System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving an utterance of a user and obtaining the utterance as voice data; means for converting the voice data into text data; means for analyzing the text data to identify a problem desired to be solved by the user; means for searching a database for retrieving an optimal solution based on the identified problem; and means for presenting the solution retrieved from the database to the user.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] Modern devices have become extremely complex to use and troubleshoot due to a variety of settings and internet connection conditions. This makes it difficult for users to quickly and accurately resolve issues that arise while using the devices. To resolve these issues, many users typically call support centers or ask for help from people around them, but this method makes it difficult to receive effective support. In particular, solutions vary depending on the individual device, such as the OS version, app installation status, and internet connection status, making it difficult to provide optimal support for each device. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system that accepts a user's speech as voice data, converts it into text data, and further analyzes the text data to identify the problem the user wants to solve. This system has a database for searching for an optimal solution based on the identified problem, and presents the solution searched from the database to the user. Specifically, the system has a recording means containing collected user device information and is equipped with a means for presenting solutions in text and voice, thereby enabling the system to provide optimal support for each device. This allows the user to receive effective support quickly.

[0006] A "user" is a person who utilizes the support application to troubleshoot a device.

[0007] "Utterance" is the act of a user verbally communicating a problem or request that they want to solve to an application.

[0008] "Voice data" refers to data that is captured by a device as voice input from a user's speech.

[0009] "Text data" is data in the form of a character string converted from voice data using voice recognition technology.

[0010] "Analysis" is the process of analyzing text data using natural language processing techniques to identify user problems.

[0011] "Problems" are problems or questions users have about their devices.

[0012] A "solution" is a specific method or procedure for addressing an identified problem.

[0013] A "database" is a collection of information that stores and makes searchable user device information and past problems and solutions.

[0014] "Recording means" is a function that collects and stores basic information about the user's device.

[0015] "Presentation means" is a function for displaying or reproducing the solution to the user in text or audio.

[0016] A "terminal" is an electronic device on which the Support Application is installed.

[0017] A "server" is a remote computer system that performs analysis, solution searches, and manages the database. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention is a support system for quickly and accurately resolving problems users have with their devices, utilizing voice recognition technology and natural language processing technology to analyze the user's speech and provide the optimal solution.

[0040] First, the user installs the supporting application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[0041] To use the app, a user launches the support app on their device and speaks to it about the problem they want to solve (for example, "I can't connect to Wi-Fi"). The device captures this speech as voice data and converts it into text data using voice recognition technology. The converted text data is then sent to the server.

[0042] The server uses natural language processing to analyze the received text data and identify the problem the user is having. For example, it extracts keywords such as "Wi-Fi" and "connection" to understand the nature of the problem. The server then searches a database along with basic device information to search for known solutions. The identified solution (e.g., "Open the Settings app and check your Wi-Fi settings. Then try reconnecting") is sent from the server to the device.

[0043] The device presents the received solution to the user. The presentation method can be either a text display or a voice playback. The user follows the instructions to implement the solution and reports the result in the app. For example, after attempting the presented solution, the device provides feedback such as "Solved" or "Not yet solved."

[0044] User feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it will perform further diagnostics and suggest another solution. By repeating this process, we aim to provide the best support for the user's device and resolve the problem as quickly as possible.

[0045] Specific examples

[0046] Scenario: A user is having internet connection issues

[0047] 1. Initial Setup

[0048] Device: When you install the support app on your iPhone (registered trademark) and launch the app for the first time, basic device information (OS: iOS 16, Internet contract status: Wi-Fi only) will be collected.

[0049] Terminal: Sends collected information to the server.

[0050] Server: Stores the information in a database and assigns a unique identifier to the device.

[0051] 2. User Interaction

[0052] User: Say "I can't connect to Wi-Fi."

[0053] Terminal: Captures speech as voice data, converts it into text data using voice recognition technology, and sends that data to the server.

[0054] 3. Problem Analysis and Diagnosis

[0055] Server: Analyze text data containing the keywords "Wi-Fi" and "cannot connect" using natural language processing technology.

[0056] Server: View device information and past problems and solutions to find the best solution.

[0057] 4. Providing a solution

[0058] Server: Generated the solution: "Open the Settings app and check your Wi-Fi settings. Then try reconnecting."

[0059] Terminal: Presents the generated solution to the user in voice and text.

[0060] 5. Feedback and final confirmation

[0061] User: Attempts to reconnect and provides feedback through the app that it's still not working.

[0062] Device: Send feedback to the server.

[0063] Server: Generate the next solution, "Please restart your router," and present it again.

[0064] By using this system, users can receive prompt, comprehensive and appropriate support tailored to the status of their device.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The user installs a supporting app on the device.

[0068] Step 2:

[0069] When a device launches the app for the first time, basic device information (OS version, app installation status, internet connection status, and device settings status) is collected.

[0070] Step 3:

[0071] The device sends the collected basic information to the server.

[0072] Step 4:

[0073] The server stores the received device information in a database and assigns a unique identifier (Device ID) to each device.

[0074] Step 5:

[0075] The user launches the app and talks about the problem they want to solve, such as "Wi-Fi won't connect."

[0076] Step 6:

[0077] The terminal acquires the user's speech as voice data and converts it into text data using voice recognition technology.

[0078] Step 7:

[0079] The terminal transmits the converted text data to the server.

[0080] Step 8:

[0081] The server analyzes the received text data using natural language processing technology to identify the problem the user is facing.

[0082] Step 9:

[0083] The server references basic information about the device and the type of problem, then searches a database for past solutions.

[0084] Step 10:

[0085] The server identifies the best solution and generates a message with specific instructions for the user.

[0086] Step 11:

[0087] The server generates a message and sends it to the terminal.

[0088] Step 12:

[0089] The terminal presents the message received from the server to the user in voice and text.

[0090] Step 13:

[0091] The user implements the proposed solution and reports the results in the app.

[0092] Step 14:

[0093] The terminal sends the user's feedback to the server.

[0094] Step 15:

[0095] The server receives the feedback and, if the problem persists, performs further diagnostics and generates additional solutions.

[0096] Step 16:

[0097] The server sends the new solution to the terminal, which presents it to the user again.

[0098] This series of steps allows users to receive the best support for their device's situation.

[0099] Example 1

[0100] 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."

[0101] Conventional user support systems required multiple manual operations and complex settings for users to solve device problems, making it difficult to solve problems quickly and accurately. Furthermore, they lacked a mechanism for providing optimal support tailored to the user's device status, resulting in low accuracy and efficiency of support. This significantly impaired user convenience.

[0102] 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.

[0103] In this invention, the server includes means for accepting a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for collecting basic information about the user's device and sending it to the server, means for having a database for searching for an optimal solution based on the identified problem, means for presenting the solution searched for from the database to the user, and means for generating further solutions based on user feedback, thereby enabling the user to receive appropriate support for quickly and accurately solving device problems.

[0104] The "means for receiving user speech" is a function for collecting speech spoken by a user into the device.

[0105] "Means for acquiring as voice data" refers to a technology for converting a user's speech into digital voice data and collecting it.

[0106] The "means for converting into text data" is a function that analyzes the voice data and converts the content into text format.

[0107] "Means for analyzing text data to identify the problem the user wants to solve" refers to a technology that extracts problem-related keywords and phrases from text data and identifies the problem the user is facing.

[0108] "Means of collecting basic information about the user's device" refers to a function that obtains information such as the device's OS version, installed applications, and Internet connection status.

[0109] The "means for transmitting to the server" is a function for transferring the collected basic information of the device to the server via the network.

[0110] "Means having a database for searching for the optimum solution" refers to a function for searching for the optimum solution by referring to a database in which past problems and their solutions are recorded.

[0111] The "means for presenting the solutions retrieved from the database to the user" is a function for providing the retrieved solutions to the user in the form of text display or audio playback.

[0112] The "means for generating further solutions based on user feedback" is a technique for analyzing feedback information from users and presenting new solutions in response to the analysis.

[0113] This invention is a support system for quickly and accurately resolving problems users have with devices, utilizing voice recognition technology and natural language processing technology to analyze the user's speech and provide the optimal solution.

[0114] First, the user installs the support application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started up. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[0115] Next, the user launches the support app and speaks to the app about the problem they want to solve. The user's speech is captured as voice data and converted into text data using voice recognition technology. This text data is then sent to the server.

[0116] The server uses natural language processing (NLP) to analyze the received text data and identify the problem the user is having. For example, it extracts keywords such as "Wi-Fi" and "connection" to understand the nature of the problem. The server then references basic device information and a database of past problems and solutions to search for the most appropriate solution. The identified solution (e.g., "Open the Settings app and check your Wi-Fi settings. Then try reconnecting") is sent from the server to the device.

[0117] The device presents the received solution to the user. The presentation method can be either a text display or a voice playback. The user follows the instructions to implement the solution and reports the result in the app. For example, after attempting the presented solution, the device provides feedback such as "Solved" or "Not yet solved."

[0118] User feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it will perform further diagnostics and suggest another solution. By repeating this process, we aim to provide the best support for the user's device and resolve the problem as quickly as possible.

[0119] As a concrete example, consider a case where a user is using an iPhone and is having trouble connecting to Wi-Fi. In this case, the user speaks to a support app saying, "I can't connect to Wi-Fi." In response, the support system captures the voice data, converts it into text data, analyzes it, and presents the optimal solution. For example, it may give instructions such as, "Open the Settings app and check your Wi-Fi settings."

[0120] An example prompt is:

[0121] "My user has an iPhone and is having trouble connecting to Wi-Fi. How can I help them?"

[0122] "After collecting basic device information, the user said 'Wi-Fi is not working'. Please provide an appropriate solution."

[0123] The system leverages speech recognition and natural language processing technologies, particularly generative AI models, to analyze and generate solutions, enabling users to receive faster and more appropriate support.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1: Install supporting applications

[0126] The user installs a support application on the device.

[0127] Input: Applications downloaded from the app store at the user's request

[0128] Output: Supporting applications installed on the device

[0129] Step 2: First boot and device information collection

[0130] When the device launches the support app for the first time, it automatically collects basic device information (e.g., OS version, app installation status, Internet connection status, and device settings).

