System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting a purchase history; means for acquiring description data of a home appliance based on the purchase history; means for analyzing the description data and extracting main functions and settings; means for generating a usage guide that is easy for a user to understand based on the extracted information; means for distributing the usage guide to a user terminal; means for receiving a question from the user and generating an answer to the question; and means for transmitting the generated answer to the user terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, many users struggle to understand the instruction manuals for home appliances they purchase. Furthermore, the manuals are often so complex that they often fail to fully utilize all of the product's features and settings. This makes it difficult for users to find the optimal way to use the appliance. The present invention aims to solve this problem by analyzing the manuals using the user's purchase history and providing instructions in a familiar and easy-to-understand format. [Means for solving the problem]

[0005] The present invention provides a system having the following configuration: a means for collecting purchase history, a means for acquiring instruction data for a home appliance based on the purchase history, a means for analyzing the instruction data to extract key functions and settings, a means for generating a user-understandable usage guide based on the extracted information, a means for distributing the instruction guide to a user terminal, a means for accepting questions from the user and generating answers to the questions, and a means for transmitting the generated answers to the user terminal. The system also collects the purchase history by acquiring data from an e-commerce site or a sales management system, and acquires the instruction data by downloading it from a manufacturer's website or other online resource. The system further generates the usage guide in a format that includes step-by-step instructions for operation, includes a means for analyzing user questions using natural language processing technology, and includes a means for generating recommendations for optimal usage methods and settings based on the analyzed instruction data, depending on the user's usage situation.

[0006] "Purchase history" is a list of information about products purchased by a user.

[0007] "Instruction data" refers to data that includes the contents of instruction manuals for home appliances and devices.

[0008] A "user terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet.

[0009] A "server" is a computer system on a network that collects, analyzes, stores, and distributes data.

[0010] "Analysis" is the process of examining data in detail to understand its components and meaning.

[0011] "Natural language processing technology" is a technology for processing and analyzing human language using a computer.

[0012] A "use guide" is a document or information that briefly explains the steps and methods for users to use a home appliance or device.

[0013] "Recommendation" refers to proposing optimal options or methods of operation based on the user's behavior and situation.

[0014] The "question receiving means" is a mechanism or method for receiving and processing questions from users.

[0015] The "answer generation means" is a component of a method or system that generates an appropriate answer to a user's question.

[0016] "Online Resources" refers to websites or databases that provide data or services accessible via the Internet. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention is a system that uses a user's purchase history to analyze the manuals for home appliances and devices, and provides users with easy-to-understand instructions for use. The "Smart Manual AI" system works through the following process.

[0039] Collection and analysis of purchase history

[0040] The server collects the user's purchase history from the e-commerce site and the sales management system.

[0041] The user terminal transmits data to the server every time the purchase history is updated.

[0042] The server analyzes the received data and stores information about each product in a database.

[0043] Examples:

[0044] When a user purchases a new refrigerator, the information is registered on the e-commerce site. The user's device collects the purchase information and sends it to the server. The server analyzes the purchase information and registers it in the database under the category "refrigerator."

[0045] Instruction data acquisition and analysis

[0046] The server retrieves corresponding product manuals from manufacturers' websites and other online resources based on the registered product information, analyzes the retrieved manual data, and extracts key features and settings.

[0047] Examples:

[0048] The server downloads the instruction manual for a new refrigerator from the manufacturer's website, analyzes the downloaded PDF, and extracts key sections such as "Basic Operation," "Temperature Setting," and "Maintenance Procedures."

[0049] Generate and distribute usage guides

[0050] The server generates a user-friendly usage guide based on the analyzed instruction data, and distributes the generated usage guide to the user's device.

[0051] Examples:

[0052] The server summarizes the refrigerator's operation procedures step by step in the form of "how to turn it on," "how to set the temperature," "how to switch to energy-saving mode," etc. The generated guide is sent to the user's device, and the user receives a notification.

[0053] User interaction support

[0054] The user inputs questions about usage from the user terminal.

[0055] The user terminal sends a query to the server.

[0056] The server receives the question, analyzes it using natural language processing technology, and generates a corresponding answer, which is then sent to the user's device.

[0057] Examples:

[0058] The user asks a question on their device: "How do I use the quick freezing feature in my refrigerator?" The server analyzes the question, extracts the relevant operating procedure from the section titled "How to use quick freezing," generates an answer, and sends it to the user's device. The user operates the refrigerator according to the answer.

[0059] Providing recommendations

[0060] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage and settings. The recommendations are then delivered to the user's device.

[0061] Examples:

[0062] The server analyzes the user's refrigerator usage history and makes recommendations such as "temperature settings to extend food storage life" and "how to use energy-saving mode." These recommendations are sent to the user's device, and the user is notified.

[0063] This invention allows users to easily understand and effectively use all the functions of the home appliances and devices they purchase, greatly improving user convenience and maximizing product performance.

[0064] The processing flow will be explained below.

[0065] Step 1: Collect your purchase history

[0066] The user terminal acquires the user's purchase history from an e-commerce site or a sales management system.

[0067] The user terminal transmits the acquired purchase history to the server.

[0068] Step 2: Analyze your purchase history

[0069] The server analyzes the received purchase history data and extracts detailed information about each product (e.g., product name, category, purchase date, etc.).

[0070] The server stores the extracted information in a database.

[0071] Step 3: Obtaining instruction data

[0072] The server retrieves instruction manual data for the corresponding home appliance or device based on the purchase history information in the database.

[0073] The server downloads instruction data from manufacturer websites and online resources.

[0074] Step 4: Analyzing the instruction data

[0075] The server parses the retrieved instruction data and converts it into a text format (e.g., using PDF parsing or OCR technology).

[0076] The server extracts key features and configuration information from the text data.

[0077] Step 5: Generate a usage guide

[0078] The server generates a usage guide in a format that is easy for the user to understand based on the analyzed instruction data.

[0079] The server stores the generated usage guide in a database.

[0080] Step 6: Distributing the usage guide

[0081] The server distributes the saved usage guide to the user terminal.

[0082] The user terminal displays an access notification of the usage guide to the user.

[0083] Step 7: Ask a question

[0084] The user inputs questions about usage from the user terminal.

[0085] The user terminal sends the question to the server.

[0086] Step 8: Parsing the Question

[0087] The server analyzes the question received from the user using natural language processing technology.

[0088] The server searches the database for instruction data and related information that corresponds to the question.

[0089] Step 9: Generate an answer

[0090] The server automatically generates an appropriate answer based on the analysis results.

[0091] The server formats the generated answer for the user.

[0092] Step 10: Publish your response

[0093] The server generates a response and sends it to the user terminal.

[0094] The user terminal displays the answer to the user.

[0095] Step 11: Serve recommendations

[0096] The server analyzes the user's usage history and current settings.

[0097] The server generates recommendations for optimal usage methods and settings based on the user's usage.

[0098] Step 12: Serving Recommendations

[0099] The server distributes the generated recommendations to the user terminal.

[0100] The user terminal notifies the user of the contents of the recommendation.

[0101] The above is a specific program processing flow of the present invention.

[0102] Example 1

[0103] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0104] Manuals for home appliances and devices are typically provided in a cumbersome and difficult-to-understand format, making it difficult for users to effectively use all of their functions. Furthermore, when users need specific usage instructions or optimal settings, they lack a way to quickly and accurately obtain that information. Furthermore, the lack of functionality to suggest optimal ways to use the product while in use reduces user convenience.

[0105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0106] In this invention, the server includes means for collecting purchase history, means for acquiring instruction manual data for the home appliance based on the purchase history, means for analyzing the instruction manual data to extract key functions and settings, means for generating a user-friendly usage guide based on the extracted information, means for distributing the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, and means for analyzing the user's usage history and settings and recommending optimal usage methods. This allows users to easily understand all the functions of home appliances and devices and use them effectively. Furthermore, information on specific usage methods and settings can be quickly obtained and appropriate recommendations tailored to individual usage situations can be received, significantly improving user convenience.

[0107] "Purchase history collection" is the process of automatically obtaining information on user purchases of home appliances and devices from electronic transaction systems and sales management systems.

[0108] "Instruction Data Acquisition" is the process of downloading relevant instructions from manufacturer websites or other online resources based on registered product information.

[0109] "Analysis of instruction manual data" is the process of analyzing the contents of the acquired instructions using a text analysis tool and extracting key functions and settings.

[0110] "Generating a usage guide" is the process of generating a guide in a form that is easy for the user to understand based on the analyzed instruction data.

[0111] "Distribution of usage guide" is a process of sending the generated usage guide to the user terminal and notifying the user.

[0112] "Accepting user questions" is the process of allowing a user to enter specific usage or configuration questions and sending those questions to the server.

[0113] "Generating an answer to a question" is the process of analyzing a user's question using natural language processing technology and creating an appropriate answer.

[0114] "Sending an answer" is the process of sending the generated answer to the user terminal so that the user can view it.

[0115] "Recommending optimal usage methods" is a process that analyzes a user's usage history and current settings and suggests appropriate usage methods and settings based on that.

[0116] This invention relates to a system that provides easy-to-understand instructions for using home appliances and devices based on a user's purchase history. The system includes collection of purchase history, acquisition and analysis of instruction manual data, generation and distribution of usage guides, support for user interaction, and provision of recommendations. The "Smart Manual AI" system is configured as follows:

[0117] First, the server automatically collects the user's purchase history from the electronic transaction system and sales management system. Specifically, it uses the electronic transaction system's API to obtain transaction data and uses the Python PANDAS library to analyze this data. For example, when a user purchases a new refrigerator, the information is recorded in the electronic transaction system. The user's device collects this purchase information and sends it to the server.

[0118] Next, the server downloads the corresponding manuals from the manufacturer's website or other online resources based on the product information stored in the database. It uses a web scraping tool (e.g., Beautiful Soup) to retrieve the manuals. It uses the Python PyPDF2 library to analyze the contents of the retrieved manuals and extract key features and settings. The extracted results are stored in the database.

[0119] The server then generates a user guide that is easy for users to understand based on the analyzed instruction data. A generative AI model (e.g., GPT-3) is used to generate the user guide. For example, a user guide is generated by concisely summarizing step-by-step operating procedures for a refrigerator, such as "how to turn it on" and "how to set the temperature." This user guide is then distributed to the user's device and notified to the user.

[0120] Furthermore, users can enter questions about specific usage or settings into a question form and send them to the server via their device. The server receives the questions, analyzes them using natural language processing techniques (e.g., the BERT model), and generates appropriate answers. The generated answers are then sent to the user's device, where they can be viewed by the user.

[0121] Finally, the server analyzes the user's usage history and current settings to recommend optimal usage methods and settings. These recommendations are generated using machine learning algorithms, and are tailored to the user's individual usage patterns. For example, the server might notify the user of a recommendation such as, "Lowering the refrigerator temperature setting by 2 degrees will reduce energy consumption by 10%."

[0122] Below are some examples of prompts to input to a generative AI model:

[0123] 1. "Please tell me the basic operation of the newly purchased refrigerator."

[0124] 2. "I'd like to know how to use flash freezing. How do I do that?"

[0125] 3. "Please explain how to switch to power saving mode."

[0126] 4. "How do I optimize my refrigerator's temperature settings?"

[0127] In this way, by implementing this system, users can easily understand all the functions of home appliances and devices and use them effectively. By utilizing appropriate technology at each processing step, it is possible to significantly improve user convenience.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] The server collects purchase history data from the electronic transaction system and sales management system. Specifically, it calls the API of the electronic transaction system and executes the API with the user ID and transaction ID as input. As output, it obtains detailed information such as the name, model number, purchase date, and purchase location of the purchased product.

[0131] Example: "Use the electronic transaction system API to retrieve the purchase history of user ID 12345."

[0132] Input: User ID, Transaction ID

[0133] Output: Product name, model number, purchase date, purchase location

[0134] Step 2:

[0135] The user device sends the collected purchase history information to the server. This is a process in which purchase history data is automatically sent when the user opens the application. Specifically, the device sends an HTTP request to the server via the API to transmit the purchase data.

[0136] For example: "When a user launches the app, purchase history information is sent to the server."

[0137] Input: Purchase history information (product name, model number, purchase date, purchase location)

[0138] Output: Purchase history information sent to the server

[0139] Step 3:

[0140] The server analyzes the received purchase history data, identifies the category and characteristics of each product, and stores the analysis results in a database using the Python PANDAS library.

[0141] Example: "Analyze purchase history information and register the relevant product information in a database."

[0142] Input: Purchase history information received

[0143] Output: Product information stored in the database (product category, features)

[0144] Step 4:

[0145] Based on the product information, the server retrieves relevant product manuals from manufacturer websites and other online resources. It uses a web scraping tool (e.g., Beautiful Soup) to download the product manual PDF.

[0146] Example: "Get product manual PDF from manufacturer's website"

[0147] Input: Product name, model number

[0148] Output: Downloaded product manual PDF

[0149] Step 5:

[0150] The server analyzes the PDF manual using a text analysis tool (e.g., PyPDF2) to extract key features and settings. During this process, it processes the data as needed and stores the extracted results in a database.

[0151] Example: "Analyze a PDF manual and register the main sections in a database."

[0152] Input: Instructions PDF

[0153] Output: Main section information stored in database (basic operation, temperature settings, maintenance procedures)

[0154] Step 6:

[0155] The server generates a user guide based on the analyzed instruction data. It uses a generative AI model (e.g., GPT-3) to generate a user guide in easy-to-understand text format.

[0156] Example: "Use a generative AI model to generate concise instructions for operating your refrigerator."

[0157] Input: Parsed instruction data

[0158] Output: Generated usage guide

[0159] Step 7:

[0160] The server delivers the generated usage guide to the user's device via email or push notification, and allows the user to view the guide within the app.

[0161] Example: "Send the generated usage guide to the user's device and notify them."

[0162] Input: Generated Usage Guide

[0163] Output: User guide delivered to user device

[0164] Step 8:

[0165] The user enters questions about how to use the app into the question form within the app and sends them to the server via the user's terminal.

[0166] Example: "A user enters 'How to use flash freezing' into a question form and sends it to the server."

[0167] Input: User question

[0168] Output: The question sent to the server

[0169] Step 9:

[0170] The server analyzes the received question using natural language processing technology (e.g., the BERT model) and generates an appropriate answer. The analysis understands the intent of the question, and extracts the appropriate information from the database to create the answer.

[0171] Example: "Use the BERT model to generate answers to user questions."

[0172] Input: User question

[0173] Output: The generated answer

[0174] Step 10:

[0175] The server sends the generated answer to the user's terminal, where the user can check the answer and take any necessary action.

[0176] Example: "Send the generated answer to the user's device and notify them."

[0177] Input: Generated answer

[0178] Output: Answer sent to user's terminal

[0179] Step 11:

[0180] The server analyzes the user's usage history and settings and recommends optimal usage methods and settings. It uses machine learning algorithms to analyze the data and make suggestions based on individual usage.

[0181] Example: "Use machine learning algorithms to recommend optimal settings for your refrigerator."

[0182] Input: Usage history, current settings

[0183] Output: Generated recommendations

[0184] Step 12:

[0185] The server sends the generated recommendations to the user's device and notifies them, allowing the user to view the recommendations within the app.

[0186] Example: "The generated recommendations are sent to the user's device and a notification is sent."

[0187] Input: Generated recommendations

[0188] Output: Recommendations sent to the user device

[0189] (Application example 1)

[0190] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0191] Currently, many home appliances and devices come with detailed and complex manuals, which are often difficult for average users to understand and operate properly. Furthermore, to obtain accurate information and setup instructions after purchase, users must manually search for and decipher the appropriate sections of the manual. This process is time-consuming and inefficient. Furthermore, the lack of real-time guidance and recommendations on optimal usage makes it difficult for users to maximize the product's performance.

[0192] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0193] In this invention, the server includes means for collecting purchase history, means for acquiring device instruction manual data based on the purchase history, means for analyzing the instruction manual data to extract key functions and settings, means for generating a user-friendly usage guide, means for distributing the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, means for the user terminal to provide real-time guidance on initial device setup and usage methods, and means for generating recommendation information based on the user's usage history and settings and distributing it to the user terminal. This allows users to easily understand all the functions of the home appliances and devices they have purchased and use them effectively. This also significantly improves user convenience and maximizes product performance.

[0194] "Purchase history" refers to historical information about products and services purchased by a user.

[0195] "Equipment" means electronic and electrical appliances for home or business use.

[0196] "Instruction data" is document data that describes information such as how to handle, install, and use the device.

[0197] "Analysis" is the process of breaking down and organizing complex data or documents into an understandable form.

[0198] "Major functions" refer to the main operations and actions provided by the device.

[0199] "Settings" refers to operations for adjusting the operating conditions and operating modes of a device.

[0200] "Usage Guide" refers to a guide that helps users correctly understand and effectively use the equipment.

[0201] "User terminal" refers to an electronic device or equipment used by a user, and specifically includes a smartphone, computer, etc.

[0202] A "question" is an inquiry submitted by a user to ask questions or clarify how to use or function the device.

[0203] An "answer" is information or explanation provided in response to a user's question.

[0204] "Real-time guidance" refers to instructions and guidance provided instantly on the spot regarding the initial setup and use of equipment.

[0205] "Recommendation information" is information about optimal usage methods and settings that are suggested based on the user's usage status and settings.

[0206] A "server" refers to a computer system that stores and processes data, and is a device that works in conjunction with user terminals via a network.

[0207] This invention is a system that allows users to easily understand and effectively use all the functions of the devices they have purchased. The system includes multiple means for collecting purchase history, analyzing and organizing device instruction manual data, and providing easy-to-understand guidance to users.

[0208] The server has the following means:

[0209] How to collect purchase history: Information on devices purchased by users is collected from e-commerce platforms and sales management systems. The user device sends data to the server every time the purchase history is updated.

[0210] Method for obtaining instruction manual data: Based on the registered device information, obtain the corresponding instruction manual from the manufacturer's website or other online resources. Analyze the obtained data and extract key functions and settings.

[0211] A means for generating a user guide: A user guide that is easy for the user to understand is generated based on the analyzed instruction data. This guide is delivered to the user's device and notified to the user.

[0212] The user terminal has the following means:

[0213] A method for accepting questions: The user inputs a question about how to use the device. The question is sent to the server.

[0214] How to generate answers to questions: The server analyzes the question and generates a corresponding answer using the generative AI model. The answer is then sent to the user's device.

[0215] Means for providing real-time guidance: The user device provides real-time guidance on how to set up and use the device, for example, by showing how to set the refrigerator temperature on a smartphone screen or through an AR display on smart glasses.

[0216] Means for delivering recommendation information: The server generates recommendation information for optimal usage methods and settings based on the user's usage history and settings, and delivers it to the user's device.

[0217] As a specific example, consider a case where a user who has purchased a new refrigerator uses this system to use the refrigerator's quick-freeze function. Once the purchase information is registered in the system, the server automatically downloads the product manual from online and analyzes its contents. When the user asks, "How do I use the quick-freeze function on my refrigerator?", the server analyzes the question, extracts the relevant operating procedures, and generates an answer. Then, with real-time guidance, the user can operate the refrigerator while checking the exact procedures on their smartphone screen or smart glasses.

[0218] An example of a prompt might be:

[0219] "Please check your purchase history. Product ID: 'new_refrigerator_123'"

[0220] "Generate a response to the following relevant user question: 'How do I use the deep freeze in my refrigerator?'"

[0221] To implement this system, the server is built using Python, Django, Flask, etc., and the client application is built using React Native or Unity. PDF analysis libraries such as PyMuPDF and pdfminer are used for data analysis, and SpaCy is used for natural language processing. This configuration allows users to quickly and easily understand the functions of the device and use it in the most optimal way.

[0222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0223] Step 1:

[0224] The server collects the user's purchase history from the e-commerce platform or sales management system. When the user purchases a new product, the user terminal updates the purchase information in the database and sends the data to the server. The server analyzes the received purchase history data and stores it in the database. The input in this step is the purchase history data, and the output is the analyzed purchase information data.

[0225] Step 2:

[0226] The server retrieves device instruction data from the manufacturer's website or other online resources based on the purchase history data. The server crawls specific URLs and downloads the relevant instruction data. It then analyzes the retrieved instruction data (e.g., in PDF format) and extracts key function and setting information. The input is the purchase history data and instruction data from the online resource, and the output is the analyzed function and setting information.

[0227] Step 3:

[0228] The server generates a user guide that is easy for users to understand based on the analyzed instruction data. Specifically, it creates a text-format guide that summarizes the main functions and setting methods in short, concise steps. The input is the analyzed function and setting information, and the output is the generated user guide. This user guide is distributed from the server to the user's device.

[0229] Step 4:

[0230] The user inputs a question about how to use the device from the user terminal. The user terminal sends the question to the server. The input in this step is the user's question, and the output is the question data sent to the server.

[0231] Step 5:

[0232] The server receives questions from users and analyzes them using natural language processing technology (e.g., SpaCy). Based on the analyzed questions, it generates appropriate answers using a generative AI model. Specifically, the server extracts the relevant operating procedures from a database and generates answer sentences using the generative AI model. The input is the received question data, and the output is the generated answer sentences.

[0233] Step 6:

[0234] The server sends the generated answer to the user terminal, which then displays the answer to the user. The input is the generated answer, and the output is the answer data sent to the user terminal.

[0235] Step 7:

[0236] The user device provides real-time guidance. For example, instructions on how to set the refrigerator temperature can be displayed on a smartphone screen or an AR display on smart glasses. Specifically, the user device displays the usage guide and setting procedures obtained from the server on its interface. The input is the obtained usage guide data, and the output is the real-time guidance provided to the user.

