System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a question by a user using a computer or a personal digital assistant; server means for receiving the question and analyzing contents thereof; artificial intelligence means for diagnosing a cause of a problem and a countermeasure based on the question analyzed by the server means; means for returning a diagnosis result generated by the artificial intelligence means to the user; and means for enabling the user to execute the countermeasure based on the diagnosis result.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, when many users encounter problems with their computers or mobile devices, it is difficult to identify the cause and resolve the problem. In particular, when an error message is displayed, it is difficult for general users without specialized knowledge to understand the content and take appropriate measures. In addition, the publicly available Q&A information is limited to general content, making it difficult to grasp the specific status of their own device and derive a solution. To solve this problem, a method is needed that allows users to quickly and accurately understand the device problems they are facing and promote self-resolution. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means: a system including means for a user to input a question using a computer or a mobile information terminal, server means for receiving the question and analyzing its content, artificial intelligence means for diagnosing the cause of a problem and a solution based on the question analyzed by the server means, means for returning the diagnosis result generated by the artificial intelligence means to the user, and means for enabling the user to implement a solution based on the diagnosis result. Furthermore, by providing means for the artificial intelligence means to utilize an external artificial intelligence model via a computer network, and means for the server means to provide an immediate answer based on keywords defined when analyzing the user's question, user convenience can be improved.

[0006] "User" refers to the general user who operates a computer or mobile information terminal.

[0007] "Computer or mobile information terminal" refers to electronic devices used for information processing and communication, such as desktop PCs, laptops, smartphones, and tablet devices.

[0008] "Means for entering questions" refers to an interface that allows users to enter their questions or concerns into the system in text form.

[0009] "Receiving server means" refers to a server and its software for temporarily storing questions received from users and processing their contents.

[0010] "Analyzing" refers to the process of analyzing the content of questions and data received from users and extracting meaning and important elements.

[0011] "Artificial intelligence means" refers to technologies that use machine learning models and neural networks to generate causes of problems and solutions based on analyzed data.

[0012] "Diagnosis" refers to the process of identifying the specific cause of a problem based on analyzed information and deriving appropriate countermeasures.

[0013] "Diagnosis Results" refers to information regarding the cause of a problem and its remedies generated after analysis and diagnosis by artificial intelligence means.

[0014] "Means for returning" refers to a system or interface for notifying or displaying diagnostic results to the user.

[0015] "Means to enable the implementation of measures" refers to a function that provides guidance and assistance to users to actually take action to resolve the problem in accordance with the presented measures.

[0016] A "computer network" refers to a communications infrastructure, such as the Internet, LAN, or WAN, that connects multiple computers and devices to each other and allows them to exchange data.

[0017] An "artificial intelligence model" refers to an algorithm or mathematical model that is trained to perform a specific task, such as text analysis or pattern recognition.

[0018] "Keywords" refer to words that are used as a basis for extracting specific important words or phrases from user questions or input data and judging the content based on them.

[0019] "Instant response methods" refers to systems and technologies that generate appropriate responses to user questions in real time or within a very short time frame. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention is a system for quickly and accurately diagnosing problems with computers and mobile information terminals that users encounter and providing appropriate solutions. Detailed embodiments of this system will be described below.

[0042] System Overview

[0043] This system implements a series of processes in which users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, returning the results. Specifically, it consists of the following elements:

[0044] 1. User Interface

[0045] 2. Server

[0046] 3. Artificial Intelligence Model

[0047] Program Description

[0048] 1. User Interface

[0049] Device:

[0050] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[0051] 2. Server

[0052] server:

[0053] The server is the central component that has many responsibilities. First, it receives the questions submitted by the user. Second, it analyzes the received questions and takes appropriate action based on their content. The server has the following functions:

[0054] Receiving questions

[0055] Question analysis

[0056] Running diagnostic processes

[0057] Generating diagnostic results

[0058] Sending diagnostic results

[0059] The server analyzes the question using internal logic and may generate an immediate diagnosis based on specific keywords, or may require a more detailed diagnosis using an external artificial intelligence model.

[0060] 3. Artificial Intelligence Model

[0061] server:

[0062] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[0063] Specific examples

[0064] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into a chat interface and submits it, the following happens:

[0065] 1. User:

[0066] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[0067] 2. Terminal:

[0068] The entered question is sent to the server.

[0069] 3. Server:

[0070] It receives a question, analyzes the keyword "slow WIFI speed," and immediately generates a diagnostic result based on the specific keyword: "Your WIFI device is clogged with packets, so please restart your WIFI device."

[0071] 4. Terminal:

[0072] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[0073] 5. User:

[0074] Follow the suggested solution and restart your WIFI device to resolve the issue.

[0075] In this way, this system greatly improves user convenience by providing quick and appropriate solutions to technical problems that users face.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] User: Opens the chat interface on their smartphone or PC and types a question into the text box, such as "Why is my Wi-Fi speed so slow on my phone?"

[0079] Step 2:

[0080] User: After entering a question, click the "Submit" button.

[0081] Step 3:

[0082] Terminal: Captures the question entered by the user and sends a POST request to the server, which contains the user's question.

[0083] Step 4:

[0084] Server: Receives the POST request and parses the data sent.

[0085] Step 5:

[0086] Server: Analyzes the user's question and extracts specific keywords (in this case, "slow WIFI speed").

[0087] Step 6:

[0088] Server: Based on the extracted keywords, it searches for predefined countermeasures (such as "The WIFI device is experiencing packet congestion, so please restart the WIFI device") and selects the appropriate diagnosis result.

[0089] Step 7:

[0090] Server: If the diagnosis result cannot be answered by a predefined answer, an external AI API is called to request a detailed analysis and diagnosis of the user's question.

[0091] Step 8:

[0092] Artificial intelligence means: Based on the received question, natural language processing is performed to generate the cause of the problem and solutions.

[0093] Step 9:

[0094] Server: Receives the diagnostic results obtained from the artificial intelligence means and formats them for transmission back to the user.

[0095] Step 10:

[0096] Server: Returns the prepared diagnostic results to the terminal.

[0097] Step 11:

[0098] Terminal: Displays diagnostic results received from the server in the chat interface.

[0099] Step 12:

[0100] User: Check the displayed diagnostic results and take the suggested measures (in this case, "restart the WIFI device").

[0101] This is the specific process flow for responding to user questions. This system allows users to quickly identify their own problems and take appropriate measures.

[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] There is a need to quickly and accurately diagnose technical problems faced by users of computers and mobile information devices and provide appropriate solutions. However, current systems sometimes fail to provide accurate diagnostic results due to insufficient analysis of the question content. Furthermore, when detailed analysis is required, it takes even more time, and the diagnostic results returned are often difficult for users to understand. There is a need to solve these problems and improve 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 a user to input a question using an information processing device; processing means for receiving the question and analyzing the content of the question; intelligence construction means for diagnosing the cause of the problem and solutions based on the analyzed question; means for returning the diagnosis result generated by the intelligence construction means to the user; means for enabling the user to take solutions based on the diagnosis result; and means for generating a prompt sentence from the content of the question and sending it to the intelligence model if the user's question requires detailed analysis by an external intelligence model, thereby providing quick and accurate diagnosis results and enabling users to solve technical problems efficiently.

[0107] "User" refers to a person who uses an information processing device to input questions and execute measures based on the questions.

[0108] "Data processing device" refers to a device, such as a computer or a personal digital assistant, that a user uses to enter a question.

[0109] A "question" refers to the content that a user inputs through an information processing device to inquire about the cause of a technical problem and how to resolve it.

[0110] "Processing means" refers to the means by which the server receives a question, analyzes its content, and generates an appropriate diagnostic result.

[0111] "Intelligence construction means" refers to a means for using an artificial intelligence model to analyze the content of a question and diagnose the cause of the problem and countermeasures.

[0112] "Diagnosis result" refers to information generated by the intelligent configuration means and presented to the user, including the cause of the problem and its solution.

[0113] A "prompt" is a document generated in response to a question that requires further analysis and sent to an external intelligence model.

[0114] An "external intelligence model" is a machine learning model that exists separately from the server and analyzes the detailed content of questions via a communication network.

[0115] "Communication network" refers to a network that connects the server with external intelligence models and sends and receives data.

[0116] The present invention provides a system for quickly and accurately diagnosing problems that users may encounter with information processing devices or mobile information terminals, and providing appropriate solutions. Specific embodiments for carrying out the present invention will be described below.

[0117] System Overview

[0118] The system consists of the following main components:

[0119] 1. User Interface

[0120] 2. Server

[0121] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[0122] Hardware and software used

[0123] 1. User Interface

[0124] Hardware: Computers and mobile devices (e.g., smartphones, tablets, PCs, etc.)

[0125] Software: Web browser and dedicated application

[0126] 2. Server

[0127] Hardware: High-performance server (e.g., cloud server, on-premise server, etc.)

[0128] Software: Web server, database management system, query analysis engine

[0129] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[0130] Software: Machine learning models for natural language processing (e.g., GPT-3 (registered trademark), BERT, etc.)

[0131] External API: A service that provides generative AI models (e.g., a cloud AI service)

[0132] System Operation

[0133] 1. User Interface

[0134] Terminal: A user uses an information processing device to enter a question into a chat-style user interface and clicks the send button.

[0135] Example: A user types "Tell me why my smartphone's Wi-Fi speed is slow" into their smartphone screen and clicks the send button.

[0136] 2. Server

[0137] Server: Receives questions sent from the device and analyzes their contents. It may generate immediate diagnostic results based on specific keywords, or if detailed analysis is required, it may generate and send prompts to an external intelligent configuration means.

[0138] Example: The server analyzes the keyword "slow WIFI speed" and generates a diagnostic result such as "Your WIFI device is clogged with packets, so please restart your WIFI device."

[0139] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[0140] Server: If further analysis is required, create a prompt sentence and send it to the generative AI model.

[0141] Generative AI model: Generates detailed diagnostic results based on prompt statements.

[0142] For example, if the question is "My smartphone's Wi-Fi speed is slow," the prompt "Please explain in detail what is causing the slow Wi-Fi connection on your smartphone" is sent to the generative AI model. As a result, the generative AI model returns a detailed diagnosis result such as "Possible causes include the placement of your router, interference from other devices, and ISP issues."

[0143] 4. Sending and displaying diagnostic results

[0144] Server: Sends the generated diagnostic results to the user's device.

[0145] Terminal: Displays the received diagnostic results in a user interface.

[0146] For example, diagnostic results such as "Please restart your WIFI device" are sent to the device and displayed to the user in the chat interface.

[0147] Specific examples

[0148] When a user types "Why is my smartphone's Wi-Fi speed slow?" into the chat interface and clicks the send button, the device sends this question to the server. The server receives the question, analyzes specific keywords (e.g., "Wi-Fi speed"), and immediately generates a diagnosis result: "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." If a more detailed analysis is required, the prompt "Please explain in detail why my smartphone's Wi-Fi connection is slow" is sent to the AI ​​model, and appropriate measures are provided to the user based on the response.

[0149] In this way, through this system, it is possible to quickly and accurately diagnose technical problems faced by users and provide appropriate solutions.

[0150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0151] Step 1:

[0152] The user types in a question and submits it

[0153] Input: The user enters a question into a text box on an information processing device (computer or mobile information terminal) and clicks the send button.

[0154] How it works: A user types in "Tell me why my phone's Wi-Fi speed is slow" and clicks send.

[0155] Output: The entered question is sent by the terminal to the server.

[0156] Step 2:

[0157] The device sends a question to the server

[0158] Input: Question data entered by the user.

[0159] Operation: The terminal forms and sends a network request to transmit this query data to the server.

[0160] Output: The query data is sent to the server.

[0161] Step 3:

[0162] The server receives and analyzes the query

[0163] Input: Question data sent from the terminal.

[0164] Operation: The server receives the question data and analyzes the question using a string analysis engine. It extracts specific keywords and performs an initial diagnosis.

[0165] Output: Initial diagnostic results based on the keyword "slow WIFI speed", or prompts if further analysis is required.

[0166] Step 4:

[0167] The server generates a diagnostic or prompt statement

[0168] Input: Parsed question content and extracted keywords.

[0169] Operation: If an answer is available immediately, the server generates a simple diagnostic result such as "Your WIFI device is experiencing packet congestion, so please restart your WIFI device." If a more detailed analysis is required, the server generates a prompt based on the question (e.g., "Please explain in detail why your smartphone's WIFI connection is slow").

[0170] Output: A brief diagnostic result or a prompt.

[0171] Step 5:

[0172] The server sends a prompt to the generative AI model (if further analysis is required).

[0173] Input: A prompt statement that is determined to require further analysis.

[0174] Action: Forms and sends an API request to send a prompt sentence to the generative AI model.

[0175] Output: Detailed diagnostic results from the generative AI model.

[0176] Step 6:

[0177] Generative AI models generate detailed diagnostic results

[0178] Input: The prompt text sent by the server.

[0179] How it works: A generative AI model uses natural language processing based on the prompt to generate a detailed diagnosis.

[0180] Output: Detailed diagnostic results generated (e.g. "This may be due to router placement, interference from other devices, or ISP issues").

[0181] Step 7:

[0182] The server receives detailed diagnostic results and sends them back to the user.

[0183] Input: Detailed diagnostic results from a generative AI model.

[0184] How it works: The server receives detailed diagnostic results, converts them into a user-friendly format, and sends them to the device.

[0185] Output: Diagnostic results sent back to the user.

[0186] Step 8:

[0187] The device displays the diagnostic results.

[0188] Input: Diagnostic results sent from the server.

[0189] Behavior: The device displays the diagnostic results it receives in the chat interface and presents them to the user.

[0190] Output: Diagnostic results displayed in the chat interface.

[0191] Step 9:

[0192] The user takes action

[0193] Input: The diagnostic results displayed on the terminal.

[0194] Action: The user takes action based on the presented diagnostic results (e.g., rebooting the WIFI device).

[0195] Output: Problem solved (e.g. improved WIFI speed).

[0196] In this way, each component of the system works together at each step, making it possible to provide quick and appropriate solutions to technical problems faced by users.

[0197] (Application example 1)

[0198] 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."

[0199] In today's brick-and-mortar stores, it is difficult to respond quickly and accurately to customer questions about products and services. Similarly, store staff have limitations in providing accurate answers to all questions. This situation can lead to lower customer satisfaction, which can have a negative impact on store sales and brand image.

[0200] 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.

[0201] In this invention, the server includes means for a user to input a question using a communication terminal, processing device means for receiving the question and analyzing its content, artificial intelligence means for diagnosing the cause of the problem and a solution based on the question analyzed by the processing device means, means for returning the diagnosis result generated by the artificial intelligence means to the user, means for enabling the user to take a solution based on the diagnosis result, and means for receiving, analyzing, and providing an answer when a user asks a question about a product or service in a physical store. This makes it possible for customers to receive a quick and accurate answer when they ask a question about a product or service in a physical store.

[0202] A "communications terminal" is a portable information device or personal computer that allows a user to input and transmit information.

[0203] The "processing device means" is a computer system for analyzing the received question and performing appropriate processing based on the content of the question.

[0204] "Artificial intelligence means" refers to computer learning models and natural language processing systems that analyze the content of questions and diagnose the causes of problems and solutions.

[0205] A "brick and mortar store" is a retail or service establishment that exists in a physical location and provides goods or services in person.

[0206] A "question" is text entered by a user about a question they want answered or information they want to know.

[0207] "Diagnosis results" are information about the results of problem analysis and countermeasures generated by artificial intelligence means.

[0208] An "external artificial intelligence model" is an external machine learning model or natural language processing system connected via a computer network.

[0209] "Keywords" are important concepts or words when analyzing the content of a question.

[0210] This invention is a system that allows users to ask questions about products and services in a physical store via a smartphone or smart glasses, and provides quick and accurate answers to those questions. Specific embodiments of this system are described below.

[0211] System Overview

[0212] The system consists of the following main components:

[0213] 1. User Interface

[0214] 2. Processing equipment

[0215] 3. Artificial Intelligence Model

[0216] User Interface

[0217] The terminal (smartphone or smart glasses) provides a chat interface for the user to input questions. This interface includes a text box and a send button, which sends the questions input by the user to the processing device.

[0218] Processing equipment

[0219] The processing device (server) receives the questions sent by the user and analyzes the content. This analysis includes extracting keywords and understanding the context. The processing device has the following functions:

[0220] Receiving questions

[0221] Question analysis

[0222] Running diagnostic processes

[0223] Generating diagnostic results

[0224] Sending diagnostic results

[0225] Based on the analyzed questions, the processing unit can invoke artificial intelligence models to diagnose the cause of the problem and remedies.

[0226] Artificial Intelligence Model

[0227] The AI ​​model analyzes the question in detail and diagnoses the cause of the problem and countermeasures. The AI ​​model used can be, for example, a generative AI model specialized in natural language processing (e.g., GPT-4 (registered trademark)). This model can be accessed via an external computer network, and can also use external APIs to obtain additional information as needed.

[0228] Specific examples

[0229] For example, if a user is in a physical store and types "Where can I buy this product?" into the chat interface and submits it, the following happens:

[0230] 1. User:

[0231] Type "Where can I buy this product?" into the chat interface and click the send button.

[0232] 2. Terminal:

[0233] The input question is sent to the processing device.

[0234] 3. Processing equipment:

[0235] The question is received and analyzed for keywords such as "product" and "where can I buy it."

[0236] Based on keywords, it instantly generates diagnostic results such as "The product is in the home appliance section on the first floor."

[0237] 4. Artificial Intelligence Model:

[0238] If necessary, an AI model (e.g., GPT-4) is invoked to perform additional analysis.

[0239] 5. Terminal:

[0240] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[0241] 6. Users:

[0242] You can search for and purchase products based on the measures presented.

[0243] Prompt Sentence Examples

[0244] Here are some example prompts for specific in-store situations:

[0245] Q: Where can I buy this product?

[0246] A: Please wait while we check product availability and location. You can find it in the following sections:

[0247] 1. Home appliance section (1st floor)

[0248] 2. You can ask about stock information directly at the information desk.

[0249] In this way, this system provides quick and accurate answers when users ask questions about products or services in physical stores, thereby improving customer satisfaction.

[0250] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0251] Step 1:

[0252] The user types a question into the chat interface and clicks the send button. Input: The text the user types into the chat interface (e.g., "Where can I buy this product?"). Output: The question data sent from the user's device to the server.

[0253] Step 2:

[0254] The terminal sends the entered question to the processing device (server). Input: Question data entered by the user. Output: Question data received by the server.

[0255] Step 3:

[0256] The server receives the question and analyzes its content. Input: Question data received by the server. Output: Analyzed keywords and context data. Specific operation: The question data is analyzed using a text analysis engine (e.g. spaCy) to extract important keywords and context.

[0257] Step 4:

[0258] The server performs diagnostic processing based on the analysis results. Input: Analyzed keywords and contextual data. Output: Diagnostic result data (e.g., "The product is located in the home appliance section on the first floor"). Specific operation: An internal database (e.g., AWS (registered trademark) DynamoDB) is queried based on the keywords, and location information for the relevant product or service is obtained.

[0259] Step 5:

[0260] If necessary, the server calls a generative AI model (e.g., GPT-4) to perform additional analysis. Input: User question data and analysis result data. Output: Supplementary information and detailed answer data from the generative AI model. Specific operation: The server calls the AI ​​model API to generate more detailed explanations and suggestions for the user's question.