[0131] Input: Basic device information collected by first launching the support app

[0132] Output: A dataset of collected device information

[0133] Specific behavior: The process of packaging collected information as structured data

[0134] Step 3: Send device information to the server

[0135] The terminal sends the collected basic device information to the server.

[0136] Input: A dataset of collected device information

[0137] Output: Device information stored on the server

[0138] What it does: Transforms the information into the appropriate format and sends it over a secure channel to the server.

[0139] Step 4: Assigning a unique identifier

[0140] The server stores the received device information in a database and assigns each device a unique identifier.

[0141] Input: Device information sent to the server

[0142] Output: Device information stored in the database and assigned unique identifier

[0143] Specific operation: Stores device information in a database and generates and assigns a UUID (Universally Unique Identifier).

[0144] Step 5: Receiving the problem statement

[0145] The user launches the support app on their device and speaks the problem they want to solve (e.g., "My Wi-Fi isn't working").

[0146] Input: User spoken utterance

[0147] Output: Speech content captured as audio data

[0148] Specific operation: Converts audio collected through a microphone into digital data

[0149] Step 6: Converting audio data to text data

[0150] The terminal converts the voice data into text data using voice recognition technology.

[0151] Input: Captured audio data

[0152] Output: Converted text data

[0153] Specific operation: Analyzes voice data using a speech recognition engine and outputs it as text.

[0154] Step 7: Sending text data to the server

[0155] The terminal transmits the converted text data to the server.

[0156] Input: Text data

[0157] Output: Text data sent to the server

[0158] Specific behavior: Encodes text data in the appropriate format and sends it to the server.

[0159] Step 8: Analyze the problem

[0160] The server analyzes the received text data using natural language processing (NLP) technology to identify the problem the user is having.

[0161] Input: Text data sent to the server

[0162] Output: Identified problem

[0163] Specific behavior: Analyzes text data, extracts keywords (e.g., "Wi-Fi," "connection"), and identifies issues

[0164] Step 9: Device Information and Database Reference

[0165] The server searches for the best solution by referring to the device's basic information and a database of past problems and solutions.

[0166] Input: Identified problem and device information

[0167] Output: The optimal solution found

[0168] Specific behavior: Run a database search algorithm to identify a solution

[0169] Step 10: Generate and present solutions

[0170] The server generates a solution to the identified problem and sends it to the terminal.

[0171] The terminal presents the generated solutions to the user in the form of a text display and audio playback.

[0172] Input: Solution data from the server

[0173] Output: The solution presented to the user

[0174] Specific Action: Format the solution in text and audio format and present it to the user

[0175] Step 11: Accepting user feedback

[0176] The user implements the proposed solution and reports the results in the support app (e.g., "Still not resolved").

[0177] Input: User feedback

[0178] Output: Feedback data

[0179] Specific operation: Enter the feedback content into the app and convert it into digital data

[0180] Step 12: Sending feedback to the server

[0181] The terminal transmits the user's feedback to the server.

[0182] Input: Feedback data

[0183] Output: Feedback sent to the server

[0184] Specific behavior: Encodes the feedback data in the appropriate format and sends it to the server

[0185] Step 13: Generate further solutions

[0186] The server performs further detailed diagnosis based on the user's feedback and generates new solutions, which are sent to the terminal.

[0187] Input: Feedback data

[0188] Output: New solution

[0189] Specific behavior: Run an algorithm that analyzes the feedback and generates a new solution based on it.

[0190] This series of steps allows users to receive support to quickly and accurately resolve issues with their devices.

[0191] (Application example 1)

[0192] 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."

[0193] When performing maintenance or troubleshooting on industrial equipment, delays in factory production lines can occur if technicians with specialized knowledge are unable to respond quickly. Furthermore, accurately understanding the complex state of equipment and proposing appropriate solutions requires advanced technology and experience. To resolve these issues, a system is needed that can instantly assess the status of the equipment and provide a fast, accurate solution.

[0194] 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.

[0195] In this invention, the server includes means for accepting user speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for having a database for searching for an optimal solution based on the identified problem, means for presenting the solution searched for from the database to the user, means for acquiring basic information about the industrial equipment, means for transmitting the basic information about the industrial equipment to the server, and means for receiving and presenting a solution to the problem of the industrial equipment from the server. This enables maintenance and troubleshooting of the industrial equipment to be performed quickly and accurately.

[0196] "User utterance" refers to the words and sentences that are the source of the voice data input to the system.

[0197] "Voice data" refers to data that is a digital recording of a user's speech.

[0198] "Text data" refers to data in the form of a string of characters converted from audio data.

[0199] "Analysis" refers to the process of understanding text data and identifying the problem the user wants to solve.

[0200] The "database" refers to a collection of information that stores basic information about users' devices and industrial equipment, as well as past problems and their solutions.

[0201] "Optimal solution" refers to the most effective and appropriate way to solve the user's problem.

[0202] "Presenting to the user" refers to visually or audibly conveying the found solution to the user.

[0203] "Industrial equipment" refers to various machines and devices used in factories and production lines.

[0204] "Basic information" refers to important data about industrial equipment, such as the model name, OS version, and last maintenance date.

[0205] "Server" refers to the central management system that analyzes voice and text data and searches for and provides optimal solutions.

[0206] "Receiving" refers to the user's device retrieving the solution provided by the server.

[0207] This invention is a support system for quickly and accurately resolving problems users have with industrial machinery. Specifically, it utilizes voice recognition technology and natural language processing technology to analyze user utterances and provide optimal solutions.

[0208] First, the user installs a support application related to the industrial equipment on the device. After installation, the device collects basic information about the industrial equipment (e.g., OS version, equipment model, and last maintenance date) when it is first started up. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[0209] When a user uses a support application to solve a problem with industrial equipment, they first launch the app on their device and then speak to the application about the problem they want to solve (for example, "The robot arm is moving slowly"). The device captures this speech as audio data and converts it into text data using speech recognition technology. The Python speech_recognition library is used for speech recognition.

[0210] The converted text data is then sent to a server. The server uses natural language processing technology to analyze the received text data and identify the problem the user is having. A natural language processing library is used for the analysis. For example, keywords such as "robot arm" and "slow movement" are extracted to understand the nature of the problem. The server then references a database along with basic information about the industrial equipment to search for known solutions. The identified solution (e.g., "Please readjust the axis of the robot arm") is then sent from the server to the device.

[0211] The device presents the received solution to the user. The presentation method can be either a text display or audio playback. For audio playback, the Python pyttsx3 library is used. The user follows the instructions to implement the solution and reports the results in the app. For example, after attempting the presented solution, the device can provide feedback such as "Solved" or "Not yet solved."

[0212] The user's feedback is sent via the device to the server. The server receives the feedback, and if the problem is not resolved, it performs a more detailed diagnosis and suggests another solution. By repeating this process, the aim is to provide optimal support for the user's industrial equipment and quickly resolve the problem.

[0213] Specific examples

[0214] If an industrial device is not working properly, for example, by saying "The robot arm has stopped working," the system will immediately suggest a solution. Examples of prompts to input to the generative AI model are as follows:

[0215] Please provide a solution for a robot that is experiencing the following issue: "The robot arm has stopped moving." The robot's basic information is as follows: OS version: FactoryOS 1.2, robot model: XJ320, last maintenance date: 2023-01-25.

[0216] By using this system, factory maintenance personnel can quickly receive comprehensive and appropriate support tailored to the status of their industrial equipment.

[0217] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0218] Step 1:

[0219] The terminal installs the supporting application and collects basic information about the industrial equipment (e.g., OS version, equipment model, and last maintenance date) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device. The input is the basic device information, and the output is the device information with the identifier stored on the server.

[0220] Step 2:

[0221] A user launches a support application to solve a problem with industrial equipment and speaks out the problem they want to solve (for example, "The robot arm is moving slowly"). The device receives this speech as voice data. The input is the user's speech, and the output is voice data.

[0222] Step 3:

[0223] The device converts the acquired voice data into text data using voice recognition technology (Python's speech_recognition library). The input is voice data, and the output is text data.

[0224] Step 4:

[0225] The terminal sends the converted text data to the server. The input is the text data, and the output is the text data sent to the server.

[0226] Step 5:

[0227] The server analyzes the received text data using natural language processing technology (natural language processing library) and identifies the problem the user is facing. The input is the text data, and the output is the identified problem.

[0228] Step 6:

[0229] The server references the database along with basic information about the industrial equipment and searches for the best solution based on the identified problem. The input is the identified problem and basic information about the industrial equipment, and the output is the solution.

[0230] Step 7:

[0231] The server sends the searched solutions to the terminal. The input is the solution and the output is the solution sent to the terminal.

[0232] Step 8:

[0233] The terminal presents the received solution to the user, either as a text display or as audio (using the Python pyttsx3 library for audio playback). The input is the solution, and the output is the solution presented to the user.

[0234] Step 9:

[0235] The user follows the instructions, implements the solution, and reports the result in the app, e.g., feedback such as "solved" or "not yet solved." The input is the user feedback, and the output is the feedback data.

[0236] Step 10:

[0237] The terminal sends the user's feedback to the server, where the input is the feedback data and the output is the feedback data sent to the server.

[0238] Step 11:

[0239] The server receives the feedback, and if the problem is not resolved, it performs further diagnostics and suggests a different solution. This process is repeated to provide optimal support. The input is the feedback data, and the output is the new solution.

[0240] 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.

[0241] This invention is a support system for quickly and accurately resolving device-related problems that users encounter, utilizing speech recognition technology, natural language processing technology, and an emotion engine for recognizing the user's emotions. This system analyzes the user's speech and provides optimal solutions, while also providing support that takes the user's emotional state into consideration.

[0242] First, the user installs the supporting application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[0243] To use the app, a user launches the support app on their device and speaks to it about the problem they want to solve (for example, "I can't connect to Wi-Fi"). The device captures this speech as voice data and converts it into text data using voice recognition technology. The converted text data is then sent to the server.

[0244] The server analyzes the received text data using natural language processing technology to identify the problem the user is experiencing. It also uses an emotion engine to extract the user's emotional state (e.g., dissatisfaction, impatience, anger, etc.) from the text and voice data. For example, it can recognize that the user is dissatisfied based on the text data "Wi-Fi is not connecting" and the tone of the voice.