[0237] Step 8:

[0238] The server generates recommendation information for optimal usage methods and settings based on the user's usage history and setting information. The generated recommendation information is distributed to the user's device. The input is the user's usage history and setting information, and the output is the generated recommendation information.

[0239] Step 9:

[0240] The user terminal receives the recommendation information delivered from the server and notifies the user. The user can adjust the product settings and usage methods based on the notification. The input is the recommendation information, and the output is the recommendation information notified to the user.

[0241] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0242] This invention is a system that uses a user's purchase history to analyze the manuals for home appliances and devices, and provides users with easy-to-understand instructions on how to use them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond according to the user's emotions. The "Smart Manual AI" system functions through the following process.

[0243] Collection and analysis of purchase history

[0244] The server collects the user's purchase history from the e-commerce site and the sales management system.

[0245] The user terminal transmits data to the server every time the purchase history is updated.

[0246] The server analyzes the received data and stores information about each product in a database.

[0247] Examples:

[0248] When a user purchases a new television, the information is registered on an e-commerce site. The user's device collects the purchase information and sends it to a server. The server analyzes the purchase information and registers it in a database under the category "television."

[0249] Instruction data acquisition and analysis

[0250] The server retrieves corresponding product manuals from manufacturers' websites and other online resources based on the registered product information, analyzes the retrieved manual data, and extracts key features and settings.

[0251] Examples:

[0252] The server downloads the instruction manual data for the new TV from the manufacturer's website, analyzes the downloaded PDF, and extracts key sections such as "Basic Operations," "Channel Settings," and "How to Use the Remote Control."

[0253] Generate and distribute usage guides

[0254] The server generates a user-friendly usage guide based on the analyzed instruction data, and distributes the generated usage guide to the user's device.

[0255] Examples:

[0256] The server summarizes the TV operation procedure step by step in a concise format such as "How to turn on the power," "How to set the channel," "How to adjust the volume," etc. The generated guide is sent to the user's device, and the user receives a notification.

[0257] User interaction support

[0258] The user inputs questions about usage from the user terminal.

[0259] The user terminal sends a query to the server.

[0260] The server receives the question, analyzes it using natural language processing technology, and generates a corresponding answer, which is then sent to the user's device.

[0261] Examples:

[0262] The user asks "How do I enable the voice guide on my TV?" on their device. The server analyzes the question, extracts the relevant operation procedure from the "Enable voice guide" section, generates an answer, and sends it to the user's device. The user then operates the TV according to the answer.

[0263] Providing recommendations

[0264] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage and settings. The recommendations are then delivered to the user's device.

[0265] Examples:

[0266] The server analyzes the user's TV usage history and makes recommendations such as "recommended picture quality settings" and "how to use energy saving mode." These recommendations are sent to the user's device, and the user is notified.

[0267] Incorporating an emotion engine

[0268] The emotion engine analyzes voice data and facial expression data from the user's device to recognize the user's emotions.

[0269] The server changes the usage guide and answer expressions for the user based on the emotion recognition results.

[0270] Examples:

[0271] When a user enters a question, if the emotion engine detects stress from the user's tone of voice or facial expression, the server will change the answer to something more polite and reassuring, such as "Don't worry. Try these steps and you'll be able to resolve the issue."

[0272] Emotional recommendations

[0273] The server adjusts the content of the recommendations based on the emotional state of the user as recognized by the emotion engine.

[0274] Examples:

[0275] If the user frequently shows signs of anxiety, the server will recommend resources such as an "initial setup guide" or "frequently asked questions" to increase the user's sense of security.

[0276] This invention allows users to easily understand all the functions of the home appliances and devices they have purchased, and receives support tailored to their emotions, allowing them to use the products more comfortably. This significantly improves user convenience and maximizes product performance.

[0277] The processing flow will be explained below.

[0278] Step 1: Collect your purchase history

[0279] The user terminal acquires the user's purchase history from an e-commerce site or a sales management system.

[0280] The user terminal transmits the acquired purchase history to the server.

[0281] Step 2: Analyze your purchase history

[0282] The server analyzes the received purchase history data and extracts detailed information about each product (e.g., product name, category, purchase date, etc.).

[0283] The server stores the extracted information in a database.

[0284] Step 3: Obtaining instruction data

[0285] The server retrieves instruction manual data for the corresponding home appliance or device based on the purchase history information in the database.

[0286] The server downloads instruction data from manufacturer websites and online resources.

[0287] Step 4: Analyzing the instruction data

[0288] The server parses the retrieved instruction data and converts it into a text format (e.g., using PDF parsing or OCR technology).

[0289] The server extracts key features and configuration information from the text data.

[0290] Step 5: Generate a usage guide

[0291] The server generates a usage guide in a format that is easy for the user to understand based on the analyzed instruction data.

[0292] The server stores the generated usage guide in a database.

[0293] Step 6: Distributing the usage guide

[0294] The server distributes the saved usage guide to the user terminal.

[0295] The user terminal displays an access notification of the usage guide to the user.

[0296] Step 7: Ask a question

[0297] The user inputs questions about usage from the user terminal.

[0298] The user terminal sends the question to the server.

[0299] Step 8: Parsing the Question

[0300] The server analyzes the question received from the user using natural language processing technology.

[0301] The server searches the database for instruction data and related information that corresponds to the question.

[0302] Step 9: Generate an answer

[0303] The server automatically generates an appropriate answer based on the analysis results.

[0304] The server formats the generated answer for the user.

[0305] Step 10: Publish your response

[0306] The server generates a response and sends it to the user terminal.

[0307] The user terminal displays the answer to the user.

[0308] Step 11: Serve recommendations

[0309] The server analyzes the user's usage history and current settings.

[0310] The server generates recommendations for optimal usage methods and settings based on the user's usage.

[0311] Step 12: Serving Recommendations

[0312] The server distributes the generated recommendations to the user terminal.

[0313] The user terminal notifies the user of the contents of the recommendation.

[0314] Step 13: Collect emotion data

[0315] The user terminal collects the user's voice data and facial expression data.

[0316] The user terminal transmits the collected emotion data to the server.

[0317] Step 14: Analyze the emotion data

[0318] The emotion data received by the server is analyzed using an emotion engine.

[0319] The server stores the emotion analysis results in a database.

[0320] Step 15: Emotion-based guidance and response adjustments

[0321] The server adjusts the expressions of the usage guide and answers provided to the user based on the analyzed emotion data.

[0322] The server delivers emotion-based responses to the user terminal.

[0323] Step 16: Emotional Recommendations

[0324] The server adjusts the content of the recommendations based on the user's emotional state recognized by the emotion engine.

[0325] The server delivers recommendations based on emotions to the user's device.

[0326] The above is a specific program processing flow of the present invention.

[0327] Example 2

[0328] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0329] Today, home appliances are becoming increasingly multifunctional, making it difficult for users to effectively utilize all the features of the products they purchase. In particular, product manuals can be overly detailed or contain a wealth of technical jargon, making it time-consuming to understand how to use them. Furthermore, uniform support that doesn't take users' feelings into consideration is a factor that reduces satisfaction. Furthermore, there is no system in place to guide users through post-purchase setup or optimal usage, leaving users struggling to resolve issues on their own. There is a need to solve these problems and enable users to make the most of their home appliances.

[0330] The identification process by the identification 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 collecting purchase history, means for acquiring product instruction data based on the purchase history, means for analyzing the instruction data to extract key functions and settings, means for generating a usage guide that is easy for the user to understand based on the extracted information, means for distributing the usage guide to a user terminal, means for accepting questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, an emotion engine for analyzing the user's voice data and facial expression data to recognize emotions, means for changing the expression of the usage guide and answers based on the results of the emotion engine, and means for generating recommendations based on the recognized emotional state. This allows the user to quickly and effectively understand how to use the home appliance they purchased and further receive support and recommendations according to their emotions.

[0331] "Purchase history" is information about products or merchandise purchased by a user in the past.

[0332] "Instruction data" refers to document data that describes how to use a product or item, how to set it up, precautions, etc.

[0333] "Analysis" is the act of examining data and information in detail to clarify meaning and relationships.

[0334] "Major Function" refers to the basic and important operation or role of a product or merchandise.

[0335] "Configuration" is the process of adjusting a product or system to determine its operating conditions and operation methods.

[0336] A "usage guide" is a guide that helps users to understand and use a product or system easily and conveniently.

[0337] A "user terminal" is a device such as a computer or smartphone that can be directly operated by a user.

[0338] "Question acceptance" is the process of receiving inquiries from users.

[0339] "Answer generation" is the task of creating an appropriate answer to a question posed by a user.

[0340] An "emotion engine" is a system that has the function of recognizing emotions by analyzing the user's voice data and facial expression data.

[0341] "Recommendations" suggest optimal actions and settings based on a user's behavioral history and emotional state.

[0342] The present invention is a system that includes "means for collecting purchase history," "means for acquiring product instruction data," "means for analyzing the instruction data and extracting key functions and settings," "means for generating a user-friendly usage guide based on the extracted information," "means for delivering the usage guide to a user terminal," "means for accepting questions from a user and generating answers to those questions," "means for sending the generated answers to the user terminal," "an emotion engine that recognizes emotions by analyzing the user's voice data and facial expression data," "means for changing the expression of the usage guide and answers based on the results of the emotion engine," and "means for generating recommendations based on the recognized emotional state."

[0343] The entire system is mainly composed of a server, a user terminal, and an emotion engine. These elements work together to enable users to quickly and effectively understand how to use the products they have purchased, and also to receive emotional support and recommendations.

[0344] Collection and analysis of purchase history

[0345] The server periodically runs a script that retrieves data from e-commerce sites and sales management systems. It uses an API to retrieve the latest purchase history and stores it in a database. For example, the server sends an API request to an e-commerce site such as Amazon or Rakuten, parses the purchase history data received in response in JSON format, and adds it to the database.

[0346] Instruction data acquisition and analysis

[0347] Based on the product information registered in the database, the server retrieves the corresponding manual from the manufacturer's website or other online resources. Specifically, the server searches for the product name using the Google Custom Search API and obtains the manual's URL. Next, the server downloads the PDF file from the obtained URL and saves it to the file system. The server then uses a PDF parsing library (e.g., Apache PDFBox) to extract text data from the downloaded manual and uses a natural language processing library (e.g., NLTK) to classify it into major sections such as "Basic Operations" and "How to Use the Remote Control."

[0348] Generate and distribute usage guides

[0349] The server automatically generates an easy-to-understand user guide based on the analyzed instruction data. The server uses a template engine (e.g., Jinja2) to document clear instructions. For example, it generates guide sentences that include specific usage procedures, such as "how to turn on the power" and "how to set the channel." The generated user guide is then distributed to the user's device. The user's device displays the guide to the user via a dedicated app or web interface and sends notifications.

[0350] User interaction support

[0351] Users can input questions about how to operate the device from their device. The user device sends the questions to the server, which analyzes the questions and generates appropriate answers. The server uses a natural language processing engine (e.g., BERT) to analyze the questions, search for relevant answers from a database, and format them for the user. The generated answers are sent to the user device and displayed on the device.

[0352] Providing recommendations

[0353] The server analyzes the user's usage history and current settings and generates recommendations for optimal usage and settings. For example, the server uses a machine learning model (e.g., XGBoost) to analyze user behavior data and generate recommendations such as "recommended image quality settings" and "how to utilize energy saving mode." These recommendations are delivered to the user's device and notified to the user.

[0354] Incorporating an emotion engine

[0355] The emotion engine analyzes the voice data and facial expression data acquired from the user's device to recognize the user's emotions. The emotion engine analyzes the voice data using an emotion analysis API (e.g., Google API) and evaluates the facial expression data using a face recognition API (e.g., Microsoft Azure Face API). Based on the recognized emotional state, the server changes the expression of the usage guide and answers. For example, if the user is feeling stressed, the tone of the answers and guide will be changed to be more polite and reassuring.

[0356] Emotional recommendations

[0357] The server adjusts the recommendations based on the results of the emotion engine. If the user feels unsure, it will recommend resources that will increase their sense of security, such as an "initial setup guide" or "frequently asked questions." This allows the user to use all the product's features without stress.

[0358] Examples of prompt statements

[0359] The following format is used as an example of a prompt statement for generating a program for this system:

[0360] Design a system that uses the user's purchase history to analyze product and equipment manuals and provide instructions for use in a way that is easy for the user to understand. In addition, add support functions that combine an emotion engine that recognizes the user's emotions.

[0361] With the above configuration, the present invention enables users to quickly and effectively understand how to use a purchased product and receive support and recommendations that correspond to their emotions.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1:

[0364] Collection of purchase history

[0365] The server periodically obtains data from e-commerce sites and sales management systems. Specifically, the server uses an API to send a request to the e-commerce site (e.g., Amazon, Rakuten) and receives the latest purchase history data. The received data is in JSON format, and the server analyzes this data and stores it in a database. The input is the purchase history data obtained from the e-commerce site, and the output is a database that stores the analyzed purchase history.

[0366] Step 2:

[0367] Send purchase history

[0368] When a user purchases a product and their purchase history is updated, the user device sends that information to the server. When a user updates their purchase history within the app, the user device sends that information to the server using an API. The input is the purchase history updated by the user, and the output is the purchase history data sent to the server.

[0369] Step 3:

[0370] Obtaining instruction data

[0371] The server retrieves product information from a database based on purchase history and collects corresponding manuals from manufacturer websites and other online resources. The server uses the Google Custom Search API to search for the product name and obtain the manual URL. It then downloads a PDF file from the URL. The input is the product name, and the output is the downloaded PDF manual.

[0372] Step 4:

[0373] Analysis of instruction data

[0374] The server analyzes the downloaded PDF manual and extracts key functions and settings. It uses a PDF analysis library (e.g., Apache PDFBox) to extract text data and uses a natural language processing library (e.g., NLTK) to classify it into sections such as "Basic Operations" and "How to Use the Remote Control." The input is the PDF manual data, and the output is the analyzed and classified manual data.

[0375] Step 5:

[0376] Creating a usage guide

[0377] The server generates an easy-to-understand user guide based on the parsed instruction data. It uses a template engine (e.g., Jinja2) to document clear instructions for each step. For example, it summarizes procedures such as "how to turn on the power" and "how to set the channel." The input is the parsed instruction data, and the output is the generated instruction guide.

[0378] Step 6:

[0379] Distribution of user guides

[0380] The server delivers the generated usage guide to the user terminal. The user terminal displays the received guide and sends a notification to the user. The input is the generated usage guide, and the output is the guide displayed on the user terminal.

[0381] Step 7:

[0382] Accepting questions from users

[0383] The user inputs a question about how to use the system from the terminal. The user terminal sends the question to the server. The input is the question from the user, and the output is the question data sent to the server.

[0384] Step 8:

[0385] Parsing questions and generating answers

[0386] The server analyzes the received question using natural language processing technology and generates an appropriate answer. It uses a natural language processing engine (e.g., BERT) to analyze the question, extract keywords, and search a database for relevant information. The input is the question data from the user, and the output is the generated answer.

[0387] Step 9:

[0388] Response distribution

[0389] The server sends the generated answer to the user terminal, which displays the answer. The input is the generated answer, and the output is the answer displayed on the user terminal.

[0390] Step 10:

[0391] Recommendation generation and delivery

[0392] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage methods and settings. Analysis is performed using a machine learning model (e.g., XGBoost) to generate recommendations. The generated recommendations are delivered to the user's device and notified to the user. The input is the user's usage history data, and the output is the generated recommendations.

[0393] Step 11:

[0394] Acquiring and analyzing emotion data

[0395] The emotion engine acquires voice and facial expression data from the user's device and analyzes them to recognize the user's emotions. It analyzes voice data using an emotion analysis API (e.g., Google API) and evaluates facial expressions using a face recognition API (e.g., Microsoft Azure Face API). The input is the user's voice and facial expression data, and the output is recognized emotion data.

[0396] Step 12:

[0397] Emotion-based responses

[0398] The server changes the expressions of the usage guide and answers given to the user based on the results of the emotion engine. If the user is feeling stressed, it will use expressions such as "Don't worry" or change the tone of the guide. The input is the recognized emotion data, and the output is the guide and answers corresponding to the emotion.

[0399] Step 13:

[0400] Providing emotional recommendations

[0401] The server adjusts the content of recommendations based on the emotional state recognized by the emotion engine. If the user is feeling anxious, it will recommend resources that will make them feel at ease (e.g., "Initial Setup Guide" or "FAQ"). The input is the recognized emotional data, and the output is a recommendation based on the emotion.

[0402] (Application example 2)

[0403] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0404] As autonomous vehicles become more multifunctional and technologically advanced, it is becoming increasingly difficult for users to understand and operate all of their functions. Even when users read the vehicle's manual, it is difficult for them to quickly find the information they need, making efficient use of the vehicle particularly challenging in stressful situations. Furthermore, as vehicle technology evolves, there is a need for a method to provide appropriate support tailored to the emotional state of each individual user. Solutions to these challenges are needed.

[0405] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting purchase history, means for acquiring function description data for the autonomously driving vehicle based on the purchase history, means for analyzing the description data and extracting key functions and settings, means for generating a usage guide that is easy for the user to understand based on the extracted information, means for delivering the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, means for analyzing the user's emotional state, and means for adjusting the guide and answer expression corresponding to the emotional state. This allows the user to use the functions of the autonomously driving vehicle quickly and easily and receive appropriate support according to their emotional state.

[0406] "Collecting purchase history" is the process of recording and compiling information about products and services purchased by users.

[0407] "Function description data" is a digital document that provides detailed explanations of the various functions and settings of an autonomous vehicle.

[0408] "Analysis" is the process of examining data in detail and understanding its contents.

[0409] "Major functions and settings" are important functions and settings that the user frequently uses or should be aware of when using the vehicle.

[0410] A "user guide" is a user guide that clearly summarizes how to operate a product or system.

[0411] A "user terminal" is a device used by a user, such as a smartphone, tablet, or in-vehicle display.

[0412] "Accepting questions" means receiving inquiries or questions from users and gathering information to respond to them.

[0413] "Generating an answer" means creating appropriate information or solutions for a question received from a user.

[0414] "Analyzing emotional state" is the process of identifying the user's current emotional and psychological state based on their voice and facial expression data.

[0415] "Adjusting the expression of the guide or answer" means changing the expression method or content of the information provided depending on the emotional state of the user.

[0416] The program of the system for realizing the present invention is configured as follows.

[0417] The server collects user purchase history and retrieves the data from e-commerce sites or sales management systems. The server then downloads the autonomous vehicle's function description data from the manufacturer's website or other online resources, and uses a PDF analysis tool (e.g., PyMuPDF) to analyze it. It extracts key functions and settings from the analyzed data and generates a user guide that is easy to understand for the user.

[0418] The generated usage guide is distributed from the cloud server to the user's device (smartphone, tablet, in-car display, etc.). The server accepts questions from the user's device, generates answers to those questions using a natural language processing tool (e.g., spaCy), and sends them to the user's device.

[0419] Furthermore, the system uses emotion analysis tools (e.g., Microsoft Azure Cognitive Services' Emotion Analysis API) to analyze the user's voice and facial expression data. Based on the analysis results, the system adjusts the guidance and response expressions to match the user's emotional state and provides them to the user. This allows users to use the autonomous vehicle quickly and easily, and receive appropriate support according to their emotional state.

[0420] As a concrete example, if a user purchases a new self-driving vehicle and wants to know about the in-car functions and how to set them up, the system operates as follows.

[0421] 1. Users gather information about new autonomous vehicles from their purchase history.

[0422] 2. The server analyzes the information and downloads the function description from the manufacturer's website.

[0423] 3. Analyze the downloaded manual, extract key functions and settings such as "how to start the vehicle" and "how to use the parking assist function," and generate a usage guide.

[0424] 4. The generated usage guide is delivered to the user's device so that the user can check it.

[0425] 5. If a user asks, "I don't know how to set the autonomous driving mode," the server uses natural language processing to generate an answer and sends it to the user's device.

[0426] Additionally, if the sentiment analysis tool determines that the user is feeling stressed, it will provide softer guidance and answers, such as "Take a slow, deep breath and try these steps."

[0427] Example prompt sentence:

[0428] "Please provide a step-by-step guide for setting up the self-parking feature for a self-driving car."

[0429] This will help users better understand the functions of the autonomous vehicle and allow them to receive appropriate support according to their emotional state.

[0430] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0431] Step 1:

[0432] The server collects the user's purchase history. The input is data from an e-commerce site or a sales management system, and the output is purchase history data. Based on this data, vehicle purchase information is identified.

[0433] Step 2:

[0434] The server obtains the function description data of the autonomous vehicle based on the purchase history. The input is the purchase history data, and the output is the URL or file of the function description. The server downloads the corresponding description from the manufacturer's website or other online resources.

[0435] Step 3:

[0436] The server parses the retrieved instruction data. The input is the feature instruction data, and the output is information about the main features and settings. Using a PDF parsing tool (e.g., PyMuPDF), it extracts sections from the instruction manual, such as "How to start the vehicle" and "How to use the parking assist function."

[0437] Step 4:

[0438] The server generates a user-friendly usage guide based on the extracted information. The input is the main features and settings information, and the output is the usage guide. A generative AI model is used to generate the user-friendly guide in text or multimedia format.

[0439] Step 5:

[0440] The server distributes the generated usage guide to the user's device. The input is the usage guide, and the output is a distribution notification to the user's device. The guide is sent via the cloud server to a smartphone, tablet, or in-car display.

[0441] Step 6:

[0442] The user inputs a question via a terminal. The input is the user's question text, and the output is the question sent to the server, which then transmits the user's question to the system.

[0443] Step 7:

[0444] The server generates answers to user questions. The input is the user's question text, and the output is the answer text. Natural language processing tools (e.g., spaCy) are used to analyze the question and generate appropriate answers.