[0261] Step 6:

[0262] The server sends the final diagnosis result to the terminal. Input: Diagnostic result data generated by the server. Output: Diagnostic result displayed on the user's terminal. Specific operation: The generated diagnostic result is converted into a chat interface format and sent to the user's terminal.

[0263] Step 7:

[0264] The device displays the diagnostic results in the chat interface and suggests countermeasures to the user. Input: Diagnostic result data sent from the server. Output: The user checks the diagnostic results displayed on the screen. Specific operation: The diagnostic results are displayed in a text box in the chat interface, providing the user with visual countermeasures.

[0265] Step 8:

[0266] The user takes specific action based on the presented solutions. Input: The diagnosis results and solutions displayed in the chat interface. Output: The specific action the user takes (e.g., "Head to the home appliance section"). Specific behavior: The user searches for the product in a physical store based on the displayed results and completes the purchase.

[0267] 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.

[0268] This invention is a system that quickly and accurately diagnoses problems with computers and mobile information terminals that users encounter, provides appropriate solutions, and also combines it with an emotion engine that recognizes the user's emotions. Detailed embodiments of this system are described below.

[0269] System Overview

[0270] This system implements a series of processes: users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, returning the results. It also includes an emotion engine that recognizes the user's emotions and responds appropriately. Specifically, it consists of the following elements:

[0271] 1. User Interface

[0272] 2. Server

[0273] 3. Artificial Intelligence Model

[0274] 4. Emotion Engine

[0275] Program Description

[0276] 1. User Interface

[0277] Device:

[0278] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[0279] 2. Server

[0280] server:

[0281] The server is the central component that performs many roles. First, it receives the questions sent by the user. Second, it analyzes the received questions and takes appropriate action based on their content. Third, it analyzes the emotional state of the user using an emotion engine. The server has the following functions:

[0282] Receiving questions

[0283] Question analysis

[0284] Emotion Analysis

[0285] Running diagnostic processes

[0286] Generating diagnostic results

[0287] Sending diagnostic results

[0288] 3. Artificial Intelligence Model

[0289] server:

[0290] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[0291] 4. Emotion Engine

[0292] server:

[0293] The emotion engine analyzes the text data received from the user and recognizes the user's emotional state. This engine infers the user's emotions from the content and tone of the text and adjusts the way it presents diagnosis results and countermeasures.

[0294] Specific examples

[0295] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into a chat interface and submits it, the following happens:

[0296] 1. User:

[0297] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[0298] 2. Terminal:

[0299] The entered question is sent to the server.

[0300] 3. Server:

[0301] The system receives the question, analyzes the keyword "slow WIFI speed", and simultaneously analyzes the user's emotional state using the emotion engine.

[0302] 4. Emotion Engine:

[0303] The system recognizes the user's emotional state as "anxious" and adjusts the way the diagnosis results are presented based on the results of this emotion analysis.

[0304] 5. Server:

[0305] Based on specific keywords, the system instantly generates a diagnostic result such as "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." At the same time, it includes an additional message based on the results of sentiment analysis, urging the user to remain calm and take appropriate action.

[0306] 6. Terminal:

[0307] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[0308] 7. Users:

[0309] Follow the suggested solution and restart your WIFI device to resolve the issue.

[0310] In this way, the system not only provides quick and appropriate solutions to technical problems faced by users, but also takes into account the user's emotional state in its response, providing a better user experience.

[0311] The processing flow will be explained below.

[0312] Step 1:

[0313] User: Opens the chat interface on their smartphone or PC and types a question into the text box, such as "Why is my Wi-Fi speed so slow on my phone?"

[0314] Step 2:

[0315] User: After entering a question, click the "Submit" button.

[0316] Step 3:

[0317] Terminal: Captures the question entered by the user and sends a POST request to the server, which contains the user's question.

[0318] Step 4:

[0319] Server: Receives the POST request and analyzes the submitted question data in text format, extracting specific keywords and phrases.

[0320] Step 5:

[0321] Server: After analyzing the content of the question, it launches the emotion engine and infers the emotional state from the question entered by the user. This inference uses a machine learning model to classify emotions (e.g., "anxious," "angry," "troubled," etc.) from the user's text.

[0322] Step 6:

[0323] Emotion engine: Analyzes emotions from user input and generates an emotional result, such as "I'm anxious." This result is reflected in the subsequent countermeasure presentation method.

[0324] Step 7:

[0325] Server: Based on the analyzed sentiment results and the question content, the server diagnoses the cause of the problem and proposes solutions. In some cases, an immediate answer can be given based on keywords and phrases, while in other cases, detailed analysis using an external AI model is required.

[0326] Step 8:

[0327] Artificial Intelligence Method: If necessary, call an external artificial intelligence API to generate detailed causes and solutions for user questions, using natural language processing (NLP) technology to generate appropriate answers.

[0328] Step 9:

[0329] Server: Receives the diagnosis results from the AI ​​tool and presents the results in the most appropriate format to the user based on the results of the emotion engine. For example, if a user appears to be "anxious," a message urging them to stay calm is added.

[0330] Step 10:

[0331] Server: Returns the prepared diagnosis results and a message based on the emotion to the device.

[0332] Step 11:

[0333] Terminal: Displays messages in the chat interface based on the diagnosis results and emotions received from the server.

[0334] Step 12:

[0335] User: Check the displayed diagnostic results and take the suggested measures (in this case, the instruction to "restart your Wi-Fi device"), while also paying attention to the emotional messages displayed.

[0336] The above is a concrete processing flow that considers the user's emotions while providing the cause of the problem and solutions. This system not only enables users to quickly identify their own problems and take appropriate measures, but also provides support that takes their emotions into consideration.

[0337] Example 2

[0338] 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."

[0339] Conventional systems lack emotional support when dealing with technical issues faced by users. This results in insufficient support for users to take prompt and appropriate measures, resulting in poor user experience. Therefore, an efficient method for simultaneously diagnosing problems and analyzing emotions is needed.

[0340] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0341] In this invention, the server includes means for a user to input a question using an information terminal, means for receiving the question and analyzing the content thereof, artificial intelligence means for diagnosing the cause of the problem and a solution based on the question analyzed by the server means, means for returning the diagnosis result and emotion analysis result generated by the artificial intelligence means to the user, and means for enabling the user to implement a solution based on the diagnosis result. This makes it possible to not only provide a quick and appropriate solution to a technical problem faced by the user, but also to respond taking into account the emotional state of the user.

[0342] "User interface" refers to a chat-style interface that allows users to input and submit questions using an information terminal.

[0343] The "server" is a computer system that receives questions sent by users, analyzes their content, and generates and returns diagnostic results.

[0344] "Artificial intelligence means" refers to machine learning models and algorithms that analyze questions based on instructions from the server and diagnose the cause of the problem and solutions.

[0345] The "emotion engine" is a system that analyzes text data received from users and recognizes their emotional state.

[0346] "Network" refers to the Internet or other network connections that connect the server to external machine learning models, databases, etc.

[0347] A "machine learning model" is an algorithm or program that is trained to make predictions or classifications based on data.

[0348] "Diagnosis results" are information that includes the causes of problems analyzed by artificial intelligence means and countermeasures for those problems.

[0349] "Emotion analysis result" is information that indicates the emotional state of the user analyzed by the emotion engine.

[0350] An "information terminal" is a device, such as a computer or smartphone, that is operated by the user to input and output information.

[0351] This invention is a system that quickly and accurately diagnoses technical problems faced by users and provides appropriate solutions. Furthermore, by combining it with an emotion engine that recognizes user emotions and responds appropriately, it provides a better user experience. This system consists of the following elements:

[0352] 1. User Interface

[0353] 2. Server

[0354] 3. Artificial Intelligence Methods (Machine Learning Models)

[0355] 4. Emotion Engine

[0356] User Interface

[0357] Device:

[0358] The user interface is a chat-style interface that allows users to enter and submit questions. This interface includes a text box and a submit button. When a user enters a question, the content is transferred to the server.

[0359] server

[0360] server:

[0361] The server receives questions sent by users and analyzes their content. The server has the following functions:

[0362] Receiving a question: The server receives the question data sent from the terminal as an HTTP request.

[0363] Question analysis: Natural language processing (NLP) technology is used to extract keywords from the question.

[0364] Emotion Analysis: Using an emotion engine, we analyze the user's emotional state from the received text.

[0365] Execute diagnostic processing: Call a generative AI model based on the analyzed data to generate appropriate diagnostic results and countermeasures.

[0366] Generating diagnostic results: Based on the diagnostic results and sentiment analysis results, a message is created to be sent back to the user.

[0367] Send diagnostic results: Send the generated message to the terminal and display it to the user.

[0368] Artificial intelligence tools (machine learning models)

[0369] Artificial intelligence means:

[0370] The artificial intelligence means is a machine learning model that analyzes the content of the question in detail based on instructions from the server and diagnoses the cause of the problem and countermeasures. For example, by using an external machine learning model specialized in natural language processing (e.g., OpenAI (registered trademark) GPT-3), the system understands the user's question and generates an appropriate answer.

[0371] Emotion Engine

[0372] Emotion Engine:

[0373] An emotion engine is a system that analyzes text data received from users and recognizes their emotional state. For example, by using an API such as IBM Watson (registered trademark) Natural Language Understanding, it can infer the user's emotions from the content and tone of the text and adjust the way it presents diagnosis results and countermeasures.

[0374] Specific examples

[0375] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and sends it, the following process will occur:

[0376] 1. User:

[0377] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[0378] 2. Terminal:

[0379] The entered question data is converted into JSON format and sent to the server as an HTTP POST request.

[0380] 3. Server:

[0381] The server receives the request and first uses an NLP library (e.g. SpaCy or NLTK) to parse the question.

[0382] 4. Emotion Engine:

[0383] Call a sentiment analysis API (e.g., IBM Watson Natural Language Understanding) to analyze sentiment from the query text. Recognize that the user is impatient.

[0384] 5. Server:

[0385] An AI model (e.g., OpenAI GPT-3) is called to generate the cause of the question and a solution, resulting in a diagnosis such as "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." Based on the results of sentiment analysis, an additional message is also generated, such as "Please stay calm and take action."

[0386] 6. Terminal:

[0387] The diagnostic results and additional messages are displayed in the user interface, allowing the user to check them and take specific measures.

[0388] Prompt Sentence Examples

[0389] For example, to provide a description of this system to a generative AI model, we might use a prompt like this:

[0390] "We have developed a system that can quickly and accurately diagnose technical issues faced by users and provide appropriate solutions. The system also incorporates an emotion engine, which tailors the results based on the user's emotions."

[0391] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0392] Step 1:

[0393] user:

[0394] A user enters a question through the chat interface and clicks the "Send" button. The input is, for example, "Why is my smartphone's Wi-Fi speed so slow?" The output is the user's input text.

[0395] Step 2:

[0396] Device:

[0397] The terminal converts the user's input question into JSON format and sends it to the server as an HTTP POST request. The input is the user's input text, and the output is JSON format data.

[0398] Step 3:

[0399] server:

[0400] The server receives the request and first uses natural language processing (NLP) techniques to analyze the question. This process is performed using an NLP library (e.g., SpaCy or NLTK). The input is the question data in JSON format, and the output is the analyzed keywords and phrases.

[0401] Step 4:

[0402] Emotion Engine:

[0403] The emotion engine analyzes the text data received from the server and calls an emotion analysis API (e.g., IBM Watson Natural Language Understanding) to recognize the user's emotional state. The input is text data containing analyzed keywords and phrases, and the output is the user's emotional state (e.g., "anxious").

[0404] Step 5:

[0405] server:

[0406] The server uses a generative AI model (e.g., OpenAI GPT-3) to generate appropriate diagnostic results and countermeasures based on the emotional state obtained by the emotion engine and the analyzed data. The input is the user's question and emotional state, and the output is the diagnostic result and an additional message. This process generates the diagnostic result "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device" and the additional message "Please stay calm and take action without panicking."

[0407] Step 6:

[0408] Device:

[0409] The terminal displays the diagnostic results and additional messages received from the server on a user interface. The input is the diagnostic results and additional messages, and the output is the display on the user interface.

[0410] Step 7:

[0411] user:

[0412] The user checks the diagnostic results and additional messages displayed on the device and takes specific measures (e.g., "restart the WIFI device"). The input is the diagnostic results and additional messages, and the output is the user's specific action.

[0413] Through these steps, the system is able to quickly and accurately diagnose the user's technical problem and provide appropriate solutions, taking into account their emotional state.

[0414] (Application example 2)

[0415] 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."

[0416] Modern self-driving vehicles use advanced technology, but they face the challenge of responding quickly and appropriately when a system malfunctions. In particular, when a user's emotional state is tense, it is important to provide not only appropriate countermeasures but also a response that reassures the user. This invention aims to not only quickly diagnose and provide solutions to problems faced by the self-driving vehicle, but also to combine an emotion engine to provide appropriate responses based on the user's emotions.

[0417] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0418] In this invention, the server includes means for a user to input a question using an information processing device or a mobile terminal, means for receiving the content of the user's question and analyzing the content, artificial intelligence means for diagnosing the cause of the problem and a solution based on the analyzed question, means for returning the diagnosis result generated by the artificial intelligence means to the user, emotion analysis means for recognizing the emotional state of the user, and means for adjusting the diagnosis result based on the emotion analysis result. This makes it possible to quickly and accurately diagnose problems with the autonomous vehicle and present solutions according to the user's emotions.

[0419] An "information processing device" is an electronic device for processing information, such as a computer or a server.

[0420] A "mobile device" is a portable information processing device such as a smartphone, tablet, or smart glasses.

[0421] A "server" is a high-performance computer that provides services to other computers on a network.

[0422] "Artificial intelligence means" is a system that uses machine learning and natural language processing technology to analyze user questions and diagnose the cause of the problem and solutions.

[0423] "Emotion analysis means" is a technology that recognizes and analyzes emotions from text and voice input by the user.

[0424] "Diagnosis results" are information about the causes of problems and their solutions generated by artificial intelligence means.

[0425] A "user interface" is an operation screen or input device that allows a user to interact with a system.

[0426] The "adjustment means" is a function that modifies the diagnostic results based on the emotion analysis means in accordance with the user's emotions.

[0427] This invention is a system that quickly and accurately diagnoses problems encountered by users in autonomous vehicles and provides appropriate countermeasures. It also incorporates an emotion engine that recognizes the user's emotions. Detailed embodiments of this system are described below.

[0428] System Overview

[0429] The system implements a series of processes: users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, then returns the results. It also has an emotion engine that recognizes the user's emotions and responds appropriately.

[0430] Program Description

[0431] 1. User Interface

[0432] Device:

[0433] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[0434] 2. Server

[0435] server:

[0436] The server is the central component that performs many roles. First, it receives the questions sent by the user. Second, it analyzes the received questions and takes appropriate action based on their content. Third, it analyzes the emotional state of the user using an emotion engine. The server has the following functions:

[0437] Receiving questions

[0438] Question analysis

[0439] Emotion Analysis

[0440] Running diagnostic processes

[0441] Generating diagnostic results

[0442] Sending diagnostic results

[0443] 3. Artificial Intelligence Model

[0444] server:

[0445] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[0446] 4. Emotion Engine

[0447] server:

[0448] The emotion engine analyzes the text data received from the user and recognizes the user's emotional state. This engine infers the user's emotions from the content and tone of the text and adjusts the way it presents diagnosis results and countermeasures.

[0449] Specific examples

[0450] For example, if a user types "My car stopped driving automatically and I don't know why. What should I do?" into the chat interface and sends it, the following happens:

[0451] 1. User:

[0452] Type "My car stopped driving automatically and I don't know why. What should I do?" into the chat interface and click the send button.

[0453] 2. Terminal:

[0454] The entered question is sent to the server.

[0455] 3. Server:

[0456] The system receives the question, analyzes the keyword "autonomous driving stopped," and simultaneously analyzes the user's emotional state using an emotion engine.

[0457] 4. Emotion Engine:

[0458] The system recognizes the user's emotional state as "anxious" and adjusts the way the diagnosis results are presented based on the results of this emotion analysis.

[0459] 5. Server:

[0460] It instantly generates a diagnostic result based on specific keywords, such as "Reboot your system and check for firmware updates," while also including an additional message based on sentiment analysis to remind users to stay calm.

[0461] 6. Terminal:

[0462] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[0463] 7. Users:

[0464] Follow the suggested solutions to resolve the issue.

[0465] Prompt Sentence Examples

[0466] User input: "My Autopilot stopped working and I don't know why. What should I do?"

[0467] Example prompts to input to a generative AI model:

[0468] Q: My car stopped working automatically and I don't know why. What should I do?

[0469] A: It may be that your system has experienced a temporary issue. Please reboot your system and then check for firmware updates. Also, please be patient and follow the guidelines below.

[0470] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0471] Step 1:

[0472] A user inputs a question using an information processing device or a mobile terminal.

[0473] Input: User types into the chat interface, "My autopilot stopped working and I don't know why. What should I do?"

[0474] Output: The entered text data is sent to the server.

[0475] Specific action: The user enters text into a chat-style interface and clicks the send button.

[0476] Step 2:

[0477] The terminal sends the entered question to the server.

[0478] Input: Text data that the user enters and presses the submit button.

[0479] Output: The text data sent to the server.

[0480] Specific operation: The terminal transmits the user's input to the server via the network.

[0481] Step 3:

[0482] The server receives the query and analyzes its contents.

[0483] Input: The user's text data sent to the server.

[0484] Output: Analyzed user question (the keyword "autonomous driving stopped" is extracted).

[0485] Specific operation: The server receives the text data, performs text analysis, and extracts important keywords.

[0486] Step 4:

[0487] The server uses emotion analysis means to analyze the user's emotional state.

[0488] Input: Parsed text data.

[0489] Output: The user's emotional state (e.g., "I'm anxious").

[0490] What it does: The server uses an emotion recognition model to analyze emotions from the context and tone of the user's text.

[0491] Step 5:

[0492] The server uses artificial intelligence means to diagnose the cause of the problem and what to do about it.

[0493] Input: Parsed user question and emotional state.

[0494] Output: Diagnostic results (e.g. "Please reboot your system and check for firmware updates").

[0495] Specific operation: The server utilizes natural language processing technology and uses a generative AI model to diagnose the cause of the problem and provide solutions.

[0496] Step 6:

[0497] The server adjusts the diagnosis results based on the emotion analysis results.

[0498] Input: Diagnosis results and user's emotional state.

[0499] Output: Tailored diagnostic results and messages (e.g. "Please stay calm and follow the steps below").

[0500] Specific operation: The server generates advice and additional messages that reflect the user's emotional state and adds them to the diagnosis results.

[0501] Step 7:

[0502] The server transmits the adjusted diagnostic results to the terminal.

[0503] Input: Adjusted diagnostic results and messages.

[0504] Output: Diagnostic results and messages sent to the terminal.

[0505] Specific operation: The server performs a process to send the adjusted diagnostic results to the terminal via the network.

[0506] Step 8:

[0507] The device will display diagnostic results and messages to the user.

[0508] Input: The adjusted diagnostic results and messages sent by the server.

[0509] Output: Diagnostic results and messages displayed in the user interface.

