OpenCV and the NLP library spaCy. The analysis result determines that "the user is surprised." This analysis result is then sent to the server.

[1469] Step 5:

[1470] The server receives the analysis results from the emotion engine and instructs the generative AI to generate a customized response that reflects the emotion. The input data consists of the emotion analysis results and the user's question. The generative AI generates a customized response such as, "This product features high-efficiency energy functions and delivers performance beyond your expectations. For more details, please see the link below," and sends the output back to the server.

[1471] Step 6:

[1472] The server receives the generated response and creates an HTML page to send back to the user. The input data is the response text from the generative AI. The template engine Handlebars.js is used to generate the HTML page. The generated HTML page is sent to the user's device.

[1473] Step 7:

[1474] The user's device displays the HTML page received from the server in its browser. The data entered is the HTML page. The user reviews the displayed page and can use the inquiry form again if they have further questions.

[1475] Step 8:

[1476] When a user wishes to proceed with a mobile phone contract, they enter the required information into a dedicated contract form and click the submit button. The user's device sends an HTTP request containing the contract information to the server. The data entered is the user's contract information.

[1477] Step 9:

[1478] The server receives contract information sent from the user terminal and verifies the data. The input data is contract information. Once verification is complete, the server instructs the generative AI to generate guidance for the next procedure. The generated guidance is then sent back to the server.

[1479] Step 10:

[1480] The server generates an HTML page based on the guidance received from the generative AI and sends it back to the user's terminal. The input data is the guidance text. The generated HTML page is sent to the user's terminal.

[1481] Step 11:

[1482] The user's device displays an HTML page received from the server in its browser, and the user proceeds with the process according to the guidance. The data entered is in the form of an HTML page. Finally, all contract procedures are completed.

[1483] The above describes the specific steps of the program processing of the present invention.

[1484] (Application Example 2)

[1485] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1486] Traditional systems failed to provide personalized conversational responses tailored to user emotions, and did not adequately streamline customer service in physical stores. Furthermore, they struggled to respond quickly and accurately to customer questions and concerns. This resulted in a poor user experience and difficulty in improving customer satisfaction.

[1487] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving sales explanations and questions from a user terminal, means for the server to receive data transmitted from the user terminal, means for sending a request to a generative AI, which then generates an answer based on the user's question, means for analyzing the user's emotional state using an emotion engine, means for the generative AI to generate a customized answer based on the emotion analysis results, means for returning the generated answer to the user terminal, and means for displaying the generated answer on a smart device. This enables personalized responses according to the user's emotional state, improving the efficiency of customer service in physical stores and enhancing the user experience.

[1488] A "user terminal" is a device used by a user, where they input questions and doubts, and where generated answers and guidance are displayed.

[1489] A "server" is a central processing unit that receives data sent from a user terminal, generates appropriate responses and guidance using generative AI and an emotion engine, and sends them back to the user terminal.

[1490] "Generative AI" refers to artificial intelligence that generates answers based on user question data, and is a system that performs the processes of analysis and generation.

[1491] An "emotion engine" is a system that analyzes a user's emotional state based on their facial expressions, tone of voice, input content, and other factors.

[1492] "Emotional state" refers to the feelings a user has in response to a question or inquiry, and includes surprise, anxiety, joy, and so on.

[1493] A "customized response" is a response generated by a generative AI that takes into account the user's emotional state, as analyzed by an emotion engine, and is tailored to the individual user's needs.

[1494] "Smart devices" refer to high-performance devices such as smartphones, smart glasses, and head-mounted displays, which are used to display responses from generative AI.

[1495] Modes for carrying out the invention

[1496] This invention is a system that streamlines customer service in physical stores and provides personalized conversational responses tailored to the user's emotional state. This system consists of the following main components:

[1497] First, the user receives service from a store employee wearing a smart device such as smart glasses. Questions and inquiries from the user are captured as voice input on the smart device. This voice input is converted into text data using a speech recognition API. Next, this text data is sent to an emotion engine, which analyzes the user's emotional state. The emotion engine uses the Microsoft Azure Emotion API, among others, to determine the user's emotional state. The analyzed emotional state is then sent to a server.

[1498] The server receives user question data and emotional state data, and instructs a generative AI to generate answers based on this data. The generative AI utilizes advanced artificial intelligence models such as GPT-4 to generate customized answers tailored to the user's emotional state. The generated answers are sent to the smart device via the server and displayed. In this way, the store clerk can provide the user with appropriate answers through the smart device's display.