[0445] Step 8:

[0446] The server sends the generated answer to the user terminal. The input is the generated answer text, and the output is a reply to the user terminal. This allows the user to obtain the necessary information.

[0447] Step 9:

[0448] The server uses an emotion analysis tool to analyze the user's emotional state. The input is the user's voice and facial expression data, and the output is the result of identifying the user's emotional state. This tool utilizes the emotion analysis API of Microsoft Azure Cognitive Services.

[0449] Step 10:

[0450] The server adjusts the expression of the guide or answer according to the user's emotional state. The input is the result of identifying the user's emotional state and the generated guide or answer, and the output is the guide or answer adjusted to an expression appropriate to the emotion. This reduces the user's psychological burden and provides stress-free support.

[0451] This allows users to use the functions of an autonomous vehicle quickly and easily, and to receive appropriate support according to their emotional state.

[0452] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0453] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0454] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0455] [Second embodiment]

[0456] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0457] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0458] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0459] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0460] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0461] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0462] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0463] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0464] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0465] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0466] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0467] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0468] This invention is a system that uses a user's purchase history to analyze the manuals for home appliances and devices, and provides users with easy-to-understand instructions for use. The "Smart Manual AI" system works through the following process.

[0469] Collection and analysis of purchase history

[0470] The server collects the user's purchase history from the e-commerce site and the sales management system.

[0471] The user terminal transmits data to the server every time the purchase history is updated.

[0472] The server analyzes the received data and stores information about each product in a database.

[0473] Examples:

[0474] When a user purchases a new refrigerator, the information is registered on the e-commerce site. The user's device collects the purchase information and sends it to the server. The server analyzes the purchase information and registers it in the database under the category "refrigerator."

[0475] Instruction data acquisition and analysis

[0476] The server retrieves corresponding product manuals from manufacturers' websites and other online resources based on the registered product information, analyzes the retrieved manual data, and extracts key features and settings.

[0477] Examples:

[0478] The server downloads the instruction manual for a new refrigerator from the manufacturer's website, analyzes the downloaded PDF, and extracts key sections such as "Basic Operation," "Temperature Setting," and "Maintenance Procedures."

[0479] Generate and distribute usage guides

[0480] The server generates a user-friendly usage guide based on the analyzed instruction data, and distributes the generated usage guide to the user's device.

[0481] Examples:

[0482] The server summarizes the refrigerator's operation procedures step by step in the form of "how to turn it on," "how to set the temperature," "how to switch to energy-saving mode," etc. The generated guide is sent to the user's device, and the user receives a notification.

[0483] User interaction support

[0484] The user inputs questions about usage from the user terminal.

[0485] The user terminal sends a query to the server.

[0486] The server receives the question, analyzes it using natural language processing technology, and generates a corresponding answer, which is then sent to the user's device.

[0487] Examples:

[0488] The user asks a question on their device: "How do I use the quick freezing feature in my refrigerator?" The server analyzes the question, extracts the relevant operating procedure from the section titled "How to use quick freezing," generates an answer, and sends it to the user's device. The user operates the refrigerator according to the answer.

[0489] Providing recommendations

[0490] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage and settings. The recommendations are then delivered to the user's device.

[0491] Examples:

[0492] The server analyzes the user's refrigerator usage history and makes recommendations such as "temperature settings to extend food storage life" and "how to use energy-saving mode." These recommendations are sent to the user's device, and the user is notified.

[0493] This invention allows users to easily understand and effectively use all the functions of the home appliances and devices they purchase, greatly improving user convenience and maximizing product performance.

[0494] The processing flow will be explained below.

[0495] Step 1: Collect your purchase history

[0496] The user terminal acquires the user's purchase history from an e-commerce site or a sales management system.

[0497] The user terminal transmits the acquired purchase history to the server.

[0498] Step 2: Analyze your purchase history

[0499] The server analyzes the received purchase history data and extracts detailed information about each product (e.g., product name, category, purchase date, etc.).

[0500] The server stores the extracted information in a database.

[0501] Step 3: Obtaining instruction data

[0502] The server retrieves instruction manual data for the corresponding home appliance or device based on the purchase history information in the database.

[0503] The server downloads instruction data from manufacturer websites and online resources.

[0504] Step 4: Analyzing the instruction data

[0505] The server parses the retrieved instruction data and converts it into a text format (e.g., using PDF parsing or OCR technology).

[0506] The server extracts key features and configuration information from the text data.

[0507] Step 5: Generate a usage guide

[0508] The server generates a usage guide in a format that is easy for the user to understand based on the analyzed instruction data.

[0509] The server stores the generated usage guide in a database.

[0510] Step 6: Distributing the usage guide

[0511] The server distributes the saved usage guide to the user terminal.

[0512] The user terminal displays an access notification of the usage guide to the user.

[0513] Step 7: Ask a question

[0514] The user inputs questions about usage from the user terminal.

[0515] The user terminal sends the question to the server.

[0516] Step 8: Parsing the Question

[0517] The server analyzes the question received from the user using natural language processing technology.

[0518] The server searches the database for instruction data and related information that corresponds to the question.

[0519] Step 9: Generate an answer

[0520] The server automatically generates an appropriate answer based on the analysis results.

[0521] The server formats the generated answer for the user.

[0522] Step 10: Publish your response

[0523] The server generates a response and sends it to the user terminal.

[0524] The user terminal displays the answer to the user.

[0525] Step 11: Serve recommendations

[0526] The server analyzes the user's usage history and current settings.

[0527] The server generates recommendations for optimal usage methods and settings based on the user's usage.

[0528] Step 12: Serving Recommendations

[0529] The server distributes the generated recommendations to the user terminal.

[0530] The user terminal notifies the user of the contents of the recommendation.

[0531] The above is a specific program processing flow of the present invention.

[0532] Example 1

[0533] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0534] Manuals for home appliances and devices are typically provided in a cumbersome and difficult-to-understand format, making it difficult for users to effectively use all of their functions. Furthermore, when users need specific usage instructions or optimal settings, they lack a way to quickly and accurately obtain that information. Furthermore, the lack of functionality to suggest optimal ways to use the product while in use reduces user convenience.

[0535] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0536] In this invention, the server includes means for collecting purchase history, means for acquiring instruction manual data for the home appliance based on the purchase history, means for analyzing the instruction manual data to extract key functions and settings, means for generating a user-friendly usage guide based on the extracted information, means for distributing the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, and means for analyzing the user's usage history and settings and recommending optimal usage methods. This allows users to easily understand all the functions of home appliances and devices and use them effectively. Furthermore, information on specific usage methods and settings can be quickly obtained and appropriate recommendations tailored to individual usage situations can be received, significantly improving user convenience.

[0537] "Purchase history collection" is the process of automatically obtaining information on user purchases of home appliances and devices from electronic transaction systems and sales management systems.

[0538] "Instruction Data Acquisition" is the process of downloading relevant instructions from manufacturer websites or other online resources based on registered product information.

[0539] "Analysis of instruction manual data" is the process of analyzing the contents of the acquired instructions using a text analysis tool and extracting key functions and settings.

[0540] "Generating a usage guide" is the process of generating a guide in a form that is easy for the user to understand based on the analyzed instruction data.

[0541] "Distribution of usage guide" is a process of sending the generated usage guide to the user terminal and notifying the user.

[0542] "Accepting user questions" is the process of allowing a user to enter specific usage or configuration questions and sending those questions to the server.

[0543] "Generating an answer to a question" is the process of analyzing a user's question using natural language processing technology and creating an appropriate answer.

[0544] "Sending an answer" is the process of sending the generated answer to the user terminal so that the user can view it.

[0545] "Recommending optimal usage methods" is a process that analyzes a user's usage history and current settings and suggests appropriate usage methods and settings based on that.

[0546] This invention relates to a system that provides easy-to-understand instructions for using home appliances and devices based on a user's purchase history. The system includes collection of purchase history, acquisition and analysis of instruction manual data, generation and distribution of usage guides, support for user interaction, and provision of recommendations. The "Smart Manual AI" system is configured as follows:

[0547] First, the server automatically collects the user's purchase history from the electronic transaction system and sales management system. Specifically, it uses the electronic transaction system's API to obtain transaction data and uses the Python PANDAS library to analyze this data. For example, when a user purchases a new refrigerator, the information is recorded in the electronic transaction system. The user's device collects this purchase information and sends it to the server.

[0548] Next, the server downloads the corresponding manuals from the manufacturer's website or other online resources based on the product information stored in the database. It uses a web scraping tool (e.g., Beautiful Soup) to retrieve the manuals. It uses the Python PyPDF2 library to analyze the contents of the retrieved manuals and extract key features and settings. The extracted results are stored in the database.

[0549] The server then generates a user guide that is easy for users to understand based on the analyzed instruction data. A generative AI model (e.g., GPT-3) is used to generate the user guide. For example, a user guide is generated by concisely summarizing step-by-step operating procedures for a refrigerator, such as "how to turn it on" and "how to set the temperature." This user guide is then distributed to the user's device and notified to the user.

[0550] Furthermore, users can enter questions about specific usage or settings into a question form and send them to the server via their device. The server receives the questions, analyzes them using natural language processing techniques (e.g., the BERT model), and generates appropriate answers. The generated answers are then sent to the user's device, where they can be viewed by the user.

[0551] Finally, the server analyzes the user's usage history and current settings to recommend optimal usage methods and settings. These recommendations are generated using machine learning algorithms, and are tailored to the user's individual usage patterns. For example, the server might notify the user of a recommendation such as, "Lowering the refrigerator temperature setting by 2 degrees will reduce energy consumption by 10%."

[0552] Below are some examples of prompts to input to a generative AI model:

[0553] 1. "Please tell me the basic operation of the newly purchased refrigerator."

[0554] 2. "I'd like to know how to use flash freezing. How do I do that?"

[0555] 3. "Please explain how to switch to power saving mode."

[0556] 4. "How do I optimize my refrigerator's temperature settings?"

[0557] In this way, by implementing this system, users can easily understand all the functions of home appliances and devices and use them effectively. By utilizing appropriate technology at each processing step, it is possible to significantly improve user convenience.

[0558] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0559] Step 1:

[0560] The server collects purchase history data from the electronic transaction system and sales management system. Specifically, it calls the API of the electronic transaction system and executes the API with the user ID and transaction ID as input. As output, it obtains detailed information such as the name, model number, purchase date, and purchase location of the purchased product.

[0561] Example: "Use the electronic transaction system API to retrieve the purchase history of user ID 12345."

[0562] Input: User ID, Transaction ID

[0563] Output: Product name, model number, purchase date, purchase location

[0564] Step 2:

[0565] The user device sends the collected purchase history information to the server. This is a process in which purchase history data is automatically sent when the user opens the application. Specifically, the device sends an HTTP request to the server via the API to transmit the purchase data.

[0566] For example: "When a user launches the app, purchase history information is sent to the server."

[0567] Input: Purchase history information (product name, model number, purchase date, purchase location)

[0568] Output: Purchase history information sent to the server

[0569] Step 3:

[0570] The server analyzes the received purchase history data, identifies the category and characteristics of each product, and stores the analysis results in a database using the Python PANDAS library.

[0571] Example: "Analyze purchase history information and register the relevant product information in a database."

[0572] Input: Purchase history information received

[0573] Output: Product information stored in the database (product category, features)

[0574] Step 4:

[0575] Based on the product information, the server retrieves relevant product manuals from manufacturer websites and other online resources. It uses a web scraping tool (e.g., Beautiful Soup) to download the product manual PDF.

[0576] Example: "Get product manual PDF from manufacturer's website"

[0577] Input: Product name, model number

[0578] Output: Downloaded product manual PDF

[0579] Step 5:

[0580] The server analyzes the PDF manual using a text analysis tool (e.g., PyPDF2) to extract key features and settings. During this process, it processes the data as needed and stores the extracted results in a database.

[0581] Example: "Analyze a PDF manual and register the main sections in a database."

[0582] Input: Instructions PDF

[0583] Output: Main section information stored in database (basic operation, temperature settings, maintenance procedures)

[0584] Step 6:

[0585] The server generates a user guide based on the analyzed instruction data. It uses a generative AI model (e.g., GPT-3) to generate a user guide in easy-to-understand text format.

[0586] Example: "Use a generative AI model to generate concise instructions for operating your refrigerator."

[0587] Input: Parsed instruction data

[0588] Output: Generated usage guide

[0589] Step 7:

[0590] The server delivers the generated usage guide to the user's device via email or push notification, and allows the user to view the guide within the app.

[0591] Example: "Send the generated usage guide to the user's device and notify them."

[0592] Input: Generated Usage Guide

[0593] Output: User guide delivered to user device

[0594] Step 8:

[0595] The user enters questions about how to use the app into the question form within the app and sends them to the server via the user's terminal.

[0596] Example: "A user enters 'How to use flash freezing' into a question form and sends it to the server."

[0597] Input: User question

[0598] Output: The question sent to the server

[0599] Step 9:

[0600] The server analyzes the received question using natural language processing technology (e.g., the BERT model) and generates an appropriate answer. The analysis understands the intent of the question, and extracts the appropriate information from the database to create the answer.

[0601] Example: "Use the BERT model to generate answers to user questions."

[0602] Input: User question

[0603] Output: The generated answer

[0604] Step 10:

[0605] The server sends the generated answer to the user's terminal, where the user can check the answer and take any necessary action.

[0606] Example: "Send the generated answer to the user's device and notify them."

[0607] Input: Generated answer

[0608] Output: Answer sent to user's terminal

[0609] Step 11:

[0610] The server analyzes the user's usage history and settings and recommends optimal usage methods and settings. It uses machine learning algorithms to analyze the data and make suggestions based on individual usage.

[0611] Example: "Use machine learning algorithms to recommend optimal settings for your refrigerator."

[0612] Input: Usage history, current settings

[0613] Output: Generated recommendations

[0614] Step 12:

[0615] The server sends the generated recommendations to the user's device and notifies them, allowing the user to view the recommendations within the app.

[0616] Example: "The generated recommendations are sent to the user's device and a notification is sent."

[0617] Input: Generated recommendations

[0618] Output: Recommendations sent to the user device

[0619] (Application example 1)

[0620] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0621] Currently, many home appliances and devices come with detailed and complex manuals, which are often difficult for average users to understand and operate properly. Furthermore, to obtain accurate information and setup instructions after purchase, users must manually search for and decipher the appropriate sections of the manual. This process is time-consuming and inefficient. Furthermore, the lack of real-time guidance and recommendations on optimal usage makes it difficult for users to maximize the product's performance.

[0622] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0623] In this invention, the server includes means for collecting purchase history, means for acquiring device instruction manual data based on the purchase history, means for analyzing the instruction manual data to extract key functions and settings, means for generating a user-friendly usage guide, means for distributing the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, means for the user terminal to provide real-time guidance on initial device setup and usage methods, and means for generating recommendation information based on the user's usage history and settings and distributing it to the user terminal. This allows users to easily understand all the functions of the home appliances and devices they have purchased and use them effectively. This also significantly improves user convenience and maximizes product performance.

[0624] "Purchase history" refers to historical information about products and services purchased by a user.

[0625] "Equipment" means electronic and electrical appliances for home or business use.

[0626] "Instruction data" is document data that describes information such as how to handle, install, and use the device.

[0627] "Analysis" is the process of breaking down and organizing complex data or documents into an understandable form.

[0628] "Major functions" refer to the main operations and actions provided by the device.

[0629] "Settings" refers to operations for adjusting the operating conditions and operating modes of a device.

[0630] "Usage Guide" refers to a guide that helps users correctly understand and effectively use the equipment.

[0631] "User terminal" refers to an electronic device or equipment used by a user, and specifically includes a smartphone, computer, etc.

[0632] A "question" is an inquiry submitted by a user to ask questions or clarify how to use or function the device.

[0633] An "answer" is information or explanation provided in response to a user's question.

[0634] "Real-time guidance" refers to instructions and guidance provided instantly on the spot regarding the initial setup and use of equipment.

[0635] "Recommendation information" is information about optimal usage methods and settings that are suggested based on the user's usage status and settings.

[0636] A "server" refers to a computer system that stores and processes data, and is a device that works in conjunction with user terminals via a network.

[0637] This invention is a system that allows users to easily understand and effectively use all the functions of the devices they have purchased. The system includes multiple means for collecting purchase history, analyzing and organizing device instruction manual data, and providing easy-to-understand guidance to users.

[0638] The server has the following means:

[0639] How to collect purchase history: Information on devices purchased by users is collected from e-commerce platforms and sales management systems. The user device sends data to the server every time the purchase history is updated.

[0640] Method for obtaining instruction manual data: Based on the registered device information, obtain the corresponding instruction manual from the manufacturer's website or other online resources. Analyze the obtained data and extract key functions and settings.

[0641] A means for generating a user guide: A user guide that is easy for the user to understand is generated based on the analyzed instruction data. This guide is delivered to the user's device and notified to the user.

[0642] The user terminal has the following means:

[0643] A method for accepting questions: The user inputs a question about how to use the device. The question is sent to the server.

[0644] How to generate answers to questions: The server analyzes the question and generates a corresponding answer using the generative AI model. The answer is then sent to the user's device.

[0645] Means for providing real-time guidance: The user device provides real-time guidance on how to set up and use the device, for example, by showing how to set the refrigerator temperature on a smartphone screen or through an AR display on smart glasses.

[0646] Means for delivering recommendation information: The server generates recommendation information for optimal usage methods and settings based on the user's usage history and settings, and delivers it to the user's device.

[0647] As a specific example, consider a case where a user who has purchased a new refrigerator uses this system to use the refrigerator's quick-freeze function. Once the purchase information is registered in the system, the server automatically downloads the product manual from online and analyzes its contents. When the user asks, "How do I use the quick-freeze function on my refrigerator?", the server analyzes the question, extracts the relevant operating procedures, and generates an answer. Then, with real-time guidance, the user can operate the refrigerator while checking the exact procedures on their smartphone screen or smart glasses.

[0648] An example of a prompt might be:

[0649] "Please check your purchase history. Product ID: 'new_refrigerator_123'"

[0650] "Generate a response to the following relevant user question: 'How do I use the deep freeze in my refrigerator?'"

[0651] To implement this system, the server is built using Python, Django, Flask, etc., and the client application is built using React Native or Unity. PDF analysis libraries such as PyMuPDF and pdfminer are used for data analysis, and SpaCy is used for natural language processing. This configuration allows users to quickly and easily understand the functions of the device and use it in the most optimal way.

[0652] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0653] Step 1:

[0654] The server collects the user's purchase history from the e-commerce platform or sales management system. When the user purchases a new product, the user terminal updates the purchase information in the database and sends the data to the server. The server analyzes the received purchase history data and stores it in the database. The input in this step is the purchase history data, and the output is the analyzed purchase information data.

[0655] Step 2:

[0656] The server retrieves device instruction data from the manufacturer's website or other online resources based on the purchase history data. The server crawls specific URLs and downloads the relevant instruction data. It then analyzes the retrieved instruction data (e.g., in PDF format) and extracts key function and setting information. The input is the purchase history data and instruction data from the online resource, and the output is the analyzed function and setting information.

[0657] Step 3:

[0658] The server generates a user guide that is easy for users to understand based on the analyzed instruction data. Specifically, it creates a text-format guide that summarizes the main functions and setting methods in short, concise steps. The input is the analyzed function and setting information, and the output is the generated user guide. This user guide is distributed from the server to the user's device.

[0659] Step 4:

[0660] The user inputs a question about how to use the device from the user terminal. The user terminal sends the question to the server. The input in this step is the user's question, and the output is the question data sent to the server.

[0661] Step 5:

[0662] The server receives questions from users and analyzes them using natural language processing technology (e.g., SpaCy). Based on the analyzed questions, it generates appropriate answers using a generative AI model. Specifically, the server extracts the relevant operating procedures from a database and generates answer sentences using the generative AI model. The input is the received question data, and the output is the generated answer sentences.

[0663] Step 6:

[0664] The server sends the generated answer to the user terminal, which then displays the answer to the user. The input is the generated answer, and the output is the answer data sent to the user terminal.

[0665] Step 7:

[0666] The user device provides real-time guidance. For example, instructions on how to set the refrigerator temperature can be displayed on a smartphone screen or an AR display on smart glasses. Specifically, the user device displays the usage guide and setting procedures obtained from the server on its interface. The input is the obtained usage guide data, and the output is the real-time guidance provided to the user.

[0667] Step 8:

[0668] The server generates recommendation information for optimal usage methods and settings based on the user's usage history and setting information. The generated recommendation information is distributed to the user's device. The input is the user's usage history and setting information, and the output is the generated recommendation information.

[0669] Step 9:

[0670] The user terminal receives the recommendation information delivered from the server and notifies the user. The user can adjust the product settings and usage methods based on the notification. The input is the recommendation information, and the output is the recommendation information notified to the user.

[0671] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0672] This invention is a system that uses a user's purchase history to analyze the manuals for home appliances and devices, and provides users with easy-to-understand instructions on how to use them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond according to the user's emotions. The "Smart Manual AI" system functions through the following process.

[0673] Collection and analysis of purchase history

[0674] The server collects the user's purchase history from the e-commerce site and the sales management system.

[0675] The user terminal transmits data to the server every time the purchase history is updated.

[0676] The server analyzes the received data and stores information about each product in a database.

[0677] Examples:

[0678] When a user purchases a new television, the information is registered on an e-commerce site. The user's device collects the purchase information and sends it to a server. The server analyzes the purchase information and registers it in a database under the category "television."

[0679] Instruction data acquisition and analysis

[0680] The server retrieves corresponding product manuals from manufacturers' websites and other online resources based on the registered product information, analyzes the retrieved manual data, and extracts key features and settings.