[0510] Specific operation: The diagnostic results and advice messages are displayed on the terminal display.

[0511] Step 9:

[0512] The user follows the suggested solutions to resolve the issue.

[0513] Input: Diagnostic results and messages displayed on the terminal.

[0514] Output: Actions taken (e.g. system reboot and firmware update).

[0515] Specific actions: The user actually performs an action based on the proposed countermeasures.

[0516] 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.

[0517] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0518] 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.

[0519] [Second embodiment]

[0520] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0521] 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.

[0522] 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).

[0523] 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.

[0524] 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.

[0525] 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).

[0526] 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.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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."

[0532] The present invention is a system for quickly and accurately diagnosing problems with computers and mobile information terminals that users encounter and providing appropriate solutions. Detailed embodiments of this system will be described below.

[0533] System Overview

[0534] This system implements a series of processes in which users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, returning the results. Specifically, it consists of the following elements:

[0535] 1. User Interface

[0536] 2. Server

[0537] 3. Artificial Intelligence Model

[0538] Program Description

[0539] 1. User Interface

[0540] Device:

[0541] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[0542] 2. Server

[0543] server:

[0544] The server is the central component that has many responsibilities. First, it receives the questions submitted by the user. Second, it analyzes the received questions and takes appropriate action based on their content. The server has the following functions:

[0545] Receiving questions

[0546] Question analysis

[0547] Running diagnostic processes

[0548] Generating diagnostic results

[0549] Sending diagnostic results

[0550] The server analyzes the question using internal logic and may generate an immediate diagnosis based on specific keywords, or may require a more detailed diagnosis using an external artificial intelligence model.

[0551] 3. Artificial Intelligence Model

[0552] server:

[0553] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[0554] Specific examples

[0555] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into a chat interface and submits it, the following happens:

[0556] 1. User:

[0557] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[0558] 2. Terminal:

[0559] The entered question is sent to the server.

[0560] 3. Server:

[0561] It receives a question, analyzes the keyword "slow WIFI speed," and immediately generates a diagnostic result based on the specific keyword: "Your WIFI device is clogged with packets, so please restart your WIFI device."

[0562] 4. Terminal:

[0563] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[0564] 5. User:

[0565] Follow the suggested solution and restart your WIFI device to resolve the issue.

[0566] In this way, this system greatly improves user convenience by providing quick and appropriate solutions to technical problems that users face.

[0567] The processing flow will be explained below.

[0568] Step 1:

[0569] User: Opens the chat interface on their smartphone or PC and types a question into the text box, such as "Why is my Wi-Fi speed so slow on my phone?"

[0570] Step 2:

[0571] User: After entering a question, click the "Submit" button.

[0572] Step 3:

[0573] Terminal: Captures the question entered by the user and sends a POST request to the server, which contains the user's question.

[0574] Step 4:

[0575] Server: Receives the POST request and parses the data sent.

[0576] Step 5:

[0577] Server: Analyzes the user's question and extracts specific keywords (in this case, "slow WIFI speed").

[0578] Step 6:

[0579] Server: Based on the extracted keywords, it searches for predefined countermeasures (such as "The WIFI device is experiencing packet congestion, so please restart the WIFI device") and selects the appropriate diagnosis result.

[0580] Step 7:

[0581] Server: If the diagnosis result cannot be answered by a predefined answer, an external AI API is called to request a detailed analysis and diagnosis of the user's question.

[0582] Step 8:

[0583] Artificial intelligence means: Based on the received question, natural language processing is performed to generate the cause of the problem and solutions.

[0584] Step 9:

[0585] Server: Receives the diagnostic results obtained from the artificial intelligence means and formats them for transmission back to the user.

[0586] Step 10:

[0587] Server: Returns the prepared diagnostic results to the terminal.

[0588] Step 11:

[0589] Terminal: Displays diagnostic results received from the server in the chat interface.

[0590] Step 12:

[0591] User: Check the displayed diagnostic results and take the suggested measures (in this case, "restart the WIFI device").

[0592] This is the specific process flow for responding to user questions. This system allows users to quickly identify their own problems and take appropriate measures.

[0593] Example 1

[0594] 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."

[0595] There is a need to quickly and accurately diagnose technical problems faced by users of computers and mobile information devices and provide appropriate solutions. However, current systems sometimes fail to provide accurate diagnostic results due to insufficient analysis of the question content. Furthermore, when detailed analysis is required, it takes even more time, and the diagnostic results returned are often difficult for users to understand. There is a need to solve these problems and improve user convenience.

[0596] 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.

[0597] In this invention, the server includes: means for a user to input a question using an information processing device; processing means for receiving the question and analyzing the content of the question; intelligence construction means for diagnosing the cause of the problem and solutions based on the analyzed question; means for returning the diagnosis result generated by the intelligence construction means to the user; means for enabling the user to take solutions based on the diagnosis result; and means for generating a prompt sentence from the content of the question and sending it to the intelligence model if the user's question requires detailed analysis by an external intelligence model, thereby providing quick and accurate diagnosis results and enabling users to solve technical problems efficiently.

[0598] "User" refers to a person who uses an information processing device to input questions and execute measures based on the questions.

[0599] "Data processing device" refers to a device, such as a computer or a personal digital assistant, that a user uses to enter a question.

[0600] A "question" refers to the content that a user inputs through an information processing device to inquire about the cause of a technical problem and how to resolve it.

[0601] "Processing means" refers to the means by which the server receives a question, analyzes its content, and generates an appropriate diagnostic result.

[0602] "Intelligence construction means" refers to a means for using an artificial intelligence model to analyze the content of a question and diagnose the cause of the problem and countermeasures.

[0603] "Diagnosis result" refers to information generated by the intelligent configuration means and presented to the user, including the cause of the problem and its solution.

[0604] A "prompt" is a document generated in response to a question that requires further analysis and sent to an external intelligence model.

[0605] An "external intelligence model" is a machine learning model that exists separately from the server and analyzes the detailed content of questions via a communication network.

[0606] "Communication network" refers to a network that connects the server with external intelligence models and sends and receives data.

[0607] The present invention provides a system for quickly and accurately diagnosing problems that users may encounter with information processing devices or mobile information terminals, and providing appropriate solutions. Specific embodiments for carrying out the present invention will be described below.

[0608] System Overview

[0609] The system consists of the following main components:

[0610] 1. User Interface

[0611] 2. Server

[0612] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[0613] Hardware and software used

[0614] 1. User Interface

[0615] Hardware: Computers and mobile devices (e.g., smartphones, tablets, PCs, etc.)

[0616] Software: Web browser and dedicated application

[0617] 2. Server

[0618] Hardware: High-performance server (e.g., cloud server, on-premise server, etc.)

[0619] Software: Web server, database management system, query analysis engine

[0620] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[0621] Software: Machine learning models for natural language processing (e.g., GPT-3, BERT, etc.)

[0622] External API: A service that provides generative AI models (e.g., a cloud AI service)

[0623] System Operation

[0624] 1. User Interface

[0625] Terminal: A user uses an information processing device to enter a question into a chat-style user interface and clicks the send button.

[0626] Example: A user types "Tell me why my smartphone's Wi-Fi speed is slow" into their smartphone screen and clicks the send button.

[0627] 2. Server

[0628] Server: Receives questions sent from the device and analyzes their contents. It may generate immediate diagnostic results based on specific keywords, or if detailed analysis is required, it may generate and send prompts to an external intelligent configuration means.

[0629] Example: The server analyzes the keyword "slow WIFI speed" and generates a diagnostic result such as "Your WIFI device is clogged with packets, so please restart your WIFI device."

[0630] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[0631] Server: If further analysis is required, create a prompt sentence and send it to the generative AI model.

[0632] Generative AI model: Generates detailed diagnostic results based on prompt statements.

[0633] For example, if the question is "My smartphone's Wi-Fi speed is slow," the prompt "Please explain in detail what is causing the slow Wi-Fi connection on your smartphone" is sent to the generative AI model. As a result, the generative AI model returns a detailed diagnosis result such as "Possible causes include the placement of your router, interference from other devices, and ISP issues."

[0634] 4. Sending and displaying diagnostic results

[0635] Server: Sends the generated diagnostic results to the user's device.

[0636] Terminal: Displays the received diagnostic results in a user interface.

[0637] For example, diagnostic results such as "Please restart your WIFI device" are sent to the device and displayed to the user in the chat interface.

[0638] Specific examples

[0639] When a user types "Why is my smartphone's Wi-Fi speed slow?" into the chat interface and clicks the send button, the device sends this question to the server. The server receives the question, analyzes specific keywords (e.g., "Wi-Fi speed"), and immediately generates a diagnosis result: "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." If a more detailed analysis is required, the prompt "Please explain in detail why my smartphone's Wi-Fi connection is slow" is sent to the AI ​​model, and appropriate measures are provided to the user based on the response.

[0640] In this way, through this system, it is possible to quickly and accurately diagnose technical problems faced by users and provide appropriate solutions.

[0641] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0642] Step 1:

[0643] The user types in a question and submits it

[0644] Input: The user enters a question into a text box on an information processing device (computer or mobile information terminal) and clicks the send button.

[0645] How it works: A user types in "Tell me why my phone's Wi-Fi speed is slow" and clicks send.

[0646] Output: The entered question is sent by the terminal to the server.

[0647] Step 2:

[0648] The device sends a question to the server

[0649] Input: Question data entered by the user.

[0650] Operation: The terminal forms and sends a network request to transmit this query data to the server.

[0651] Output: The query data is sent to the server.

[0652] Step 3:

[0653] The server receives and analyzes the query

[0654] Input: Question data sent from the terminal.

[0655] Operation: The server receives the question data and analyzes the question using a string analysis engine. It extracts specific keywords and performs an initial diagnosis.

[0656] Output: Initial diagnostic results based on the keyword "slow WIFI speed", or prompts if further analysis is required.

[0657] Step 4:

[0658] The server generates a diagnostic or prompt statement

[0659] Input: Parsed question content and extracted keywords.

[0660] Operation: If an answer is available immediately, the server generates a simple diagnostic result such as "Your WIFI device is experiencing packet congestion, so please restart your WIFI device." If a more detailed analysis is required, the server generates a prompt based on the question (e.g., "Please explain in detail why your smartphone's WIFI connection is slow").

[0661] Output: A brief diagnostic result or a prompt.

[0662] Step 5:

[0663] The server sends a prompt to the generative AI model (if further analysis is required).

[0664] Input: A prompt statement that is determined to require further analysis.

[0665] Action: Forms and sends an API request to send a prompt sentence to the generative AI model.

[0666] Output: Detailed diagnostic results from the generative AI model.

[0667] Step 6:

[0668] Generative AI models generate detailed diagnostic results

[0669] Input: The prompt text sent by the server.

[0670] How it works: A generative AI model uses natural language processing based on the prompt to generate a detailed diagnosis.

[0671] Output: Detailed diagnostic results generated (e.g. "This may be due to router placement, interference from other devices, or ISP issues").

[0672] Step 7:

[0673] The server receives detailed diagnostic results and sends them back to the user.

[0674] Input: Detailed diagnostic results from a generative AI model.

[0675] How it works: The server receives detailed diagnostic results, converts them into a user-friendly format, and sends them to the device.

[0676] Output: Diagnostic results sent back to the user.

[0677] Step 8:

[0678] The device displays the diagnostic results.

[0679] Input: Diagnostic results sent from the server.

[0680] Behavior: The device displays the diagnostic results it receives in the chat interface and presents them to the user.

[0681] Output: Diagnostic results displayed in the chat interface.

[0682] Step 9:

[0683] The user takes action

[0684] Input: The diagnostic results displayed on the terminal.

[0685] Action: The user takes action based on the presented diagnostic results (e.g., rebooting the WIFI device).

[0686] Output: Problem solved (e.g. improved WIFI speed).

[0687] In this way, each component of the system works together at each step, making it possible to provide quick and appropriate solutions to technical problems faced by users.

[0688] (Application example 1)

[0689] 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."

[0690] In today's brick-and-mortar stores, it is difficult to respond quickly and accurately to customer questions about products and services. Similarly, store staff have limitations in providing accurate answers to all questions. This situation can lead to lower customer satisfaction, which can have a negative impact on store sales and brand image.

[0691] 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.

[0692] In this invention, the server includes means for a user to input a question using a communication terminal, processing device means for receiving the question and analyzing its content, artificial intelligence means for diagnosing the cause of the problem and a solution based on the question analyzed by the processing device means, means for returning the diagnosis result generated by the artificial intelligence means to the user, means for enabling the user to take a solution based on the diagnosis result, and means for receiving, analyzing, and providing an answer when a user asks a question about a product or service in a physical store. This makes it possible for customers to receive a quick and accurate answer when they ask a question about a product or service in a physical store.

[0693] A "communications terminal" is a portable information device or personal computer that allows a user to input and transmit information.

[0694] The "processing device means" is a computer system for analyzing the received question and performing appropriate processing based on the content of the question.

[0695] "Artificial intelligence means" refers to computer learning models and natural language processing systems that analyze the content of questions and diagnose the causes of problems and solutions.

[0696] A "brick and mortar store" is a retail or service establishment that exists in a physical location and provides goods or services in person.

[0697] A "question" is text entered by a user about a question they want answered or information they want to know.

[0698] "Diagnosis results" are information about the results of problem analysis and countermeasures generated by artificial intelligence means.

[0699] An "external artificial intelligence model" is an external machine learning model or natural language processing system connected via a computer network.

[0700] "Keywords" are important concepts or words when analyzing the content of a question.

[0701] This invention is a system that allows users to ask questions about products and services in a physical store via a smartphone or smart glasses, and provides quick and accurate answers to those questions. Specific embodiments of this system are described below.

[0702] System Overview

[0703] The system consists of the following main components:

[0704] 1. User Interface

[0705] 2. Processing equipment

[0706] 3. Artificial Intelligence Model

[0707] User Interface

[0708] The terminal (smartphone or smart glasses) provides a chat interface for the user to input questions. This interface includes a text box and a send button, which sends the questions input by the user to the processing device.

[0709] Processing equipment

[0710] The processing device (server) receives the questions sent by the user and analyzes the content. This analysis includes extracting keywords and understanding the context. The processing device has the following functions:

[0711] Receiving questions

[0712] Question analysis

[0713] Running diagnostic processes

[0714] Generating diagnostic results

[0715] Sending diagnostic results

[0716] Based on the analyzed questions, the processing unit can invoke artificial intelligence models to diagnose the cause of the problem and remedies.

[0717] Artificial Intelligence Model

[0718] The AI ​​model analyzes the question in detail and diagnoses the cause of the problem and countermeasures. The AI ​​model used can be, for example, a generative AI model specialized in natural language processing (e.g., GPT-4). This model can be accessed via an external computer network, and can also use external APIs to obtain additional information as needed.

[0719] Specific examples

[0720] For example, if a user is in a physical store and types "Where can I buy this product?" into the chat interface and submits it, the following happens:

[0721] 1. User:

[0722] Type "Where can I buy this product?" into the chat interface and click the send button.

[0723] 2. Terminal:

[0724] The input question is sent to the processing device.

[0725] 3. Processing equipment:

[0726] The question is received and analyzed for keywords such as "product" and "where can I buy it."

[0727] Based on keywords, it instantly generates diagnostic results such as "The product is in the home appliance section on the first floor."

[0728] 4. Artificial Intelligence Model:

[0729] If necessary, an AI model (e.g., GPT-4) is invoked to perform additional analysis.

[0730] 5. Terminal:

[0731] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[0732] 6. Users:

[0733] You can search for and purchase products based on the measures presented.

[0734] Prompt Sentence Examples

[0735] Here are some example prompts for specific in-store situations:

[0736] Q: Where can I buy this product?

[0737] A: Please wait while we check product availability and location. You can find it in the following sections:

[0738] 1. Home appliance section (1st floor)

[0739] 2. You can ask about stock information directly at the information desk.

[0740] In this way, this system provides quick and accurate answers when users ask questions about products or services in physical stores, thereby improving customer satisfaction.

[0741] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0742] Step 1:

[0743] The user types a question into the chat interface and clicks the send button. Input: The text the user types into the chat interface (e.g., "Where can I buy this product?"). Output: The question data sent from the user's device to the server.

[0744] Step 2:

[0745] The terminal sends the entered question to the processing device (server). Input: Question data entered by the user. Output: Question data received by the server.

[0746] Step 3:

[0747] The server receives the question and analyzes its content. Input: Question data received by the server. Output: Analyzed keywords and context data. Specific operation: The question data is analyzed using a text analysis engine (e.g. spaCy) to extract important keywords and context.

[0748] Step 4:

[0749] The server performs diagnostic processing based on the analysis results. Input: Analyzed keywords and contextual data. Output: Diagnostic result data (e.g., "The product is located in the home appliance section on the first floor"). Specific operation: An internal database (e.g., AWS DynamoDB) is queried based on the keywords, and location information for the relevant product or service is obtained.

[0750] Step 5:

[0751] If necessary, the server calls a generative AI model (e.g., GPT-4) to perform additional analysis. Input: User question data and analysis result data. Output: Supplementary information and detailed answer data from the generative AI model. Specific operation: The server calls the AI ​​model API to generate more detailed explanations and suggestions for the user's question.

[0752] Step 6:

[0753] The server sends the final diagnosis result to the terminal. Input: Diagnostic result data generated by the server. Output: Diagnostic result displayed on the user's terminal. Specific operation: The generated diagnostic result is converted into a chat interface format and sent to the user's terminal.

[0754] Step 7:

[0755] The device displays the diagnostic results in the chat interface and suggests countermeasures to the user. Input: Diagnostic result data sent from the server. Output: The user checks the diagnostic results displayed on the screen. Specific operation: The diagnostic results are displayed in a text box in the chat interface, providing the user with visual countermeasures.

[0756] Step 8:

[0757] The user takes specific action based on the presented solutions. Input: The diagnosis results and solutions displayed in the chat interface. Output: The specific action the user takes (e.g., "Head to the home appliance section"). Specific behavior: The user searches for the product in a physical store based on the displayed results and completes the purchase.

[0758] 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.

[0759] This invention is a system that quickly and accurately diagnoses problems with computers and mobile information terminals that users encounter, provides appropriate solutions, and also combines it with an emotion engine that recognizes the user's emotions. Detailed embodiments of this system are described below.

[0760] System Overview

[0761] This system implements a series of processes: users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, returning the results. It also includes an emotion engine that recognizes the user's emotions and responds appropriately. Specifically, it consists of the following elements:

[0762] 1. User Interface

[0763] 2. Server

[0764] 3. Artificial Intelligence Model

[0765] 4. Emotion Engine

[0766] Program Description

[0767] 1. User Interface

[0768] Device:

[0769] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[0770] 2. Server

[0771] server:

[0772] The server is the central component that performs many roles. First, it receives the questions sent by the user. Second, it analyzes the received questions and takes appropriate action based on their content. Third, it analyzes the emotional state of the user using an emotion engine. The server has the following functions:

[0773] Receiving questions

[0774] Question analysis

[0775] Emotion Analysis

[0776] Running diagnostic processes

[0777] Generating diagnostic results

[0778] Sending diagnostic results

[0779] 3. Artificial Intelligence Model

[0780] server:

[0781] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[0782] 4. Emotion Engine

[0783] server:

[0784] The emotion engine analyzes the text data received from the user and recognizes the user's emotional state. This engine infers the user's emotions from the content and tone of the text and adjusts the way it presents diagnosis results and countermeasures.