[0245] The server then searches the database for the optimal solution, taking into account the user's emotional state, basic device information, and the type of problem. For example, if the user is frustrated, a solution containing kind words and encouraging messages will be generated in addition to the usual solution. A specific solution might be something like, "Open the Settings app and check your Wi-Fi settings. Then try reconnecting," with an additional message depending on the user's condition.

[0246] The solution sent from the server is presented to the user via the device. The presentation method can be either text display or audio playback. The user attempts to solve the problem according to the presented solution and reports the result in the app (for example, feedback such as "Solved" or "Not yet solved").

[0247] The feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it performs a more detailed diagnosis and proposes a new solution. At this time, it again utilizes the emotion engine to recognize the user's emotional state and adjust the appropriate support method.

[0248] Specific examples

[0249] Scenario: A user is having internet connection issues

[0250] 1. Initial Setup

[0251] Device: When you install the support app on your iPhone and launch the app for the first time, basic device information (OS: iOS 16, Internet contract status: Wi-Fi only) will be collected.

[0252] Terminal: Sends collected information to the server.

[0253] Server: Stores the information in a database and assigns a unique identifier to the device.

[0254] 2. User Interaction

[0255] User: Say "I can't connect to Wi-Fi."

[0256] Terminal: Captures speech as voice data, converts it into text data using voice recognition technology, and sends that data to the server.

[0257] 3. Problem analysis and emotion recognition

[0258] Server: Analyzes text data containing the keywords "Wi-Fi" and "cannot connect" using natural language processing technology to identify the problem.

[0259] Server: Using the emotion engine, recognize the user's emotion from the text data and voice data. For example, recognize that the user is dissatisfied.

[0260] 4. Solution Search and Generation

[0261] Server: Generates the optimal solution by taking into account the user's emotional state and device information. Example: "Open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[0262] 5. Providing a solution

[0263] Server: Sends the generated solution to the device.

[0264] Terminal: Presents audio and text to the user.

[0265] 6. Feedback and Final Review

[0266] User: Try the proposed solution and provide feedback on the results. Example: "Still not solved."

[0267] Device: Sends feedback to the server.

[0268] Server: Perform additional diagnostics and generate a different solution: "Reboot your router" and provide the appropriate support message again.

[0269] This system allows users to quickly receive comprehensive and appropriate support tailored to the situation of their device, and provides more customized support by taking into account the user's emotional state.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] The user installs a supporting app on the device.

[0273] Step 2:

[0274] When a device launches the app for the first time, basic device information (OS version, app installation status, internet connection status, and device settings status) is collected.

[0275] Step 3:

[0276] The device sends the collected basic information to the server.

[0277] Step 4:

[0278] The server stores the received device information in a database and assigns a unique identifier (Device ID) to each device.

[0279] Step 5:

[0280] The user launches the app and talks about the problem they want to solve, such as "Wi-Fi won't connect."

[0281] Step 6:

[0282] The terminal acquires the user's speech as voice data and converts it into text data using voice recognition technology.

[0283] Step 7:

[0284] The terminal transmits the converted text data to the server.

[0285] Step 8:

[0286] The server analyzes the received text data using natural language processing technology to identify the problem the user is facing.

[0287] Step 9:

[0288] The server uses an emotion engine to recognize the user's emotion from the text data and voice data, for example, to detect that the user is dissatisfied.

[0289] Step 10:

[0290] The server takes into account the device's basic information and emotional state to search for past solutions from a database.

[0291] Step 11:

[0292] The server identifies the best solution and generates a message for the user that takes into account specific steps and emotions, such as "Open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[0293] Step 12:

[0294] The server generates a message and sends it to the terminal.

[0295] Step 13:

[0296] The terminal presents the message received from the server to the user in voice and text.

[0297] Step 14:

[0298] The user implements the proposed solution and reports the results in the app, for example, by providing feedback such as "still not solved."

[0299] Step 15:

[0300] The terminal sends the user's feedback to the server.

[0301] Step 16:

[0302] The server receives the feedback, and if the problem is not resolved, it performs a more detailed diagnosis and generates a new solution, again leveraging the emotion engine to recognize the user's emotional state.

[0303] Step 17:

[0304] The server sends a new solution to the device, which then presents it to the user again, for example, "Please restart your router. Please wait a moment."

[0305] This series of steps allows users to receive optimal support based on the context of their device. Combined with the emotion engine, customized support is provided that takes into account the user's emotional state.

[0306] Example 2

[0307] 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."

[0308] In conventional support systems, when a user reports a device problem, it is difficult to accurately and quickly identify the problem and provide the optimal solution. Furthermore, the quality of support can be compromised by not taking the user's emotional state into consideration. This often leads to stress for users and delays in resolving the problem.

[0309] 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.

[0310] In this invention, the server includes means for receiving a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for having a database for searching for an optimal solution based on the identified problem, means for recognizing the user's emotional state, and means for presenting the solution searched for in the database to the user. This makes it possible to quickly and accurately solve device problems while taking the user's emotional state into consideration.

[0311] "Means for accepting user speech and obtaining it as voice data" refers to a device or software that captures information that a user inputs into a device by voice and converts it into a format that can be stored electronically.

[0312] The "means for converting voice data into text data" refers to a device or program that analyzes voice data and converts it into corresponding text format information.

[0313] "Means for analyzing text data to identify the problem the user wants to solve" refers to a method or technology for analyzing the converted text data and extracting the user's problem or inquiry from its contents.

[0314] "Means having a database for searching for an optimal solution based on an identified problem" refers to a device or system that has the function of searching for an optimal response from a database that stores related solutions based on analyzed text data.

[0315] The "means for recognizing the user's emotional state" is a technology for analyzing the user's emotions from the tone of their voice and the contents of their text data, and recognizing their state.

[0316] The "means for presenting the solutions retrieved from the database to the user" is a device or software for visually or audibly presenting the solutions retrieved to the user.

[0317] The present invention is a support system for quickly and accurately resolving device-related problems that users encounter, utilizing speech recognition technology, natural language processing technology, and an emotion engine for recognizing the user's emotions. This system analyzes the user's speech and provides optimal solutions while also providing support that takes the user's emotional state into consideration.

[0318] First, the user installs the supporting application on their device. After installation, when the application is launched, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) at first launch. This information is sent to the server and stored in a database, and a unique identifier is assigned to each device.

[0319] The user then launches the support app and speaks out the problem they want to solve. For example, if the user says, "My Wi-Fi isn't connecting," the device captures this speech as voice data. Next, the device uses voice recognition technology to convert this voice data into text data. Specifically, the device converts the voice data into text format using the Google® Cloud Speech-to-Text API or similar. The converted text data is then sent to the server.

[0320] The server analyzes the received text data using natural language processing technology (e.g., Amazon Comprehend) to identify the problem the user is experiencing. At the same time, it uses an emotion engine to extract the user's emotional state from the text and voice data. For example, it recognizes that the user is dissatisfied based on the utterance "My Wi-Fi isn't connecting" and the tone of the voice.

[0321] Next, the server searches for the optimal solution from a database, taking into account the user's emotional state, basic device information, and the type of problem. For example, if the user is frustrated, the server generates a solution that includes kind words and an encouraging message in addition to the usual solution. A specific solution might be, "Open the Settings app and check your Wi-Fi settings. Then, try reconnecting. We apologize for the inconvenience." This solution is sent from the server to the device and presented to the user via the device. It can be presented as text or audio.

[0322] The user attempts to solve the problem using the proposed solutions and reports the results in the app. For example, they can provide feedback such as "Solved" or "Not yet solved." This feedback is sent to the server via the device. The server receives the feedback, and if the problem is not solved, it performs a more detailed diagnosis and suggests a new solution. At this time, the emotion engine is again utilized to recognize the user's emotional state and adjust the appropriate support method.

[0323] Specific examples

[0324] Scenario: A user is having internet connection issues

[0325] Initial Setup

[0326] 1. Device: When you install the support app on your smartphone and launch the app for the first time, basic device information (OS version, internet connection status, etc.) will be collected.

[0327] 2. Terminal: Sends collected information to the server.

[0328] 3. Server: Stores the information in a database and assigns a unique identifier to the device.

[0329] User Interaction

[0330] 1. User: Says, "Wi-Fi is not connecting."

[0331] 2. Terminal: The speech is acquired as audio data, converted into text data using the Google Cloud Speech-to-Text API, and sent to the server.

[0332] Problem analysis and emotion recognition

[0333] 1. Server: Analyze text data containing the keywords "Wi-Fi" and "unable to connect" using Amazon Comprehend to identify problems.

[0334] 2. Server: Using the emotion engine, recognize the user's dissatisfaction from text and voice data.

[0335] Solution generation and delivery

[0336] 1. Server: Generates the optimal solution by taking into account the user's emotional state and device information. Example: "Please open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[0337] 2. Server: Sends the generated solution to the device.

[0338] 3. Terminal: Presents the user with voice and text.

[0339] Feedback and final confirmation

[0340] 1. User: Try the proposed solution and provide feedback on the results. Example: "Still not solved."

[0341] 2. Device: Sends feedback to the server.

[0342] 3. Server: Performs additional diagnostics and generates another solution, "Please restart the router," and sends it to the device again.

[0343] Using this system, users can quickly and accurately resolve problems with their devices and receive customized support tailored to their emotional state.

[0344] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0345] Step 1:

[0346] The user installs a supporting application on the device.

[0347] Input: Application installation request

[0348] Data processing / calculation: The installer copies the application files to the device and performs the necessary settings.

[0349] Output: Supporting applications installed on the device

[0350] Specific operations: Download the supporting app from the App Store or GOOGLE PLAY(R) store and complete the installation.

[0351] Step 2:

[0352] The device collects basic device information when the application is first launched.

[0353] Input: First launch of application

[0354] Data processing / calculation: Collects information such as the device's OS version, internet connection status, and installed apps, and generates data in JSON format.

[0355] Output: Basic device information collected

[0356] Specific operation: The device obtains device information and captures it in JSON format.

[0357] Step 3:

[0358] The device sends the collected information to the server.

[0359] Input: Collected device information (JSON format)

[0360] Data processing / calculation: Device information is encrypted using the HTTPS protocol and sent to the server.

[0361] Output: Basic device information sent to the server

[0362] Specific operation: The terminal sends JSON data using the HTTPS protocol.

[0363] Step 4:

[0364] The server stores the received information in a database and assigns each device a unique identifier.