[0681] Examples:

[0682] The server downloads the instruction manual data for the new TV from the manufacturer's website, analyzes the downloaded PDF, and extracts key sections such as "Basic Operations," "Channel Settings," and "How to Use the Remote Control."

[0683] Generate and distribute usage guides

[0684] The server generates a user-friendly usage guide based on the analyzed instruction data, and distributes the generated usage guide to the user's device.

[0685] Examples:

[0686] The server summarizes the TV operation procedure step by step in a concise format such as "How to turn on the power," "How to set the channel," "How to adjust the volume," etc. The generated guide is sent to the user's device, and the user receives a notification.

[0687] User interaction support

[0688] The user inputs questions about usage from the user terminal.

[0689] The user terminal sends a query to the server.

[0690] The server receives the question, analyzes it using natural language processing technology, and generates a corresponding answer, which is then sent to the user's device.

[0691] Examples:

[0692] The user asks "How do I enable the voice guide on my TV?" on their device. The server analyzes the question, extracts the relevant operation procedure from the "Enable voice guide" section, generates an answer, and sends it to the user's device. The user then operates the TV according to the answer.

[0693] Providing recommendations

[0694] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage and settings. The recommendations are then delivered to the user's device.

[0695] Examples:

[0696] The server analyzes the user's TV usage history and makes recommendations such as "recommended picture quality settings" and "how to use energy saving mode." These recommendations are sent to the user's device, and the user is notified.

[0697] Incorporating an emotion engine

[0698] The emotion engine analyzes voice data and facial expression data from the user's device to recognize the user's emotions.

[0699] The server changes the usage guide and answer expressions for the user based on the emotion recognition results.

[0700] Examples:

[0701] When a user enters a question, if the emotion engine detects stress from the user's tone of voice or facial expression, the server will change the answer to something more polite and reassuring, such as "Don't worry. Try these steps and you'll be able to resolve the issue."

[0702] Emotional recommendations

[0703] The server adjusts the content of the recommendations based on the emotional state of the user as recognized by the emotion engine.

[0704] Examples:

[0705] If the user frequently shows signs of anxiety, the server will recommend resources such as an "initial setup guide" or "frequently asked questions" to increase the user's sense of security.

[0706] This invention allows users to easily understand all the functions of the home appliances and devices they have purchased, and receives support tailored to their emotions, allowing them to use the products more comfortably. This significantly improves user convenience and maximizes product performance.

[0707] The processing flow will be explained below.

[0708] Step 1: Collect your purchase history

[0709] The user terminal acquires the user's purchase history from an e-commerce site or a sales management system.

[0710] The user terminal transmits the acquired purchase history to the server.

[0711] Step 2: Analyze your purchase history

[0712] The server analyzes the received purchase history data and extracts detailed information about each product (e.g., product name, category, purchase date, etc.).

[0713] The server stores the extracted information in a database.

[0714] Step 3: Obtaining instruction data

[0715] The server retrieves instruction manual data for the corresponding home appliance or device based on the purchase history information in the database.

[0716] The server downloads instruction data from manufacturer websites and online resources.

[0717] Step 4: Analyzing the instruction data

[0718] The server parses the retrieved instruction data and converts it into a text format (e.g., using PDF parsing or OCR technology).

[0719] The server extracts key features and configuration information from the text data.

[0720] Step 5: Generate a usage guide

[0721] The server generates a usage guide in a format that is easy for the user to understand based on the analyzed instruction data.

[0722] The server stores the generated usage guide in a database.

[0723] Step 6: Distributing the usage guide

[0724] The server distributes the saved usage guide to the user terminal.

[0725] The user terminal displays an access notification of the usage guide to the user.

[0726] Step 7: Ask a question

[0727] The user inputs questions about usage from the user terminal.

[0728] The user terminal sends the question to the server.

[0729] Step 8: Parsing the Question

[0730] The server analyzes the question received from the user using natural language processing technology.

[0731] The server searches the database for instruction data and related information that corresponds to the question.

[0732] Step 9: Generate an answer

[0733] The server automatically generates an appropriate answer based on the analysis results.

[0734] The server formats the generated answer for the user.

[0735] Step 10: Publish your response

[0736] The server generates a response and sends it to the user terminal.

[0737] The user terminal displays the answer to the user.

[0738] Step 11: Serve recommendations

[0739] The server analyzes the user's usage history and current settings.

[0740] The server generates recommendations for optimal usage methods and settings based on the user's usage.

[0741] Step 12: Serving Recommendations

[0742] The server distributes the generated recommendations to the user terminal.

[0743] The user terminal notifies the user of the contents of the recommendation.

[0744] Step 13: Collect emotion data

[0745] The user terminal collects the user's voice data and facial expression data.

[0746] The user terminal transmits the collected emotion data to the server.

[0747] Step 14: Analyze the emotion data

[0748] The emotion data received by the server is analyzed using an emotion engine.

[0749] The server stores the emotion analysis results in a database.

[0750] Step 15: Emotion-based guidance and response adjustments

[0751] The server adjusts the expressions of the usage guide and answers provided to the user based on the analyzed emotion data.

[0752] The server delivers emotion-based responses to the user terminal.

[0753] Step 16: Emotional Recommendations

[0754] The server adjusts the content of the recommendations based on the user's emotional state recognized by the emotion engine.

[0755] The server delivers recommendations based on emotions to the user's device.

[0756] The above is a specific program processing flow of the present invention.

[0757] Example 2

[0758] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0759] Today, home appliances are becoming increasingly multifunctional, making it difficult for users to effectively utilize all the features of the products they purchase. In particular, product manuals can be overly detailed or contain a wealth of technical jargon, making it time-consuming to understand how to use them. Furthermore, uniform support that doesn't take users' feelings into consideration is a factor that reduces satisfaction. Furthermore, there is no system in place to guide users through post-purchase setup or optimal usage, leaving users struggling to resolve issues on their own. There is a need to solve these problems and enable users to make the most of their home appliances.

[0760] The identification process by the identification 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 collecting purchase history, means for acquiring product instruction data based on the purchase history, means for analyzing the instruction data to extract key functions and settings, means for generating a usage guide that is easy for the user to understand based on the extracted information, means for distributing the usage guide to a user terminal, means for accepting questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, an emotion engine for analyzing the user's voice data and facial expression data to recognize emotions, means for changing the expression of the usage guide and answers based on the results of the emotion engine, and means for generating recommendations based on the recognized emotional state. This allows the user to quickly and effectively understand how to use the home appliance they purchased and further receive support and recommendations according to their emotions.

[0761] "Purchase history" is information about products or merchandise purchased by a user in the past.

[0762] "Instruction data" refers to document data that describes how to use a product or item, how to set it up, precautions, etc.

[0763] "Analysis" is the act of examining data and information in detail to clarify meaning and relationships.

[0764] "Major Function" refers to the basic and important operation or role of a product or merchandise.

[0765] "Configuration" is the process of adjusting a product or system to determine its operating conditions and operation methods.

[0766] A "usage guide" is a guide that helps users to understand and use a product or system easily and conveniently.

[0767] A "user terminal" is a device such as a computer or smartphone that can be directly operated by a user.

[0768] "Question acceptance" is the process of receiving inquiries from users.

[0769] "Answer generation" is the task of creating an appropriate answer to a question posed by a user.

[0770] An "emotion engine" is a system that has the function of recognizing emotions by analyzing the user's voice data and facial expression data.

[0771] "Recommendations" suggest optimal actions and settings based on a user's behavioral history and emotional state.

[0772] The present invention is a system that includes "means for collecting purchase history," "means for acquiring product instruction data," "means for analyzing the instruction data and extracting key functions and settings," "means for generating a user-friendly usage guide based on the extracted information," "means for delivering the usage guide to a user terminal," "means for accepting questions from a user and generating answers to those questions," "means for sending the generated answers to the user terminal," "an emotion engine that recognizes emotions by analyzing the user's voice data and facial expression data," "means for changing the expression of the usage guide and answers based on the results of the emotion engine," and "means for generating recommendations based on the recognized emotional state."

[0773] The entire system is mainly composed of a server, a user terminal, and an emotion engine. These elements work together to enable users to quickly and effectively understand how to use the products they have purchased, and also to receive emotional support and recommendations.

[0774] Collection and analysis of purchase history

[0775] The server periodically runs a script that retrieves data from e-commerce sites and sales management systems. It uses an API to retrieve the latest purchase history and stores it in a database. For example, the server sends an API request to an e-commerce site such as Amazon or Rakuten, parses the purchase history data received in response in JSON format, and adds it to the database.

[0776] Instruction data acquisition and analysis

[0777] Based on the product information registered in the database, the server retrieves the corresponding manual from the manufacturer's website or other online resources. Specifically, the server searches for the product name using the Google Custom Search API and obtains the manual's URL. Next, the server downloads the PDF file from the obtained URL and saves it to the file system. The server then uses a PDF parsing library (e.g., Apache PDFBox) to extract text data from the downloaded manual and uses a natural language processing library (e.g., NLTK) to classify it into major sections such as "Basic Operations" and "How to Use the Remote Control."

[0778] Generate and distribute usage guides

[0779] The server automatically generates an easy-to-understand user guide based on the analyzed instruction data. The server uses a template engine (e.g., Jinja2) to document clear instructions. For example, it generates guide sentences that include specific usage procedures, such as "how to turn on the power" and "how to set the channel." The generated user guide is then distributed to the user's device. The user's device displays the guide to the user via a dedicated app or web interface and sends notifications.

[0780] User interaction support

[0781] Users can input questions about how to operate the device from their device. The user device sends the questions to the server, which analyzes the questions and generates appropriate answers. The server uses a natural language processing engine (e.g., BERT) to analyze the questions, search for relevant answers from a database, and format them for the user. The generated answers are sent to the user device and displayed on the device.

[0782] Providing recommendations

[0783] The server analyzes the user's usage history and current settings and generates recommendations for optimal usage and settings. For example, the server uses a machine learning model (e.g., XGBoost) to analyze user behavior data and generate recommendations such as "recommended image quality settings" and "how to utilize energy saving mode." These recommendations are delivered to the user's device and notified to the user.

[0784] Incorporating an emotion engine

[0785] The emotion engine analyzes the voice data and facial expression data acquired from the user's device to recognize the user's emotions. The emotion engine analyzes the voice data using an emotion analysis API (e.g., Google API) and evaluates the facial expression data using a face recognition API (e.g., Microsoft Azure Face API). Based on the recognized emotional state, the server changes the expression of the usage guide and answers. For example, if the user is feeling stressed, the tone of the answers and guide will be changed to be more polite and reassuring.

[0786] Emotional recommendations

[0787] The server adjusts the recommendations based on the results of the emotion engine. If the user feels unsure, it will recommend resources that will increase their sense of security, such as an "initial setup guide" or "frequently asked questions." This allows the user to use all the product's features without stress.

[0788] Examples of prompt statements

[0789] The following format is used as an example of a prompt statement for generating a program for this system:

[0790] Design a system that uses the user's purchase history to analyze product and equipment manuals and provide instructions for use in a way that is easy for the user to understand. In addition, add support functions that combine an emotion engine that recognizes the user's emotions.

[0791] With the above configuration, the present invention enables users to quickly and effectively understand how to use a purchased product and receive support and recommendations that correspond to their emotions.

[0792] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0793] Step 1:

[0794] Collection of purchase history

[0795] The server periodically obtains data from e-commerce sites and sales management systems. Specifically, the server uses an API to send a request to the e-commerce site (e.g., Amazon, Rakuten) and receives the latest purchase history data. The received data is in JSON format, and the server analyzes this data and stores it in a database. The input is the purchase history data obtained from the e-commerce site, and the output is a database that stores the analyzed purchase history.

[0796] Step 2:

[0797] Send purchase history

[0798] When a user purchases a product and their purchase history is updated, the user device sends that information to the server. When a user updates their purchase history within the app, the user device sends that information to the server using an API. The input is the purchase history updated by the user, and the output is the purchase history data sent to the server.

[0799] Step 3:

[0800] Obtaining instruction data

[0801] The server retrieves product information from a database based on purchase history and collects corresponding manuals from manufacturer websites and other online resources. The server uses the Google Custom Search API to search for the product name and obtain the manual URL. It then downloads a PDF file from the URL. The input is the product name, and the output is the downloaded PDF manual.

[0802] Step 4:

[0803] Analysis of instruction data

[0804] The server analyzes the downloaded PDF manual and extracts key functions and settings. It uses a PDF analysis library (e.g., Apache PDFBox) to extract text data and uses a natural language processing library (e.g., NLTK) to classify it into sections such as "Basic Operations" and "How to Use the Remote Control." The input is the PDF manual data, and the output is the analyzed and classified manual data.

[0805] Step 5:

[0806] Creating a usage guide

[0807] The server generates an easy-to-understand user guide based on the parsed instruction data. It uses a template engine (e.g., Jinja2) to document clear instructions for each step. For example, it summarizes procedures such as "how to turn on the power" and "how to set the channel." The input is the parsed instruction data, and the output is the generated instruction guide.

[0808] Step 6:

[0809] Distribution of user guides

[0810] The server delivers the generated usage guide to the user terminal. The user terminal displays the received guide and sends a notification to the user. The input is the generated usage guide, and the output is the guide displayed on the user terminal.

[0811] Step 7:

[0812] Accepting questions from users

[0813] The user inputs a question about how to use the system from the terminal. The user terminal sends the question to the server. The input is the question from the user, and the output is the question data sent to the server.

[0814] Step 8:

[0815] Parsing questions and generating answers

[0816] The server analyzes the received question using natural language processing technology and generates an appropriate answer. It uses a natural language processing engine (e.g., BERT) to analyze the question, extract keywords, and search a database for relevant information. The input is the question data from the user, and the output is the generated answer.

[0817] Step 9:

[0818] Response distribution

[0819] The server sends the generated answer to the user terminal, which displays the answer. The input is the generated answer, and the output is the answer displayed on the user terminal.

[0820] Step 10:

[0821] Recommendation generation and delivery

[0822] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage methods and settings. Analysis is performed using a machine learning model (e.g., XGBoost) to generate recommendations. The generated recommendations are delivered to the user's device and notified to the user. The input is the user's usage history data, and the output is the generated recommendations.

[0823] Step 11:

[0824] Acquiring and analyzing emotion data

[0825] The emotion engine acquires voice and facial expression data from the user's device and analyzes them to recognize the user's emotions. It analyzes voice data using an emotion analysis API (e.g., Google API) and evaluates facial expressions using a face recognition API (e.g., Microsoft Azure Face API). The input is the user's voice and facial expression data, and the output is recognized emotion data.

[0826] Step 12:

[0827] Emotion-based responses

[0828] The server changes the expressions of the usage guide and answers given to the user based on the results of the emotion engine. If the user is feeling stressed, it will use expressions such as "Don't worry" or change the tone of the guide. The input is the recognized emotion data, and the output is the guide and answers corresponding to the emotion.

[0829] Step 13:

[0830] Providing emotional recommendations

[0831] The server adjusts the content of recommendations based on the emotional state recognized by the emotion engine. If the user is feeling anxious, it will recommend resources that will make them feel at ease (e.g., "Initial Setup Guide" or "FAQ"). The input is the recognized emotional data, and the output is a recommendation based on the emotion.

[0832] (Application example 2)

[0833] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0834] As autonomous vehicles become more multifunctional and technologically advanced, it is becoming increasingly difficult for users to understand and operate all of their functions. Even when users read the vehicle's manual, it is difficult for them to quickly find the information they need, making efficient use of the vehicle particularly challenging in stressful situations. Furthermore, as vehicle technology evolves, there is a need for a method to provide appropriate support tailored to the emotional state of each individual user. Solutions to these challenges are needed.

[0835] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting purchase history, means for acquiring function description data for the autonomously driving vehicle based on the purchase history, means for analyzing the description data and extracting key functions and settings, means for generating a usage guide that is easy for the user to understand based on the extracted information, means for delivering the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, means for analyzing the user's emotional state, and means for adjusting the guide and answer expression corresponding to the emotional state. This allows the user to use the functions of the autonomously driving vehicle quickly and easily and receive appropriate support according to their emotional state.

[0836] "Collecting purchase history" is the process of recording and compiling information about products and services purchased by users.

[0837] "Function description data" is a digital document that provides detailed explanations of the various functions and settings of an autonomous vehicle.

[0838] "Analysis" is the process of examining data in detail and understanding its contents.

[0839] "Major functions and settings" are important functions and settings that the user frequently uses or should be aware of when using the vehicle.

[0840] A "user guide" is a user guide that clearly summarizes how to operate a product or system.

[0841] A "user terminal" is a device used by a user, such as a smartphone, tablet, or in-vehicle display.

[0842] "Accepting questions" means receiving inquiries or questions from users and gathering information to respond to them.

[0843] "Generating an answer" means creating appropriate information or solutions for a question received from a user.

[0844] "Analyzing emotional state" is the process of identifying the user's current emotional and psychological state based on their voice and facial expression data.

[0845] "Adjusting the expression of the guide or answer" means changing the expression method or content of the information provided depending on the emotional state of the user.

[0846] The program of the system for realizing the present invention is configured as follows.

[0847] The server collects user purchase history and retrieves the data from e-commerce sites or sales management systems. The server then downloads the autonomous vehicle's function description data from the manufacturer's website or other online resources, and uses a PDF analysis tool (e.g., PyMuPDF) to analyze it. It extracts key functions and settings from the analyzed data and generates a user guide that is easy to understand for the user.

[0848] The generated usage guide is distributed from the cloud server to the user's device (smartphone, tablet, in-car display, etc.). The server accepts questions from the user's device, generates answers to those questions using a natural language processing tool (e.g., spaCy), and sends them to the user's device.

[0849] Furthermore, the system uses emotion analysis tools (e.g., Microsoft Azure Cognitive Services' Emotion Analysis API) to analyze the user's voice and facial expression data. Based on the analysis results, the system adjusts the guidance and response expressions to match the user's emotional state and provides them to the user. This allows users to use the autonomous vehicle quickly and easily, and receive appropriate support according to their emotional state.

[0850] As a concrete example, if a user purchases a new self-driving vehicle and wants to know about the in-car functions and how to set them up, the system operates as follows.

[0851] 1. Users gather information about new autonomous vehicles from their purchase history.

[0852] 2. The server analyzes the information and downloads the function description from the manufacturer's website.

[0853] 3. Analyze the downloaded manual, extract key functions and settings such as "how to start the vehicle" and "how to use the parking assist function," and generate a usage guide.

[0854] 4. The generated usage guide is delivered to the user's device so that the user can check it.

[0855] 5. If a user asks, "I don't know how to set the autonomous driving mode," the server uses natural language processing to generate an answer and sends it to the user's device.

[0856] Additionally, if the sentiment analysis tool determines that the user is feeling stressed, it will provide softer guidance and answers, such as "Take a slow, deep breath and try these steps."

[0857] Example prompt sentence:

[0858] "Please provide a step-by-step guide for setting up the self-parking feature for a self-driving car."

[0859] This will help users better understand the functions of the autonomous vehicle and allow them to receive appropriate support according to their emotional state.

[0860] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0861] Step 1:

[0862] The server collects the user's purchase history. The input is data from an e-commerce site or a sales management system, and the output is purchase history data. Based on this data, vehicle purchase information is identified.

[0863] Step 2:

[0864] The server obtains the function description data of the autonomous vehicle based on the purchase history. The input is the purchase history data, and the output is the URL or file of the function description. The server downloads the corresponding description from the manufacturer's website or other online resources.

[0865] Step 3:

[0866] The server parses the retrieved instruction data. The input is the feature instruction data, and the output is information about the main features and settings. Using a PDF parsing tool (e.g., PyMuPDF), it extracts sections from the instruction manual, such as "How to start the vehicle" and "How to use the parking assist function."

[0867] Step 4:

[0868] The server generates a user-friendly usage guide based on the extracted information. The input is the main features and settings information, and the output is the usage guide. A generative AI model is used to generate the user-friendly guide in text or multimedia format.

[0869] Step 5:

[0870] The server distributes the generated usage guide to the user's device. The input is the usage guide, and the output is a distribution notification to the user's device. The guide is sent via the cloud server to a smartphone, tablet, or in-car display.

[0871] Step 6:

[0872] The user inputs a question via a terminal. The input is the user's question text, and the output is the question sent to the server, which then transmits the user's question to the system.

[0873] Step 7:

[0874] The server generates answers to user questions. The input is the user's question text, and the output is the answer text. Natural language processing tools (e.g., spaCy) are used to analyze the question and generate appropriate answers.

[0875] Step 8:

[0876] The server sends the generated answer to the user terminal. The input is the generated answer text, and the output is a reply to the user terminal. This allows the user to obtain the necessary information.

[0877] Step 9:

[0878] The server uses an emotion analysis tool to analyze the user's emotional state. The input is the user's voice and facial expression data, and the output is the result of identifying the user's emotional state. This tool utilizes the emotion analysis API of Microsoft Azure Cognitive Services.

[0879] Step 10:

[0880] The server adjusts the expression of the guide or answer according to the user's emotional state. The input is the result of identifying the user's emotional state and the generated guide or answer, and the output is the guide or answer adjusted to an expression appropriate to the emotion. This reduces the user's psychological burden and provides stress-free support.

[0881] This allows users to use the functions of an autonomous vehicle quickly and easily, and to receive appropriate support according to their emotional state.

[0882] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0883] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0884] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0885] [Third embodiment]

[0886] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0887] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0888] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0889] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0890] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0891] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0892] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0893] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0894] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0895] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0896] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0897] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0898] This invention is a system that uses a user's purchase history to analyze the manuals for home appliances and devices, and provides users with easy-to-understand instructions for use. The "Smart Manual AI" system works through the following process.

[0899] Collection and analysis of purchase history

[0900] The server collects the user's purchase history from the e-commerce site and the sales management system.