[0785] Specific examples

[0786] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into a chat interface and submits it, the following happens:

[0787] 1. User:

[0788] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[0789] 2. Terminal:

[0790] The entered question is sent to the server.

[0791] 3. Server:

[0792] The system receives the question, analyzes the keyword "slow WIFI speed", and simultaneously analyzes the user's emotional state using the emotion engine.

[0793] 4. Emotion Engine:

[0794] The system recognizes the user's emotional state as "anxious" and adjusts the way the diagnosis results are presented based on the results of this emotion analysis.

[0795] 5. Server:

[0796] Based on specific keywords, the system instantly generates a diagnostic result such as "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." At the same time, it includes an additional message based on the results of sentiment analysis, urging the user to remain calm and take appropriate action.

[0797] 6. Terminal:

[0798] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[0799] 7. Users:

[0800] Follow the suggested solution and restart your WIFI device to resolve the issue.

[0801] In this way, the system not only provides quick and appropriate solutions to technical problems faced by users, but also takes into account the user's emotional state in its response, providing a better user experience.

[0802] The processing flow will be explained below.

[0803] Step 1:

[0804] User: Opens the chat interface on their smartphone or PC and types a question into the text box, such as "Why is my Wi-Fi speed so slow on my phone?"

[0805] Step 2:

[0806] User: After entering a question, click the "Submit" button.

[0807] Step 3:

[0808] Terminal: Captures the question entered by the user and sends a POST request to the server, which contains the user's question.

[0809] Step 4:

[0810] Server: Receives the POST request and analyzes the submitted question data in text format, extracting specific keywords and phrases.

[0811] Step 5:

[0812] Server: After analyzing the content of the question, it launches the emotion engine and infers the emotional state from the question entered by the user. This inference uses a machine learning model to classify emotions (e.g., "anxious," "angry," "troubled," etc.) from the user's text.

[0813] Step 6:

[0814] Emotion engine: Analyzes emotions from user input and generates an emotional result, such as "I'm anxious." This result is reflected in the subsequent countermeasure presentation method.

[0815] Step 7:

[0816] Server: Based on the analyzed sentiment results and the question content, the server diagnoses the cause of the problem and proposes solutions. In some cases, an immediate answer can be given based on keywords and phrases, while in other cases, detailed analysis using an external AI model is required.

[0817] Step 8:

[0818] Artificial Intelligence Method: If necessary, call an external artificial intelligence API to generate detailed causes and solutions for user questions, using natural language processing (NLP) technology to generate appropriate answers.

[0819] Step 9:

[0820] Server: Receives the diagnosis results from the AI ​​tool and presents the results in the most appropriate format to the user based on the results of the emotion engine. For example, if a user appears to be "anxious," a message urging them to stay calm is added.

[0821] Step 10:

[0822] Server: Returns the prepared diagnosis results and a message based on the emotion to the device.

[0823] Step 11:

[0824] Terminal: Displays messages in the chat interface based on the diagnosis results and emotions received from the server.

[0825] Step 12:

[0826] User: Check the displayed diagnostic results and take the suggested measures (in this case, the instruction to "restart your Wi-Fi device"), while also paying attention to the emotional messages displayed.

[0827] The above is a concrete processing flow that considers the user's emotions while providing the cause of the problem and solutions. This system not only enables users to quickly identify their own problems and take appropriate measures, but also provides support that takes their emotions into consideration.

[0828] Example 2

[0829] 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."

[0830] Conventional systems lack emotional support when dealing with technical issues faced by users. This results in insufficient support for users to take prompt and appropriate measures, resulting in poor user experience. Therefore, an efficient method for simultaneously diagnosing problems and analyzing emotions is needed.

[0831] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0832] In this invention, the server includes means for a user to input a question using an information terminal, means for receiving the question and analyzing the content thereof, artificial intelligence means for diagnosing the cause of the problem and a solution based on the question analyzed by the server means, means for returning the diagnosis result and emotion analysis result generated by the artificial intelligence means to the user, and means for enabling the user to implement a solution based on the diagnosis result. This makes it possible to not only provide a quick and appropriate solution to a technical problem faced by the user, but also to respond taking into account the emotional state of the user.

[0833] "User interface" refers to a chat-style interface that allows users to input and submit questions using an information terminal.

[0834] The "server" is a computer system that receives questions sent by users, analyzes their content, and generates and returns diagnostic results.

[0835] "Artificial intelligence means" refers to machine learning models and algorithms that analyze questions based on instructions from the server and diagnose the cause of the problem and solutions.

[0836] The "emotion engine" is a system that analyzes text data received from users and recognizes their emotional state.

[0837] "Network" refers to the Internet or other network connections that connect the server to external machine learning models, databases, etc.

[0838] A "machine learning model" is an algorithm or program that is trained to make predictions or classifications based on data.

[0839] "Diagnosis results" are information that includes the causes of problems analyzed by artificial intelligence means and countermeasures for those problems.

[0840] "Emotion analysis result" is information that indicates the emotional state of the user analyzed by the emotion engine.

[0841] An "information terminal" is a device, such as a computer or smartphone, that is operated by the user to input and output information.

[0842] This invention is a system that quickly and accurately diagnoses technical problems faced by users and provides appropriate solutions. Furthermore, by combining it with an emotion engine that recognizes user emotions and responds appropriately, it provides a better user experience. This system consists of the following elements:

[0843] 1. User Interface

[0844] 2. Server

[0845] 3. Artificial Intelligence Methods (Machine Learning Models)

[0846] 4. Emotion Engine

[0847] User Interface

[0848] Device:

[0849] The user interface is a chat-style interface that allows users to enter and submit questions. This interface includes a text box and a submit button. When a user enters a question, the content is transferred to the server.

[0850] server

[0851] server:

[0852] The server receives questions sent by users and analyzes their content. The server has the following functions:

[0853] Receiving a question: The server receives the question data sent from the terminal as an HTTP request.

[0854] Question analysis: Natural language processing (NLP) technology is used to extract keywords from the question.

[0855] Emotion Analysis: Using an emotion engine, we analyze the user's emotional state from the received text.

[0856] Execute diagnostic processing: Call a generative AI model based on the analyzed data to generate appropriate diagnostic results and countermeasures.

[0857] Generating diagnostic results: Based on the diagnostic results and sentiment analysis results, a message is created to be sent back to the user.

[0858] Send diagnostic results: Send the generated message to the terminal and display it to the user.

[0859] Artificial intelligence tools (machine learning models)

[0860] Artificial intelligence means:

[0861] The artificial intelligence means is a machine learning model that analyzes the content of the question in detail based on instructions from the server and diagnoses the cause of the problem and countermeasures. For example, by using an external machine learning model specialized in natural language processing (e.g., OpenAI GPT-3), the system can understand the user's question and generate an appropriate answer.

[0862] Emotion Engine

[0863] Emotion Engine:

[0864] An emotion engine is a system that analyzes text data received from users and recognizes their emotional state. For example, by using an API such as IBM Watson Natural Language Understanding, it can infer the user's emotions from the content and tone of the text and adjust the way it presents diagnosis results and countermeasures.

[0865] Specific examples

[0866] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and sends it, the following process will occur:

[0867] 1. User:

[0868] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[0869] 2. Terminal:

[0870] The entered question data is converted into JSON format and sent to the server as an HTTP POST request.

[0871] 3. Server:

[0872] The server receives the request and first uses an NLP library (e.g. SpaCy or NLTK) to parse the question.

[0873] 4. Emotion Engine:

[0874] Call a sentiment analysis API (e.g., IBM Watson Natural Language Understanding) to analyze sentiment from the query text. Recognize that the user is impatient.

[0875] 5. Server:

[0876] An AI model (e.g., OpenAI GPT-3) is called to generate the cause of the question and a solution, resulting in a diagnosis such as "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." Based on the results of sentiment analysis, an additional message is also generated, such as "Please stay calm and take action."

[0877] 6. Terminal:

[0878] The diagnostic results and additional messages are displayed in the user interface, allowing the user to check them and take specific measures.

[0879] Prompt Sentence Examples

[0880] For example, to provide a description of this system to a generative AI model, we might use a prompt like this:

[0881] "We have developed a system that can quickly and accurately diagnose technical issues faced by users and provide appropriate solutions. The system also incorporates an emotion engine, which tailors the results based on the user's emotions."

[0882] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0883] Step 1:

[0884] user:

[0885] A user enters a question through the chat interface and clicks the "Send" button. The input is, for example, "Why is my smartphone's Wi-Fi speed so slow?" The output is the user's input text.

[0886] Step 2:

[0887] Device:

[0888] The terminal converts the user's input question into JSON format and sends it to the server as an HTTP POST request. The input is the user's input text, and the output is JSON format data.

[0889] Step 3:

[0890] server:

[0891] The server receives the request and first uses natural language processing (NLP) techniques to analyze the question. This process is performed using an NLP library (e.g., SpaCy or NLTK). The input is the question data in JSON format, and the output is the analyzed keywords and phrases.

[0892] Step 4:

[0893] Emotion Engine:

[0894] The emotion engine analyzes the text data received from the server and calls an emotion analysis API (e.g., IBM Watson Natural Language Understanding) to recognize the user's emotional state. The input is text data containing analyzed keywords and phrases, and the output is the user's emotional state (e.g., "anxious").

[0895] Step 5:

[0896] server:

[0897] The server uses a generative AI model (e.g., OpenAI GPT-3) to generate appropriate diagnostic results and countermeasures based on the emotional state obtained by the emotion engine and the analyzed data. The input is the user's question and emotional state, and the output is the diagnostic result and an additional message. This process generates the diagnostic result "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device" and the additional message "Please stay calm and take action without panicking."

[0898] Step 6:

[0899] Device:

[0900] The terminal displays the diagnostic results and additional messages received from the server on a user interface. The input is the diagnostic results and additional messages, and the output is the display on the user interface.

[0901] Step 7:

[0902] user:

[0903] The user checks the diagnostic results and additional messages displayed on the device and takes specific measures (e.g., "restart the WIFI device"). The input is the diagnostic results and additional messages, and the output is the user's specific action.

[0904] Through these steps, the system is able to quickly and accurately diagnose the user's technical problem and provide appropriate solutions, taking into account their emotional state.

[0905] (Application example 2)

[0906] 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."

[0907] Modern self-driving vehicles use advanced technology, but they face the challenge of responding quickly and appropriately when a system malfunctions. In particular, when a user's emotional state is tense, it is important to provide not only appropriate countermeasures but also a response that reassures the user. This invention aims to not only quickly diagnose and provide solutions to problems faced by the self-driving vehicle, but also to combine an emotion engine to provide appropriate responses based on the user's emotions.

[0908] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0909] In this invention, the server includes means for a user to input a question using an information processing device or a mobile terminal, means for receiving the content of the user's question and analyzing the content, artificial intelligence means for diagnosing the cause of the problem and a solution based on the analyzed question, means for returning the diagnosis result generated by the artificial intelligence means to the user, emotion analysis means for recognizing the emotional state of the user, and means for adjusting the diagnosis result based on the emotion analysis result. This makes it possible to quickly and accurately diagnose problems with the autonomous vehicle and present solutions according to the user's emotions.

[0910] An "information processing device" is an electronic device for processing information, such as a computer or a server.

[0911] A "mobile device" is a portable information processing device such as a smartphone, tablet, or smart glasses.

[0912] A "server" is a high-performance computer that provides services to other computers on a network.

[0913] "Artificial intelligence means" is a system that uses machine learning and natural language processing technology to analyze user questions and diagnose the cause of the problem and solutions.

[0914] "Emotion analysis means" is a technology that recognizes and analyzes emotions from text and voice input by the user.

[0915] "Diagnosis results" are information about the causes of problems and their solutions generated by artificial intelligence means.

[0916] A "user interface" is an operation screen or input device that allows a user to interact with a system.

[0917] The "adjustment means" is a function that modifies the diagnostic results based on the emotion analysis means in accordance with the user's emotions.

[0918] This invention is a system that quickly and accurately diagnoses problems encountered by users in autonomous vehicles and provides appropriate countermeasures. It also incorporates an emotion engine that recognizes the user's emotions. Detailed embodiments of this system are described below.

[0919] System Overview

[0920] The system implements a series of processes: users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, then returns the results. It also has an emotion engine that recognizes the user's emotions and responds appropriately.

[0921] Program Description

[0922] 1. User Interface

[0923] Device:

[0924] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[0925] 2. Server

[0926] server:

[0927] The server is the central component that performs many roles. First, it receives the questions sent by the user. Second, it analyzes the received questions and takes appropriate action based on their content. Third, it analyzes the emotional state of the user using an emotion engine. The server has the following functions:

[0928] Receiving questions

[0929] Question analysis

[0930] Emotion Analysis

[0931] Running diagnostic processes

[0932] Generating diagnostic results

[0933] Sending diagnostic results

[0934] 3. Artificial Intelligence Model

[0935] server:

[0936] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[0937] 4. Emotion Engine

[0938] server:

[0939] The emotion engine analyzes the text data received from the user and recognizes the user's emotional state. This engine infers the user's emotions from the content and tone of the text and adjusts the way it presents diagnosis results and countermeasures.

[0940] Specific examples

[0941] For example, if a user types "My car stopped driving automatically and I don't know why. What should I do?" into the chat interface and sends it, the following happens:

[0942] 1. User:

[0943] Type "My car stopped driving automatically and I don't know why. What should I do?" into the chat interface and click the send button.

[0944] 2. Terminal:

[0945] The entered question is sent to the server.

[0946] 3. Server:

[0947] The system receives the question, analyzes the keyword "autonomous driving stopped," and simultaneously analyzes the user's emotional state using an emotion engine.

[0948] 4. Emotion Engine:

[0949] The system recognizes the user's emotional state as "anxious" and adjusts the way the diagnosis results are presented based on the results of this emotion analysis.

[0950] 5. Server:

[0951] It instantly generates a diagnostic result based on specific keywords, such as "Reboot your system and check for firmware updates," while also including an additional message based on sentiment analysis to remind users to stay calm.

[0952] 6. Terminal:

[0953] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[0954] 7. Users:

[0955] Follow the suggested solutions to resolve the issue.

[0956] Prompt Sentence Examples

[0957] User input: "My Autopilot stopped working and I don't know why. What should I do?"

[0958] Example prompts to input to a generative AI model:

[0959] Q: My car stopped working automatically and I don't know why. What should I do?

[0960] A: It may be that your system has experienced a temporary issue. Please reboot your system and then check for firmware updates. Also, please be patient and follow the guidelines below.

[0961] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0962] Step 1:

[0963] A user inputs a question using an information processing device or a mobile terminal.

[0964] Input: User types into the chat interface, "My autopilot stopped working and I don't know why. What should I do?"

[0965] Output: The entered text data is sent to the server.

[0966] Specific action: The user enters text into a chat-style interface and clicks the send button.

[0967] Step 2:

[0968] The terminal sends the entered question to the server.

[0969] Input: Text data that the user enters and presses the submit button.

[0970] Output: The text data sent to the server.

[0971] Specific operation: The terminal transmits the user's input to the server via the network.

[0972] Step 3:

[0973] The server receives the query and analyzes its contents.

[0974] Input: The user's text data sent to the server.

[0975] Output: Analyzed user question (the keyword "autonomous driving stopped" is extracted).

[0976] Specific operation: The server receives the text data, performs text analysis, and extracts important keywords.

[0977] Step 4:

[0978] The server uses emotion analysis means to analyze the user's emotional state.

[0979] Input: Parsed text data.

[0980] Output: The user's emotional state (e.g., "I'm anxious").

[0981] What it does: The server uses an emotion recognition model to analyze emotions from the context and tone of the user's text.

[0982] Step 5:

[0983] The server uses artificial intelligence means to diagnose the cause of the problem and what to do about it.

[0984] Input: Parsed user question and emotional state.

[0985] Output: Diagnostic results (e.g. "Please reboot your system and check for firmware updates").

[0986] Specific operation: The server utilizes natural language processing technology and uses a generative AI model to diagnose the cause of the problem and provide solutions.

[0987] Step 6:

[0988] The server adjusts the diagnosis results based on the emotion analysis results.

[0989] Input: Diagnosis results and user's emotional state.

[0990] Output: Tailored diagnostic results and messages (e.g. "Please stay calm and follow the steps below").

[0991] Specific operation: The server generates advice and additional messages that reflect the user's emotional state and adds them to the diagnosis results.

[0992] Step 7:

[0993] The server transmits the adjusted diagnostic results to the terminal.

[0994] Input: Adjusted diagnostic results and messages.

[0995] Output: Diagnostic results and messages sent to the terminal.

[0996] Specific operation: The server performs a process to send the adjusted diagnostic results to the terminal via the network.

[0997] Step 8:

[0998] The device will display diagnostic results and messages to the user.

[0999] Input: The adjusted diagnostic results and messages sent by the server.

[1000] Output: Diagnostic results and messages displayed in the user interface.

[1001] Specific operation: The diagnostic results and advice messages are displayed on the terminal display.

[1002] Step 9:

[1003] The user follows the suggested solutions to resolve the issue.

[1004] Input: Diagnostic results and messages displayed on the terminal.

[1005] Output: Actions taken (e.g. system reboot and firmware update).

[1006] Specific actions: The user actually performs an action based on the proposed countermeasures.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] [Third embodiment]

[1011] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1012] 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.

[1013] 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).

[1014] 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.

[1015] 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.

[1016] 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).

[1017] 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.

[1018] 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.

[1019] 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.

[1020] 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.

[1021] 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.

[1022] 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."

[1023] The present invention is a system for quickly and accurately diagnosing problems with computers and mobile information terminals that users encounter and providing appropriate solutions. Detailed embodiments of this system will be described below.

[1024] System Overview

[1025] This system implements a series of processes in which users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, returning the results. Specifically, it consists of the following elements:

[1026] 1. User Interface

[1027] 2. Server

[1028] 3. Artificial Intelligence Model

[1029] Program Description

[1030] 1. User Interface

[1031] Device:

[1032] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[1033] 2. Server

[1034] server:

[1035] The server is the central component that has many responsibilities. First, it receives the questions submitted by the user. Second, it analyzes the received questions and takes appropriate action based on their content. The server has the following functions:

[1036] Receiving questions

[1037] Question analysis

[1038] Running diagnostic processes

[1039] Generating diagnostic results

[1040] Sending diagnostic results

[1041] The server analyzes the question using internal logic and may generate an immediate diagnosis based on specific keywords, or may require a more detailed diagnosis using an external artificial intelligence model.

[1042] 3. Artificial Intelligence Model

[1043] server:

[1044] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[1045] Specific examples

[1046] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into a chat interface and submits it, the following happens:

[1047] 1. User:

[1048] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[1049] 2. Terminal:

[1050] The entered question is sent to the server.

[1051] 3. Server:

[1052] It receives a question, analyzes the keyword "slow WIFI speed," and immediately generates a diagnostic result based on the specific keyword: "Your WIFI device is clogged with packets, so please restart your WIFI device."

[1053] 4. Terminal:

[1054] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[1055] 5. User:

[1056] Follow the suggested solution and restart your WIFI device to resolve the issue.