[0365] Input: Sent device information (JSON format)

[0366] Data processing / calculation: Save device information in an RDBMS (e.g., MySQL (registered trademark)), generate a UUID, and assign it to the device.

[0367] Output: A unique identifier assigned to the device.

[0368] Specific operation: The server generates a UUID and stores it in the database along with the information.

[0369] Step 5:

[0370] The user launches the support app and talks about the problem they want to solve.

[0371] Input: User utterance

[0372] Data processing / calculation: Acquire speech as audio data

[0373] Output: User's voice data

[0374] Specific action: The user talks about the problem into the device (e.g., "My Wi-Fi isn't working").

[0375] Step 6:

[0376] The device captures the user's speech as voice data.

[0377] Input: User utterance

[0378] Data processing / calculation: Capture audio data through a microphone and convert it into binary data format

[0379] Output: Captured audio data

[0380] Specific operation: The device records audio data.

[0381] Step 7:

[0382] The device uses voice recognition technology to convert the voice data into text data.

[0383] Input: Audio data

[0384] Data processing / calculation: Converting voice data into text data using the Google Cloud Speech-to-Text API

[0385] Output: Text data

[0386] Specific operation: Sends audio data to the Google Cloud Speech-to-Text API and receives the conversion result.

[0387] Step 8:

[0388] The terminal sends the converted text data to the server.

[0389] Input: Text data

[0390] Data processing / calculation: Encrypt the text data using the HTTPS protocol and send it to the server.

[0391] Output: Text data sent to the server

[0392] Specific operation: The terminal transmits the converted text data.

[0393] Step 9:

[0394] The server analyzes the received text data using natural language processing technology to identify problems.

[0395] Input: Text data

[0396] Data processing / calculation: Analyze text data using Amazon Comprehend and identify problems

[0397] Output: Identified issues

[0398] Specific operation: The server analyzes the text data and extracts the keywords "Wi-Fi" and "cannot connect."

[0399] Step 10:

[0400] The server uses an emotion engine to recognize the user's emotional state.

[0401] Input: Text data and audio data

[0402] Data processing / calculation: Analyze text and voice data using an emotion engine to determine emotional states

[0403] Output: Recognized emotional state of the user

[0404] What happens: The server analyzes the text and tone of voice and recognizes that the user is frustrated.

[0405] Step 11:

[0406] The server takes into account the user's emotional state, references the device's basic information and the type of problem, and generates the optimal solution.

[0407] Input: User's emotional state, basic device information, identified issues

[0408] Data processing / calculation: Searching the database and generating optimal solutions

[0409] Output: Generated solution

[0410] What happens: The server retrieves the solution from the database and generates a customized message.

[0411] Step 12:

[0412] The server sends the generated solution to the device.

[0413] Input: Generated solution

[0414] Data processing / calculation: Generate a solution in JSON format and send it to the terminal using the HTTPS protocol.

[0415] Output: Solution sent to terminal

[0416] Specific operation: The server sends the solution to the terminal.

[0417] Step 13:

[0418] The terminal presents the solution to the user by playing it aloud and displaying it as text.

[0419] Input: Solution sent by the server

[0420] Data processing / calculation: Converting the solution into a text display and audio playback format

[0421] Output: The solution presented to the user

[0422] Specific operation: The device displays the solution on the screen and plays it aloud using the text-to-speech function.

[0423] Step 14:

[0424] The user follows the suggested solution and reports the solution in the app.

[0425] Input: Solution attempt results

[0426] Data processing / calculation: Input the solution results into the terminal and generate feedback

[0427] Output: Feedback data

[0428] What happens: The user reports feedback in the app, such as "Resolved" or "Still not resolved."

[0429] Step 15:

[0430] The device sends the feedback to the server.

[0431] Input: User feedback

[0432] Data processing / calculation: Feedback data is converted into JSON format and sent to the server using the HTTPS protocol.

[0433] Output: Feedback data sent to the server

[0434] Specific operation: The terminal transmits feedback data.

[0435] Step 16:

[0436] The server receives the feedback and, if the problem persists, provides further diagnostics and a new solution.

[0437] Input: Feedback data

[0438] Data processing / computation: Analyze feedback, perform additional diagnostics, and generate new solutions

[0439] Output: New solution

[0440] Specific action: The server re-analyzes the problem, generates a different solution (e.g., reboot the router), and sends it again to the device.

[0441] This series of processes allows users to quickly and accurately resolve device problems and receive support that takes their emotional state into consideration.

[0442] (Application example 2)

[0443] 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."

[0444] Conventional user support systems only provide a uniform solution without considering the user's emotions or stress when reporting a problem. As a result, users are unable to receive appropriate support for their emotional state, which can leave them dissatisfied. This invention aims to provide more accurate and customized support by taking into account the user's emotional state when they encounter a problem in services such as food delivery.

[0445] 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.

[0446] In this invention, the server includes means for accepting a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data to identify a problem the user wants to solve, means having a database for searching for an optimal solution based on the identified problem, means having an emotion engine for recognizing the user's emotional state, means for adjusting a solution based on the emotional state, and means for presenting the solution searched for from the database to the user. This makes it possible to provide support for quickly and accurately solving a problem when the user is in a situation where the user has a problem, taking into account the user's emotional state.

[0447] "User utterance" refers to an expression of intent made by a user through speech directed at a device.

[0448] "Voice data" refers to data that is a digital recording of a user's speech.

[0449] "Text data" refers to data obtained by converting voice data into character information.

[0450] A "database" refers to a collection of data that is systematically organized and stored, and can be searched and used.

[0451] "Emotion engine" refers to technology for recognizing a user's emotional state from text and voice data.

[0452] "Solution" refers to the method or procedure offered to solve a user's problem.

[0453] "Presentation medium" refers to the technology or method used to convey information to a user.

[0454] "Feedback" refers to the user's evaluation and reaction to the presented solution.

[0455] "Diagnosis" refers to the process of analyzing a user's problem and identifying its cause and solution.

[0456] "Customized support" refers to assistance that is tailored to the user's specific situation and emotional state.

[0457] To implement this invention, a comprehensive support system is required to acquire user utterances as voice data, convert them into text data, and analyze the data to identify problems. The support system performs a series of processes, including initial device configuration, user interaction, utilization of an emotion recognition engine, generation and presentation of solutions to problems, and feedback processing. This process is described in detail below.

[0458] Hardware and software used

[0459] Hardware: Smartphones, smart glasses

[0460] Software: Google Cloud Speech-to-Text API (voice recognition engine), IBM Watson (registered trademark) Natural Language Understanding (natural language processing engine), Microsoft (registered trademark) Azure (registered trademark) Emotion API (emotion engine), cloud server, database management system

[0461] Data processing and calculation

[0462] First, a user installs a supporting application on their smartphone or smart glasses. When the application is launched for the first time, it collects basic device information (e.g., account information, delivery address, and network connection status) and sends it to a server. This assigns a unique identifier to the device, which is then stored in a database.

[0463] User Interaction

[0464] When a user reports a problem, such as "My order hasn't arrived" or "My order hasn't arrived even after the estimated delivery time," the app captures the voice and converts it into text data using the Google Cloud Speech-to-Text API. This text data is then sent to the server.

[0465] Natural Language Processing and Emotion Recognition

[0466] The server uses IBM Watson Natural Language Understanding to analyze the text data and identify the user's problem, while at the same time using the Microsoft Azure Emotion API to recognize the user's emotional state (e.g., irritation, impatience) from the user's voice and text data.

[0467] Solution generation and presentation

[0468] Based on the identified problem and emotional state, the system searches for the best solution from a database and generates an emotionally sensitive message. The generated solution is presented to the user via text and voice, and may include a message such as, "Sorry for the wait. Your driver is currently on his way to your address and is expected to arrive in 15 minutes."

[0469] Feedback and re-solution

[0470] When the user attempts a solution and provides feedback on the results, the server receives that feedback, performs a detailed diagnosis again, and generates a new solution. If the problem is reported as "still not solved," new steps or other measures are suggested.

[0471] Specific examples

[0472] When a user reports that their order hasn't arrived even after the estimated delivery time, the app converts the voice to text and recognizes the user's emotional state (irritation, impatience). The server checks the delivery status from the database, generates a solution such as "We are currently on our way to deliver it. It should arrive in about 10 minutes. We apologize for the wait," and presents it to the user in text and voice.

[0473] Prompt Sentence Examples

[0474] "My order hasn't arrived even after the estimated delivery time. Please check the delivery status and generate a response that takes the user's emotions into consideration."

[0475] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0476] Step 1:

[0477] The device receives the problem reported by the user as voice data. For example, the user speaks into an application on a smartphone or smart glasses, saying, "My order hasn't arrived even though the scheduled delivery time has passed." The device records this speech as voice data. This inputs the voice data.

[0478] Step 2:

[0479] The device converts this voice data into text data. Specifically, it uses the Google Cloud Speech-to-Text API to analyze the voice data and convert it into text information. This text data is output in a form that is sent to the server.

[0480] Step 3:

[0481] The server analyzes the received text data using natural language processing technology. IBM Watson Natural Language Understanding is used to identify the problem the user is having from the text data. For example, the keywords "estimated delivery time" and "not delivered" are extracted to identify the nature of the problem. The results of this analysis are used as input for the next process.

[0482] Step 4:

[0483] The server recognizes the user's emotional state from the analyzed text and voice data. It uses the Microsoft Azure Emotion API to extract the user's emotions (e.g., frustration, impatience). It saves the analysis results along with the emotional state as integrated data and proceeds to the next step.

[0484] Step 5:

[0485] The server searches the database for the optimal solution based on the identified problem and the recognized emotional state. The server refers to the device information and order history and generates a solution such as "We are currently on our way to deliver your order. It should arrive in about 10 minutes. We apologize for the wait." The solution is output as text data.

[0486] Step 6:

[0487] The server sends the generated solution to the device. The device then presents this solution to the user. The device displays or plays a message to the user in text or voice, such as "We are currently on our way to deliver your item and will arrive in about 10 minutes. We apologize for the wait." This presents the solution to the user.

[0488] Step 7:

[0489] The user tries the proposed solution and provides feedback on the results, such as "It hasn't arrived yet." This feedback data is sent from the terminal to the server.