[0901] The user terminal transmits data to the server every time the purchase history is updated.

[0902] The server analyzes the received data and stores information about each product in a database.

[0903] Examples:

[0904] When a user purchases a new refrigerator, the information is registered on the e-commerce site. The user's device collects the purchase information and sends it to the server. The server analyzes the purchase information and registers it in the database under the category "refrigerator."

[0905] Instruction data acquisition and analysis

[0906] The server retrieves corresponding product manuals from manufacturers' websites and other online resources based on the registered product information, analyzes the retrieved manual data, and extracts key features and settings.

[0907] Examples:

[0908] The server downloads the instruction manual for a new refrigerator from the manufacturer's website, analyzes the downloaded PDF, and extracts key sections such as "Basic Operation," "Temperature Setting," and "Maintenance Procedures."

[0909] Generate and distribute usage guides

[0910] The server generates a user-friendly usage guide based on the analyzed instruction data, and distributes the generated usage guide to the user's device.

[0911] Examples:

[0912] The server summarizes the refrigerator's operation procedures step by step in the form of "how to turn it on," "how to set the temperature," "how to switch to energy-saving mode," etc. The generated guide is sent to the user's device, and the user receives a notification.

[0913] User interaction support

[0914] The user inputs questions about usage from the user terminal.

[0915] The user terminal sends a query to the server.

[0916] The server receives the question, analyzes it using natural language processing technology, and generates a corresponding answer, which is then sent to the user's device.

[0917] Examples:

[0918] The user asks a question on their device: "How do I use the quick freezing feature in my refrigerator?" The server analyzes the question, extracts the relevant operating procedure from the section titled "How to use quick freezing," generates an answer, and sends it to the user's device. The user operates the refrigerator according to the answer.

[0919] Providing recommendations

[0920] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage and settings. The recommendations are then delivered to the user's device.

[0921] Examples:

[0922] The server analyzes the user's refrigerator usage history and makes recommendations such as "temperature settings to extend food storage life" and "how to use energy-saving mode." These recommendations are sent to the user's device, and the user is notified.

[0923] This invention allows users to easily understand and effectively use all the functions of the home appliances and devices they purchase, greatly improving user convenience and maximizing product performance.

[0924] The processing flow will be explained below.

[0925] Step 1: Collect your purchase history

[0926] The user terminal acquires the user's purchase history from an e-commerce site or a sales management system.

[0927] The user terminal transmits the acquired purchase history to the server.

[0928] Step 2: Analyze your purchase history

[0929] The server analyzes the received purchase history data and extracts detailed information about each product (e.g., product name, category, purchase date, etc.).

[0930] The server stores the extracted information in a database.

[0931] Step 3: Obtaining instruction data

[0932] The server retrieves instruction manual data for the corresponding home appliance or device based on the purchase history information in the database.

[0933] The server downloads instruction data from manufacturer websites and online resources.

[0934] Step 4: Analyzing the instruction data

[0935] The server parses the retrieved instruction data and converts it into a text format (e.g., using PDF parsing or OCR technology).

[0936] The server extracts key features and configuration information from the text data.

[0937] Step 5: Generate a usage guide

[0938] The server generates a usage guide in a format that is easy for the user to understand based on the analyzed instruction data.

[0939] The server stores the generated usage guide in a database.

[0940] Step 6: Distributing the usage guide

[0941] The server distributes the saved usage guide to the user terminal.

[0942] The user terminal displays an access notification of the usage guide to the user.

[0943] Step 7: Ask a question

[0944] The user inputs questions about usage from the user terminal.

[0945] The user terminal sends the question to the server.

[0946] Step 8: Parsing the Question

[0947] The server analyzes the question received from the user using natural language processing technology.

[0948] The server searches the database for instruction data and related information that corresponds to the question.

[0949] Step 9: Generate an answer

[0950] The server automatically generates an appropriate answer based on the analysis results.

[0951] The server formats the generated answer for the user.

[0952] Step 10: Publish your response

[0953] The server generates a response and sends it to the user terminal.

[0954] The user terminal displays the answer to the user.

[0955] Step 11: Serve recommendations

[0956] The server analyzes the user's usage history and current settings.

[0957] The server generates recommendations for optimal usage methods and settings based on the user's usage.

[0958] Step 12: Serving Recommendations

[0959] The server distributes the generated recommendations to the user terminal.

[0960] The user terminal notifies the user of the contents of the recommendation.

[0961] The above is a specific program processing flow of the present invention.

[0962] Example 1

[0963] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0964] Manuals for home appliances and devices are typically provided in a cumbersome and difficult-to-understand format, making it difficult for users to effectively use all of their functions. Furthermore, when users need specific usage instructions or optimal settings, they lack a way to quickly and accurately obtain that information. Furthermore, the lack of functionality to suggest optimal ways to use the product while in use reduces user convenience.

[0965] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0966] In this invention, the server includes means for collecting purchase history, means for acquiring instruction manual data for the home appliance based on the purchase history, means for analyzing the instruction manual data to extract key functions and settings, means for generating a user-friendly usage guide based on the extracted information, means for distributing the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, and means for analyzing the user's usage history and settings and recommending optimal usage methods. This allows users to easily understand all the functions of home appliances and devices and use them effectively. Furthermore, information on specific usage methods and settings can be quickly obtained and appropriate recommendations tailored to individual usage situations can be received, significantly improving user convenience.

[0967] "Purchase history collection" is the process of automatically obtaining information on user purchases of home appliances and devices from electronic transaction systems and sales management systems.

[0968] "Instruction Data Acquisition" is the process of downloading relevant instructions from manufacturer websites or other online resources based on registered product information.

[0969] "Analysis of instruction manual data" is the process of analyzing the contents of the acquired instructions using a text analysis tool and extracting key functions and settings.

[0970] "Generating a usage guide" is the process of generating a guide in a form that is easy for the user to understand based on the analyzed instruction data.

[0971] "Distribution of usage guide" is a process of sending the generated usage guide to the user terminal and notifying the user.

[0972] "Accepting user questions" is the process of allowing a user to enter specific usage or configuration questions and sending those questions to the server.

[0973] "Generating an answer to a question" is the process of analyzing a user's question using natural language processing technology and creating an appropriate answer.

[0974] "Sending an answer" is the process of sending the generated answer to the user terminal so that the user can view it.

[0975] "Recommending optimal usage methods" is a process that analyzes a user's usage history and current settings and suggests appropriate usage methods and settings based on that.

[0976] This invention relates to a system that provides easy-to-understand instructions for using home appliances and devices based on a user's purchase history. The system includes collection of purchase history, acquisition and analysis of instruction manual data, generation and distribution of usage guides, support for user interaction, and provision of recommendations. The "Smart Manual AI" system is configured as follows:

[0977] First, the server automatically collects the user's purchase history from the electronic transaction system and sales management system. Specifically, it uses the electronic transaction system's API to obtain transaction data and uses the Python PANDAS library to analyze this data. For example, when a user purchases a new refrigerator, the information is recorded in the electronic transaction system. The user's device collects this purchase information and sends it to the server.

[0978] Next, the server downloads the corresponding manuals from the manufacturer's website or other online resources based on the product information stored in the database. It uses a web scraping tool (e.g., Beautiful Soup) to retrieve the manuals. It uses the Python PyPDF2 library to analyze the contents of the retrieved manuals and extract key features and settings. The extracted results are stored in the database.

[0979] The server then generates a user guide that is easy for users to understand based on the analyzed instruction data. A generative AI model (e.g., GPT-3) is used to generate the user guide. For example, a user guide is generated by concisely summarizing step-by-step operating procedures for a refrigerator, such as "how to turn it on" and "how to set the temperature." This user guide is then distributed to the user's device and notified to the user.

[0980] Furthermore, users can enter questions about specific usage or settings into a question form and send them to the server via their device. The server receives the questions, analyzes them using natural language processing techniques (e.g., the BERT model), and generates appropriate answers. The generated answers are then sent to the user's device, where they can be viewed by the user.

[0981] Finally, the server analyzes the user's usage history and current settings to recommend optimal usage methods and settings. These recommendations are generated using machine learning algorithms, and are tailored to the user's individual usage patterns. For example, the server might notify the user of a recommendation such as, "Lowering the refrigerator temperature setting by 2 degrees will reduce energy consumption by 10%."

[0982] Below are some examples of prompts to input to a generative AI model:

[0983] 1. "Please tell me the basic operation of the newly purchased refrigerator."

[0984] 2. "I'd like to know how to use flash freezing. How do I do that?"

[0985] 3. "Please explain how to switch to power saving mode."

[0986] 4. "How do I optimize my refrigerator's temperature settings?"

[0987] In this way, by implementing this system, users can easily understand all the functions of home appliances and devices and use them effectively. By utilizing appropriate technology at each processing step, it is possible to significantly improve user convenience.

[0988] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0989] Step 1:

[0990] The server collects purchase history data from the electronic transaction system and sales management system. Specifically, it calls the API of the electronic transaction system and executes the API with the user ID and transaction ID as input. As output, it obtains detailed information such as the name, model number, purchase date, and purchase location of the purchased product.

[0991] Example: "Use the electronic transaction system API to retrieve the purchase history of user ID 12345."

[0992] Input: User ID, Transaction ID

[0993] Output: Product name, model number, purchase date, purchase location

[0994] Step 2:

[0995] The user device sends the collected purchase history information to the server. This is a process in which purchase history data is automatically sent when the user opens the application. Specifically, the device sends an HTTP request to the server via the API to transmit the purchase data.

[0996] For example: "When a user launches the app, purchase history information is sent to the server."

[0997] Input: Purchase history information (product name, model number, purchase date, purchase location)

[0998] Output: Purchase history information sent to the server

[0999] Step 3:

[1000] The server analyzes the received purchase history data, identifies the category and characteristics of each product, and stores the analysis results in a database using the Python PANDAS library.

[1001] Example: "Analyze purchase history information and register the relevant product information in a database."

[1002] Input: Purchase history information received

[1003] Output: Product information stored in the database (product category, features)

[1004] Step 4:

[1005] Based on the product information, the server retrieves relevant product manuals from manufacturer websites and other online resources. It uses a web scraping tool (e.g., Beautiful Soup) to download the product manual PDF.

[1006] Example: "Get product manual PDF from manufacturer's website"

[1007] Input: Product name, model number

[1008] Output: Downloaded product manual PDF

[1009] Step 5:

[1010] The server analyzes the PDF manual using a text analysis tool (e.g., PyPDF2) to extract key features and settings. During this process, it processes the data as needed and stores the extracted results in a database.

[1011] Example: "Analyze a PDF manual and register the main sections in a database."

[1012] Input: Instructions PDF

[1013] Output: Main section information stored in database (basic operation, temperature settings, maintenance procedures)

[1014] Step 6:

[1015] The server generates a user guide based on the analyzed instruction data. It uses a generative AI model (e.g., GPT-3) to generate a user guide in easy-to-understand text format.

[1016] Example: "Use a generative AI model to generate concise instructions for operating your refrigerator."

[1017] Input: Parsed instruction data

[1018] Output: Generated usage guide

[1019] Step 7:

[1020] The server delivers the generated usage guide to the user's device via email or push notification, and allows the user to view the guide within the app.

[1021] Example: "Send the generated usage guide to the user's device and notify them."

[1022] Input: Generated Usage Guide

[1023] Output: User guide delivered to user device

[1024] Step 8:

[1025] The user enters questions about how to use the app into the question form within the app and sends them to the server via the user's terminal.

[1026] Example: "A user enters 'How to use flash freezing' into a question form and sends it to the server."

[1027] Input: User question

[1028] Output: The question sent to the server

[1029] Step 9:

[1030] The server analyzes the received question using natural language processing technology (e.g., the BERT model) and generates an appropriate answer. The analysis understands the intent of the question, and extracts the appropriate information from the database to create the answer.

[1031] Example: "Use the BERT model to generate answers to user questions."

[1032] Input: User question

[1033] Output: The generated answer

[1034] Step 10:

[1035] The server sends the generated answer to the user's terminal, where the user can check the answer and take any necessary action.

[1036] Example: "Send the generated answer to the user's device and notify them."

[1037] Input: Generated answer

[1038] Output: Answer sent to user's terminal

[1039] Step 11:

[1040] The server analyzes the user's usage history and settings and recommends optimal usage methods and settings. It uses machine learning algorithms to analyze the data and make suggestions based on individual usage.

[1041] Example: "Use machine learning algorithms to recommend optimal settings for your refrigerator."

[1042] Input: Usage history, current settings

[1043] Output: Generated recommendations

[1044] Step 12:

[1045] The server sends the generated recommendations to the user's device and notifies them, allowing the user to view the recommendations within the app.

[1046] Example: "The generated recommendations are sent to the user's device and a notification is sent."

[1047] Input: Generated recommendations

[1048] Output: Recommendations sent to the user device

[1049] (Application example 1)

[1050] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1051] Currently, many home appliances and devices come with detailed and complex manuals, which are often difficult for average users to understand and operate properly. Furthermore, to obtain accurate information and setup instructions after purchase, users must manually search for and decipher the appropriate sections of the manual. This process is time-consuming and inefficient. Furthermore, the lack of real-time guidance and recommendations on optimal usage makes it difficult for users to maximize the product's performance.

[1052] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1053] In this invention, the server includes means for collecting purchase history, means for acquiring device instruction manual data based on the purchase history, means for analyzing the instruction manual data to extract key functions and settings, means for generating a user-friendly usage guide, means for distributing the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, means for the user terminal to provide real-time guidance on initial device setup and usage methods, and means for generating recommendation information based on the user's usage history and settings and distributing it to the user terminal. This allows users to easily understand all the functions of the home appliances and devices they have purchased and use them effectively. This also significantly improves user convenience and maximizes product performance.

[1054] "Purchase history" refers to historical information about products and services purchased by a user.

[1055] "Equipment" means electronic and electrical appliances for home or business use.

[1056] "Instruction data" is document data that describes information such as how to handle, install, and use the device.

[1057] "Analysis" is the process of breaking down and organizing complex data or documents into an understandable form.

[1058] "Major functions" refer to the main operations and actions provided by the device.

[1059] "Settings" refers to operations for adjusting the operating conditions and operating modes of a device.

[1060] "Usage Guide" refers to a guide that helps users correctly understand and effectively use the equipment.

[1061] "User terminal" refers to an electronic device or equipment used by a user, and specifically includes a smartphone, computer, etc.

[1062] A "question" is an inquiry submitted by a user to ask questions or clarify how to use or function the device.

[1063] An "answer" is information or explanation provided in response to a user's question.

[1064] "Real-time guidance" refers to instructions and guidance provided instantly on the spot regarding the initial setup and use of equipment.

[1065] "Recommendation information" is information about optimal usage methods and settings that are suggested based on the user's usage status and settings.

[1066] A "server" refers to a computer system that stores and processes data, and is a device that works in conjunction with user terminals via a network.

[1067] This invention is a system that allows users to easily understand and effectively use all the functions of the devices they have purchased. The system includes multiple means for collecting purchase history, analyzing and organizing device instruction manual data, and providing easy-to-understand guidance to users.

[1068] The server has the following means:

[1069] How to collect purchase history: Information on devices purchased by users is collected from e-commerce platforms and sales management systems. The user device sends data to the server every time the purchase history is updated.

[1070] Method for obtaining instruction manual data: Based on the registered device information, obtain the corresponding instruction manual from the manufacturer's website or other online resources. Analyze the obtained data and extract key functions and settings.

[1071] A means for generating a user guide: A user guide that is easy for the user to understand is generated based on the analyzed instruction data. This guide is delivered to the user's device and notified to the user.

[1072] The user terminal has the following means:

[1073] A method for accepting questions: The user inputs a question about how to use the device. The question is sent to the server.

[1074] How to generate answers to questions: The server analyzes the question and generates a corresponding answer using the generative AI model. The answer is then sent to the user's device.

[1075] Means for providing real-time guidance: The user device provides real-time guidance on how to set up and use the device, for example, by showing how to set the refrigerator temperature on a smartphone screen or through an AR display on smart glasses.

[1076] Means for delivering recommendation information: The server generates recommendation information for optimal usage methods and settings based on the user's usage history and settings, and delivers it to the user's device.

[1077] As a specific example, consider a case where a user who has purchased a new refrigerator uses this system to use the refrigerator's quick-freeze function. Once the purchase information is registered in the system, the server automatically downloads the product manual from online and analyzes its contents. When the user asks, "How do I use the quick-freeze function on my refrigerator?", the server analyzes the question, extracts the relevant operating procedures, and generates an answer. Then, with real-time guidance, the user can operate the refrigerator while checking the exact procedures on their smartphone screen or smart glasses.

[1078] An example of a prompt might be:

[1079] "Please check your purchase history. Product ID: 'new_refrigerator_123'"

[1080] "Generate a response to the following relevant user question: 'How do I use the deep freeze in my refrigerator?'"

[1081] To implement this system, the server is built using Python, Django, Flask, etc., and the client application is built using React Native or Unity. PDF analysis libraries such as PyMuPDF and pdfminer are used for data analysis, and SpaCy is used for natural language processing. This configuration allows users to quickly and easily understand the functions of the device and use it in the most optimal way.

[1082] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1083] Step 1:

[1084] The server collects the user's purchase history from the e-commerce platform or sales management system. When the user purchases a new product, the user terminal updates the purchase information in the database and sends the data to the server. The server analyzes the received purchase history data and stores it in the database. The input in this step is the purchase history data, and the output is the analyzed purchase information data.

[1085] Step 2:

[1086] The server retrieves device instruction data from the manufacturer's website or other online resources based on the purchase history data. The server crawls specific URLs and downloads the relevant instruction data. It then analyzes the retrieved instruction data (e.g., in PDF format) and extracts key function and setting information. The input is the purchase history data and instruction data from the online resource, and the output is the analyzed function and setting information.

[1087] Step 3:

[1088] The server generates a user guide that is easy for users to understand based on the analyzed instruction data. Specifically, it creates a text-format guide that summarizes the main functions and setting methods in short, concise steps. The input is the analyzed function and setting information, and the output is the generated user guide. This user guide is distributed from the server to the user's device.

[1089] Step 4:

[1090] The user inputs a question about how to use the device from the user terminal. The user terminal sends the question to the server. The input in this step is the user's question, and the output is the question data sent to the server.

[1091] Step 5:

[1092] The server receives questions from users and analyzes them using natural language processing technology (e.g., SpaCy). Based on the analyzed questions, it generates appropriate answers using a generative AI model. Specifically, the server extracts the relevant operating procedures from a database and generates answer sentences using the generative AI model. The input is the received question data, and the output is the generated answer sentences.

[1093] Step 6:

[1094] The server sends the generated answer to the user terminal, which then displays the answer to the user. The input is the generated answer, and the output is the answer data sent to the user terminal.

[1095] Step 7:

[1096] The user device provides real-time guidance. For example, instructions on how to set the refrigerator temperature can be displayed on a smartphone screen or an AR display on smart glasses. Specifically, the user device displays the usage guide and setting procedures obtained from the server on its interface. The input is the obtained usage guide data, and the output is the real-time guidance provided to the user.

[1097] Step 8:

[1098] The server generates recommendation information for optimal usage methods and settings based on the user's usage history and setting information. The generated recommendation information is distributed to the user's device. The input is the user's usage history and setting information, and the output is the generated recommendation information.

[1099] Step 9:

[1100] The user terminal receives the recommendation information delivered from the server and notifies the user. The user can adjust the product settings and usage methods based on the notification. The input is the recommendation information, and the output is the recommendation information notified to the user.

[1101] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1102] This invention is a system that uses a user's purchase history to analyze the manuals for home appliances and devices, and provides users with easy-to-understand instructions on how to use them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond according to the user's emotions. The "Smart Manual AI" system functions through the following process.

[1103] Collection and analysis of purchase history

[1104] The server collects the user's purchase history from the e-commerce site and the sales management system.

[1105] The user terminal transmits data to the server every time the purchase history is updated.

[1106] The server analyzes the received data and stores information about each product in a database.

[1107] Examples:

[1108] When a user purchases a new television, the information is registered on an e-commerce site. The user's device collects the purchase information and sends it to a server. The server analyzes the purchase information and registers it in a database under the category "television."

[1109] Instruction data acquisition and analysis

[1110] The server retrieves corresponding product manuals from manufacturers' websites and other online resources based on the registered product information, analyzes the retrieved manual data, and extracts key features and settings.

[1111] Examples:

[1112] The server downloads the instruction manual data for the new TV from the manufacturer's website, analyzes the downloaded PDF, and extracts key sections such as "Basic Operations," "Channel Settings," and "How to Use the Remote Control."

[1113] Generate and distribute usage guides

[1114] The server generates a user-friendly usage guide based on the analyzed instruction data, and distributes the generated usage guide to the user's device.

[1115] Examples:

[1116] The server summarizes the TV operation procedure step by step in a concise format such as "How to turn on the power," "How to set the channel," "How to adjust the volume," etc. The generated guide is sent to the user's device, and the user receives a notification.

[1117] User interaction support

[1118] The user inputs questions about usage from the user terminal.

[1119] The user terminal sends a query to the server.

[1120] The server receives the question, analyzes it using natural language processing technology, and generates a corresponding answer, which is then sent to the user's device.

[1121] Examples:

[1122] The user asks "How do I enable the voice guide on my TV?" on their device. The server analyzes the question, extracts the relevant operation procedure from the "Enable voice guide" section, generates an answer, and sends it to the user's device. The user then operates the TV according to the answer.

[1123] Providing recommendations

[1124] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage and settings. The recommendations are then delivered to the user's device.

[1125] Examples:

[1126] The server analyzes the user's TV usage history and makes recommendations such as "recommended picture quality settings" and "how to use energy saving mode." These recommendations are sent to the user's device, and the user is notified.