[1057] In this way, this system greatly improves user convenience by providing quick and appropriate solutions to technical problems that users face.

[1058] The processing flow will be explained below.

[1059] Step 1:

[1060] User: Opens the chat interface on their smartphone or PC and types a question into the text box, such as "Why is my Wi-Fi speed so slow on my phone?"

[1061] Step 2:

[1062] User: After entering a question, click the "Submit" button.

[1063] Step 3:

[1064] Terminal: Captures the question entered by the user and sends a POST request to the server, which contains the user's question.

[1065] Step 4:

[1066] Server: Receives the POST request and parses the data sent.

[1067] Step 5:

[1068] Server: Analyzes the user's question and extracts specific keywords (in this case, "slow WIFI speed").

[1069] Step 6:

[1070] Server: Based on the extracted keywords, it searches for predefined countermeasures (such as "The WIFI device is experiencing packet congestion, so please restart the WIFI device") and selects the appropriate diagnosis result.

[1071] Step 7:

[1072] Server: If the diagnosis result cannot be answered by a predefined answer, an external AI API is called to request a detailed analysis and diagnosis of the user's question.

[1073] Step 8:

[1074] Artificial intelligence means: Based on the received question, natural language processing is performed to generate the cause of the problem and solutions.

[1075] Step 9:

[1076] Server: Receives the diagnostic results obtained from the artificial intelligence means and formats them for transmission back to the user.

[1077] Step 10:

[1078] Server: Returns the prepared diagnostic results to the terminal.

[1079] Step 11:

[1080] Terminal: Displays diagnostic results received from the server in the chat interface.

[1081] Step 12:

[1082] User: Check the displayed diagnostic results and take the suggested measures (in this case, "restart the WIFI device").

[1083] This is the specific process flow for responding to user questions. This system allows users to quickly identify their own problems and take appropriate measures.

[1084] Example 1

[1085] 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."

[1086] There is a need to quickly and accurately diagnose technical problems faced by users of computers and mobile information devices and provide appropriate solutions. However, current systems sometimes fail to provide accurate diagnostic results due to insufficient analysis of the question content. Furthermore, when detailed analysis is required, it takes even more time, and the diagnostic results returned are often difficult for users to understand. There is a need to solve these problems and improve user convenience.

[1087] 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.

[1088] In this invention, the server includes: means for a user to input a question using an information processing device; processing means for receiving the question and analyzing the content of the question; intelligence construction means for diagnosing the cause of the problem and solutions based on the analyzed question; means for returning the diagnosis result generated by the intelligence construction means to the user; means for enabling the user to take solutions based on the diagnosis result; and means for generating a prompt sentence from the content of the question and sending it to the intelligence model if the user's question requires detailed analysis by an external intelligence model, thereby providing quick and accurate diagnosis results and enabling users to solve technical problems efficiently.

[1089] "User" refers to a person who uses an information processing device to input questions and execute measures based on the questions.

[1090] "Data processing device" refers to a device, such as a computer or a personal digital assistant, that a user uses to enter a question.

[1091] A "question" refers to the content that a user inputs through an information processing device to inquire about the cause of a technical problem and how to resolve it.

[1092] "Processing means" refers to the means by which the server receives a question, analyzes its content, and generates an appropriate diagnostic result.

[1093] "Intelligence construction means" refers to a means for using an artificial intelligence model to analyze the content of a question and diagnose the cause of the problem and countermeasures.

[1094] "Diagnosis result" refers to information generated by the intelligent configuration means and presented to the user, including the cause of the problem and its solution.

[1095] A "prompt" is a document generated in response to a question that requires further analysis and sent to an external intelligence model.

[1096] An "external intelligence model" is a machine learning model that exists separately from the server and analyzes the detailed content of questions via a communication network.

[1097] "Communication network" refers to a network that connects the server with external intelligence models and sends and receives data.

[1098] The present invention provides a system for quickly and accurately diagnosing problems that users may encounter with information processing devices or mobile information terminals, and providing appropriate solutions. Specific embodiments for carrying out the present invention will be described below.

[1099] System Overview

[1100] The system consists of the following main components:

[1101] 1. User Interface

[1102] 2. Server

[1103] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[1104] Hardware and software used

[1105] 1. User Interface

[1106] Hardware: Computers and mobile devices (e.g., smartphones, tablets, PCs, etc.)

[1107] Software: Web browser and dedicated application

[1108] 2. Server

[1109] Hardware: High-performance server (e.g., cloud server, on-premise server, etc.)

[1110] Software: Web server, database management system, query analysis engine

[1111] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[1112] Software: Machine learning models for natural language processing (e.g., GPT-3, BERT, etc.)

[1113] External API: A service that provides generative AI models (e.g., a cloud AI service)

[1114] System Operation

[1115] 1. User Interface

[1116] Terminal: A user uses an information processing device to enter a question into a chat-style user interface and clicks the send button.

[1117] Example: A user types "Tell me why my smartphone's Wi-Fi speed is slow" into their smartphone screen and clicks the send button.

[1118] 2. Server

[1119] Server: Receives questions sent from the device and analyzes their contents. It may generate immediate diagnostic results based on specific keywords, or if detailed analysis is required, it may generate and send prompts to an external intelligent configuration means.

[1120] Example: The server analyzes the keyword "slow WIFI speed" and generates a diagnostic result such as "Your WIFI device is clogged with packets, so please restart your WIFI device."

[1121] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[1122] Server: If further analysis is required, create a prompt sentence and send it to the generative AI model.

[1123] Generative AI model: Generates detailed diagnostic results based on prompt statements.

[1124] For example, if the question is "My smartphone's Wi-Fi speed is slow," the prompt "Please explain in detail what is causing the slow Wi-Fi connection on your smartphone" is sent to the generative AI model. As a result, the generative AI model returns a detailed diagnosis result such as "Possible causes include the placement of your router, interference from other devices, and ISP issues."

[1125] 4. Sending and displaying diagnostic results

[1126] Server: Sends the generated diagnostic results to the user's device.

[1127] Terminal: Displays the received diagnostic results in a user interface.

[1128] For example, diagnostic results such as "Please restart your WIFI device" are sent to the device and displayed to the user in the chat interface.

[1129] Specific examples

[1130] When a user types "Why is my smartphone's Wi-Fi speed slow?" into the chat interface and clicks the send button, the device sends this question to the server. The server receives the question, analyzes specific keywords (e.g., "Wi-Fi speed"), and immediately generates a diagnosis result: "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." If a more detailed analysis is required, the prompt "Please explain in detail why my smartphone's Wi-Fi connection is slow" is sent to the AI ​​model, and appropriate measures are provided to the user based on the response.

[1131] In this way, through this system, it is possible to quickly and accurately diagnose technical problems faced by users and provide appropriate solutions.

[1132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1133] Step 1:

[1134] The user types in a question and submits it

[1135] Input: The user enters a question into a text box on an information processing device (computer or mobile information terminal) and clicks the send button.

[1136] How it works: A user types in "Tell me why my phone's Wi-Fi speed is slow" and clicks send.

[1137] Output: The entered question is sent by the terminal to the server.

[1138] Step 2:

[1139] The device sends a question to the server

[1140] Input: Question data entered by the user.

[1141] Operation: The terminal forms and sends a network request to transmit this query data to the server.

[1142] Output: The query data is sent to the server.

[1143] Step 3:

[1144] The server receives and analyzes the query

[1145] Input: Question data sent from the terminal.

[1146] Operation: The server receives the question data and analyzes the question using a string analysis engine. It extracts specific keywords and performs an initial diagnosis.

[1147] Output: Initial diagnostic results based on the keyword "slow WIFI speed", or prompts if further analysis is required.

[1148] Step 4:

[1149] The server generates a diagnostic or prompt statement

[1150] Input: Parsed question content and extracted keywords.

[1151] Operation: If an answer is available immediately, the server generates a simple diagnostic result such as "Your WIFI device is experiencing packet congestion, so please restart your WIFI device." If a more detailed analysis is required, the server generates a prompt based on the question (e.g., "Please explain in detail why your smartphone's WIFI connection is slow").

[1152] Output: A brief diagnostic result or a prompt.

[1153] Step 5:

[1154] The server sends a prompt to the generative AI model (if further analysis is required).

[1155] Input: A prompt statement that is determined to require further analysis.

[1156] Action: Forms and sends an API request to send a prompt sentence to the generative AI model.

[1157] Output: Detailed diagnostic results from the generative AI model.

[1158] Step 6:

[1159] Generative AI models generate detailed diagnostic results

[1160] Input: The prompt text sent by the server.

[1161] How it works: A generative AI model uses natural language processing based on the prompt to generate a detailed diagnosis.

[1162] Output: Detailed diagnostic results generated (e.g. "This may be due to router placement, interference from other devices, or ISP issues").

[1163] Step 7:

[1164] The server receives detailed diagnostic results and sends them back to the user.

[1165] Input: Detailed diagnostic results from a generative AI model.

[1166] How it works: The server receives detailed diagnostic results, converts them into a user-friendly format, and sends them to the device.

[1167] Output: Diagnostic results sent back to the user.

[1168] Step 8:

[1169] The device displays the diagnostic results.

[1170] Input: Diagnostic results sent from the server.

[1171] Behavior: The device displays the diagnostic results it receives in the chat interface and presents them to the user.

[1172] Output: Diagnostic results displayed in the chat interface.

[1173] Step 9:

[1174] The user takes action

[1175] Input: The diagnostic results displayed on the terminal.

[1176] Action: The user takes action based on the presented diagnostic results (e.g., rebooting the WIFI device).

[1177] Output: Problem solved (e.g. improved WIFI speed).

[1178] In this way, each component of the system works together at each step, making it possible to provide quick and appropriate solutions to technical problems faced by users.

[1179] (Application example 1)

[1180] 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."

[1181] In today's brick-and-mortar stores, it is difficult to respond quickly and accurately to customer questions about products and services. Similarly, store staff have limitations in providing accurate answers to all questions. This situation can lead to lower customer satisfaction, which can have a negative impact on store sales and brand image.

[1182] 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.

[1183] In this invention, the server includes means for a user to input a question using a communication terminal, processing device means for receiving the question and analyzing its content, artificial intelligence means for diagnosing the cause of the problem and a solution based on the question analyzed by the processing device means, means for returning the diagnosis result generated by the artificial intelligence means to the user, means for enabling the user to take a solution based on the diagnosis result, and means for receiving, analyzing, and providing an answer when a user asks a question about a product or service in a physical store. This makes it possible for customers to receive a quick and accurate answer when they ask a question about a product or service in a physical store.

[1184] A "communications terminal" is a portable information device or personal computer that allows a user to input and transmit information.

[1185] The "processing device means" is a computer system for analyzing the received question and performing appropriate processing based on the content of the question.

[1186] "Artificial intelligence means" refers to computer learning models and natural language processing systems that analyze the content of questions and diagnose the causes of problems and solutions.

[1187] A "brick and mortar store" is a retail or service establishment that exists in a physical location and provides goods or services in person.

[1188] A "question" is text entered by a user about a question they want answered or information they want to know.

[1189] "Diagnosis results" are information about the results of problem analysis and countermeasures generated by artificial intelligence means.

[1190] An "external artificial intelligence model" is an external machine learning model or natural language processing system connected via a computer network.

[1191] "Keywords" are important concepts or words when analyzing the content of a question.

[1192] This invention is a system that allows users to ask questions about products and services in a physical store via a smartphone or smart glasses, and provides quick and accurate answers to those questions. Specific embodiments of this system are described below.

[1193] System Overview

[1194] The system consists of the following main components:

[1195] 1. User Interface

[1196] 2. Processing equipment

[1197] 3. Artificial Intelligence Model

[1198] User Interface

[1199] The terminal (smartphone or smart glasses) provides a chat interface for the user to input questions. This interface includes a text box and a send button, which sends the questions input by the user to the processing device.

[1200] Processing equipment

[1201] The processing device (server) receives the questions sent by the user and analyzes the content. This analysis includes extracting keywords and understanding the context. The processing device has the following functions:

[1202] Receiving questions

[1203] Question analysis

[1204] Running diagnostic processes

[1205] Generating diagnostic results

[1206] Sending diagnostic results

[1207] Based on the analyzed questions, the processing unit can invoke artificial intelligence models to diagnose the cause of the problem and remedies.

[1208] Artificial Intelligence Model

[1209] The AI ​​model analyzes the question in detail and diagnoses the cause of the problem and countermeasures. The AI ​​model used can be, for example, a generative AI model specialized in natural language processing (e.g., GPT-4). This model can be accessed via an external computer network, and can also use external APIs to obtain additional information as needed.

[1210] Specific examples

[1211] For example, if a user is in a physical store and types "Where can I buy this product?" into the chat interface and submits it, the following happens:

[1212] 1. User:

[1213] Type "Where can I buy this product?" into the chat interface and click the send button.

[1214] 2. Terminal:

[1215] The input question is sent to the processing device.

[1216] 3. Processing equipment:

[1217] The question is received and analyzed for keywords such as "product" and "where can I buy it."

[1218] Based on keywords, it instantly generates diagnostic results such as "The product is in the home appliance section on the first floor."

[1219] 4. Artificial Intelligence Model:

[1220] If necessary, an AI model (e.g., GPT-4) is invoked to perform additional analysis.

[1221] 5. Terminal:

[1222] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[1223] 6. Users:

[1224] You can search for and purchase products based on the measures presented.

[1225] Prompt Sentence Examples

[1226] Here are some example prompts for specific in-store situations:

[1227] Q: Where can I buy this product?

[1228] A: Please wait while we check product availability and location. You can find it in the following sections:

[1229] 1. Home appliance section (1st floor)

[1230] 2. You can ask about stock information directly at the information desk.

[1231] In this way, this system provides quick and accurate answers when users ask questions about products or services in physical stores, thereby improving customer satisfaction.

[1232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1233] Step 1:

[1234] The user types a question into the chat interface and clicks the send button. Input: The text the user types into the chat interface (e.g., "Where can I buy this product?"). Output: The question data sent from the user's device to the server.

[1235] Step 2:

[1236] The terminal sends the entered question to the processing device (server). Input: Question data entered by the user. Output: Question data received by the server.

[1237] Step 3:

[1238] The server receives the question and analyzes its content. Input: Question data received by the server. Output: Analyzed keywords and context data. Specific operation: The question data is analyzed using a text analysis engine (e.g. spaCy) to extract important keywords and context.

[1239] Step 4:

[1240] The server performs diagnostic processing based on the analysis results. Input: Analyzed keywords and contextual data. Output: Diagnostic result data (e.g., "The product is located in the home appliance section on the first floor"). Specific operation: An internal database (e.g., AWS DynamoDB) is queried based on the keywords, and location information for the relevant product or service is obtained.

[1241] Step 5:

[1242] If necessary, the server calls a generative AI model (e.g., GPT-4) to perform additional analysis. Input: User question data and analysis result data. Output: Supplementary information and detailed answer data from the generative AI model. Specific operation: The server calls the AI ​​model API to generate more detailed explanations and suggestions for the user's question.

[1243] Step 6:

[1244] The server sends the final diagnosis result to the terminal. Input: Diagnostic result data generated by the server. Output: Diagnostic result displayed on the user's terminal. Specific operation: The generated diagnostic result is converted into a chat interface format and sent to the user's terminal.

[1245] Step 7:

[1246] The device displays the diagnostic results in the chat interface and suggests countermeasures to the user. Input: Diagnostic result data sent from the server. Output: The user checks the diagnostic results displayed on the screen. Specific operation: The diagnostic results are displayed in a text box in the chat interface, providing the user with visual countermeasures.

[1247] Step 8:

[1248] The user takes specific action based on the presented solutions. Input: The diagnosis results and solutions displayed in the chat interface. Output: The specific action the user takes (e.g., "Head to the home appliance section"). Specific behavior: The user searches for the product in a physical store based on the displayed results and completes the purchase.

[1249] 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.

[1250] This invention is a system that quickly and accurately diagnoses problems with computers and mobile information terminals that users encounter, provides appropriate solutions, and also combines it with an emotion engine that recognizes the user's emotions. Detailed embodiments of this system are described below.

[1251] System Overview

[1252] This system implements a series of processes: users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, returning the results. It also includes an emotion engine that recognizes the user's emotions and responds appropriately. Specifically, it consists of the following elements:

[1253] 1. User Interface

[1254] 2. Server

[1255] 3. Artificial Intelligence Model

[1256] 4. Emotion Engine

[1257] Program Description

[1258] 1. User Interface

[1259] Device:

[1260] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[1261] 2. Server

[1262] server:

[1263] The server is the central component that performs many roles. First, it receives the questions sent by the user. Second, it analyzes the received questions and takes appropriate action based on their content. Third, it analyzes the emotional state of the user using an emotion engine. The server has the following functions:

[1264] Receiving questions

[1265] Question analysis

[1266] Emotion Analysis

[1267] Running diagnostic processes

[1268] Generating diagnostic results

[1269] Sending diagnostic results

[1270] 3. Artificial Intelligence Model

[1271] server:

[1272] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[1273] 4. Emotion Engine

[1274] server:

[1275] The emotion engine analyzes the text data received from the user and recognizes the user's emotional state. This engine infers the user's emotions from the content and tone of the text and adjusts the way it presents diagnosis results and countermeasures.

[1276] Specific examples

[1277] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into a chat interface and submits it, the following happens:

[1278] 1. User:

[1279] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[1280] 2. Terminal:

[1281] The entered question is sent to the server.

[1282] 3. Server:

[1283] The system receives the question, analyzes the keyword "slow WIFI speed", and simultaneously analyzes the user's emotional state using the emotion engine.

[1284] 4. Emotion Engine:

[1285] The system recognizes the user's emotional state as "anxious" and adjusts the way the diagnosis results are presented based on the results of this emotion analysis.

[1286] 5. Server:

[1287] Based on specific keywords, the system instantly generates a diagnostic result such as "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." At the same time, it includes an additional message based on the results of sentiment analysis, urging the user to remain calm and take appropriate action.

[1288] 6. Terminal:

[1289] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[1290] 7. Users:

[1291] Follow the suggested solution and restart your WIFI device to resolve the issue.

[1292] In this way, the system not only provides quick and appropriate solutions to technical problems faced by users, but also takes into account the user's emotional state in its response, providing a better user experience.

[1293] The processing flow will be explained below.

[1294] Step 1:

[1295] User: Opens the chat interface on their smartphone or PC and types a question into the text box, such as "Why is my Wi-Fi speed so slow on my phone?"

[1296] Step 2:

[1297] User: After entering a question, click the "Submit" button.

[1298] Step 3:

[1299] Terminal: Captures the question entered by the user and sends a POST request to the server, which contains the user's question.

[1300] Step 4:

[1301] Server: Receives the POST request and analyzes the submitted question data in text format, extracting specific keywords and phrases.

[1302] Step 5:

[1303] Server: After analyzing the content of the question, it launches the emotion engine and infers the emotional state from the question entered by the user. This inference uses a machine learning model to classify emotions (e.g., "anxious," "angry," "troubled," etc.) from the user's text.

[1304] Step 6:

[1305] Emotion engine: Analyzes emotions from user input and generates an emotional result, such as "I'm anxious." This result is reflected in the subsequent countermeasure presentation method.