[0490] Step 8:

[0491] The server receives this feedback, analyzes it, and if necessary performs further diagnostics, e.g., recalculating the delivery progress and estimated arrival time, and generating a different solution (e.g., "Please check again. The estimated arrival time is about 15 minutes later."). The analysis results and the new solution are output.

[0492] Step 9:

[0493] The server sends the regenerated solution to the terminal, which then presents it to the user. The re-presented solution is again displayed or played back to the user in text or audio, allowing the user to continue working until they find the appropriate solution.

[0494] Some examples of actual examples and prompts include:

[0495] "My order hasn't arrived even after the estimated delivery time. Please check the delivery status and generate a response that takes the user's emotions into consideration."

[0496] 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.

[0497] 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.

[0498] 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.

[0499] [Second embodiment]

[0500] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0501] 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.

[0502] 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).

[0503] 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.

[0504] 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.

[0505] 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).

[0506] 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.

[0507] 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.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] 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."

[0512] The present invention is a support system for quickly and accurately resolving problems users have with their devices, utilizing voice recognition technology and natural language processing technology to analyze the user's speech and provide the optimal solution.

[0513] First, the user installs the supporting application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[0514] To use the app, a user launches the support app on their device and speaks to it about the problem they want to solve (for example, "I can't connect to Wi-Fi"). The device captures this speech as voice data and converts it into text data using voice recognition technology. The converted text data is then sent to the server.

[0515] The server uses natural language processing to analyze the received text data and identify the problem the user is having. For example, it extracts keywords such as "Wi-Fi" and "connection" to understand the nature of the problem. The server then searches a database along with basic device information to search for known solutions. The identified solution (e.g., "Open the Settings app and check your Wi-Fi settings. Then try reconnecting") is sent from the server to the device.

[0516] The device presents the received solution to the user. The presentation method can be either a text display or a voice playback. The user follows the instructions to implement the solution and reports the result in the app. For example, after attempting the presented solution, the device provides feedback such as "Solved" or "Not yet solved."

[0517] User feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it will perform further diagnostics and suggest another solution. By repeating this process, we aim to provide the best support for the user's device and resolve the problem as quickly as possible.

[0518] Specific examples

[0519] Scenario: A user is having internet connection issues

[0520] 1. Initial Setup

[0521] Device: When you install the support app on your iPhone and launch the app for the first time, basic device information (OS: iOS 16, Internet contract status: Wi-Fi only) will be collected.

[0522] Terminal: Sends collected information to the server.

[0523] Server: Stores the information in a database and assigns a unique identifier to the device.

[0524] 2. User Interaction

[0525] User: Say "I can't connect to Wi-Fi."

[0526] Terminal: Captures speech as voice data, converts it into text data using voice recognition technology, and sends that data to the server.

[0527] 3. Problem Analysis and Diagnosis

[0528] Server: Analyze text data containing the keywords "Wi-Fi" and "cannot connect" using natural language processing technology.

[0529] Server: View device information and past problems and solutions to find the best solution.

[0530] 4. Providing a solution

[0531] Server: Generated the solution: "Open the Settings app and check your Wi-Fi settings. Then try reconnecting."

[0532] Terminal: Presents the generated solution to the user in voice and text.

[0533] 5. Feedback and final confirmation

[0534] User: Attempts to reconnect and provides feedback through the app that it's still not working.

[0535] Device: Send feedback to the server.

[0536] Server: Generate the next solution, "Please restart your router," and present it again.

[0537] By using this system, users can receive prompt, comprehensive and appropriate support tailored to the status of their device.

[0538] The processing flow will be explained below.

[0539] Step 1:

[0540] The user installs a supporting app on the device.

[0541] Step 2:

[0542] When a device launches the app for the first time, basic device information (OS version, app installation status, internet connection status, and device settings status) is collected.

[0543] Step 3:

[0544] The device sends the collected basic information to the server.

[0545] Step 4:

[0546] The server stores the received device information in a database and assigns a unique identifier (Device ID) to each device.

[0547] Step 5:

[0548] The user launches the app and talks about the problem they want to solve, such as "Wi-Fi won't connect."

[0549] Step 6:

[0550] The terminal acquires the user's speech as voice data and converts it into text data using voice recognition technology.

[0551] Step 7:

[0552] The terminal transmits the converted text data to the server.

[0553] Step 8:

[0554] The server analyzes the received text data using natural language processing technology to identify the problem the user is facing.

[0555] Step 9:

[0556] The server references basic information about the device and the type of problem, then searches a database for past solutions.

[0557] Step 10:

[0558] The server identifies the best solution and generates a message with specific instructions for the user.

[0559] Step 11:

[0560] The server generates a message and sends it to the terminal.

[0561] Step 12:

[0562] The terminal presents the message received from the server to the user in voice and text.

[0563] Step 13:

[0564] The user implements the proposed solution and reports the results in the app.

[0565] Step 14:

[0566] The terminal sends the user's feedback to the server.

[0567] Step 15:

[0568] The server receives the feedback and, if the problem persists, performs further diagnostics and generates additional solutions.

[0569] Step 16:

[0570] The server sends the new solution to the terminal, which presents it to the user again.

[0571] This series of steps allows users to receive the best support for their device's situation.

[0572] Example 1

[0573] 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."

[0574] Conventional user support systems required multiple manual operations and complex settings for users to solve device problems, making it difficult to solve problems quickly and accurately. Furthermore, they lacked a mechanism for providing optimal support tailored to the user's device status, resulting in low accuracy and efficiency of support. This significantly impaired user convenience.

[0575] 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.

[0576] In this invention, the server includes means for accepting a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for collecting basic information about the user's device and sending it to the server, means for having a database for searching for an optimal solution based on the identified problem, means for presenting the solution searched for from the database to the user, and means for generating further solutions based on user feedback, thereby enabling the user to receive appropriate support for quickly and accurately solving device problems.

[0577] The "means for receiving user speech" is a function for collecting speech spoken by a user into the device.

[0578] "Means for acquiring as voice data" refers to a technology for converting a user's speech into digital voice data and collecting it.

[0579] The "means for converting into text data" is a function that analyzes the voice data and converts the content into text format.

[0580] "Means for analyzing text data to identify the problem the user wants to solve" refers to a technology that extracts problem-related keywords and phrases from text data and identifies the problem the user is facing.

[0581] "Means of collecting basic information about the user's device" refers to a function that obtains information such as the device's OS version, installed applications, and Internet connection status.

[0582] The "means for transmitting to the server" is a function for transferring the collected basic information of the device to the server via the network.

[0583] "Means having a database for searching for the optimum solution" refers to a function for searching for the optimum solution by referring to a database in which past problems and their solutions are recorded.

[0584] The "means for presenting the solutions retrieved from the database to the user" is a function for providing the retrieved solutions to the user in the form of text display or audio playback.

[0585] The "means for generating further solutions based on user feedback" is a technique for analyzing feedback information from users and presenting new solutions in response to the analysis.

[0586] This invention is a support system for quickly and accurately resolving problems users have with devices, utilizing voice recognition technology and natural language processing technology to analyze the user's speech and provide the optimal solution.

[0587] First, the user installs the support application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started up. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[0588] Next, the user launches the support app and speaks to the app about the problem they want to solve. The user's speech is captured as voice data and converted into text data using voice recognition technology. This text data is then sent to the server.

[0589] The server uses natural language processing (NLP) to analyze the received text data and identify the problem the user is having. For example, it extracts keywords such as "Wi-Fi" and "connection" to understand the nature of the problem. The server then references basic device information and a database of past problems and solutions to search for the most appropriate solution. The identified solution (e.g., "Open the Settings app and check your Wi-Fi settings. Then try reconnecting") is sent from the server to the device.

[0590] The device presents the received solution to the user. The presentation method can be either a text display or a voice playback. The user follows the instructions to implement the solution and reports the result in the app. For example, after attempting the presented solution, the device provides feedback such as "Solved" or "Not yet solved."

[0591] User feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it will perform further diagnostics and suggest another solution. By repeating this process, we aim to provide the best support for the user's device and resolve the problem as quickly as possible.

[0592] As a concrete example, consider a case where a user is using an iPhone and is having trouble connecting to Wi-Fi. In this case, the user speaks to a support app saying, "I can't connect to Wi-Fi." In response, the support system captures the voice data, converts it into text data, analyzes it, and presents the optimal solution. For example, it may give instructions such as, "Open the Settings app and check your Wi-Fi settings."

[0593] An example prompt is:

[0594] "My user has an iPhone and is having trouble connecting to Wi-Fi. How can I help them?"

[0595] "After collecting basic device information, the user said 'Wi-Fi is not working'. Please provide an appropriate solution."

[0596] The system leverages speech recognition and natural language processing technologies, particularly generative AI models, to analyze and generate solutions, enabling users to receive faster and more appropriate support.

[0597] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0598] Step 1: Install supporting applications

[0599] The user installs a support application on the device.

[0600] Input: Applications downloaded from the app store at the user's request

[0601] Output: Supporting applications installed on the device

[0602] Step 2: First boot and device information collection

[0603] When the device launches the support app for the first time, it automatically collects basic device information (e.g., OS version, app installation status, Internet connection status, and device settings).

[0604] Input: Basic device information collected by first launching the support app

[0605] Output: A dataset of collected device information

[0606] Specific behavior: The process of packaging collected information as structured data

[0607] Step 3: Send device information to the server

[0608] The terminal sends the collected basic device information to the server.

[0609] Input: A dataset of collected device information

[0610] Output: Device information stored on the server

[0611] What it does: Transforms the information into the appropriate format and sends it over a secure channel to the server.

[0612] Step 4: Assigning a unique identifier

[0613] The server stores the received device information in a database and assigns each device a unique identifier.

[0614] Input: Device information sent to the server

[0615] Output: Device information stored in the database and assigned unique identifier

[0616] Specific operation: Stores device information in a database and generates and assigns a UUID (Universally Unique Identifier).

[0617] Step 5: Receiving the problem statement

[0618] The user launches the support app on their device and speaks the problem they want to solve (e.g., "My Wi-Fi isn't working").

[0619] Input: User spoken utterance

[0620] Output: Speech content captured as audio data

[0621] Specific operation: Converts audio collected through a microphone into digital data

[0622] Step 6: Converting audio data to text data

[0623] The terminal converts the voice data into text data using voice recognition technology.