[1127] Incorporating an emotion engine

[1128] The emotion engine analyzes voice data and facial expression data from the user's device to recognize the user's emotions.

[1129] The server changes the usage guide and answer expressions for the user based on the emotion recognition results.

[1130] Examples:

[1131] When a user enters a question, if the emotion engine detects stress from the user's tone of voice or facial expression, the server will change the answer to something more polite and reassuring, such as "Don't worry. Try these steps and you'll be able to resolve the issue."

[1132] Emotional recommendations

[1133] The server adjusts the content of the recommendations based on the emotional state of the user as recognized by the emotion engine.

[1134] Examples:

[1135] If the user frequently shows signs of anxiety, the server will recommend resources such as an "initial setup guide" or "frequently asked questions" to increase the user's sense of security.

[1136] This invention allows users to easily understand all the functions of the home appliances and devices they have purchased, and receives support tailored to their emotions, allowing them to use the products more comfortably. This significantly improves user convenience and maximizes product performance.

[1137] The processing flow will be explained below.

[1138] Step 1: Collect your purchase history

[1139] The user terminal acquires the user's purchase history from an e-commerce site or a sales management system.

[1140] The user terminal transmits the acquired purchase history to the server.

[1141] Step 2: Analyze your purchase history

[1142] The server analyzes the received purchase history data and extracts detailed information about each product (e.g., product name, category, purchase date, etc.).

[1143] The server stores the extracted information in a database.

[1144] Step 3: Obtaining instruction data

[1145] The server retrieves instruction manual data for the corresponding home appliance or device based on the purchase history information in the database.

[1146] The server downloads instruction data from manufacturer websites and online resources.

[1147] Step 4: Analyzing the instruction data

[1148] The server parses the retrieved instruction data and converts it into a text format (e.g., using PDF parsing or OCR technology).

[1149] The server extracts key features and configuration information from the text data.

[1150] Step 5: Generate a usage guide

[1151] The server generates a usage guide in a format that is easy for the user to understand based on the analyzed instruction data.

[1152] The server stores the generated usage guide in a database.

[1153] Step 6: Distributing the usage guide

[1154] The server distributes the saved usage guide to the user terminal.

[1155] The user terminal displays an access notification of the usage guide to the user.

[1156] Step 7: Ask a question

[1157] The user inputs questions about usage from the user terminal.

[1158] The user terminal sends the question to the server.

[1159] Step 8: Parsing the Question

[1160] The server analyzes the question received from the user using natural language processing technology.

[1161] The server searches the database for instruction data and related information that corresponds to the question.

[1162] Step 9: Generate an answer

[1163] The server automatically generates an appropriate answer based on the analysis results.

[1164] The server formats the generated answer for the user.

[1165] Step 10: Publish your response

[1166] The server generates a response and sends it to the user terminal.

[1167] The user terminal displays the answer to the user.

[1168] Step 11: Serve recommendations

[1169] The server analyzes the user's usage history and current settings.

[1170] The server generates recommendations for optimal usage methods and settings based on the user's usage.

[1171] Step 12: Serving Recommendations

[1172] The server distributes the generated recommendations to the user terminal.

[1173] The user terminal notifies the user of the contents of the recommendation.

[1174] Step 13: Collect emotion data

[1175] The user terminal collects the user's voice data and facial expression data.

[1176] The user terminal transmits the collected emotion data to the server.

[1177] Step 14: Analyze the emotion data

[1178] The emotion data received by the server is analyzed using an emotion engine.

[1179] The server stores the emotion analysis results in a database.

[1180] Step 15: Emotion-based guidance and response adjustments

[1181] The server adjusts the expressions of the usage guide and answers provided to the user based on the analyzed emotion data.

[1182] The server delivers emotion-based responses to the user terminal.

[1183] Step 16: Emotional Recommendations

[1184] The server adjusts the content of the recommendations based on the user's emotional state recognized by the emotion engine.

[1185] The server delivers recommendations based on emotions to the user's device.

[1186] The above is a specific program processing flow of the present invention.

[1187] Example 2

[1188] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1189] Today, home appliances are becoming increasingly multifunctional, making it difficult for users to effectively utilize all the features of the products they purchase. In particular, product manuals can be overly detailed or contain a wealth of technical jargon, making it time-consuming to understand how to use them. Furthermore, uniform support that doesn't take users' feelings into consideration is a factor that reduces satisfaction. Furthermore, there is no system in place to guide users through post-purchase setup or optimal usage, leaving users struggling to resolve issues on their own. There is a need to solve these problems and enable users to make the most of their home appliances.

[1190] The identification process by the identification 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 collecting purchase history, means for acquiring product instruction data based on the purchase history, means for analyzing the instruction data to extract key functions and settings, means for generating a usage guide that is easy for the user to understand based on the extracted information, means for distributing the usage guide to a user terminal, means for accepting questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, an emotion engine for analyzing the user's voice data and facial expression data to recognize emotions, means for changing the expression of the usage guide and answers based on the results of the emotion engine, and means for generating recommendations based on the recognized emotional state. This allows the user to quickly and effectively understand how to use the home appliance they purchased and further receive support and recommendations according to their emotions.

[1191] "Purchase history" is information about products or merchandise purchased by a user in the past.

[1192] "Instruction data" refers to document data that describes how to use a product or item, how to set it up, precautions, etc.

[1193] "Analysis" is the act of examining data and information in detail to clarify meaning and relationships.

[1194] "Major Function" refers to the basic and important operation or role of a product or merchandise.

[1195] "Configuration" is the process of adjusting a product or system to determine its operating conditions and operation methods.

[1196] A "usage guide" is a guide that helps users to understand and use a product or system easily and conveniently.

[1197] A "user terminal" is a device such as a computer or smartphone that can be directly operated by a user.

[1198] "Question acceptance" is the process of receiving inquiries from users.

[1199] "Answer generation" is the task of creating an appropriate answer to a question posed by a user.

[1200] An "emotion engine" is a system that has the function of recognizing emotions by analyzing the user's voice data and facial expression data.

[1201] "Recommendations" suggest optimal actions and settings based on a user's behavioral history and emotional state.

[1202] The present invention is a system that includes "means for collecting purchase history," "means for acquiring product instruction data," "means for analyzing the instruction data and extracting key functions and settings," "means for generating a user-friendly usage guide based on the extracted information," "means for delivering the usage guide to a user terminal," "means for accepting questions from a user and generating answers to those questions," "means for sending the generated answers to the user terminal," "an emotion engine that recognizes emotions by analyzing the user's voice data and facial expression data," "means for changing the expression of the usage guide and answers based on the results of the emotion engine," and "means for generating recommendations based on the recognized emotional state."

[1203] The entire system is mainly composed of a server, a user terminal, and an emotion engine. These elements work together to enable users to quickly and effectively understand how to use the products they have purchased, and also to receive emotional support and recommendations.

[1204] Collection and analysis of purchase history

[1205] The server periodically runs a script that retrieves data from e-commerce sites and sales management systems. It uses an API to retrieve the latest purchase history and stores it in a database. For example, the server sends an API request to an e-commerce site such as Amazon or Rakuten, parses the purchase history data received in response in JSON format, and adds it to the database.

[1206] Instruction data acquisition and analysis

[1207] Based on the product information registered in the database, the server retrieves the corresponding manual from the manufacturer's website or other online resources. Specifically, the server searches for the product name using the Google Custom Search API and obtains the manual's URL. Next, the server downloads the PDF file from the obtained URL and saves it to the file system. The server then uses a PDF parsing library (e.g., Apache PDFBox) to extract text data from the downloaded manual and uses a natural language processing library (e.g., NLTK) to classify it into major sections such as "Basic Operations" and "How to Use the Remote Control."

[1208] Generate and distribute usage guides

[1209] The server automatically generates an easy-to-understand user guide based on the analyzed instruction data. The server uses a template engine (e.g., Jinja2) to document clear instructions. For example, it generates guide sentences that include specific usage procedures, such as "how to turn on the power" and "how to set the channel." The generated user guide is then distributed to the user's device. The user's device displays the guide to the user via a dedicated app or web interface and sends notifications.

[1210] User interaction support

[1211] Users can input questions about how to operate the device from their device. The user device sends the questions to the server, which analyzes the questions and generates appropriate answers. The server uses a natural language processing engine (e.g., BERT) to analyze the questions, search for relevant answers from a database, and format them for the user. The generated answers are sent to the user device and displayed on the device.

[1212] Providing recommendations

[1213] The server analyzes the user's usage history and current settings and generates recommendations for optimal usage and settings. For example, the server uses a machine learning model (e.g., XGBoost) to analyze user behavior data and generate recommendations such as "recommended image quality settings" and "how to utilize energy saving mode." These recommendations are delivered to the user's device and notified to the user.

[1214] Incorporating an emotion engine

[1215] The emotion engine analyzes the voice data and facial expression data acquired from the user's device to recognize the user's emotions. The emotion engine analyzes the voice data using an emotion analysis API (e.g., Google API) and evaluates the facial expression data using a face recognition API (e.g., Microsoft Azure Face API). Based on the recognized emotional state, the server changes the expression of the usage guide and answers. For example, if the user is feeling stressed, the tone of the answers and guide will be changed to be more polite and reassuring.

[1216] Emotional recommendations

[1217] The server adjusts the recommendations based on the results of the emotion engine. If the user feels unsure, it will recommend resources that will increase their sense of security, such as an "initial setup guide" or "frequently asked questions." This allows the user to use all the product's features without stress.

[1218] Examples of prompt statements

[1219] The following format is used as an example of a prompt statement for generating a program for this system:

[1220] Design a system that uses the user's purchase history to analyze product and equipment manuals and provide instructions for use in a way that is easy for the user to understand. In addition, add support functions that combine an emotion engine that recognizes the user's emotions.

[1221] With the above configuration, the present invention enables users to quickly and effectively understand how to use a purchased product and receive support and recommendations that correspond to their emotions.

[1222] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1223] Step 1:

[1224] Collection of purchase history

[1225] The server periodically obtains data from e-commerce sites and sales management systems. Specifically, the server uses an API to send a request to the e-commerce site (e.g., Amazon, Rakuten) and receives the latest purchase history data. The received data is in JSON format, and the server analyzes this data and stores it in a database. The input is the purchase history data obtained from the e-commerce site, and the output is a database that stores the analyzed purchase history.

[1226] Step 2:

[1227] Send purchase history

[1228] When a user purchases a product and their purchase history is updated, the user device sends that information to the server. When a user updates their purchase history within the app, the user device sends that information to the server using an API. The input is the purchase history updated by the user, and the output is the purchase history data sent to the server.

[1229] Step 3:

[1230] Obtaining instruction data

[1231] The server retrieves product information from a database based on purchase history and collects corresponding manuals from manufacturer websites and other online resources. The server uses the Google Custom Search API to search for the product name and obtain the manual URL. It then downloads a PDF file from the URL. The input is the product name, and the output is the downloaded PDF manual.

[1232] Step 4:

[1233] Analysis of instruction data

[1234] The server analyzes the downloaded PDF manual and extracts key functions and settings. It uses a PDF analysis library (e.g., Apache PDFBox) to extract text data and uses a natural language processing library (e.g., NLTK) to classify it into sections such as "Basic Operations" and "How to Use the Remote Control." The input is the PDF manual data, and the output is the analyzed and classified manual data.

[1235] Step 5:

[1236] Creating a usage guide

[1237] The server generates an easy-to-understand user guide based on the parsed instruction data. It uses a template engine (e.g., Jinja2) to document clear instructions for each step. For example, it summarizes procedures such as "how to turn on the power" and "how to set the channel." The input is the parsed instruction data, and the output is the generated instruction guide.

[1238] Step 6:

[1239] Distribution of user guides

[1240] The server delivers the generated usage guide to the user terminal. The user terminal displays the received guide and sends a notification to the user. The input is the generated usage guide, and the output is the guide displayed on the user terminal.

[1241] Step 7:

[1242] Accepting questions from users

[1243] The user inputs a question about how to use the system from the terminal. The user terminal sends the question to the server. The input is the question from the user, and the output is the question data sent to the server.

[1244] Step 8:

[1245] Parsing questions and generating answers

[1246] The server analyzes the received question using natural language processing technology and generates an appropriate answer. It uses a natural language processing engine (e.g., BERT) to analyze the question, extract keywords, and search a database for relevant information. The input is the question data from the user, and the output is the generated answer.

[1247] Step 9:

[1248] Response distribution

[1249] The server sends the generated answer to the user terminal, which displays the answer. The input is the generated answer, and the output is the answer displayed on the user terminal.

[1250] Step 10:

[1251] Recommendation generation and delivery

[1252] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage methods and settings. Analysis is performed using a machine learning model (e.g., XGBoost) to generate recommendations. The generated recommendations are delivered to the user's device and notified to the user. The input is the user's usage history data, and the output is the generated recommendations.

[1253] Step 11:

[1254] Acquiring and analyzing emotion data

[1255] The emotion engine acquires voice and facial expression data from the user's device and analyzes them to recognize the user's emotions. It analyzes voice data using an emotion analysis API (e.g., Google API) and evaluates facial expressions using a face recognition API (e.g., Microsoft Azure Face API). The input is the user's voice and facial expression data, and the output is recognized emotion data.

[1256] Step 12:

[1257] Emotion-based responses

[1258] The server changes the expressions of the usage guide and answers given to the user based on the results of the emotion engine. If the user is feeling stressed, it will use expressions such as "Don't worry" or change the tone of the guide. The input is the recognized emotion data, and the output is the guide and answers corresponding to the emotion.

[1259] Step 13:

[1260] Providing emotional recommendations

[1261] The server adjusts the content of recommendations based on the emotional state recognized by the emotion engine. If the user is feeling anxious, it will recommend resources that will make them feel at ease (e.g., "Initial Setup Guide" or "FAQ"). The input is the recognized emotional data, and the output is a recommendation based on the emotion.

[1262] (Application example 2)

[1263] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1264] As autonomous vehicles become more multifunctional and technologically advanced, it is becoming increasingly difficult for users to understand and operate all of their functions. Even when users read the vehicle's manual, it is difficult for them to quickly find the information they need, making efficient use of the vehicle particularly challenging in stressful situations. Furthermore, as vehicle technology evolves, there is a need for a method to provide appropriate support tailored to the emotional state of each individual user. Solutions to these challenges are needed.

[1265] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting purchase history, means for acquiring function description data for the autonomously driving vehicle based on the purchase history, means for analyzing the description data and extracting key functions and settings, means for generating a usage guide that is easy for the user to understand based on the extracted information, means for delivering the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, means for analyzing the user's emotional state, and means for adjusting the guide and answer expression corresponding to the emotional state. This allows the user to use the functions of the autonomously driving vehicle quickly and easily and receive appropriate support according to their emotional state.

[1266] "Collecting purchase history" is the process of recording and compiling information about products and services purchased by users.

[1267] "Function description data" is a digital document that provides detailed explanations of the various functions and settings of an autonomous vehicle.

[1268] "Analysis" is the process of examining data in detail and understanding its contents.

[1269] "Major functions and settings" are important functions and settings that the user frequently uses or should be aware of when using the vehicle.

[1270] A "user guide" is a user guide that clearly summarizes how to operate a product or system.

[1271] A "user terminal" is a device used by a user, such as a smartphone, tablet, or in-vehicle display.

[1272] "Accepting questions" means receiving inquiries or questions from users and gathering information to respond to them.

[1273] "Generating an answer" means creating appropriate information or solutions for a question received from a user.

[1274] "Analyzing emotional state" is the process of identifying the user's current emotional and psychological state based on their voice and facial expression data.

[1275] "Adjusting the expression of the guide or answer" means changing the expression method or content of the information provided depending on the emotional state of the user.

[1276] The program of the system for realizing the present invention is configured as follows.

[1277] The server collects user purchase history and retrieves the data from e-commerce sites or sales management systems. The server then downloads the autonomous vehicle's function description data from the manufacturer's website or other online resources, and uses a PDF analysis tool (e.g., PyMuPDF) to analyze it. It extracts key functions and settings from the analyzed data and generates a user guide that is easy to understand for the user.

[1278] The generated usage guide is distributed from the cloud server to the user's device (smartphone, tablet, in-car display, etc.). The server accepts questions from the user's device, generates answers to those questions using a natural language processing tool (e.g., spaCy), and sends them to the user's device.

[1279] Furthermore, the system uses emotion analysis tools (e.g., Microsoft Azure Cognitive Services' Emotion Analysis API) to analyze the user's voice and facial expression data. Based on the analysis results, the system adjusts the guidance and response expressions to match the user's emotional state and provides them to the user. This allows users to use the autonomous vehicle quickly and easily, and receive appropriate support according to their emotional state.

[1280] As a concrete example, if a user purchases a new self-driving vehicle and wants to know about the in-car functions and how to set them up, the system operates as follows.

[1281] 1. Users gather information about new autonomous vehicles from their purchase history.

[1282] 2. The server analyzes the information and downloads the function description from the manufacturer's website.

[1283] 3. Analyze the downloaded manual, extract key functions and settings such as "how to start the vehicle" and "how to use the parking assist function," and generate a usage guide.

[1284] 4. The generated usage guide is delivered to the user's device so that the user can check it.

[1285] 5. If a user asks, "I don't know how to set the autonomous driving mode," the server uses natural language processing to generate an answer and sends it to the user's device.

[1286] Additionally, if the sentiment analysis tool determines that the user is feeling stressed, it will provide softer guidance and answers, such as "Take a slow, deep breath and try these steps."

[1287] Example prompt sentence:

[1288] "Please provide a step-by-step guide for setting up the self-parking feature for a self-driving car."

[1289] This will help users better understand the functions of the autonomous vehicle and allow them to receive appropriate support according to their emotional state.

[1290] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1291] Step 1:

[1292] The server collects the user's purchase history. The input is data from an e-commerce site or a sales management system, and the output is purchase history data. Based on this data, vehicle purchase information is identified.

[1293] Step 2:

[1294] The server obtains the function description data of the autonomous vehicle based on the purchase history. The input is the purchase history data, and the output is the URL or file of the function description. The server downloads the corresponding description from the manufacturer's website or other online resources.

[1295] Step 3:

[1296] The server parses the retrieved instruction data. The input is the feature instruction data, and the output is information about the main features and settings. Using a PDF parsing tool (e.g., PyMuPDF), it extracts sections from the instruction manual, such as "How to start the vehicle" and "How to use the parking assist function."

[1297] Step 4:

[1298] The server generates a user-friendly usage guide based on the extracted information. The input is the main features and settings information, and the output is the usage guide. A generative AI model is used to generate the user-friendly guide in text or multimedia format.

[1299] Step 5:

[1300] The server distributes the generated usage guide to the user's device. The input is the usage guide, and the output is a distribution notification to the user's device. The guide is sent via the cloud server to a smartphone, tablet, or in-car display.

[1301] Step 6:

[1302] The user inputs a question via a terminal. The input is the user's question text, and the output is the question sent to the server, which then transmits the user's question to the system.

[1303] Step 7:

[1304] The server generates answers to user questions. The input is the user's question text, and the output is the answer text. Natural language processing tools (e.g., spaCy) are used to analyze the question and generate appropriate answers.

[1305] Step 8:

[1306] The server sends the generated answer to the user terminal. The input is the generated answer text, and the output is a reply to the user terminal. This allows the user to obtain the necessary information.

[1307] Step 9:

[1308] The server uses an emotion analysis tool to analyze the user's emotional state. The input is the user's voice and facial expression data, and the output is the result of identifying the user's emotional state. This tool utilizes the emotion analysis API of Microsoft Azure Cognitive Services.

[1309] Step 10:

[1310] The server adjusts the expression of the guide or answer according to the user's emotional state. The input is the result of identifying the user's emotional state and the generated guide or answer, and the output is the guide or answer adjusted to an expression appropriate to the emotion. This reduces the user's psychological burden and provides stress-free support.

[1311] This allows users to use the functions of an autonomous vehicle quickly and easily, and to receive appropriate support according to their emotional state.

[1312] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1313] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1314] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1315] [Fourth embodiment]

[1316] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1317] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1318] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1319] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1320] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1321] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1322] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1323] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1324] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1325] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1326] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1327] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1328] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1329] This invention is a system that uses a user's purchase history to analyze the manuals for home appliances and devices, and provides users with easy-to-understand instructions for use. The "Smart Manual AI" system works through the following process.

[1330] Collection and analysis of purchase history

[1331] The server collects the user's purchase history from the e-commerce site and the sales management system.

[1332] The user terminal transmits data to the server every time the purchase history is updated.

[1333] The server analyzes the received data and stores information about each product in a database.

[1334] Examples:

[1335] When a user purchases a new refrigerator, the information is registered on the e-commerce site. The user's device collects the purchase information and sends it to the server. The server analyzes the purchase information and registers it in the database under the category "refrigerator."

[1336] Instruction data acquisition and analysis

[1337] The server retrieves corresponding product manuals from manufacturers' websites and other online resources based on the registered product information, analyzes the retrieved manual data, and extracts key features and settings.

[1338] Examples:

[1339] The server downloads the instruction manual for a new refrigerator from the manufacturer's website, analyzes the downloaded PDF, and extracts key sections such as "Basic Operation," "Temperature Setting," and "Maintenance Procedures."

[1340] Generate and distribute usage guides

[1341] The server generates a user-friendly usage guide based on the analyzed instruction data, and distributes the generated usage guide to the user's device.

[1342] Examples:

[1343] The server summarizes the refrigerator's operation procedures step by step in the form of "how to turn it on," "how to set the temperature," "how to switch to energy-saving mode," etc. The generated guide is sent to the user's device, and the user receives a notification.