[1306] Step 7:

[1307] Server: Based on the analyzed sentiment results and the question content, the server diagnoses the cause of the problem and proposes solutions. In some cases, an immediate answer can be given based on keywords and phrases, while in other cases, detailed analysis using an external AI model is required.

[1308] Step 8:

[1309] Artificial Intelligence Method: If necessary, call an external artificial intelligence API to generate detailed causes and solutions for user questions, using natural language processing (NLP) technology to generate appropriate answers.

[1310] Step 9:

[1311] Server: Receives the diagnosis results from the AI ​​tool and presents the results in the most appropriate format to the user based on the results of the emotion engine. For example, if a user appears to be "anxious," a message urging them to stay calm is added.

[1312] Step 10:

[1313] Server: Returns the prepared diagnosis results and a message based on the emotion to the device.

[1314] Step 11:

[1315] Terminal: Displays messages in the chat interface based on the diagnosis results and emotions received from the server.

[1316] Step 12:

[1317] User: Check the displayed diagnostic results and take the suggested measures (in this case, the instruction to "restart your Wi-Fi device"), while also paying attention to the emotional messages displayed.

[1318] The above is a concrete processing flow that considers the user's emotions while providing the cause of the problem and solutions. This system not only enables users to quickly identify their own problems and take appropriate measures, but also provides support that takes their emotions into consideration.

[1319] Example 2

[1320] 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."

[1321] Conventional systems lack emotional support when dealing with technical issues faced by users. This results in insufficient support for users to take prompt and appropriate measures, resulting in poor user experience. Therefore, an efficient method for simultaneously diagnosing problems and analyzing emotions is needed.

[1322] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1323] In this invention, the server includes means for a user to input a question using an information terminal, means for receiving the question and analyzing the content thereof, artificial intelligence means for diagnosing the cause of the problem and a solution based on the question analyzed by the server means, means for returning the diagnosis result and emotion analysis result generated by the artificial intelligence means to the user, and means for enabling the user to implement a solution based on the diagnosis result. This makes it possible to not only provide a quick and appropriate solution to a technical problem faced by the user, but also to respond taking into account the emotional state of the user.

[1324] "User interface" refers to a chat-style interface that allows users to input and submit questions using an information terminal.

[1325] The "server" is a computer system that receives questions sent by users, analyzes their content, and generates and returns diagnostic results.

[1326] "Artificial intelligence means" refers to machine learning models and algorithms that analyze questions based on instructions from the server and diagnose the cause of the problem and solutions.

[1327] The "emotion engine" is a system that analyzes text data received from users and recognizes their emotional state.

[1328] "Network" refers to the Internet or other network connections that connect the server to external machine learning models, databases, etc.

[1329] A "machine learning model" is an algorithm or program that is trained to make predictions or classifications based on data.

[1330] "Diagnosis results" are information that includes the causes of problems analyzed by artificial intelligence means and countermeasures for those problems.

[1331] "Emotion analysis result" is information that indicates the emotional state of the user analyzed by the emotion engine.

[1332] An "information terminal" is a device, such as a computer or smartphone, that is operated by the user to input and output information.

[1333] This invention is a system that quickly and accurately diagnoses technical problems faced by users and provides appropriate solutions. Furthermore, by combining it with an emotion engine that recognizes user emotions and responds appropriately, it provides a better user experience. This system consists of the following elements:

[1334] 1. User Interface

[1335] 2. Server

[1336] 3. Artificial Intelligence Methods (Machine Learning Models)

[1337] 4. Emotion Engine

[1338] User Interface

[1339] Device:

[1340] The user interface is a chat-style interface that allows users to enter and submit questions. This interface includes a text box and a submit button. When a user enters a question, the content is transferred to the server.

[1341] server

[1342] server:

[1343] The server receives questions sent by users and analyzes their content. The server has the following functions:

[1344] Receiving a question: The server receives the question data sent from the terminal as an HTTP request.

[1345] Question analysis: Natural language processing (NLP) technology is used to extract keywords from the question.

[1346] Emotion Analysis: Using an emotion engine, we analyze the user's emotional state from the received text.

[1347] Execute diagnostic processing: Call a generative AI model based on the analyzed data to generate appropriate diagnostic results and countermeasures.

[1348] Generating diagnostic results: Based on the diagnostic results and sentiment analysis results, a message is created to be sent back to the user.

[1349] Send diagnostic results: Send the generated message to the terminal and display it to the user.

[1350] Artificial intelligence tools (machine learning models)

[1351] Artificial intelligence means:

[1352] The artificial intelligence means is a machine learning model that analyzes the content of the question in detail based on instructions from the server and diagnoses the cause of the problem and countermeasures. For example, by using an external machine learning model specialized in natural language processing (e.g., OpenAI GPT-3), the system can understand the user's question and generate an appropriate answer.

[1353] Emotion Engine

[1354] Emotion Engine:

[1355] An emotion engine is a system that analyzes text data received from users and recognizes their emotional state. For example, by using an API such as IBM Watson Natural Language Understanding, it can infer the user's emotions from the content and tone of the text and adjust the way it presents diagnosis results and countermeasures.

[1356] Specific examples

[1357] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and sends it, the following process will occur:

[1358] 1. User:

[1359] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[1360] 2. Terminal:

[1361] The entered question data is converted into JSON format and sent to the server as an HTTP POST request.

[1362] 3. Server:

[1363] The server receives the request and first uses an NLP library (e.g. SpaCy or NLTK) to parse the question.

[1364] 4. Emotion Engine:

[1365] Call a sentiment analysis API (e.g., IBM Watson Natural Language Understanding) to analyze sentiment from the query text. Recognize that the user is impatient.

[1366] 5. Server:

[1367] An AI model (e.g., OpenAI GPT-3) is called to generate the cause of the question and a solution, resulting in a diagnosis such as "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." Based on the results of sentiment analysis, an additional message is also generated, such as "Please stay calm and take action."

[1368] 6. Terminal:

[1369] The diagnostic results and additional messages are displayed in the user interface, allowing the user to check them and take specific measures.

[1370] Prompt Sentence Examples

[1371] For example, to provide a description of this system to a generative AI model, we might use a prompt like this:

[1372] "We have developed a system that can quickly and accurately diagnose technical issues faced by users and provide appropriate solutions. The system also incorporates an emotion engine, which tailors the results based on the user's emotions."

[1373] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1374] Step 1:

[1375] user:

[1376] A user enters a question through the chat interface and clicks the "Send" button. The input is, for example, "Why is my smartphone's Wi-Fi speed so slow?" The output is the user's input text.

[1377] Step 2:

[1378] Device:

[1379] The terminal converts the user's input question into JSON format and sends it to the server as an HTTP POST request. The input is the user's input text, and the output is JSON format data.

[1380] Step 3:

[1381] server:

[1382] The server receives the request and first uses natural language processing (NLP) techniques to analyze the question. This process is performed using an NLP library (e.g., SpaCy or NLTK). The input is the question data in JSON format, and the output is the analyzed keywords and phrases.

[1383] Step 4:

[1384] Emotion Engine:

[1385] The emotion engine analyzes the text data received from the server and calls an emotion analysis API (e.g., IBM Watson Natural Language Understanding) to recognize the user's emotional state. The input is text data containing analyzed keywords and phrases, and the output is the user's emotional state (e.g., "anxious").

[1386] Step 5:

[1387] server:

[1388] The server uses a generative AI model (e.g., OpenAI GPT-3) to generate appropriate diagnostic results and countermeasures based on the emotional state obtained by the emotion engine and the analyzed data. The input is the user's question and emotional state, and the output is the diagnostic result and an additional message. This process generates the diagnostic result "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device" and the additional message "Please stay calm and take action without panicking."

[1389] Step 6:

[1390] Device:

[1391] The terminal displays the diagnostic results and additional messages received from the server on a user interface. The input is the diagnostic results and additional messages, and the output is the display on the user interface.

[1392] Step 7:

[1393] user:

[1394] The user checks the diagnostic results and additional messages displayed on the device and takes specific measures (e.g., "restart the WIFI device"). The input is the diagnostic results and additional messages, and the output is the user's specific action.

[1395] Through these steps, the system is able to quickly and accurately diagnose the user's technical problem and provide appropriate solutions, taking into account their emotional state.

[1396] (Application example 2)

[1397] 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."

[1398] Modern self-driving vehicles use advanced technology, but they face the challenge of responding quickly and appropriately when a system malfunctions. In particular, when a user's emotional state is tense, it is important to provide not only appropriate countermeasures but also a response that reassures the user. This invention aims to not only quickly diagnose and provide solutions to problems faced by the self-driving vehicle, but also to combine an emotion engine to provide appropriate responses based on the user's emotions.

[1399] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1400] In this invention, the server includes means for a user to input a question using an information processing device or a mobile terminal, means for receiving the content of the user's question and analyzing the content, artificial intelligence means for diagnosing the cause of the problem and a solution based on the analyzed question, means for returning the diagnosis result generated by the artificial intelligence means to the user, emotion analysis means for recognizing the emotional state of the user, and means for adjusting the diagnosis result based on the emotion analysis result. This makes it possible to quickly and accurately diagnose problems with the autonomous vehicle and present solutions according to the user's emotions.

[1401] An "information processing device" is an electronic device for processing information, such as a computer or a server.

[1402] A "mobile device" is a portable information processing device such as a smartphone, tablet, or smart glasses.

[1403] A "server" is a high-performance computer that provides services to other computers on a network.

[1404] "Artificial intelligence means" is a system that uses machine learning and natural language processing technology to analyze user questions and diagnose the cause of the problem and solutions.

[1405] "Emotion analysis means" is a technology that recognizes and analyzes emotions from text and voice input by the user.

[1406] "Diagnosis results" are information about the causes of problems and their solutions generated by artificial intelligence means.

[1407] A "user interface" is an operation screen or input device that allows a user to interact with a system.

[1408] The "adjustment means" is a function that modifies the diagnostic results based on the emotion analysis means in accordance with the user's emotions.

[1409] This invention is a system that quickly and accurately diagnoses problems encountered by users in autonomous vehicles and provides appropriate countermeasures. It also incorporates an emotion engine that recognizes the user's emotions. Detailed embodiments of this system are described below.

[1410] System Overview

[1411] The system implements a series of processes: users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, then returns the results. It also has an emotion engine that recognizes the user's emotions and responds appropriately.

[1412] Program Description

[1413] 1. User Interface

[1414] Device:

[1415] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[1416] 2. Server

[1417] server:

[1418] The server is the central component that performs many roles. First, it receives the questions sent by the user. Second, it analyzes the received questions and takes appropriate action based on their content. Third, it analyzes the emotional state of the user using an emotion engine. The server has the following functions:

[1419] Receiving questions

[1420] Question analysis

[1421] Emotion Analysis

[1422] Running diagnostic processes

[1423] Generating diagnostic results

[1424] Sending diagnostic results

[1425] 3. Artificial Intelligence Model

[1426] server:

[1427] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[1428] 4. Emotion Engine

[1429] server:

[1430] The emotion engine analyzes the text data received from the user and recognizes the user's emotional state. This engine infers the user's emotions from the content and tone of the text and adjusts the way it presents diagnosis results and countermeasures.

[1431] Specific examples

[1432] For example, if a user types "My car stopped driving automatically and I don't know why. What should I do?" into the chat interface and sends it, the following happens:

[1433] 1. User:

[1434] Type "My car stopped driving automatically and I don't know why. What should I do?" into the chat interface and click the send button.

[1435] 2. Terminal:

[1436] The entered question is sent to the server.

[1437] 3. Server:

[1438] The system receives the question, analyzes the keyword "autonomous driving stopped," and simultaneously analyzes the user's emotional state using an emotion engine.

[1439] 4. Emotion Engine:

[1440] The system recognizes the user's emotional state as "anxious" and adjusts the way the diagnosis results are presented based on the results of this emotion analysis.

[1441] 5. Server:

[1442] It instantly generates a diagnostic result based on specific keywords, such as "Reboot your system and check for firmware updates," while also including an additional message based on sentiment analysis to remind users to stay calm.

[1443] 6. Terminal:

[1444] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[1445] 7. Users:

[1446] Follow the suggested solutions to resolve the issue.

[1447] Prompt Sentence Examples

[1448] User input: "My Autopilot stopped working and I don't know why. What should I do?"

[1449] Example prompts to input to a generative AI model:

[1450] Q: My car stopped working automatically and I don't know why. What should I do?

[1451] A: It may be that your system has experienced a temporary issue. Please reboot your system and then check for firmware updates. Also, please be patient and follow the guidelines below.

[1452] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1453] Step 1:

[1454] A user inputs a question using an information processing device or a mobile terminal.

[1455] Input: User types into the chat interface, "My autopilot stopped working and I don't know why. What should I do?"

[1456] Output: The entered text data is sent to the server.

[1457] Specific action: The user enters text into a chat-style interface and clicks the send button.

[1458] Step 2:

[1459] The terminal sends the entered question to the server.

[1460] Input: Text data that the user enters and presses the submit button.

[1461] Output: The text data sent to the server.

[1462] Specific operation: The terminal transmits the user's input to the server via the network.

[1463] Step 3:

[1464] The server receives the query and analyzes its contents.

[1465] Input: The user's text data sent to the server.

[1466] Output: Analyzed user question (the keyword "autonomous driving stopped" is extracted).

[1467] Specific operation: The server receives the text data, performs text analysis, and extracts important keywords.

[1468] Step 4:

[1469] The server uses emotion analysis means to analyze the user's emotional state.

[1470] Input: Parsed text data.

[1471] Output: The user's emotional state (e.g., "I'm anxious").

[1472] What it does: The server uses an emotion recognition model to analyze emotions from the context and tone of the user's text.

[1473] Step 5:

[1474] The server uses artificial intelligence means to diagnose the cause of the problem and what to do about it.

[1475] Input: Parsed user question and emotional state.

[1476] Output: Diagnostic results (e.g. "Please reboot your system and check for firmware updates").

[1477] Specific operation: The server utilizes natural language processing technology and uses a generative AI model to diagnose the cause of the problem and provide solutions.

[1478] Step 6:

[1479] The server adjusts the diagnosis results based on the emotion analysis results.

[1480] Input: Diagnosis results and user's emotional state.

[1481] Output: Tailored diagnostic results and messages (e.g. "Please stay calm and follow the steps below").

[1482] Specific operation: The server generates advice and additional messages that reflect the user's emotional state and adds them to the diagnosis results.

[1483] Step 7:

[1484] The server transmits the adjusted diagnostic results to the terminal.

[1485] Input: Adjusted diagnostic results and messages.

[1486] Output: Diagnostic results and messages sent to the terminal.

[1487] Specific operation: The server performs a process to send the adjusted diagnostic results to the terminal via the network.

[1488] Step 8:

[1489] The device will display diagnostic results and messages to the user.

[1490] Input: The adjusted diagnostic results and messages sent by the server.

[1491] Output: Diagnostic results and messages displayed in the user interface.

[1492] Specific operation: The diagnostic results and advice messages are displayed on the terminal display.

[1493] Step 9:

[1494] The user follows the suggested solutions to resolve the issue.

[1495] Input: Diagnostic results and messages displayed on the terminal.

[1496] Output: Actions taken (e.g. system reboot and firmware update).

[1497] Specific actions: The user actually performs an action based on the proposed countermeasures.

[1498] 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.

[1499] 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.

[1500] 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.

[1501] [Fourth embodiment]

[1502] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1503] 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.

[1504] 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).

[1505] 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.

[1506] 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.

[1507] 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).

[1508] 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.

[1509] 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.

[1510] 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.

[1511] 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.

[1512] 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.

[1513] 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.

[1514] 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."

[1515] The present invention is a system for quickly and accurately diagnosing problems with computers and mobile information terminals that users encounter and providing appropriate solutions. Detailed embodiments of this system will be described below.

[1516] System Overview

[1517] This system implements a series of processes in which users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, returning the results. Specifically, it consists of the following elements:

[1518] 1. User Interface

[1519] 2. Server

[1520] 3. Artificial Intelligence Model

[1521] Program Description

[1522] 1. User Interface

[1523] Device:

[1524] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[1525] 2. Server

[1526] server:

[1527] The server is the central component that has many responsibilities. First, it receives the questions submitted by the user. Second, it analyzes the received questions and takes appropriate action based on their content. The server has the following functions:

[1528] Receiving questions

[1529] Question analysis

[1530] Running diagnostic processes

[1531] Generating diagnostic results

[1532] Sending diagnostic results

[1533] The server analyzes the question using internal logic and may generate an immediate diagnosis based on specific keywords, or may require a more detailed diagnosis using an external artificial intelligence model.

[1534] 3. Artificial Intelligence Model

[1535] server:

[1536] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[1537] Specific examples

[1538] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into a chat interface and submits it, the following happens:

[1539] 1. User:

[1540] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[1541] 2. Terminal:

[1542] The entered question is sent to the server.

[1543] 3. Server:

[1544] It receives a question, analyzes the keyword "slow WIFI speed," and immediately generates a diagnostic result based on the specific keyword: "Your WIFI device is clogged with packets, so please restart your WIFI device."

[1545] 4. Terminal:

[1546] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[1547] 5. User:

[1548] Follow the suggested solution and restart your WIFI device to resolve the issue.

[1549] In this way, this system greatly improves user convenience by providing quick and appropriate solutions to technical problems that users face.

[1550] The processing flow will be explained below.

[1551] Step 1:

[1552] User: Opens the chat interface on their smartphone or PC and types a question into the text box, such as "Why is my Wi-Fi speed so slow on my phone?"

[1553] Step 2:

[1554] User: After entering a question, click the "Submit" button.

[1555] Step 3:

[1556] Terminal: Captures the question entered by the user and sends a POST request to the server, which contains the user's question.

[1557] Step 4:

[1558] Server: Receives the POST request and parses the data sent.

[1559] Step 5:

[1560] Server: Analyzes the user's question and extracts specific keywords (in this case, "slow WIFI speed").

[1561] Step 6:

[1562] Server: Based on the extracted keywords, it searches for predefined countermeasures (such as "The WIFI device is experiencing packet congestion, so please restart the WIFI device") and selects the appropriate diagnosis result.

[1563] Step 7:

[1564] Server: If the diagnosis result cannot be answered by a predefined answer, an external AI API is called to request a detailed analysis and diagnosis of the user's question.

[1565] Step 8:

[1566] Artificial intelligence means: Based on the received question, natural language processing is performed to generate the cause of the problem and solutions.

[1567] Step 9:

[1568] Server: Receives the diagnostic results obtained from the artificial intelligence means and formats them for transmission back to the user.

[1569] Step 10:

[1570] Server: Returns the prepared diagnostic results to the terminal.

[1571] Step 11:

[1572] Terminal: Displays diagnostic results received from the server in the chat interface.

[1573] Step 12:

[1574] User: Check the displayed diagnostic results and take the suggested measures (in this case, "restart the WIFI device").

[1575] This is the specific process flow for responding to user questions. This system allows users to quickly identify their own problems and take appropriate measures.

[1576] Example 1

[1577] 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."

[1578] There is a need to quickly and accurately diagnose technical problems faced by users of computers and mobile information devices and provide appropriate solutions. However, current systems sometimes fail to provide accurate diagnostic results due to insufficient analysis of the question content. Furthermore, when detailed analysis is required, it takes even more time, and the diagnostic results returned are often difficult for users to understand. There is a need to solve these problems and improve user convenience.