[1499] As a concrete example, let's consider a scenario where a user asks about the features of a new product. When the user asks, "I want to know about the features of the new product," the smart device uses a speech recognition API to convert the question into text data. If the emotion engine analyzes the user's emotional state and determines that they are "surprised," the following prompt is passed to the generative AI:

[1500] Question: "I would like to know about the features of the new product."

[1501] Emotion: "Surprise"

[1502] Answer: "This product features highly efficient energy-saving functions and delivers performance beyond your expectations. For more details, please see the link below."

[1503] A generative AI model (such as GPT-4) generates a customized response based on this prompt and sends it back to the server. This response is then displayed on the smart device, and the store clerk uses this to provide information to the user.

[1504] The components of this system include:

[1505] Voice recognition (voice_recognition API)

[1506] Emotion analysis (Microsoft Azure Emotion API)

[1507] Generative AI (GPT-4, etc.)

[1508] Dedicated SDK for servers and smart devices

[1509] This enables personalized responses tailored to the user's emotional state, leading to increased efficiency in customer service at physical stores and an improved user experience.

[1510] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1511] Step 1:

[1512] The user inputs the question by voice. The smart device captures the voice and converts it into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text API). In this step, the input is voice data and the output is text data.

[1513] Step 2:

[1514] The converted text data is sent to an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state. The emotion engine determines the emotion based on factors such as tone of voice and word choice. The input for this step is text data, and the output is the analyzed emotion data.

[1515] Step 3:

[1516] Emotional data and text data are sent to the server. The server receives this data and sends a request to the generative AI to generate a response based on the data. In this specific example, the input is emotional data and text data, and the output is the prompt text of the generative AI.

[1517] Step 4:

[1518] A generative AI (e.g., GPT-4) generates an answer based on the prompt text. This generated answer is customized to take into account the user's emotional state. The input for this step is the prompt text, and the output is the generated text answer.

[1519] Step 5:

[1520] The generated text response is sent back to the server. The server then sends the received response back to the smart device. In this step, the input is the generated text response, and the output is the response sent to the smart device.

[1521] Step 6:

[1522] The smart device displays the received response, and the store clerk uses this to provide an appropriate reply to the user. In this step, the input is the text response sent from the server, and the output is what is displayed on the smart device's screen.

[1523] As a concrete example of how this works, if a question is asked about the features of a new product, the process will be as follows:

[1524] Step 1: The user asks a voice message saying, "I want to know about the features of the new product."

[1525] Step 2: This audio is converted into text data, which reads, "I want to know about the features of the new product."

[1526] Step 3: The emotion engine analyzes the user's surprise and determines that "surprise" is present.

[1527] Step 4: The server sends this information to the generative AI and generates a prompt along with the emotion of "surprise".

[1528] Step 5: The generation AI generates a customized response: "This product features high-efficiency energy functions and delivers performance beyond your expectations. For more details, please see the link below."

[1529] Step 6: The server sends this response to the smart device, and it is displayed on the smart device's screen. The store clerk then provides the user with an appropriate explanation based on this display.

[1530] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1531] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1532] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1533] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1534] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1535] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1536] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1537] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1538] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1539] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1540] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1541] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1542] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1543] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1544] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1545] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1546] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1547] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1548] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1549] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1550] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1551] The following is further disclosed regarding the embodiments described above.

[1552] (Claim 1)

[1553] A means by which sales explanations and questions are entered from the user's terminal,

[1554] A means by which the server receives data sent from the user terminal,

[1555] A means for sending a request to a generative AI, and for the generative AI to generate an answer based on the user's question,

[1556] A means of returning the generated response to the user's terminal,

[1557] A system that includes this.

[1558] (Claim 2)

[1559] A means of receiving contract information from the user's terminal and verifying the necessary data,

[1560] A means of generating guidance for the next procedure using a generative AI,

[1561] A means of displaying the generated guidance on the user terminal,

[1562] The system according to claim 1, including the following:

[1563] (Claim 3)

[1564] The system according to claim 1, characterized by having means for consistently providing sales explanations, answers to questions, and guidance on contract procedures to a user terminal.

[1565] "Example 1"

[1566] (Claim 1)

[1567] A means by which sales explanations and questions are entered from the user's terminal,

[1568] A means by which the server receives data sent from the user terminal,

[1569] A means for analyzing the data received by the server,

[1570] A means for sending a request to a generative AI, and for the generative AI to generate an answer based on the user's question,

[1571] A means of returning the generated response to the user's terminal,

[1572] A system that includes this.

[1573] (Claim 2)

[1574] A means of receiving contract information from the user's terminal and verifying the necessary data,

[1575] A means of generating guidance for the next procedure using a generative AI,

[1576] A means of displaying the generated guidance on the user terminal,

[1577] The system according to claim 1, including the following:

[1578] (Claim 3)

[1579] The system according to claim 1, characterized by having means for consistently providing sales explanations, answers to questions, and guidance on contract procedures to a user terminal.