[1344] User interaction support

[1345] The user inputs questions about usage from the user terminal.

[1346] The user terminal sends a query to the server.

[1347] The server receives the question, analyzes it using natural language processing technology, and generates a corresponding answer, which is then sent to the user's device.

[1348] Examples:

[1349] The user asks a question on their device: "How do I use the quick freezing feature in my refrigerator?" The server analyzes the question, extracts the relevant operating procedure from the section titled "How to use quick freezing," generates an answer, and sends it to the user's device. The user operates the refrigerator according to the answer.

[1350] Providing recommendations

[1351] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage and settings. The recommendations are then delivered to the user's device.

[1352] Examples:

[1353] The server analyzes the user's refrigerator usage history and makes recommendations such as "temperature settings to extend food storage life" and "how to use energy-saving mode." These recommendations are sent to the user's device, and the user is notified.

[1354] This invention allows users to easily understand and effectively use all the functions of the home appliances and devices they purchase, greatly improving user convenience and maximizing product performance.

[1355] The processing flow will be explained below.

[1356] Step 1: Collect your purchase history

[1357] The user terminal acquires the user's purchase history from an e-commerce site or a sales management system.

[1358] The user terminal transmits the acquired purchase history to the server.

[1359] Step 2: Analyze your purchase history

[1360] The server analyzes the received purchase history data and extracts detailed information about each product (e.g., product name, category, purchase date, etc.).

[1361] The server stores the extracted information in a database.

[1362] Step 3: Obtaining instruction data

[1363] The server retrieves instruction manual data for the corresponding home appliance or device based on the purchase history information in the database.

[1364] The server downloads instruction data from manufacturer websites and online resources.

[1365] Step 4: Analyzing the instruction data

[1366] The server parses the retrieved instruction data and converts it into a text format (e.g., using PDF parsing or OCR technology).

[1367] The server extracts key features and configuration information from the text data.

[1368] Step 5: Generate a usage guide

[1369] The server generates a usage guide in a format that is easy for the user to understand based on the analyzed instruction data.

[1370] The server stores the generated usage guide in a database.

[1371] Step 6: Distributing the usage guide

[1372] The server distributes the saved usage guide to the user terminal.

[1373] The user terminal displays an access notification of the usage guide to the user.

[1374] Step 7: Ask a question

[1375] The user inputs questions about usage from the user terminal.

[1376] The user terminal sends the question to the server.

[1377] Step 8: Parsing the Question

[1378] The server analyzes the question received from the user using natural language processing technology.

[1379] The server searches the database for instruction data and related information that corresponds to the question.

[1380] Step 9: Generate an answer

[1381] The server automatically generates an appropriate answer based on the analysis results.

[1382] The server formats the generated answer for the user.

[1383] Step 10: Publish your response

[1384] The server generates a response and sends it to the user terminal.

[1385] The user terminal displays the answer to the user.

[1386] Step 11: Serve recommendations

[1387] The server analyzes the user's usage history and current settings.

[1388] The server generates recommendations for optimal usage methods and settings based on the user's usage.

[1389] Step 12: Serving Recommendations

[1390] The server distributes the generated recommendations to the user terminal.

[1391] The user terminal notifies the user of the contents of the recommendation.

[1392] The above is a specific program processing flow of the present invention.

[1393] Example 1

[1394] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1395] Manuals for home appliances and devices are typically provided in a cumbersome and difficult-to-understand format, making it difficult for users to effectively use all of their functions. Furthermore, when users need specific usage instructions or optimal settings, they lack a way to quickly and accurately obtain that information. Furthermore, the lack of functionality to suggest optimal ways to use the product while in use reduces user convenience.

[1396] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1397] In this invention, the server includes means for collecting purchase history, means for acquiring instruction manual data for the home appliance based on the purchase history, means for analyzing the instruction manual data to extract key functions and settings, means for generating a user-friendly usage guide based on the extracted information, means for distributing the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, and means for analyzing the user's usage history and settings and recommending optimal usage methods. This allows users to easily understand all the functions of home appliances and devices and use them effectively. Furthermore, information on specific usage methods and settings can be quickly obtained and appropriate recommendations tailored to individual usage situations can be received, significantly improving user convenience.

[1398] "Purchase history collection" is the process of automatically obtaining information on user purchases of home appliances and devices from electronic transaction systems and sales management systems.

[1399] "Instruction Data Acquisition" is the process of downloading relevant instructions from manufacturer websites or other online resources based on registered product information.

[1400] "Analysis of instruction manual data" is the process of analyzing the contents of the acquired instructions using a text analysis tool and extracting key functions and settings.

[1401] "Generating a usage guide" is the process of generating a guide in a form that is easy for the user to understand based on the analyzed instruction data.

[1402] "Distribution of usage guide" is a process of sending the generated usage guide to the user terminal and notifying the user.

[1403] "Accepting user questions" is the process of allowing a user to enter specific usage or configuration questions and sending those questions to the server.

[1404] "Generating an answer to a question" is the process of analyzing a user's question using natural language processing technology and creating an appropriate answer.

[1405] "Sending an answer" is the process of sending the generated answer to the user terminal so that the user can view it.

[1406] "Recommending optimal usage methods" is a process that analyzes a user's usage history and current settings and suggests appropriate usage methods and settings based on that.

[1407] This invention relates to a system that provides easy-to-understand instructions for using home appliances and devices based on a user's purchase history. The system includes collection of purchase history, acquisition and analysis of instruction manual data, generation and distribution of usage guides, support for user interaction, and provision of recommendations. The "Smart Manual AI" system is configured as follows:

[1408] First, the server automatically collects the user's purchase history from the electronic transaction system and sales management system. Specifically, it uses the electronic transaction system's API to obtain transaction data and uses the Python PANDAS library to analyze this data. For example, when a user purchases a new refrigerator, the information is recorded in the electronic transaction system. The user's device collects this purchase information and sends it to the server.

[1409] Next, the server downloads the corresponding manuals from the manufacturer's website or other online resources based on the product information stored in the database. It uses a web scraping tool (e.g., Beautiful Soup) to retrieve the manuals. It uses the Python PyPDF2 library to analyze the contents of the retrieved manuals and extract key features and settings. The extracted results are stored in the database.

[1410] The server then generates a user guide that is easy for users to understand based on the analyzed instruction data. A generative AI model (e.g., GPT-3) is used to generate the user guide. For example, a user guide is generated by concisely summarizing step-by-step operating procedures for a refrigerator, such as "how to turn it on" and "how to set the temperature." This user guide is then distributed to the user's device and notified to the user.

[1411] Furthermore, users can enter questions about specific usage or settings into a question form and send them to the server via their device. The server receives the questions, analyzes them using natural language processing techniques (e.g., the BERT model), and generates appropriate answers. The generated answers are then sent to the user's device, where they can be viewed by the user.

[1412] Finally, the server analyzes the user's usage history and current settings to recommend optimal usage methods and settings. These recommendations are generated using machine learning algorithms, and are tailored to the user's individual usage patterns. For example, the server might notify the user of a recommendation such as, "Lowering the refrigerator temperature setting by 2 degrees will reduce energy consumption by 10%."

[1413] Below are some examples of prompts to input to a generative AI model:

[1414] 1. "Please tell me the basic operation of the newly purchased refrigerator."

[1415] 2. "I'd like to know how to use flash freezing. How do I do that?"

[1416] 3. "Please explain how to switch to power saving mode."

[1417] 4. "How do I optimize my refrigerator's temperature settings?"

[1418] In this way, by implementing this system, users can easily understand all the functions of home appliances and devices and use them effectively. By utilizing appropriate technology at each processing step, it is possible to significantly improve user convenience.

[1419] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1420] Step 1:

[1421] The server collects purchase history data from the electronic transaction system and sales management system. Specifically, it calls the API of the electronic transaction system and executes the API with the user ID and transaction ID as input. As output, it obtains detailed information such as the name, model number, purchase date, and purchase location of the purchased product.

[1422] Example: "Use the electronic transaction system API to retrieve the purchase history of user ID 12345."

[1423] Input: User ID, Transaction ID

[1424] Output: Product name, model number, purchase date, purchase location

[1425] Step 2:

[1426] The user device sends the collected purchase history information to the server. This is a process in which purchase history data is automatically sent when the user opens the application. Specifically, the device sends an HTTP request to the server via the API to transmit the purchase data.

[1427] For example: "When a user launches the app, purchase history information is sent to the server."

[1428] Input: Purchase history information (product name, model number, purchase date, purchase location)

[1429] Output: Purchase history information sent to the server

[1430] Step 3:

[1431] The server analyzes the received purchase history data, identifies the category and characteristics of each product, and stores the analysis results in a database using the Python PANDAS library.

[1432] Example: "Analyze purchase history information and register the relevant product information in a database."

[1433] Input: Purchase history information received

[1434] Output: Product information stored in the database (product category, features)

[1435] Step 4:

[1436] Based on the product information, the server retrieves relevant product manuals from manufacturer websites and other online resources. It uses a web scraping tool (e.g., Beautiful Soup) to download the product manual PDF.

[1437] Example: "Get product manual PDF from manufacturer's website"

[1438] Input: Product name, model number

[1439] Output: Downloaded product manual PDF

[1440] Step 5:

[1441] The server analyzes the PDF manual using a text analysis tool (e.g., PyPDF2) to extract key features and settings. During this process, it processes the data as needed and stores the extracted results in a database.

[1442] Example: "Analyze a PDF manual and register the main sections in a database."

[1443] Input: Instructions PDF

[1444] Output: Main section information stored in database (basic operation, temperature settings, maintenance procedures)

[1445] Step 6:

[1446] The server generates a user guide based on the analyzed instruction data. It uses a generative AI model (e.g., GPT-3) to generate a user guide in easy-to-understand text format.

[1447] Example: "Use a generative AI model to generate concise instructions for operating your refrigerator."

[1448] Input: Parsed instruction data

[1449] Output: Generated usage guide

[1450] Step 7:

[1451] The server delivers the generated usage guide to the user's device via email or push notification, and allows the user to view the guide within the app.

[1452] Example: "Send the generated usage guide to the user's device and notify them."

[1453] Input: Generated Usage Guide

[1454] Output: User guide delivered to user device

[1455] Step 8:

[1456] The user enters questions about how to use the app into the question form within the app and sends them to the server via the user's terminal.

[1457] Example: "A user enters 'How to use flash freezing' into a question form and sends it to the server."

[1458] Input: User question

[1459] Output: The question sent to the server

[1460] Step 9:

[1461] The server analyzes the received question using natural language processing technology (e.g., the BERT model) and generates an appropriate answer. The analysis understands the intent of the question, and extracts the appropriate information from the database to create the answer.

[1462] Example: "Use the BERT model to generate answers to user questions."

[1463] Input: User question

[1464] Output: The generated answer

[1465] Step 10:

[1466] The server sends the generated answer to the user's terminal, where the user can check the answer and take any necessary action.

[1467] Example: "Send the generated answer to the user's device and notify them."

[1468] Input: Generated answer

[1469] Output: Answer sent to user's terminal

[1470] Step 11:

[1471] The server analyzes the user's usage history and settings and recommends optimal usage methods and settings. It uses machine learning algorithms to analyze the data and make suggestions based on individual usage.

[1472] Example: "Use machine learning algorithms to recommend optimal settings for your refrigerator."

[1473] Input: Usage history, current settings

[1474] Output: Generated recommendations

[1475] Step 12:

[1476] The server sends the generated recommendations to the user's device and notifies them, allowing the user to view the recommendations within the app.

[1477] Example: "The generated recommendations are sent to the user's device and a notification is sent."

[1478] Input: Generated recommendations

[1479] Output: Recommendations sent to the user device

[1480] (Application example 1)

[1481] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1482] Currently, many home appliances and devices come with detailed and complex manuals, which are often difficult for average users to understand and operate properly. Furthermore, to obtain accurate information and setup instructions after purchase, users must manually search for and decipher the appropriate sections of the manual. This process is time-consuming and inefficient. Furthermore, the lack of real-time guidance and recommendations on optimal usage makes it difficult for users to maximize the product's performance.

[1483] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1484] In this invention, the server includes means for collecting purchase history, means for acquiring device instruction manual data based on the purchase history, means for analyzing the instruction manual data to extract key functions and settings, means for generating a user-friendly usage guide, means for distributing the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, means for the user terminal to provide real-time guidance on initial device setup and usage methods, and means for generating recommendation information based on the user's usage history and settings and distributing it to the user terminal. This allows users to easily understand all the functions of the home appliances and devices they have purchased and use them effectively. This also significantly improves user convenience and maximizes product performance.

[1485] "Purchase history" refers to historical information about products and services purchased by a user.

[1486] "Equipment" means electronic and electrical appliances for home or business use.

[1487] "Instruction data" is document data that describes information such as how to handle, install, and use the device.

[1488] "Analysis" is the process of breaking down and organizing complex data or documents into an understandable form.

[1489] "Major functions" refer to the main operations and actions provided by the device.

[1490] "Settings" refers to operations for adjusting the operating conditions and operating modes of a device.

[1491] "Usage Guide" refers to a guide that helps users correctly understand and effectively use the equipment.

[1492] "User terminal" refers to an electronic device or equipment used by a user, and specifically includes a smartphone, computer, etc.

[1493] A "question" is an inquiry submitted by a user to ask questions or clarify how to use or function the device.

[1494] An "answer" is information or explanation provided in response to a user's question.

[1495] "Real-time guidance" refers to instructions and guidance provided instantly on the spot regarding the initial setup and use of equipment.

[1496] "Recommendation information" is information about optimal usage methods and settings that are suggested based on the user's usage status and settings.

[1497] A "server" refers to a computer system that stores and processes data, and is a device that works in conjunction with user terminals via a network.

[1498] This invention is a system that allows users to easily understand and effectively use all the functions of the devices they have purchased. The system includes multiple means for collecting purchase history, analyzing and organizing device instruction manual data, and providing easy-to-understand guidance to users.

[1499] The server has the following means:

[1500] How to collect purchase history: Information on devices purchased by users is collected from e-commerce platforms and sales management systems. The user device sends data to the server every time the purchase history is updated.

[1501] Method for obtaining instruction manual data: Based on the registered device information, obtain the corresponding instruction manual from the manufacturer's website or other online resources. Analyze the obtained data and extract key functions and settings.

[1502] A means for generating a user guide: A user guide that is easy for the user to understand is generated based on the analyzed instruction data. This guide is delivered to the user's device and notified to the user.

[1503] The user terminal has the following means:

[1504] A method for accepting questions: The user inputs a question about how to use the device. The question is sent to the server.

[1505] How to generate answers to questions: The server analyzes the question and generates a corresponding answer using the generative AI model. The answer is then sent to the user's device.

[1506] Means for providing real-time guidance: The user device provides real-time guidance on how to set up and use the device, for example, by showing how to set the refrigerator temperature on a smartphone screen or through an AR display on smart glasses.

[1507] Means for delivering recommendation information: The server generates recommendation information for optimal usage methods and settings based on the user's usage history and settings, and delivers it to the user's device.

[1508] As a specific example, consider a case where a user who has purchased a new refrigerator uses this system to use the refrigerator's quick-freeze function. Once the purchase information is registered in the system, the server automatically downloads the product manual from online and analyzes its contents. When the user asks, "How do I use the quick-freeze function on my refrigerator?", the server analyzes the question, extracts the relevant operating procedures, and generates an answer. Then, with real-time guidance, the user can operate the refrigerator while checking the exact procedures on their smartphone screen or smart glasses.

[1509] An example of a prompt might be:

[1510] "Please check your purchase history. Product ID: 'new_refrigerator_123'"

[1511] "Generate a response to the following relevant user question: 'How do I use the deep freeze in my refrigerator?'"

[1512] To implement this system, the server is built using Python, Django, Flask, etc., and the client application is built using React Native or Unity. PDF analysis libraries such as PyMuPDF and pdfminer are used for data analysis, and SpaCy is used for natural language processing. This configuration allows users to quickly and easily understand the functions of the device and use it in the most optimal way.

[1513] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1514] Step 1:

[1515] The server collects the user's purchase history from the e-commerce platform or sales management system. When the user purchases a new product, the user terminal updates the purchase information in the database and sends the data to the server. The server analyzes the received purchase history data and stores it in the database. The input in this step is the purchase history data, and the output is the analyzed purchase information data.

[1516] Step 2:

[1517] The server retrieves device instruction data from the manufacturer's website or other online resources based on the purchase history data. The server crawls specific URLs and downloads the relevant instruction data. It then analyzes the retrieved instruction data (e.g., in PDF format) and extracts key function and setting information. The input is the purchase history data and instruction data from the online resource, and the output is the analyzed function and setting information.

[1518] Step 3:

[1519] The server generates a user guide that is easy for users to understand based on the analyzed instruction data. Specifically, it creates a text-format guide that summarizes the main functions and setting methods in short, concise steps. The input is the analyzed function and setting information, and the output is the generated user guide. This user guide is distributed from the server to the user's device.

[1520] Step 4:

[1521] The user inputs a question about how to use the device from the user terminal. The user terminal sends the question to the server. The input in this step is the user's question, and the output is the question data sent to the server.

[1522] Step 5:

[1523] The server receives questions from users and analyzes them using natural language processing technology (e.g., SpaCy). Based on the analyzed questions, it generates appropriate answers using a generative AI model. Specifically, the server extracts the relevant operating procedures from a database and generates answer sentences using the generative AI model. The input is the received question data, and the output is the generated answer sentences.

[1524] Step 6:

[1525] The server sends the generated answer to the user terminal, which then displays the answer to the user. The input is the generated answer, and the output is the answer data sent to the user terminal.

[1526] Step 7:

[1527] The user device provides real-time guidance. For example, instructions on how to set the refrigerator temperature can be displayed on a smartphone screen or an AR display on smart glasses. Specifically, the user device displays the usage guide and setting procedures obtained from the server on its interface. The input is the obtained usage guide data, and the output is the real-time guidance provided to the user.

[1528] Step 8:

[1529] The server generates recommendation information for optimal usage methods and settings based on the user's usage history and setting information. The generated recommendation information is distributed to the user's device. The input is the user's usage history and setting information, and the output is the generated recommendation information.

[1530] Step 9:

[1531] The user terminal receives the recommendation information delivered from the server and notifies the user. The user can adjust the product settings and usage methods based on the notification. The input is the recommendation information, and the output is the recommendation information notified to the user.

[1532] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1533] This invention is a system that uses a user's purchase history to analyze the manuals for home appliances and devices, and provides users with easy-to-understand instructions on how to use them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond according to the user's emotions. The "Smart Manual AI" system functions through the following process.

[1534] Collection and analysis of purchase history

[1535] The server collects the user's purchase history from the e-commerce site and the sales management system.

[1536] The user terminal transmits data to the server every time the purchase history is updated.

[1537] The server analyzes the received data and stores information about each product in a database.

[1538] Examples:

[1539] When a user purchases a new television, the information is registered on an e-commerce site. The user's device collects the purchase information and sends it to a server. The server analyzes the purchase information and registers it in a database under the category "television."

[1540] Instruction data acquisition and analysis

[1541] The server retrieves corresponding product manuals from manufacturers' websites and other online resources based on the registered product information, analyzes the retrieved manual data, and extracts key features and settings.

[1542] Examples:

[1543] The server downloads the instruction manual data for the new TV from the manufacturer's website, analyzes the downloaded PDF, and extracts key sections such as "Basic Operations," "Channel Settings," and "How to Use the Remote Control."

[1544] Generate and distribute usage guides

[1545] The server generates a user-friendly usage guide based on the analyzed instruction data, and distributes the generated usage guide to the user's device.

[1546] Examples:

[1547] The server summarizes the TV operation procedure step by step in a concise format such as "How to turn on the power," "How to set the channel," "How to adjust the volume," etc. The generated guide is sent to the user's device, and the user receives a notification.

[1548] User interaction support

[1549] The user inputs questions about usage from the user terminal.

[1550] The user terminal sends a query to the server.

[1551] The server receives the question, analyzes it using natural language processing technology, and generates a corresponding answer, which is then sent to the user's device.

[1552] Examples:

[1553] The user asks "How do I enable the voice guide on my TV?" on their device. The server analyzes the question, extracts the relevant operation procedure from the "Enable voice guide" section, generates an answer, and sends it to the user's device. The user then operates the TV according to the answer.

[1554] Providing recommendations

[1555] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage and settings. The recommendations are then delivered to the user's device.

[1556] Examples:

[1557] The server analyzes the user's TV usage history and makes recommendations such as "recommended picture quality settings" and "how to use energy saving mode." These recommendations are sent to the user's device, and the user is notified.

[1558] Incorporating an emotion engine

[1559] The emotion engine analyzes voice data and facial expression data from the user's device to recognize the user's emotions.

[1560] The server changes the usage guide and answer expressions for the user based on the emotion recognition results.

[1561] Examples:

[1562] When a user enters a question, if the emotion engine detects stress from the user's tone of voice or facial expression, the server will change the answer to something more polite and reassuring, such as "Don't worry. Try these steps and you'll be able to resolve the issue."

[1563] Emotional recommendations

[1564] The server adjusts the content of the recommendations based on the emotional state of the user as recognized by the emotion engine.

[1565] Examples:

[1566] If the user frequently shows signs of anxiety, the server will recommend resources such as an "initial setup guide" or "frequently asked questions" to increase the user's sense of security.

[1567] This invention allows users to easily understand all the functions of the home appliances and devices they have purchased, and receives support tailored to their emotions, allowing them to use the products more comfortably. This significantly improves user convenience and maximizes product performance.

[1568] The processing flow will be explained below.

[1569] Step 1: Collect your purchase history

[1570] The user terminal acquires the user's purchase history from an e-commerce site or a sales management system.