[0624] Input: Captured audio data

[0625] Output: Converted text data

[0626] Specific operation: Analyzes voice data using a speech recognition engine and outputs it as text.

[0627] Step 7: Sending text data to the server

[0628] The terminal transmits the converted text data to the server.

[0629] Input: Text data

[0630] Output: Text data sent to the server

[0631] Specific behavior: Encodes text data in the appropriate format and sends it to the server.

[0632] Step 8: Analyze the problem

[0633] The server analyzes the received text data using natural language processing (NLP) technology to identify the problem the user is having.

[0634] Input: Text data sent to the server

[0635] Output: Identified problem

[0636] Specific behavior: Analyzes text data, extracts keywords (e.g., "Wi-Fi," "connection"), and identifies issues

[0637] Step 9: Device Information and Database Reference

[0638] The server searches for the best solution by referring to the device's basic information and a database of past problems and solutions.

[0639] Input: Identified problem and device information

[0640] Output: The optimal solution found

[0641] Specific behavior: Run a database search algorithm to identify a solution

[0642] Step 10: Generate and present solutions

[0643] The server generates a solution to the identified problem and sends it to the terminal.

[0644] The terminal presents the generated solutions to the user in the form of a text display and audio playback.

[0645] Input: Solution data from the server

[0646] Output: The solution presented to the user

[0647] Specific Action: Format the solution in text and audio format and present it to the user

[0648] Step 11: Accepting user feedback

[0649] The user implements the proposed solution and reports the results in the support app (e.g., "Still not resolved").

[0650] Input: User feedback

[0651] Output: Feedback data

[0652] Specific operation: Enter the feedback content into the app and convert it into digital data

[0653] Step 12: Sending feedback to the server

[0654] The terminal transmits the user's feedback to the server.

[0655] Input: Feedback data

[0656] Output: Feedback sent to the server

[0657] Specific behavior: Encodes the feedback data in the appropriate format and sends it to the server

[0658] Step 13: Generate further solutions

[0659] The server performs further detailed diagnosis based on the user's feedback and generates new solutions, which are sent to the terminal.

[0660] Input: Feedback data

[0661] Output: New solution

[0662] Specific behavior: Run an algorithm that analyzes the feedback and generates a new solution based on it.

[0663] This series of steps allows users to receive support to quickly and accurately resolve issues with their devices.

[0664] (Application example 1)

[0665] 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."

[0666] When performing maintenance or troubleshooting on industrial equipment, delays in factory production lines can occur if technicians with specialized knowledge are unable to respond quickly. Furthermore, accurately understanding the complex state of equipment and proposing appropriate solutions requires advanced technology and experience. To resolve these issues, a system is needed that can instantly assess the status of the equipment and provide a fast, accurate solution.

[0667] 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.

[0668] In this invention, the server includes means for accepting user speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for having a database for searching for an optimal solution based on the identified problem, means for presenting the solution searched for from the database to the user, means for acquiring basic information about the industrial equipment, means for transmitting the basic information about the industrial equipment to the server, and means for receiving and presenting a solution to the problem of the industrial equipment from the server. This enables maintenance and troubleshooting of the industrial equipment to be performed quickly and accurately.

[0669] "User utterance" refers to the words and sentences that are the source of the voice data input to the system.

[0670] "Voice data" refers to data that is a digital recording of a user's speech.

[0671] "Text data" refers to data in the form of a string of characters converted from audio data.

[0672] "Analysis" refers to the process of understanding text data and identifying the problem the user wants to solve.

[0673] The "database" refers to a collection of information that stores basic information about users' devices and industrial equipment, as well as past problems and their solutions.

[0674] "Optimal solution" refers to the most effective and appropriate way to solve the user's problem.

[0675] "Presenting to the user" refers to visually or audibly conveying the found solution to the user.

[0676] "Industrial equipment" refers to various machines and devices used in factories and production lines.

[0677] "Basic information" refers to important data about industrial equipment, such as the model name, OS version, and last maintenance date.

[0678] "Server" refers to the central management system that analyzes voice and text data and searches for and provides optimal solutions.

[0679] "Receiving" refers to the user's device retrieving the solution provided by the server.

[0680] This invention is a support system for quickly and accurately resolving problems users have with industrial machinery. Specifically, it utilizes voice recognition technology and natural language processing technology to analyze user utterances and provide optimal solutions.

[0681] First, the user installs a support application related to the industrial equipment on the device. After installation, the device collects basic information about the industrial equipment (e.g., OS version, equipment model, and last maintenance date) when it is first started up. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[0682] When a user uses a support application to solve a problem with industrial equipment, they first launch the app on their device and then speak to the application about the problem they want to solve (for example, "The robot arm is moving slowly"). The device captures this speech as audio data and converts it into text data using speech recognition technology. The Python speech_recognition library is used for speech recognition.

[0683] The converted text data is then sent to a server. The server uses natural language processing technology to analyze the received text data and identify the problem the user is having. A natural language processing library is used for the analysis. For example, keywords such as "robot arm" and "slow movement" are extracted to understand the nature of the problem. The server then references a database along with basic information about the industrial equipment to search for known solutions. The identified solution (e.g., "Please readjust the axis of the robot arm") is then sent from the server to the device.

[0684] The device presents the received solution to the user. The presentation method can be either a text display or audio playback. For audio playback, the Python pyttsx3 library is used. The user follows the instructions to implement the solution and reports the results in the app. For example, after attempting the presented solution, the device can provide feedback such as "Solved" or "Not yet solved."

[0685] The user's feedback is sent via the device to the server. The server receives the feedback, and if the problem is not resolved, it performs a more detailed diagnosis and suggests another solution. By repeating this process, the aim is to provide optimal support for the user's industrial equipment and quickly resolve the problem.

[0686] Specific examples

[0687] If an industrial device is not working properly, for example, by saying "The robot arm has stopped working," the system will immediately suggest a solution. Examples of prompts to input to the generative AI model are as follows:

[0688] Please provide a solution for a robot that is experiencing the following issue: "The robot arm has stopped moving." The robot's basic information is as follows: OS version: FactoryOS 1.2, robot model: XJ320, last maintenance date: 2023-01-25.

[0689] By using this system, factory maintenance personnel can quickly receive comprehensive and appropriate support tailored to the status of their industrial equipment.

[0690] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0691] Step 1:

[0692] The terminal installs the supporting application and collects basic information about the industrial equipment (e.g., OS version, equipment model, and last maintenance date) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device. The input is the basic device information, and the output is the device information with the identifier stored on the server.

[0693] Step 2:

[0694] A user launches a support application to solve a problem with industrial equipment and speaks out the problem they want to solve (for example, "The robot arm is moving slowly"). The device receives this speech as voice data. The input is the user's speech, and the output is voice data.

[0695] Step 3:

[0696] The device converts the acquired voice data into text data using voice recognition technology (Python's speech_recognition library). The input is voice data, and the output is text data.

[0697] Step 4:

[0698] The terminal sends the converted text data to the server. The input is the text data, and the output is the text data sent to the server.

[0699] Step 5:

[0700] The server analyzes the received text data using natural language processing technology (natural language processing library) and identifies the problem the user is facing. The input is the text data, and the output is the identified problem.

[0701] Step 6:

[0702] The server references the database along with basic information about the industrial equipment and searches for the best solution based on the identified problem. The input is the identified problem and basic information about the industrial equipment, and the output is the solution.

[0703] Step 7:

[0704] The server sends the searched solutions to the terminal. The input is the solution and the output is the solution sent to the terminal.

[0705] Step 8:

[0706] The terminal presents the received solution to the user, either as a text display or as audio (using the Python pyttsx3 library for audio playback). The input is the solution, and the output is the solution presented to the user.

[0707] Step 9:

[0708] The user follows the instructions, implements the solution, and reports the result in the app, e.g., feedback such as "solved" or "not yet solved." The input is the user feedback, and the output is the feedback data.

[0709] Step 10:

[0710] The terminal sends the user's feedback to the server, where the input is the feedback data and the output is the feedback data sent to the server.

[0711] Step 11:

[0712] The server receives the feedback, and if the problem is not resolved, it performs further diagnostics and suggests a different solution. This process is repeated to provide optimal support. The input is the feedback data, and the output is the new solution.

[0713] 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.

[0714] This invention is a support system for quickly and accurately resolving device-related problems that users encounter, utilizing speech recognition technology, natural language processing technology, and an emotion engine for recognizing the user's emotions. This system analyzes the user's speech and provides optimal solutions, while also providing support that takes the user's emotional state into consideration.

[0715] First, the user installs the supporting application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[0716] To use the app, a user launches the support app on their device and speaks to it about the problem they want to solve (for example, "I can't connect to Wi-Fi"). The device captures this speech as voice data and converts it into text data using voice recognition technology. The converted text data is then sent to the server.

[0717] The server analyzes the received text data using natural language processing technology to identify the problem the user is experiencing. It also uses an emotion engine to extract the user's emotional state (e.g., dissatisfaction, impatience, anger, etc.) from the text and voice data. For example, it can recognize that the user is dissatisfied based on the text data "Wi-Fi is not connecting" and the tone of the voice.

[0718] The server then searches the database for the optimal solution, taking into account the user's emotional state, basic device information, and the type of problem. For example, if the user is frustrated, a solution containing kind words and encouraging messages will be generated in addition to the usual solution. A specific solution might be something like, "Open the Settings app and check your Wi-Fi settings. Then try reconnecting," with an additional message depending on the user's condition.

[0719] The solution sent from the server is presented to the user via the device. The presentation method can be either text display or audio playback. The user attempts to solve the problem according to the presented solution and reports the result in the app (for example, feedback such as "Solved" or "Not yet solved").

[0720] The feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it performs a more detailed diagnosis and proposes a new solution. At this time, it again utilizes the emotion engine to recognize the user's emotional state and adjust the appropriate support method.

[0721] Specific examples

[0722] Scenario: A user is having internet connection issues

[0723] 1. Initial Setup

[0724] Device: When you install the support app on your iPhone and launch the app for the first time, basic device information (OS: iOS 16, Internet contract status: Wi-Fi only) will be collected.

[0725] Terminal: Sends collected information to the server.

[0726] Server: Stores the information in a database and assigns a unique identifier to the device.