[1579] 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.

[1580] In this invention, the server includes: means for a user to input a question using an information processing device; processing means for receiving the question and analyzing the content of the question; intelligence construction means for diagnosing the cause of the problem and solutions based on the analyzed question; means for returning the diagnosis result generated by the intelligence construction means to the user; means for enabling the user to take solutions based on the diagnosis result; and means for generating a prompt sentence from the content of the question and sending it to the intelligence model if the user's question requires detailed analysis by an external intelligence model, thereby providing quick and accurate diagnosis results and enabling users to solve technical problems efficiently.

[1581] "User" refers to a person who uses an information processing device to input questions and execute measures based on the questions.

[1582] "Data processing device" refers to a device, such as a computer or a personal digital assistant, that a user uses to enter a question.

[1583] A "question" refers to the content that a user inputs through an information processing device to inquire about the cause of a technical problem and how to resolve it.

[1584] "Processing means" refers to the means by which the server receives a question, analyzes its content, and generates an appropriate diagnostic result.

[1585] "Intelligence construction means" refers to a means for using an artificial intelligence model to analyze the content of a question and diagnose the cause of the problem and countermeasures.

[1586] "Diagnosis result" refers to information generated by the intelligent configuration means and presented to the user, including the cause of the problem and its solution.

[1587] A "prompt" is a document generated in response to a question that requires further analysis and sent to an external intelligence model.

[1588] An "external intelligence model" is a machine learning model that exists separately from the server and analyzes the detailed content of questions via a communication network.

[1589] "Communication network" refers to a network that connects the server with external intelligence models and sends and receives data.

[1590] The present invention provides a system for quickly and accurately diagnosing problems that users may encounter with information processing devices or mobile information terminals, and providing appropriate solutions. Specific embodiments for carrying out the present invention will be described below.

[1591] System Overview

[1592] The system consists of the following main components:

[1593] 1. User Interface

[1594] 2. Server

[1595] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[1596] Hardware and software used

[1597] 1. User Interface

[1598] Hardware: Computers and mobile devices (e.g., smartphones, tablets, PCs, etc.)

[1599] Software: Web browser and dedicated application

[1600] 2. Server

[1601] Hardware: High-performance server (e.g., cloud server, on-premise server, etc.)

[1602] Software: Web server, database management system, query analysis engine

[1603] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[1604] Software: Machine learning models for natural language processing (e.g., GPT-3, BERT, etc.)

[1605] External API: A service that provides generative AI models (e.g., a cloud AI service)

[1606] System Operation

[1607] 1. User Interface

[1608] Terminal: A user uses an information processing device to enter a question into a chat-style user interface and clicks the send button.

[1609] Example: A user types "Tell me why my smartphone's Wi-Fi speed is slow" into their smartphone screen and clicks the send button.

[1610] 2. Server

[1611] Server: Receives questions sent from the device and analyzes their contents. It may generate immediate diagnostic results based on specific keywords, or if detailed analysis is required, it may generate and send prompts to an external intelligent configuration means.

[1612] Example: The server analyzes the keyword "slow WIFI speed" and generates a diagnostic result such as "Your WIFI device is clogged with packets, so please restart your WIFI device."

[1613] 3. Intelligence Construction Methods (Artificial Intelligence Models)

[1614] Server: If further analysis is required, create a prompt sentence and send it to the generative AI model.

[1615] Generative AI model: Generates detailed diagnostic results based on prompt statements.

[1616] For example, if the question is "My smartphone's Wi-Fi speed is slow," the prompt "Please explain in detail what is causing the slow Wi-Fi connection on your smartphone" is sent to the generative AI model. As a result, the generative AI model returns a detailed diagnosis result such as "Possible causes include the placement of your router, interference from other devices, and ISP issues."

[1617] 4. Sending and displaying diagnostic results

[1618] Server: Sends the generated diagnostic results to the user's device.

[1619] Terminal: Displays the received diagnostic results in a user interface.

[1620] For example, diagnostic results such as "Please restart your WIFI device" are sent to the device and displayed to the user in the chat interface.

[1621] Specific examples

[1622] When a user types "Why is my smartphone's Wi-Fi speed slow?" into the chat interface and clicks the send button, the device sends this question to the server. The server receives the question, analyzes specific keywords (e.g., "Wi-Fi speed"), and immediately generates a diagnosis result: "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." If a more detailed analysis is required, the prompt "Please explain in detail why my smartphone's Wi-Fi connection is slow" is sent to the AI ​​model, and appropriate measures are provided to the user based on the response.

[1623] In this way, through this system, it is possible to quickly and accurately diagnose technical problems faced by users and provide appropriate solutions.

[1624] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1625] Step 1:

[1626] The user types in a question and submits it

[1627] Input: The user enters a question into a text box on an information processing device (computer or mobile information terminal) and clicks the send button.

[1628] How it works: A user types in "Tell me why my phone's Wi-Fi speed is slow" and clicks send.

[1629] Output: The entered question is sent by the terminal to the server.

[1630] Step 2:

[1631] The device sends a question to the server

[1632] Input: Question data entered by the user.

[1633] Operation: The terminal forms and sends a network request to transmit this query data to the server.

[1634] Output: The query data is sent to the server.

[1635] Step 3:

[1636] The server receives and analyzes the query

[1637] Input: Question data sent from the terminal.

[1638] Operation: The server receives the question data and analyzes the question using a string analysis engine. It extracts specific keywords and performs an initial diagnosis.

[1639] Output: Initial diagnostic results based on the keyword "slow WIFI speed", or prompts if further analysis is required.

[1640] Step 4:

[1641] The server generates a diagnostic or prompt statement

[1642] Input: Parsed question content and extracted keywords.

[1643] Operation: If an answer is available immediately, the server generates a simple diagnostic result such as "Your WIFI device is experiencing packet congestion, so please restart your WIFI device." If a more detailed analysis is required, the server generates a prompt based on the question (e.g., "Please explain in detail why your smartphone's WIFI connection is slow").

[1644] Output: A brief diagnostic result or a prompt.

[1645] Step 5:

[1646] The server sends a prompt to the generative AI model (if further analysis is required).

[1647] Input: A prompt statement that is determined to require further analysis.

[1648] Action: Forms and sends an API request to send a prompt sentence to the generative AI model.

[1649] Output: Detailed diagnostic results from the generative AI model.

[1650] Step 6:

[1651] Generative AI models generate detailed diagnostic results

[1652] Input: The prompt text sent by the server.

[1653] How it works: A generative AI model uses natural language processing based on the prompt to generate a detailed diagnosis.

[1654] Output: Detailed diagnostic results generated (e.g. "This may be due to router placement, interference from other devices, or ISP issues").

[1655] Step 7:

[1656] The server receives detailed diagnostic results and sends them back to the user.

[1657] Input: Detailed diagnostic results from a generative AI model.

[1658] How it works: The server receives detailed diagnostic results, converts them into a user-friendly format, and sends them to the device.

[1659] Output: Diagnostic results sent back to the user.

[1660] Step 8:

[1661] The device displays the diagnostic results.

[1662] Input: Diagnostic results sent from the server.

[1663] Behavior: The device displays the diagnostic results it receives in the chat interface and presents them to the user.

[1664] Output: Diagnostic results displayed in the chat interface.

[1665] Step 9:

[1666] The user takes action

[1667] Input: The diagnostic results displayed on the terminal.

[1668] Action: The user takes action based on the presented diagnostic results (e.g., rebooting the WIFI device).

[1669] Output: Problem solved (e.g. improved WIFI speed).

[1670] In this way, each component of the system works together at each step, making it possible to provide quick and appropriate solutions to technical problems faced by users.

[1671] (Application example 1)

[1672] 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."

[1673] In today's brick-and-mortar stores, it is difficult to respond quickly and accurately to customer questions about products and services. Similarly, store staff have limitations in providing accurate answers to all questions. This situation can lead to lower customer satisfaction, which can have a negative impact on store sales and brand image.

[1674] 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.

[1675] In this invention, the server includes means for a user to input a question using a communication terminal, processing device means for receiving the question and analyzing its content, artificial intelligence means for diagnosing the cause of the problem and a solution based on the question analyzed by the processing device means, means for returning the diagnosis result generated by the artificial intelligence means to the user, means for enabling the user to take a solution based on the diagnosis result, and means for receiving, analyzing, and providing an answer when a user asks a question about a product or service in a physical store. This makes it possible for customers to receive a quick and accurate answer when they ask a question about a product or service in a physical store.

[1676] A "communications terminal" is a portable information device or personal computer that allows a user to input and transmit information.

[1677] The "processing device means" is a computer system for analyzing the received question and performing appropriate processing based on the content of the question.

[1678] "Artificial intelligence means" refers to computer learning models and natural language processing systems that analyze the content of questions and diagnose the causes of problems and solutions.

[1679] A "brick and mortar store" is a retail or service establishment that exists in a physical location and provides goods or services in person.

[1680] A "question" is text entered by a user about a question they want answered or information they want to know.

[1681] "Diagnosis results" are information about the results of problem analysis and countermeasures generated by artificial intelligence means.

[1682] An "external artificial intelligence model" is an external machine learning model or natural language processing system connected via a computer network.

[1683] "Keywords" are important concepts or words when analyzing the content of a question.

[1684] This invention is a system that allows users to ask questions about products and services in a physical store via a smartphone or smart glasses, and provides quick and accurate answers to those questions. Specific embodiments of this system are described below.

[1685] System Overview

[1686] The system consists of the following main components:

[1687] 1. User Interface

[1688] 2. Processing equipment

[1689] 3. Artificial Intelligence Model

[1690] User Interface

[1691] The terminal (smartphone or smart glasses) provides a chat interface for the user to input questions. This interface includes a text box and a send button, which sends the questions input by the user to the processing device.

[1692] Processing equipment

[1693] The processing device (server) receives the questions sent by the user and analyzes the content. This analysis includes extracting keywords and understanding the context. The processing device has the following functions:

[1694] Receiving questions

[1695] Question analysis

[1696] Running diagnostic processes

[1697] Generating diagnostic results

[1698] Sending diagnostic results

[1699] Based on the analyzed questions, the processing unit can invoke artificial intelligence models to diagnose the cause of the problem and remedies.

[1700] Artificial Intelligence Model

[1701] The AI ​​model analyzes the question in detail and diagnoses the cause of the problem and countermeasures. The AI ​​model used can be, for example, a generative AI model specialized in natural language processing (e.g., GPT-4). This model can be accessed via an external computer network, and can also use external APIs to obtain additional information as needed.

[1702] Specific examples

[1703] For example, if a user is in a physical store and types "Where can I buy this product?" into the chat interface and submits it, the following happens:

[1704] 1. User:

[1705] Type "Where can I buy this product?" into the chat interface and click the send button.

[1706] 2. Terminal:

[1707] The input question is sent to the processing device.

[1708] 3. Processing equipment:

[1709] The question is received and analyzed for keywords such as "product" and "where can I buy it."

[1710] Based on keywords, it instantly generates diagnostic results such as "The product is in the home appliance section on the first floor."

[1711] 4. Artificial Intelligence Model:

[1712] If necessary, an AI model (e.g., GPT-4) is invoked to perform additional analysis.

[1713] 5. Terminal:

[1714] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[1715] 6. Users:

[1716] You can search for and purchase products based on the measures presented.

[1717] Prompt Sentence Examples

[1718] Here are some example prompts for specific in-store situations:

[1719] Q: Where can I buy this product?

[1720] A: Please wait while we check product availability and location. You can find it in the following sections:

[1721] 1. Home appliance section (1st floor)

[1722] 2. You can ask about stock information directly at the information desk.

[1723] In this way, this system provides quick and accurate answers when users ask questions about products or services in physical stores, thereby improving customer satisfaction.

[1724] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1725] Step 1:

[1726] The user types a question into the chat interface and clicks the send button. Input: The text the user types into the chat interface (e.g., "Where can I buy this product?"). Output: The question data sent from the user's device to the server.

[1727] Step 2:

[1728] The terminal sends the entered question to the processing device (server). Input: Question data entered by the user. Output: Question data received by the server.

[1729] Step 3:

[1730] The server receives the question and analyzes its content. Input: Question data received by the server. Output: Analyzed keywords and context data. Specific operation: The question data is analyzed using a text analysis engine (e.g. spaCy) to extract important keywords and context.

[1731] Step 4:

[1732] The server performs diagnostic processing based on the analysis results. Input: Analyzed keywords and contextual data. Output: Diagnostic result data (e.g., "The product is located in the home appliance section on the first floor"). Specific operation: An internal database (e.g., AWS DynamoDB) is queried based on the keywords, and location information for the relevant product or service is obtained.

[1733] Step 5:

[1734] If necessary, the server calls a generative AI model (e.g., GPT-4) to perform additional analysis. Input: User question data and analysis result data. Output: Supplementary information and detailed answer data from the generative AI model. Specific operation: The server calls the AI ​​model API to generate more detailed explanations and suggestions for the user's question.

[1735] Step 6:

[1736] The server sends the final diagnosis result to the terminal. Input: Diagnostic result data generated by the server. Output: Diagnostic result displayed on the user's terminal. Specific operation: The generated diagnostic result is converted into a chat interface format and sent to the user's terminal.

[1737] Step 7:

[1738] The device displays the diagnostic results in the chat interface and suggests countermeasures to the user. Input: Diagnostic result data sent from the server. Output: The user checks the diagnostic results displayed on the screen. Specific operation: The diagnostic results are displayed in a text box in the chat interface, providing the user with visual countermeasures.

[1739] Step 8:

[1740] The user takes specific action based on the presented solutions. Input: The diagnosis results and solutions displayed in the chat interface. Output: The specific action the user takes (e.g., "Head to the home appliance section"). Specific behavior: The user searches for the product in a physical store based on the displayed results and completes the purchase.

[1741] 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.

[1742] This invention is a system that quickly and accurately diagnoses problems with computers and mobile information terminals that users encounter, provides appropriate solutions, and also combines it with an emotion engine that recognizes the user's emotions. Detailed embodiments of this system are described below.

[1743] System Overview

[1744] This system implements a series of processes: users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, returning the results. It also includes an emotion engine that recognizes the user's emotions and responds appropriately. Specifically, it consists of the following elements:

[1745] 1. User Interface

[1746] 2. Server

[1747] 3. Artificial Intelligence Model

[1748] 4. Emotion Engine

[1749] Program Description

[1750] 1. User Interface

[1751] Device:

[1752] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[1753] 2. Server

[1754] server:

[1755] The server is the central component that performs many roles. First, it receives the questions sent by the user. Second, it analyzes the received questions and takes appropriate action based on their content. Third, it analyzes the emotional state of the user using an emotion engine. The server has the following functions:

[1756] Receiving questions

[1757] Question analysis

[1758] Emotion Analysis

[1759] Running diagnostic processes

[1760] Generating diagnostic results

[1761] Sending diagnostic results

[1762] 3. Artificial Intelligence Model

[1763] server:

[1764] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[1765] 4. Emotion Engine

[1766] server:

[1767] The emotion engine analyzes the text data received from the user and recognizes the user's emotional state. This engine infers the user's emotions from the content and tone of the text and adjusts the way it presents diagnosis results and countermeasures.

[1768] Specific examples

[1769] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into a chat interface and submits it, the following happens:

[1770] 1. User:

[1771] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[1772] 2. Terminal:

[1773] The entered question is sent to the server.

[1774] 3. Server:

[1775] The system receives the question, analyzes the keyword "slow WIFI speed", and simultaneously analyzes the user's emotional state using the emotion engine.

[1776] 4. Emotion Engine:

[1777] The system recognizes the user's emotional state as "anxious" and adjusts the way the diagnosis results are presented based on the results of this emotion analysis.

[1778] 5. Server:

[1779] Based on specific keywords, the system instantly generates a diagnostic result such as "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." At the same time, it includes an additional message based on the results of sentiment analysis, urging the user to remain calm and take appropriate action.

[1780] 6. Terminal:

[1781] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[1782] 7. Users:

[1783] Follow the suggested solution and restart your WIFI device to resolve the issue.

[1784] In this way, the system not only provides quick and appropriate solutions to technical problems faced by users, but also takes into account the user's emotional state in its response, providing a better user experience.

[1785] The processing flow will be explained below.

[1786] Step 1:

[1787] User: Opens the chat interface on their smartphone or PC and types a question into the text box, such as "Why is my Wi-Fi speed so slow on my phone?"

[1788] Step 2:

[1789] User: After entering a question, click the "Submit" button.

[1790] Step 3:

[1791] Terminal: Captures the question entered by the user and sends a POST request to the server, which contains the user's question.

[1792] Step 4:

[1793] Server: Receives the POST request and analyzes the submitted question data in text format, extracting specific keywords and phrases.

[1794] Step 5:

[1795] Server: After analyzing the content of the question, it launches the emotion engine and infers the emotional state from the question entered by the user. This inference uses a machine learning model to classify emotions (e.g., "anxious," "angry," "troubled," etc.) from the user's text.

[1796] Step 6:

[1797] Emotion engine: Analyzes emotions from user input and generates an emotional result, such as "I'm anxious." This result is reflected in the subsequent countermeasure presentation method.

[1798] Step 7:

[1799] Server: Based on the analyzed sentiment results and the question content, the server diagnoses the cause of the problem and proposes solutions. In some cases, an immediate answer can be given based on keywords and phrases, while in other cases, detailed analysis using an external AI model is required.

[1800] Step 8:

[1801] Artificial Intelligence Method: If necessary, call an external artificial intelligence API to generate detailed causes and solutions for user questions, using natural language processing (NLP) technology to generate appropriate answers.

[1802] Step 9:

[1803] Server: Receives the diagnosis results from the AI ​​tool and presents the results in the most appropriate format to the user based on the results of the emotion engine. For example, if a user appears to be "anxious," a message urging them to stay calm is added.

[1804] Step 10:

[1805] Server: Returns the prepared diagnosis results and a message based on the emotion to the device.

[1806] Step 11:

[1807] Terminal: Displays messages in the chat interface based on the diagnosis results and emotions received from the server.

[1808] Step 12:

[1809] User: Check the displayed diagnostic results and take the suggested measures (in this case, the instruction to "restart your Wi-Fi device"), while also paying attention to the emotional messages displayed.

[1810] The above is a concrete processing flow that considers the user's emotions while providing the cause of the problem and solutions. This system not only enables users to quickly identify their own problems and take appropriate measures, but also provides support that takes their emotions into consideration.

[1811] Example 2

[1812] 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."

[1813] Conventional systems lack emotional support when dealing with technical issues faced by users. This results in insufficient support for users to take prompt and appropriate measures, resulting in poor user experience. Therefore, an efficient method for simultaneously diagnosing problems and analyzing emotions is needed.

[1814] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1815] In this invention, the server includes means for a user to input a question using an information terminal, means for receiving the question and analyzing the content thereof, artificial intelligence means for diagnosing the cause of the problem and a solution based on the question analyzed by the server means, means for returning the diagnosis result and emotion analysis result generated by the artificial intelligence means to the user, and means for enabling the user to implement a solution based on the diagnosis result. This makes it possible to not only provide a quick and appropriate solution to a technical problem faced by the user, but also to respond taking into account the emotional state of the user.