[1571] The user terminal transmits the acquired purchase history to the server.

[1572] Step 2: Analyze your purchase history

[1573] The server analyzes the received purchase history data and extracts detailed information about each product (e.g., product name, category, purchase date, etc.).

[1574] The server stores the extracted information in a database.

[1575] Step 3: Obtaining instruction data

[1576] The server retrieves instruction manual data for the corresponding home appliance or device based on the purchase history information in the database.

[1577] The server downloads instruction data from manufacturer websites and online resources.

[1578] Step 4: Analyzing the instruction data

[1579] The server parses the retrieved instruction data and converts it into a text format (e.g., using PDF parsing or OCR technology).

[1580] The server extracts key features and configuration information from the text data.

[1581] Step 5: Generate a usage guide

[1582] The server generates a usage guide in a format that is easy for the user to understand based on the analyzed instruction data.

[1583] The server stores the generated usage guide in a database.

[1584] Step 6: Distributing the usage guide

[1585] The server distributes the saved usage guide to the user terminal.

[1586] The user terminal displays an access notification of the usage guide to the user.

[1587] Step 7: Ask a question

[1588] The user inputs questions about usage from the user terminal.

[1589] The user terminal sends the question to the server.

[1590] Step 8: Parsing the Question

[1591] The server analyzes the question received from the user using natural language processing technology.

[1592] The server searches the database for instruction data and related information that corresponds to the question.

[1593] Step 9: Generate an answer

[1594] The server automatically generates an appropriate answer based on the analysis results.

[1595] The server formats the generated answer for the user.

[1596] Step 10: Publish your response

[1597] The server generates a response and sends it to the user terminal.

[1598] The user terminal displays the answer to the user.

[1599] Step 11: Serve recommendations

[1600] The server analyzes the user's usage history and current settings.

[1601] The server generates recommendations for optimal usage methods and settings based on the user's usage.

[1602] Step 12: Serving Recommendations

[1603] The server distributes the generated recommendations to the user terminal.

[1604] The user terminal notifies the user of the contents of the recommendation.

[1605] Step 13: Collect emotion data

[1606] The user terminal collects the user's voice data and facial expression data.

[1607] The user terminal transmits the collected emotion data to the server.

[1608] Step 14: Analyze the emotion data

[1609] The emotion data received by the server is analyzed using an emotion engine.

[1610] The server stores the emotion analysis results in a database.

[1611] Step 15: Emotion-based guidance and response adjustments

[1612] The server adjusts the expressions of the usage guide and answers provided to the user based on the analyzed emotion data.

[1613] The server delivers emotion-based responses to the user terminal.

[1614] Step 16: Emotional Recommendations

[1615] The server adjusts the content of the recommendations based on the user's emotional state recognized by the emotion engine.

[1616] The server delivers recommendations based on emotions to the user's device.

[1617] The above is a specific program processing flow of the present invention.

[1618] Example 2

[1619] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1620] Today, home appliances are becoming increasingly multifunctional, making it difficult for users to effectively utilize all the features of the products they purchase. In particular, product manuals can be overly detailed or contain a wealth of technical jargon, making it time-consuming to understand how to use them. Furthermore, uniform support that doesn't take users' feelings into consideration is a factor that reduces satisfaction. Furthermore, there is no system in place to guide users through post-purchase setup or optimal usage, leaving users struggling to resolve issues on their own. There is a need to solve these problems and enable users to make the most of their home appliances.

[1621] The identification process by the identification 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 collecting purchase history, means for acquiring product instruction data based on the purchase history, means for analyzing the instruction data to extract key functions and settings, means for generating a usage guide that is easy for the user to understand based on the extracted information, means for distributing the usage guide to a user terminal, means for accepting questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, an emotion engine for analyzing the user's voice data and facial expression data to recognize emotions, means for changing the expression of the usage guide and answers based on the results of the emotion engine, and means for generating recommendations based on the recognized emotional state. This allows the user to quickly and effectively understand how to use the home appliance they purchased and further receive support and recommendations according to their emotions.

[1622] "Purchase history" is information about products or merchandise purchased by a user in the past.

[1623] "Instruction data" refers to document data that describes how to use a product or item, how to set it up, precautions, etc.

[1624] "Analysis" is the act of examining data and information in detail to clarify meaning and relationships.

[1625] "Major Function" refers to the basic and important operation or role of a product or merchandise.

[1626] "Configuration" is the process of adjusting a product or system to determine its operating conditions and operation methods.

[1627] A "usage guide" is a guide that helps users to understand and use a product or system easily and conveniently.

[1628] A "user terminal" is a device such as a computer or smartphone that can be directly operated by a user.

[1629] "Question acceptance" is the process of receiving inquiries from users.

[1630] "Answer generation" is the task of creating an appropriate answer to a question posed by a user.

[1631] An "emotion engine" is a system that has the function of recognizing emotions by analyzing the user's voice data and facial expression data.

[1632] "Recommendations" suggest optimal actions and settings based on a user's behavioral history and emotional state.

[1633] The present invention is a system that includes "means for collecting purchase history," "means for acquiring product instruction data," "means for analyzing the instruction data and extracting key functions and settings," "means for generating a user-friendly usage guide based on the extracted information," "means for delivering the usage guide to a user terminal," "means for accepting questions from a user and generating answers to those questions," "means for sending the generated answers to the user terminal," "an emotion engine that recognizes emotions by analyzing the user's voice data and facial expression data," "means for changing the expression of the usage guide and answers based on the results of the emotion engine," and "means for generating recommendations based on the recognized emotional state."

[1634] The entire system is mainly composed of a server, a user terminal, and an emotion engine. These elements work together to enable users to quickly and effectively understand how to use the products they have purchased, and also to receive emotional support and recommendations.

[1635] Collection and analysis of purchase history

[1636] The server periodically runs a script that retrieves data from e-commerce sites and sales management systems. It uses an API to retrieve the latest purchase history and stores it in a database. For example, the server sends an API request to an e-commerce site such as Amazon or Rakuten, parses the purchase history data received in response in JSON format, and adds it to the database.

[1637] Instruction data acquisition and analysis

[1638] Based on the product information registered in the database, the server retrieves the corresponding manual from the manufacturer's website or other online resources. Specifically, the server searches for the product name using the Google Custom Search API and obtains the manual's URL. Next, the server downloads the PDF file from the obtained URL and saves it to the file system. The server then uses a PDF parsing library (e.g., Apache PDFBox) to extract text data from the downloaded manual and uses a natural language processing library (e.g., NLTK) to classify it into major sections such as "Basic Operations" and "How to Use the Remote Control."

[1639] Generate and distribute usage guides

[1640] The server automatically generates an easy-to-understand user guide based on the analyzed instruction data. The server uses a template engine (e.g., Jinja2) to document clear instructions. For example, it generates guide sentences that include specific usage procedures, such as "how to turn on the power" and "how to set the channel." The generated user guide is then distributed to the user's device. The user's device displays the guide to the user via a dedicated app or web interface and sends notifications.

[1641] User interaction support

[1642] Users can input questions about how to operate the device from their device. The user device sends the questions to the server, which analyzes the questions and generates appropriate answers. The server uses a natural language processing engine (e.g., BERT) to analyze the questions, search for relevant answers from a database, and format them for the user. The generated answers are sent to the user device and displayed on the device.

[1643] Providing recommendations

[1644] The server analyzes the user's usage history and current settings and generates recommendations for optimal usage and settings. For example, the server uses a machine learning model (e.g., XGBoost) to analyze user behavior data and generate recommendations such as "recommended image quality settings" and "how to utilize energy saving mode." These recommendations are delivered to the user's device and notified to the user.

[1645] Incorporating an emotion engine

[1646] The emotion engine analyzes the voice data and facial expression data acquired from the user's device to recognize the user's emotions. The emotion engine analyzes the voice data using an emotion analysis API (e.g., Google API) and evaluates the facial expression data using a face recognition API (e.g., Microsoft Azure Face API). Based on the recognized emotional state, the server changes the expression of the usage guide and answers. For example, if the user is feeling stressed, the tone of the answers and guide will be changed to be more polite and reassuring.

[1647] Emotional recommendations

[1648] The server adjusts the recommendations based on the results of the emotion engine. If the user feels unsure, it will recommend resources that will increase their sense of security, such as an "initial setup guide" or "frequently asked questions." This allows the user to use all the product's features without stress.

[1649] Examples of prompt statements

[1650] The following format is used as an example of a prompt statement for generating a program for this system:

[1651] Design a system that uses the user's purchase history to analyze product and equipment manuals and provide instructions for use in a way that is easy for the user to understand. In addition, add support functions that combine an emotion engine that recognizes the user's emotions.

[1652] With the above configuration, the present invention enables users to quickly and effectively understand how to use a purchased product and receive support and recommendations that correspond to their emotions.

[1653] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1654] Step 1:

[1655] Collection of purchase history

[1656] The server periodically obtains data from e-commerce sites and sales management systems. Specifically, the server uses an API to send a request to the e-commerce site (e.g., Amazon, Rakuten) and receives the latest purchase history data. The received data is in JSON format, and the server analyzes this data and stores it in a database. The input is the purchase history data obtained from the e-commerce site, and the output is a database that stores the analyzed purchase history.

[1657] Step 2:

[1658] Send purchase history

[1659] When a user purchases a product and their purchase history is updated, the user device sends that information to the server. When a user updates their purchase history within the app, the user device sends that information to the server using an API. The input is the purchase history updated by the user, and the output is the purchase history data sent to the server.

[1660] Step 3:

[1661] Obtaining instruction data

[1662] The server retrieves product information from a database based on purchase history and collects corresponding manuals from manufacturer websites and other online resources. The server uses the Google Custom Search API to search for the product name and obtain the manual URL. It then downloads a PDF file from the URL. The input is the product name, and the output is the downloaded PDF manual.

[1663] Step 4:

[1664] Analysis of instruction data

[1665] The server analyzes the downloaded PDF manual and extracts key functions and settings. It uses a PDF analysis library (e.g., Apache PDFBox) to extract text data and uses a natural language processing library (e.g., NLTK) to classify it into sections such as "Basic Operations" and "How to Use the Remote Control." The input is the PDF manual data, and the output is the analyzed and classified manual data.

[1666] Step 5:

[1667] Creating a usage guide

[1668] The server generates an easy-to-understand user guide based on the parsed instruction data. It uses a template engine (e.g., Jinja2) to document clear instructions for each step. For example, it summarizes procedures such as "how to turn on the power" and "how to set the channel." The input is the parsed instruction data, and the output is the generated instruction guide.

[1669] Step 6:

[1670] Distribution of user guides

[1671] The server delivers the generated usage guide to the user terminal. The user terminal displays the received guide and sends a notification to the user. The input is the generated usage guide, and the output is the guide displayed on the user terminal.

[1672] Step 7:

[1673] Accepting questions from users

[1674] The user inputs a question about how to use the system from the terminal. The user terminal sends the question to the server. The input is the question from the user, and the output is the question data sent to the server.

[1675] Step 8:

[1676] Parsing questions and generating answers

[1677] The server analyzes the received question using natural language processing technology and generates an appropriate answer. It uses a natural language processing engine (e.g., BERT) to analyze the question, extract keywords, and search a database for relevant information. The input is the question data from the user, and the output is the generated answer.

[1678] Step 9:

[1679] Response distribution

[1680] The server sends the generated answer to the user terminal, which displays the answer. The input is the generated answer, and the output is the answer displayed on the user terminal.

[1681] Step 10:

[1682] Recommendation generation and delivery

[1683] The server analyzes the user's usage history and current settings, and generates recommendations for optimal usage methods and settings. Analysis is performed using a machine learning model (e.g., XGBoost) to generate recommendations. The generated recommendations are delivered to the user's device and notified to the user. The input is the user's usage history data, and the output is the generated recommendations.

[1684] Step 11:

[1685] Acquiring and analyzing emotion data

[1686] The emotion engine acquires voice and facial expression data from the user's device and analyzes them to recognize the user's emotions. It analyzes voice data using an emotion analysis API (e.g., Google API) and evaluates facial expressions using a face recognition API (e.g., Microsoft Azure Face API). The input is the user's voice and facial expression data, and the output is recognized emotion data.

[1687] Step 12:

[1688] Emotion-based responses

[1689] The server changes the expressions of the usage guide and answers given to the user based on the results of the emotion engine. If the user is feeling stressed, it will use expressions such as "Don't worry" or change the tone of the guide. The input is the recognized emotion data, and the output is the guide and answers corresponding to the emotion.

[1690] Step 13:

[1691] Providing emotional recommendations

[1692] The server adjusts the content of recommendations based on the emotional state recognized by the emotion engine. If the user is feeling anxious, it will recommend resources that will make them feel at ease (e.g., "Initial Setup Guide" or "FAQ"). The input is the recognized emotional data, and the output is a recommendation based on the emotion.

[1693] (Application example 2)

[1694] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1695] As autonomous vehicles become more multifunctional and technologically advanced, it is becoming increasingly difficult for users to understand and operate all of their functions. Even when users read the vehicle's manual, it is difficult for them to quickly find the information they need, making efficient use of the vehicle particularly challenging in stressful situations. Furthermore, as vehicle technology evolves, there is a need for a method to provide appropriate support tailored to the emotional state of each individual user. Solutions to these challenges are needed.

[1696] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting purchase history, means for acquiring function description data for the autonomously driving vehicle based on the purchase history, means for analyzing the description data and extracting key functions and settings, means for generating a usage guide that is easy for the user to understand based on the extracted information, means for delivering the usage guide to a user terminal, means for receiving questions from the user and generating answers to the questions, means for transmitting the generated answers to the user terminal, means for analyzing the user's emotional state, and means for adjusting the guide and answer expression corresponding to the emotional state. This allows the user to use the functions of the autonomously driving vehicle quickly and easily and receive appropriate support according to their emotional state.

[1697] "Collecting purchase history" is the process of recording and compiling information about products and services purchased by users.

[1698] "Function description data" is a digital document that provides detailed explanations of the various functions and settings of an autonomous vehicle.

[1699] "Analysis" is the process of examining data in detail and understanding its contents.

[1700] "Major functions and settings" are important functions and settings that the user frequently uses or should be aware of when using the vehicle.

[1701] A "user guide" is a user guide that clearly summarizes how to operate a product or system.

[1702] A "user terminal" is a device used by a user, such as a smartphone, tablet, or in-vehicle display.

[1703] "Accepting questions" means receiving inquiries or questions from users and gathering information to respond to them.

[1704] "Generating an answer" means creating appropriate information or solutions for a question received from a user.

[1705] "Analyzing emotional state" is the process of identifying the user's current emotional and psychological state based on their voice and facial expression data.

[1706] "Adjusting the expression of the guide or answer" means changing the expression method or content of the information provided depending on the emotional state of the user.

[1707] The program of the system for realizing the present invention is configured as follows.

[1708] The server collects user purchase history and retrieves the data from e-commerce sites or sales management systems. The server then downloads the autonomous vehicle's function description data from the manufacturer's website or other online resources, and uses a PDF analysis tool (e.g., PyMuPDF) to analyze it. It extracts key functions and settings from the analyzed data and generates a user guide that is easy to understand for the user.

[1709] The generated usage guide is distributed from the cloud server to the user's device (smartphone, tablet, in-car display, etc.). The server accepts questions from the user's device, generates answers to those questions using a natural language processing tool (e.g., spaCy), and sends them to the user's device.

[1710] Furthermore, the system uses emotion analysis tools (e.g., Microsoft Azure Cognitive Services' Emotion Analysis API) to analyze the user's voice and facial expression data. Based on the analysis results, the system adjusts the guidance and response expressions to match the user's emotional state and provides them to the user. This allows users to use the autonomous vehicle quickly and easily, and receive appropriate support according to their emotional state.

[1711] As a concrete example, if a user purchases a new self-driving vehicle and wants to know about the in-car functions and how to set them up, the system operates as follows.

[1712] 1. Users gather information about new autonomous vehicles from their purchase history.

[1713] 2. The server analyzes the information and downloads the function description from the manufacturer's website.

[1714] 3. Analyze the downloaded manual, extract key functions and settings such as "how to start the vehicle" and "how to use the parking assist function," and generate a usage guide.

[1715] 4. The generated usage guide is delivered to the user's device so that the user can check it.

[1716] 5. If a user asks, "I don't know how to set the autonomous driving mode," the server uses natural language processing to generate an answer and sends it to the user's device.

[1717] Additionally, if the sentiment analysis tool determines that the user is feeling stressed, it will provide softer guidance and answers, such as "Take a slow, deep breath and try these steps."

[1718] Example prompt sentence:

[1719] "Please provide a step-by-step guide for setting up the self-parking feature for a self-driving car."

[1720] This will help users better understand the functions of the autonomous vehicle and allow them to receive appropriate support according to their emotional state.

[1721] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1722] Step 1:

[1723] The server collects the user's purchase history. The input is data from an e-commerce site or a sales management system, and the output is purchase history data. Based on this data, vehicle purchase information is identified.

[1724] Step 2:

[1725] The server obtains the function description data of the autonomous vehicle based on the purchase history. The input is the purchase history data, and the output is the URL or file of the function description. The server downloads the corresponding description from the manufacturer's website or other online resources.

[1726] Step 3:

[1727] The server parses the retrieved instruction data. The input is the feature instruction data, and the output is information about the main features and settings. Using a PDF parsing tool (e.g., PyMuPDF), it extracts sections from the instruction manual, such as "How to start the vehicle" and "How to use the parking assist function."

[1728] Step 4:

[1729] The server generates a user-friendly usage guide based on the extracted information. The input is the main features and settings information, and the output is the usage guide. A generative AI model is used to generate the user-friendly guide in text or multimedia format.

[1730] Step 5:

[1731] The server distributes the generated usage guide to the user's device. The input is the usage guide, and the output is a distribution notification to the user's device. The guide is sent via the cloud server to a smartphone, tablet, or in-car display.

[1732] Step 6:

[1733] The user inputs a question via a terminal. The input is the user's question text, and the output is the question sent to the server, which then transmits the user's question to the system.

[1734] Step 7:

[1735] The server generates answers to user questions. The input is the user's question text, and the output is the answer text. Natural language processing tools (e.g., spaCy) are used to analyze the question and generate appropriate answers.

[1736] Step 8:

[1737] The server sends the generated answer to the user terminal. The input is the generated answer text, and the output is a reply to the user terminal. This allows the user to obtain the necessary information.

[1738] Step 9:

[1739] The server uses an emotion analysis tool to analyze the user's emotional state. The input is the user's voice and facial expression data, and the output is the result of identifying the user's emotional state. This tool utilizes the emotion analysis API of Microsoft Azure Cognitive Services.

[1740] Step 10:

[1741] The server adjusts the expression of the guide or answer according to the user's emotional state. The input is the result of identifying the user's emotional state and the generated guide or answer, and the output is the guide or answer adjusted to an expression appropriate to the emotion. This reduces the user's psychological burden and provides stress-free support.

[1742] This allows users to use the functions of an autonomous vehicle quickly and easily, and to receive appropriate support according to their emotional state.

[1743] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1744] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1745] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1746] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1747] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1748] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1749] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1750] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1751] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1752] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1753] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1754] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1755] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1756] 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.

[1757] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1758] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1759] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1760] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1761] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1762] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1763] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1764] The following is further disclosed regarding the above embodiment.

[1765] (Claim 1)

[1766] A means of collecting purchase history;

[1767] means for acquiring instruction manual data for the home appliance based on the purchase history;

[1768] means for analyzing the instruction data to extract key functions and settings;

[1769] means for generating a usage guide that is easy for a user to understand based on the extracted information;

[1770] means for distributing the usage guide to a user terminal;

[1771] means for accepting questions from users and generating answers to those questions;

[1772] means for transmitting the generated response to the user terminal;

[1773] A system including:

[1774] (Claim 2)

[1775] 2. The system according to claim 1, wherein the collection of the purchase history is performed by acquiring data from an e-commerce site or a sales management system.

[1776] (Claim 3)

[1777] 2. The system of claim 1, wherein the instruction data is obtained by downloading it from a manufacturer's website or other online resource.

[1778] (Claim 4)

[1779] 2. The system according to claim 1, wherein the generation of the usage guide is performed in a format including step-by-step operation instructions.

[1780] (Claim 5)

[1781] 2. The system according to claim 1, further comprising means for analyzing the question from the user using a natural language processing technique.

[1782] (Claim 6)

[1783] The system according to claim 1, further comprising means for generating recommendations for optimal usage methods and settings according to the user's usage situation based on the analyzed instruction data.

[1784] "Example 1"

[1785] (Claim 1)

[1786] A means of collecting purchase history;

[1787] means for acquiring instruction manual data for the home appliance based on the purchase history;

[1788] means for analyzing the instruction data to extract key functions and settings;

[1789] means for generating a usage guide that is easy for a user to ...

Claims

1. A means of collecting purchase history; means for acquiring instruction manual data for the home appliance based on the purchase history; means for analyzing the instruction data to extract key functions and settings; means for generating a usage guide that is easy for a user to understand based on the extracted information; means for distributing the usage guide to a user terminal; means for accepting questions from users and generating answers to those questions; means for transmitting the generated response to the user terminal; A system including:

2. 2. The system according to claim 1, wherein the collection of the purchase history is performed by acquiring data from an electronic commerce site or a sales management system.

3. 2. The system of claim 1, wherein the instruction data is obtained by downloading it from a manufacturer's website or other online resource.

4. 2. The system according to claim 1, wherein the generation of the usage guide is performed in a format including step-by-step operation instructions.

5. The system according to claim 1, further comprising means for analyzing the question from the user using natural language processing technology.

6. The system according to claim 1, further comprising means for generating recommendations for optimal usage methods and settings according to the user's usage situation based on the analyzed instruction data.

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