[0727] 2. User Interaction

[0728] User: Say "I can't connect to Wi-Fi."

[0729] Terminal: Captures speech as voice data, converts it into text data using voice recognition technology, and sends that data to the server.

[0730] 3. Problem analysis and emotion recognition

[0731] Server: Analyzes text data containing the keywords "Wi-Fi" and "cannot connect" using natural language processing technology to identify the problem.

[0732] Server: Using the emotion engine, recognize the user's emotion from the text data and voice data. For example, recognize that the user is dissatisfied.

[0733] 4. Solution Search and Generation

[0734] Server: Generates the optimal solution by taking into account the user's emotional state and device information. Example: "Open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[0735] 5. Providing a solution

[0736] Server: Sends the generated solution to the device.

[0737] Terminal: Presents audio and text to the user.

[0738] 6. Feedback and Final Review

[0739] User: Try the proposed solution and provide feedback on the results. Example: "Still not solved."

[0740] Device: Sends feedback to the server.

[0741] Server: Perform additional diagnostics and generate a different solution: "Reboot your router" and provide the appropriate support message again.

[0742] This system allows users to quickly receive comprehensive and appropriate support tailored to the situation of their device, and provides more customized support by taking into account the user's emotional state.

[0743] The processing flow will be explained below.

[0744] Step 1:

[0745] The user installs a supporting app on the device.

[0746] Step 2:

[0747] When a device launches the app for the first time, basic device information (OS version, app installation status, internet connection status, and device settings status) is collected.

[0748] Step 3:

[0749] The device sends the collected basic information to the server.

[0750] Step 4:

[0751] The server stores the received device information in a database and assigns a unique identifier (Device ID) to each device.

[0752] Step 5:

[0753] The user launches the app and talks about the problem they want to solve, such as "Wi-Fi won't connect."

[0754] Step 6:

[0755] The terminal acquires the user's speech as voice data and converts it into text data using voice recognition technology.

[0756] Step 7:

[0757] The terminal transmits the converted text data to the server.

[0758] Step 8:

[0759] The server analyzes the received text data using natural language processing technology to identify the problem the user is facing.

[0760] Step 9:

[0761] The server uses an emotion engine to recognize the user's emotion from the text data and voice data, for example, to detect that the user is dissatisfied.

[0762] Step 10:

[0763] The server takes into account the device's basic information and emotional state to search for past solutions from a database.

[0764] Step 11:

[0765] The server identifies the best solution and generates a message for the user that takes into account specific steps and emotions, such as "Open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[0766] Step 12:

[0767] The server generates a message and sends it to the terminal.

[0768] Step 13:

[0769] The terminal presents the message received from the server to the user in voice and text.

[0770] Step 14:

[0771] The user implements the proposed solution and reports the results in the app, for example, by providing feedback such as "still not solved."

[0772] Step 15:

[0773] The terminal sends the user's feedback to the server.

[0774] Step 16:

[0775] The server receives the feedback, and if the problem is not resolved, it performs a more detailed diagnosis and generates a new solution, again leveraging the emotion engine to recognize the user's emotional state.

[0776] Step 17:

[0777] The server sends a new solution to the device, which then presents it to the user again, for example, "Please restart your router. Please wait a moment."

[0778] This series of steps allows users to receive optimal support based on the context of their device. Combined with the emotion engine, customized support is provided that takes into account the user's emotional state.

[0779] Example 2

[0780] 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."

[0781] In conventional support systems, when a user reports a device problem, it is difficult to accurately and quickly identify the problem and provide the optimal solution. Furthermore, the quality of support can be compromised by not taking the user's emotional state into consideration. This often leads to stress for users and delays in resolving the problem.

[0782] 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.

[0783] In this invention, the server includes means for receiving a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for having a database for searching for an optimal solution based on the identified problem, means for recognizing the user's emotional state, and means for presenting the solution searched for in the database to the user. This makes it possible to quickly and accurately solve device problems while taking the user's emotional state into consideration.

[0784] "Means for accepting user speech and obtaining it as voice data" refers to a device or software that captures information that a user inputs into a device by voice and converts it into a format that can be stored electronically.

[0785] The "means for converting voice data into text data" refers to a device or program that analyzes voice data and converts it into corresponding text format information.

[0786] "Means for analyzing text data to identify the problem the user wants to solve" refers to a method or technology for analyzing the converted text data and extracting the user's problem or inquiry from its contents.

[0787] "Means having a database for searching for an optimal solution based on an identified problem" refers to a device or system that has the function of searching for an optimal response from a database that stores related solutions based on analyzed text data.

[0788] The "means for recognizing the user's emotional state" is a technology for analyzing the user's emotions from the tone of their voice and the contents of their text data, and recognizing their state.

[0789] The "means for presenting the solutions retrieved from the database to the user" is a device or software for visually or audibly presenting the solutions retrieved to the user.

[0790] The present invention is a support system for quickly and accurately resolving device-related problems that users encounter, utilizing speech recognition technology, natural language processing technology, and an emotion engine for recognizing the user's emotions. This system analyzes the user's speech and provides optimal solutions while also providing support that takes the user's emotional state into consideration.

[0791] First, the user installs the supporting application on their device. After installation, when the application is launched, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) at first launch. This information is sent to the server and stored in a database, and a unique identifier is assigned to each device.

[0792] The user then launches the support app and speaks out the problem they want to solve. For example, if the user says, "My Wi-Fi isn't connecting," the device captures this speech as voice data. Next, it uses voice recognition technology to convert this voice data into text data. Specifically, it uses the Google Cloud Speech-to-Text API or similar to convert the voice data into text format. This converted text data is then sent to the server.

[0793] The server analyzes the received text data using natural language processing technology (e.g., Amazon Comprehend) to identify the problem the user is experiencing. At the same time, it uses an emotion engine to extract the user's emotional state from the text and voice data. For example, it recognizes that the user is dissatisfied based on the utterance "My Wi-Fi isn't connecting" and the tone of the voice.

[0794] Next, the server searches for the optimal solution from a database, taking into account the user's emotional state, basic device information, and the type of problem. For example, if the user is frustrated, the server generates a solution that includes kind words and an encouraging message in addition to the usual solution. A specific solution might be, "Open the Settings app and check your Wi-Fi settings. Then, try reconnecting. We apologize for the inconvenience." This solution is sent from the server to the device and presented to the user via the device. It can be presented as text or audio.

[0795] The user attempts to solve the problem using the proposed solutions and reports the results in the app. For example, they can provide feedback such as "Solved" or "Not yet solved." This feedback is sent to the server via the device. The server receives the feedback, and if the problem is not solved, it performs a more detailed diagnosis and suggests a new solution. At this time, the emotion engine is again utilized to recognize the user's emotional state and adjust the appropriate support method.

[0796] Specific examples

[0797] Scenario: A user is having internet connection issues

[0798] Initial Setup

[0799] 1. Device: When you install the support app on your smartphone and launch the app for the first time, basic device information (OS version, internet connection status, etc.) will be collected.

[0800] 2. Terminal: Sends collected information to the server.

[0801] 3. Server: Stores the information in a database and assigns a unique identifier to the device.

[0802] User Interaction

[0803] 1. User: Says, "Wi-Fi is not connecting."

[0804] 2. Terminal: The speech is acquired as audio data, converted into text data using the Google Cloud Speech-to-Text API, and sent to the server.

[0805] Problem analysis and emotion recognition

[0806] 1. Server: Analyze text data containing the keywords "Wi-Fi" and "unable to connect" using Amazon Comprehend to identify problems.

[0807] 2. Server: Using the emotion engine, recognize the user's dissatisfaction from text and voice data.

[0808] Solution generation and delivery

[0809] 1. Server: Generates the optimal solution by taking into account the user's emotional state and device information. Example: "Please open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[0810] 2. Server: Sends the generated solution to the device.

[0811] 3. Terminal: Presents the user with voice and text.

[0812] Feedback and final confirmation

[0813] 1. User: Try the proposed solution and provide feedback on the results. Example: "Still not solved."

[0814] 2. Device: Sends feedback to the server.

[0815] 3. Server: Performs additional diagnostics and generates another solution, "Please restart the router," and sends it to the device again.

[0816] Using this system, users can quickly and accurately resolve problems with their devices and receive customized support tailored to their emotional state.

[0817] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0818] Step 1:

[0819] The user installs a supporting application on the device.

[0820] Input: Application installation request

[0821] Data processing / calculation: The installer copies the application files to the device and performs the necessary settings.

[0822] Output: Supporting applications installed on the device

[0823] Specific operations: Download the support app from the App Store or Google Play Store and complete the installation.

[0824] Step 2:

[0825] The device collects basic device information when the application is first launched.

[0826] Input: First launch of application

[0827] Data processing / calculation: Collects information such as the device's OS version, internet connection status, and installed apps, and generates data in JSON format.

[0828] Output: Basic device information collected

[0829] Specific operation: The device obtains device information and captures it in JSON format.

[0830] Step 3:

[0831] The device sends the collected information to the server.

[0832] Input: Collected device information (JSON format)

[0833] Data processing / calculation: Device information is encrypted using the HTTPS protocol and sent to the server.

[0834] Output: Basic device information sent to the server

[0835] Specific operation: The terminal sends JSON data using the HTTPS protocol.

[0836] Step 4:

[0837] The server stores the received information in a database and assigns each device a unique identifier.

[0838] Input: Sent device information (JSON format)

[0839] Data processing / calculation: Save device information in an RDBMS (e.g., MySQL), generate a UUID, and assign it to the device.

[0840] Output: A unique identifier assigned to the device.

[0841] Specific operation: The server generates a UUID and stores it in the database along with the information.

[0842] Step 5:

[0843] The user launches the support app and talks about the problem they want to solve.

[0844] Input: User utterance

[0845] Data processing / calculation: Acquire speech as audio data

[0846] Output: User's voice data

[0847] Specific action: The user talks about the problem into the device (e.g., "My Wi-Fi isn't working").

[0848] Step 6:

[0849] The device captures the user's speech as voice data.

[0850] Input: User utterance

[0851] Data processing / calculation: Capture audio data through a microphone and convert it into binary data format

[0852] Output: Captured audio data

[0853] Specific operation: The device records audio data.