[1580] "Application Example 1"

[1581] (Claim 1)

[1582] A means by which sales explanations and questions are entered from the user's terminal,

[1583] A means by which the server receives data sent from the user terminal,

[1584] A means for sending a request to a generative AI, and for the generative AI to generate an answer based on the user's question,

[1585] A means of returning the generated response to the user's terminal,

[1586] A system that includes this.

[1587] (Claim 2)

[1588] A means of receiving contract information from the user's terminal and verifying the necessary data,

[1589] A means of generating guidance for the next procedure using a generative AI,

[1590] A means of displaying the generated guidance on the user terminal,

[1591] The system according to claim 1, including the following:

[1592] (Claim 3)

[1593] The system according to claim 1, characterized by having means for consistently providing sales explanations, answers to questions, and guidance on contract procedures to a user terminal.

[1594] New parts of the application example

[1595] Purchase procedure support

[1596] Managing purchase history and user account information

[1597] (Claim 1)

[1598] A means by which sales explanations and questions are entered from the user's terminal,

[1599] A means by which the server receives data sent from the user terminal,

[1600] A means for sending a request to a generative AI model, and for the generative AI model to generate an answer based on the user's question,

[1601] A means of returning the generated response to the user's terminal,

[1602] A means of providing guidance on the purchase process using a generative AI model,

[1603] A system that includes this.

[1604] (Claim 2)

[1605] A means of receiving contract information and purchase information from user terminals and verifying the necessary data,

[1606] A means for generating guidance for the next procedure using a generative AI model,

[1607] A means of displaying the generated guidance on the user terminal,

[1608] The system according to claim 1, including the following:

[1609] (Claim 3)

[1610] The system according to claim 1, characterized by having means to consistently provide sales explanations, answers to questions, and guidance on contract and purchase procedures to user terminals.

[1611] "Example 2 of combining an emotion engine"

[1612] (Claim 1)

[1613] A means by which sales explanations and questions are entered from the user's terminal,

[1614] A means by which the server receives data sent from the user terminal,

[1615] A means for sending a request to a generative AI, and for the generative AI to generate an answer based on the user's question,

[1616] A means of returning the generated response to the user's terminal,

[1617] The emotion engine provides a means for analyzing the user's emotional state,

[1618] A means for causing a generative AI to generate a customized response based on the emotions analyzed by the aforementioned emotion engine,

[1619] A system that includes this.

[1620] (Claim 2)

[1621] A means of receiving contract information from the user's terminal and verifying the necessary data,

[1622] A means of generating guidance for the next procedure using a generative AI,

[1623] A means of displaying the generated guidance on the user terminal,

[1624] The system according to claim 1, including the following:

[1625] (Claim 3)

[1626] The system according to claim 1, characterized by having means for consistently providing sales explanations, answers to questions, and guidance on contract procedures to a user terminal.

[1627] "Application example 2 when combining with an emotional engine"

[1628] (Claim 1)

[1629] A means by which sales explanations and questions are entered from the user's terminal,

[1630] A means by which the server receives data sent from the user terminal,

[1631] A means for sending a request to a generative AI, and for the generative AI to generate an answer based on the user's question,

[1632] A means of analyzing the user's emotional state using an emotion engine,

[1633] A means by which a generative AI generates a customized response based on the emotion analysis results,

[1634] A means of returning the generated response to the user's terminal,

[1635] A means of displaying the generated response on a smart device,

[1636] A system that includes this.

[1637] (Claim 2)

[1638] A means of receiving contract information from the user's terminal and verifying the necessary data,

[1639] A means of generating guidance for the next procedure using a generative AI,

[1640] A means of displaying the generated guidance on the user terminal,

[1641] The system according to claim 1, including the following:

[1642] (Claim 3)

[1643] The system according to claim 1, characterized by having means for consistently providing sales explanations, answers to questions, and guidance on contract procedures to a user terminal. [Explanation of symbols]

[1644] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means by which sales explanations and questions are entered from the user's terminal, A means by which the server receives data sent from the user terminal, A means for sending a request to a generative AI, and for the generative AI to generate an answer based on the user's question, A means of returning the generated response to the user's terminal, A system that includes this.

2. A means of receiving contract information from the user's terminal and verifying the necessary data, A means of generating guidance for the next procedure using a generative AI, A means of displaying the generated guidance on the user terminal, The system according to claim 1, including the following:

3. The system according to claim 1, characterized by comprising means for consistently providing sales explanations, answers to questions, and guidance on contract procedures to a user terminal.

Images