[1816] "User interface" refers to a chat-style interface that allows users to input and submit questions using an information terminal.

[1817] The "server" is a computer system that receives questions sent by users, analyzes their content, and generates and returns diagnostic results.

[1818] "Artificial intelligence means" refers to machine learning models and algorithms that analyze questions based on instructions from the server and diagnose the cause of the problem and solutions.

[1819] The "emotion engine" is a system that analyzes text data received from users and recognizes their emotional state.

[1820] "Network" refers to the Internet or other network connections that connect the server to external machine learning models, databases, etc.

[1821] A "machine learning model" is an algorithm or program that is trained to make predictions or classifications based on data.

[1822] "Diagnosis results" are information that includes the causes of problems analyzed by artificial intelligence means and countermeasures for those problems.

[1823] "Emotion analysis result" is information that indicates the emotional state of the user analyzed by the emotion engine.

[1824] An "information terminal" is a device, such as a computer or smartphone, that is operated by the user to input and output information.

[1825] This invention is a system that quickly and accurately diagnoses technical problems faced by users and provides appropriate solutions. Furthermore, by combining it with an emotion engine that recognizes user emotions and responds appropriately, it provides a better user experience. This system consists of the following elements:

[1826] 1. User Interface

[1827] 2. Server

[1828] 3. Artificial Intelligence Methods (Machine Learning Models)

[1829] 4. Emotion Engine

[1830] User Interface

[1831] Device:

[1832] The user interface is a chat-style interface that allows users to enter and submit questions. This interface includes a text box and a submit button. When a user enters a question, the content is transferred to the server.

[1833] server

[1834] server:

[1835] The server receives questions sent by users and analyzes their content. The server has the following functions:

[1836] Receiving a question: The server receives the question data sent from the terminal as an HTTP request.

[1837] Question analysis: Natural language processing (NLP) technology is used to extract keywords from the question.

[1838] Emotion Analysis: Using an emotion engine, we analyze the user's emotional state from the received text.

[1839] Execute diagnostic processing: Call a generative AI model based on the analyzed data to generate appropriate diagnostic results and countermeasures.

[1840] Generating diagnostic results: Based on the diagnostic results and sentiment analysis results, a message is created to be sent back to the user.

[1841] Send diagnostic results: Send the generated message to the terminal and display it to the user.

[1842] Artificial intelligence tools (machine learning models)

[1843] Artificial intelligence means:

[1844] The artificial intelligence means is a machine learning model that analyzes the content of the question in detail based on instructions from the server and diagnoses the cause of the problem and countermeasures. For example, by using an external machine learning model specialized in natural language processing (e.g., OpenAI GPT-3), the system can understand the user's question and generate an appropriate answer.

[1845] Emotion Engine

[1846] Emotion Engine:

[1847] An emotion engine is a system that analyzes text data received from users and recognizes their emotional state. For example, by using an API such as IBM Watson Natural Language Understanding, it can infer the user's emotions from the content and tone of the text and adjust the way it presents diagnosis results and countermeasures.

[1848] Specific examples

[1849] For example, if a user types "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and sends it, the following process will occur:

[1850] 1. User:

[1851] Type "Tell me why my phone's Wi-Fi speed is slow" into the chat interface and click send.

[1852] 2. Terminal:

[1853] The entered question data is converted into JSON format and sent to the server as an HTTP POST request.

[1854] 3. Server:

[1855] The server receives the request and first uses an NLP library (e.g. SpaCy or NLTK) to parse the question.

[1856] 4. Emotion Engine:

[1857] Call a sentiment analysis API (e.g., IBM Watson Natural Language Understanding) to analyze sentiment from the query text. Recognize that the user is impatient.

[1858] 5. Server:

[1859] An AI model (e.g., OpenAI GPT-3) is called to generate the cause of the question and a solution, resulting in a diagnosis such as "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device." Based on the results of sentiment analysis, an additional message is also generated, such as "Please stay calm and take action."

[1860] 6. Terminal:

[1861] The diagnostic results and additional messages are displayed in the user interface, allowing the user to check them and take specific measures.

[1862] Prompt Sentence Examples

[1863] For example, to provide a description of this system to a generative AI model, we might use a prompt like this:

[1864] "We have developed a system that can quickly and accurately diagnose technical issues faced by users and provide appropriate solutions. The system also incorporates an emotion engine, which tailors the results based on the user's emotions."

[1865] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1866] Step 1:

[1867] user:

[1868] A user enters a question through the chat interface and clicks the "Send" button. The input is, for example, "Why is my smartphone's Wi-Fi speed so slow?" The output is the user's input text.

[1869] Step 2:

[1870] Device:

[1871] The terminal converts the user's input question into JSON format and sends it to the server as an HTTP POST request. The input is the user's input text, and the output is JSON format data.

[1872] Step 3:

[1873] server:

[1874] The server receives the request and first uses natural language processing (NLP) techniques to analyze the question. This process is performed using an NLP library (e.g., SpaCy or NLTK). The input is the question data in JSON format, and the output is the analyzed keywords and phrases.

[1875] Step 4:

[1876] Emotion Engine:

[1877] The emotion engine analyzes the text data received from the server and calls an emotion analysis API (e.g., IBM Watson Natural Language Understanding) to recognize the user's emotional state. The input is text data containing analyzed keywords and phrases, and the output is the user's emotional state (e.g., "anxious").

[1878] Step 5:

[1879] server:

[1880] The server uses a generative AI model (e.g., OpenAI GPT-3) to generate appropriate diagnostic results and countermeasures based on the emotional state obtained by the emotion engine and the analyzed data. The input is the user's question and emotional state, and the output is the diagnostic result and an additional message. This process generates the diagnostic result "Your Wi-Fi device is experiencing packet congestion, so please restart your Wi-Fi device" and the additional message "Please stay calm and take action without panicking."

[1881] Step 6:

[1882] Device:

[1883] The terminal displays the diagnostic results and additional messages received from the server on a user interface. The input is the diagnostic results and additional messages, and the output is the display on the user interface.

[1884] Step 7:

[1885] user:

[1886] The user checks the diagnostic results and additional messages displayed on the device and takes specific measures (e.g., "restart the WIFI device"). The input is the diagnostic results and additional messages, and the output is the user's specific action.

[1887] Through these steps, the system is able to quickly and accurately diagnose the user's technical problem and provide appropriate solutions, taking into account their emotional state.

[1888] (Application example 2)

[1889] 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."

[1890] Modern self-driving vehicles use advanced technology, but they face the challenge of responding quickly and appropriately when a system malfunctions. In particular, when a user's emotional state is tense, it is important to provide not only appropriate countermeasures but also a response that reassures the user. This invention aims to not only quickly diagnose and provide solutions to problems faced by the self-driving vehicle, but also to combine an emotion engine to provide appropriate responses based on the user's emotions.

[1891] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1892] In this invention, the server includes means for a user to input a question using an information processing device or a mobile terminal, means for receiving the content of the user's question and analyzing the content, artificial intelligence means for diagnosing the cause of the problem and a solution based on the analyzed question, means for returning the diagnosis result generated by the artificial intelligence means to the user, emotion analysis means for recognizing the emotional state of the user, and means for adjusting the diagnosis result based on the emotion analysis result. This makes it possible to quickly and accurately diagnose problems with the autonomous vehicle and present solutions according to the user's emotions.

[1893] An "information processing device" is an electronic device for processing information, such as a computer or a server.

[1894] A "mobile device" is a portable information processing device such as a smartphone, tablet, or smart glasses.

[1895] A "server" is a high-performance computer that provides services to other computers on a network.

[1896] "Artificial intelligence means" is a system that uses machine learning and natural language processing technology to analyze user questions and diagnose the cause of the problem and solutions.

[1897] "Emotion analysis means" is a technology that recognizes and analyzes emotions from text and voice input by the user.

[1898] "Diagnosis results" are information about the causes of problems and their solutions generated by artificial intelligence means.

[1899] A "user interface" is an operation screen or input device that allows a user to interact with a system.

[1900] The "adjustment means" is a function that modifies the diagnostic results based on the emotion analysis means in accordance with the user's emotions.

[1901] This invention is a system that quickly and accurately diagnoses problems encountered by users in autonomous vehicles and provides appropriate countermeasures. It also incorporates an emotion engine that recognizes the user's emotions. Detailed embodiments of this system are described below.

[1902] System Overview

[1903] The system implements a series of processes: users input questions via a chat interface, the server receives and analyzes the questions, and artificial intelligence (AI) diagnoses the cause of the problem and provides solutions, then returns the results. It also has an emotion engine that recognizes the user's emotions and responds appropriately.

[1904] Program Description

[1905] 1. User Interface

[1906] Device:

[1907] The user interface provides a chat-style interface for users to enter and submit questions. The interface includes a text box and a submit button. When a user enters a question, the content is transmitted to the server.

[1908] 2. Server

[1909] server:

[1910] The server is the central component that performs many roles. First, it receives the questions sent by the user. Second, it analyzes the received questions and takes appropriate action based on their content. Third, it analyzes the emotional state of the user using an emotion engine. The server has the following functions:

[1911] Receiving questions

[1912] Question analysis

[1913] Emotion Analysis

[1914] Running diagnostic processes

[1915] Generating diagnostic results

[1916] Sending diagnostic results

[1917] 3. Artificial Intelligence Model

[1918] server:

[1919] The AI ​​model analyzes the content of the question in detail based on instructions from the server, and diagnoses the cause of the problem and possible solutions. This model can use external APIs, and for example, by using a machine learning model specialized in natural language processing, it can understand the user's question and generate an appropriate answer.

[1920] 4. Emotion Engine

[1921] server:

[1922] The emotion engine analyzes the text data received from the user and recognizes the user's emotional state. This engine infers the user's emotions from the content and tone of the text and adjusts the way it presents diagnosis results and countermeasures.

[1923] Specific examples

[1924] For example, if a user types "My car stopped driving automatically and I don't know why. What should I do?" into the chat interface and sends it, the following happens:

[1925] 1. User:

[1926] Type "My car stopped driving automatically and I don't know why. What should I do?" into the chat interface and click the send button.

[1927] 2. Terminal:

[1928] The entered question is sent to the server.

[1929] 3. Server:

[1930] The system receives the question, analyzes the keyword "autonomous driving stopped," and simultaneously analyzes the user's emotional state using an emotion engine.

[1931] 4. Emotion Engine:

[1932] The system recognizes the user's emotional state as "anxious" and adjusts the way the diagnosis results are presented based on the results of this emotion analysis.

[1933] 5. Server:

[1934] It instantly generates a diagnostic result based on specific keywords, such as "Reboot your system and check for firmware updates," while also including an additional message based on sentiment analysis to remind users to stay calm.

[1935] 6. Terminal:

[1936] The diagnostic results are displayed in the chat interface and solutions are suggested to the user.

[1937] 7. Users:

[1938] Follow the suggested solutions to resolve the issue.

[1939] Prompt Sentence Examples

[1940] User input: "My Autopilot stopped working and I don't know why. What should I do?"

[1941] Example prompts to input to a generative AI model:

[1942] Q: My car stopped working automatically and I don't know why. What should I do?

[1943] A: It may be that your system has experienced a temporary issue. Please reboot your system and then check for firmware updates. Also, please be patient and follow the guidelines below.

[1944] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1945] Step 1:

[1946] A user inputs a question using an information processing device or a mobile terminal.

[1947] Input: User types into the chat interface, "My autopilot stopped working and I don't know why. What should I do?"

[1948] Output: The entered text data is sent to the server.

[1949] Specific action: The user enters text into a chat-style interface and clicks the send button.

[1950] Step 2:

[1951] The terminal sends the entered question to the server.

[1952] Input: Text data that the user enters and presses the submit button.

[1953] Output: The text data sent to the server.

[1954] Specific operation: The terminal transmits the user's input to the server via the network.

[1955] Step 3:

[1956] The server receives the query and analyzes its contents.

[1957] Input: The user's text data sent to the server.

[1958] Output: Analyzed user question (the keyword "autonomous driving stopped" is extracted).

[1959] Specific operation: The server receives the text data, performs text analysis, and extracts important keywords.

[1960] Step 4:

[1961] The server uses emotion analysis means to analyze the user's emotional state.

[1962] Input: Parsed text data.

[1963] Output: The user's emotional state (e.g., "I'm anxious").

[1964] What it does: The server uses an emotion recognition model to analyze emotions from the context and tone of the user's text.

[1965] Step 5:

[1966] The server uses artificial intelligence means to diagnose the cause of the problem and what to do about it.

[1967] Input: Parsed user question and emotional state.

[1968] Output: Diagnostic results (e.g. "Please reboot your system and check for firmware updates").

[1969] Specific operation: The server utilizes natural language processing technology and uses a generative AI model to diagnose the cause of the problem and provide solutions.

[1970] Step 6:

[1971] The server adjusts the diagnosis results based on the emotion analysis results.

[1972] Input: Diagnosis results and user's emotional state.

[1973] Output: Tailored diagnostic results and messages (e.g. "Please stay calm and follow the steps below").

[1974] Specific operation: The server generates advice and additional messages that reflect the user's emotional state and adds them to the diagnosis results.

[1975] Step 7:

[1976] The server transmits the adjusted diagnostic results to the terminal.

[1977] Input: Adjusted diagnostic results and messages.

[1978] Output: Diagnostic results and messages sent to the terminal.

[1979] Specific operation: The server performs a process to send the adjusted diagnostic results to the terminal via the network.

[1980] Step 8:

[1981] The device will display diagnostic results and messages to the user.

[1982] Input: The adjusted diagnostic results and messages sent by the server.

[1983] Output: Diagnostic results and messages displayed in the user interface.

[1984] Specific operation: The diagnostic results and advice messages are displayed on the terminal display.

[1985] Step 9:

[1986] The user follows the suggested solutions to resolve the issue.

[1987] Input: Diagnostic results and messages displayed on the terminal.

[1988] Output: Actions taken (e.g. system reboot and firmware update).

[1989] Specific actions: The user actually performs an action based on the proposed countermeasures.

[1990] 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.

[1991] 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.

[1992] 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.

[1993] 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.

[1994] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1995] 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.

[1996] 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).

[1997] 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.

[1998] 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."

[1999] 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.

[2000] 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).

[2001] 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.

[2002] 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.

[2003] 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.

[2004] 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.

[2005] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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 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.

[2006] 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.

[2007] 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.

[2008] 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.

[2009] 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.

[2010] 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.

[2011] The following is further disclosed regarding the above embodiment.

[2012] (Claim 1)

[2013] a means for a user to input a question using a computer or personal digital assistant;

[2014] a server means for receiving the question and analyzing its contents;

[2015] an artificial intelligence means for diagnosing the cause of a problem and a solution based on the question analyzed by the server means;

[2016] means for returning the diagnostic results generated by said artificial intelligence means to the user;

[2017] The system includes means for enabling a user to take countermeasures based on the diagnostic results.

[2018] (Claim 2)

[2019] 2. The system of claim 1, wherein the artificial intelligence means includes means for utilizing an external artificial intelligence model via a computer network.

[2020] (Claim 3)

[2021] 2. The system of claim 1, wherein the server means includes means for providing an immediate answer based on keywords defined when analyzing a user's question.

[2022] "Example 1"

[2023] (Claim 1)

[2024] A means for a user to input a question using an information processing device;

[2025] processing means for receiving the query and analyzing its content;

[2026] an intelligence construction means for diagnosing the cause of a problem and a solution based on the question analyzed by said processing means;

[2027] means for returning the diagnostic results generated by said intelligent configuration means to the user;

[2028] The system includes means for enabling a user to take countermeasures based on the diagnostic results.

[2029] (Claim 2)

[2030] 2. The system according to claim 1, wherein said intelligence configuration means includes means for utilizing an external intelligence model via a communication network.

[2031] (Claim 3)

[2032] 10. The system of claim 1, wherein the processing means includes means for generating instant answers based on keywords defined in analyzing the user's question.

[2033] (Claim 4)

[2034] 2. The system of claim 1, wherein the processing means includes means for generating a prompt sentence from the content of a user's question and sending it to the external intelligence model when the user's question requires detailed analysis by the external intelligence model.

[2035] "Application Example 1"

[2036] (Claim 1)

[2037] a means for a user to input a question using a communications terminal;

[2038] processing means for receiving the query and analyzing its content;

[2039] an artificial intelligence means for diagnosing the cause of a problem and a solution based on the question analyzed by said processing device means;

[2040] means for returning the diagnostic results generated by said artificial intelligence means to the user;

[2041] means for enabling a user to take measures based on the diagnostic results;

[2042] A system that includes a means for receiving, analyzing, and providing answers to questions when a user asks about products or services in a physical store.

[2043] (Claim 2)

[2044] 2. The system according to claim 1, wherein said artificial intelligence means includes means for utilizing an external artificial intelligence model via a computer network.

[2045] (Claim 3)

[2046] 2. The system of claim 1, wherein the processing device means includes means for providing instant answers based on keywords defined when analyzing a user's question, and means for responding to customer questions within a physical store.

[2047] "Example 2: Combining Emotion Engines"

[2048] (Claim 1)

[2049] a means for a user to input a question using an information terminal;

[2050] a server means for receiving the question and analyzing its contents;

[2051] an artificial intelligence means for diagnosing the cause of a problem and a solution based on the question analyzed by the server means;

[2052] a means for returning the diagnosis result and emotion analysis result generated by the artificial intelligence means to the user;

[2053] The system includes means for enabling a user to take countermeasures based on the diagnostic results.

[2054] (Claim 2)

[2055] 2. The system according to claim 1, wherein the artificial intelligence means includes means for utilizing an external machine learning model via a communication network.

[2056] (Claim 3)

[2057] 2. The system of claim 1, wherein the server means includes means for generating instant answers and sentiment-sensitive messages based on keywords and sentiment analysis defined when analyzing a user's question.

[2058] "Application example 2 when combining emotion engines"

[2059] (Claim 1)

[2060] A means for a user to input a question using an information processing device or a mobile terminal;

[2061] a server means for receiving the question and analyzing its contents;

[2062] an artificial intelligence means for diagnosing the cause of a problem and a solution based on the question analyzed by the server means;

[2063] means for returning the diagnostic results generated by said artificial intelligence means to the user;

[2064] an emotion analysis means for recognizing the emotional state of a user;

[2065] means for adjusting the diagnosis result based on the emotion analysis result;

[2066] and means for enabling a user to take countermeasures based on said adjusted diagnostic results.

[2067] (Claim 2)

[2068] 2. The system of claim 1, wherein the artificial intelligence means includes means for utilizing an external artificial intelligence model via an information processing network.

[2069] (Claim 3)

[2070] 2. The system of claim 1, wherein the server means includes means for providing an immediate answer based on keywords defined when analyzing a user's question. [Explanation of symbols]

[2071] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input a question using a computer or personal digital assistant; a server means for receiving the question and analyzing its contents; an artificial intelligence means for diagnosing the cause of a problem and a solution based on the question analyzed by the server means; means for returning the diagnostic results generated by said artificial intelligence means to the user; The system includes means for enabling a user to take countermeasures based on the diagnostic results.

2. 2. The system of claim 1, wherein said artificial intelligence means includes means for utilizing an external artificial intelligence model via a computer network.

3. 2. The system of claim 1, wherein said server means includes means for providing an immediate answer based on keywords defined in analyzing a user's question.

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