[0854] Step 7:

[0855] The device uses voice recognition technology to convert the voice data into text data.

[0856] Input: Audio data

[0857] Data processing / calculation: Converting voice data into text data using the Google Cloud Speech-to-Text API

[0858] Output: Text data

[0859] Specific operation: Sends audio data to the Google Cloud Speech-to-Text API and receives the conversion result.

[0860] Step 8:

[0861] The terminal sends the converted text data to the server.

[0862] Input: Text data

[0863] Data processing / calculation: Encrypt the text data using the HTTPS protocol and send it to the server.

[0864] Output: Text data sent to the server

[0865] Specific operation: The terminal transmits the converted text data.

[0866] Step 9:

[0867] The server analyzes the received text data using natural language processing technology to identify problems.

[0868] Input: Text data

[0869] Data processing / calculation: Analyze text data using Amazon Comprehend and identify problems

[0870] Output: Identified issues

[0871] Specific operation: The server analyzes the text data and extracts the keywords "Wi-Fi" and "cannot connect."

[0872] Step 10:

[0873] The server uses an emotion engine to recognize the user's emotional state.

[0874] Input: Text data and audio data

[0875] Data processing / calculation: Analyze text and voice data using an emotion engine to determine emotional states

[0876] Output: Recognized emotional state of the user

[0877] What happens: The server analyzes the text and tone of voice and recognizes that the user is frustrated.

[0878] Step 11:

[0879] The server takes into account the user's emotional state, references the device's basic information and the type of problem, and generates the optimal solution.

[0880] Input: User's emotional state, basic device information, identified issues

[0881] Data processing / calculation: Searching the database and generating optimal solutions

[0882] Output: Generated solution

[0883] What happens: The server retrieves the solution from the database and generates a customized message.

[0884] Step 12:

[0885] The server sends the generated solution to the device.

[0886] Input: Generated solution

[0887] Data processing / calculation: Generate a solution in JSON format and send it to the terminal using the HTTPS protocol.

[0888] Output: Solution sent to terminal

[0889] Specific operation: The server sends the solution to the terminal.

[0890] Step 13:

[0891] The terminal presents the solution to the user by playing it aloud and displaying it as text.

[0892] Input: Solution sent by the server

[0893] Data processing / calculation: Converting the solution into a text display and audio playback format

[0894] Output: The solution presented to the user

[0895] Specific operation: The device displays the solution on the screen and plays it aloud using the text-to-speech function.

[0896] Step 14:

[0897] The user follows the suggested solution and reports the solution in the app.

[0898] Input: Solution attempt results

[0899] Data processing / calculation: Input the solution results into the terminal and generate feedback

[0900] Output: Feedback data

[0901] What happens: The user reports feedback in the app, such as "Resolved" or "Still not resolved."

[0902] Step 15:

[0903] The device sends the feedback to the server.

[0904] Input: User feedback

[0905] Data processing / calculation: Feedback data is converted into JSON format and sent to the server using the HTTPS protocol.

[0906] Output: Feedback data sent to the server

[0907] Specific operation: The terminal transmits feedback data.

[0908] Step 16:

[0909] The server receives the feedback and, if the problem persists, provides further diagnostics and a new solution.

[0910] Input: Feedback data

[0911] Data processing / computation: Analyze feedback, perform additional diagnostics, and generate new solutions

[0912] Output: New solution

[0913] Specific action: The server re-analyzes the problem, generates a different solution (e.g., reboot the router), and sends it again to the device.

[0914] This series of processes allows users to quickly and accurately resolve device problems and receive support that takes their emotional state into consideration.

[0915] (Application example 2)

[0916] 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."

[0917] Conventional user support systems only provide a uniform solution without considering the user's emotions or stress when reporting a problem. As a result, users are unable to receive appropriate support for their emotional state, which can leave them dissatisfied. This invention aims to provide more accurate and customized support by taking into account the user's emotional state when they encounter a problem in services such as food delivery.

[0918] 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.

[0919] In this invention, the server includes means for accepting a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data to identify a problem the user wants to solve, means having a database for searching for an optimal solution based on the identified problem, means having an emotion engine for recognizing the user's emotional state, means for adjusting a solution based on the emotional state, and means for presenting the solution searched for from the database to the user. This makes it possible to provide support for quickly and accurately solving a problem when the user is in a situation where the user has a problem, taking into account the user's emotional state.

[0920] "User utterance" refers to an expression of intent made by a user through speech directed at a device.

[0921] "Voice data" refers to data that is a digital recording of a user's speech.

[0922] "Text data" refers to data obtained by converting voice data into character information.

[0923] A "database" refers to a collection of data that is systematically organized and stored, and can be searched and used.

[0924] "Emotion engine" refers to technology for recognizing a user's emotional state from text and voice data.

[0925] "Solution" refers to the method or procedure offered to solve a user's problem.

[0926] "Presentation medium" refers to the technology or method used to convey information to a user.

[0927] "Feedback" refers to the user's evaluation and reaction to the presented solution.

[0928] "Diagnosis" refers to the process of analyzing a user's problem and identifying its cause and solution.

[0929] "Customized support" refers to assistance that is tailored to the user's specific situation and emotional state.

[0930] To implement this invention, a comprehensive support system is required to acquire user utterances as voice data, convert them into text data, and analyze the data to identify problems. The support system performs a series of processes, including initial device configuration, user interaction, utilization of an emotion recognition engine, generation and presentation of solutions to problems, and feedback processing. This process is described in detail below.

[0931] Hardware and software used

[0932] Hardware: Smartphones, smart glasses

[0933] Software: Google Cloud Speech-to-Text API (speech recognition engine), IBM Watson Natural Language Understanding (natural language processing engine), Microsoft Azure Emotion API (emotion engine), cloud server, database management system

[0934] Data processing and calculation

[0935] First, a user installs a supporting application on their smartphone or smart glasses. When the application is launched for the first time, it collects basic device information (e.g., account information, delivery address, and network connection status) and sends it to a server. This assigns a unique identifier to the device, which is then stored in a database.

[0936] User Interaction

[0937] When a user reports a problem, such as "My order hasn't arrived" or "My order hasn't arrived even after the estimated delivery time," the app captures the voice and converts it into text data using the Google Cloud Speech-to-Text API. This text data is then sent to the server.

[0938] Natural Language Processing and Emotion Recognition

[0939] The server uses IBM Watson Natural Language Understanding to analyze the text data and identify the user's problem, while at the same time using the Microsoft Azure Emotion API to recognize the user's emotional state (e.g., irritation, impatience) from the user's voice and text data.

[0940] Solution generation and presentation

[0941] Based on the identified problem and emotional state, the system searches for the best solution from a database and generates an emotionally sensitive message. The generated solution is presented to the user via text and voice, and may include a message such as, "Sorry for the wait. Your driver is currently on his way to your address and is expected to arrive in 15 minutes."

[0942] Feedback and re-solution

[0943] When the user attempts a solution and provides feedback on the results, the server receives that feedback, performs a detailed diagnosis again, and generates a new solution. If the problem is reported as "still not solved," new steps or other measures are suggested.

[0944] Specific examples

[0945] When a user reports that their order hasn't arrived even after the estimated delivery time, the app converts the voice to text and recognizes the user's emotional state (irritation, impatience). The server checks the delivery status from the database, generates a solution such as "We are currently on our way to deliver it. It should arrive in about 10 minutes. We apologize for the wait," and presents it to the user in text and voice.

[0946] Prompt Sentence Examples

[0947] "My order hasn't arrived even after the estimated delivery time. Please check the delivery status and generate a response that takes the user's emotions into consideration."

[0948] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0949] Step 1:

[0950] The device receives the problem reported by the user as voice data. For example, the user speaks into an application on a smartphone or smart glasses, saying, "My order hasn't arrived even though the scheduled delivery time has passed." The device records this speech as voice data. This inputs the voice data.

[0951] Step 2:

[0952] The device converts this voice data into text data. Specifically, it uses the Google Cloud Speech-to-Text API to analyze the voice data and convert it into text information. This text data is output in a form that is sent to the server.

[0953] Step 3:

[0954] The server analyzes the received text data using natural language processing technology. IBM Watson Natural Language Understanding is used to identify the problem the user is having from the text data. For example, the keywords "estimated delivery time" and "not delivered" are extracted to identify the nature of the problem. The results of this analysis are used as input for the next process.

[0955] Step 4:

[0956] The server recognizes the user's emotional state from the analyzed text and voice data. It uses the Microsoft Azure Emotion API to extract the user's emotions (e.g., frustration, impatience). It saves the analysis results along with the emotional state as integrated data and proceeds to the next step.

[0957] Step 5:

[0958] The server searches the database for the optimal solution based on the identified problem and the recognized emotional state. The server refers to the device information and order history and generates a solution such as "We are currently on our way to deliver your order. It should arrive in about 10 minutes. We apologize for the wait." The solution is output as text data.

[0959] Step 6:

[0960] The server sends the generated solution to the device. The device then presents this solution to the user. The device displays or plays a message to the user in text or voice, such as "We are currently on our way to deliver your item and will arrive in about 10 minutes. We apologize for the wait." This presents the solution to the user.

[0961] Step 7:

[0962] The user tries the proposed solution and provides feedback on the results, such as "It hasn't arrived yet." This feedback data is sent from the terminal to the server.

[0963] Step 8:

[0964] The server receives this feedback, analyzes it, and if necessary performs further diagnostics, e.g., recalculating the delivery progress and estimated arrival time, and generating a different solution (e.g., "Please check again. The estimated arrival time is about 15 minutes later."). The analysis results and the new solution are output.

[0965] Step 9:

[0966] The server sends the regenerated solution to the terminal, which then presents it to the user. The re-presented solution is again displayed or played back to the user in text or audio, allowing the user to continue working until they find the appropriate solution.

[0967] Some examples of actual examples and prompts include:

[0968] "My order hasn't arrived even after the estimated delivery time. Please check the delivery status and generate a response that takes the user's emotions into consideration."

[0969] 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.

[0970] 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.

[0971] 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.

[0972] [Third embodiment]

[0973] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0974] 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.

[0975] 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).

[0976] 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.

[0977] 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.

[0978] 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).

[0979] 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.

[0980] 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.

[0981] 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.

[0982] 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.

[0983] 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.

[0984] 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."

[0985] The present invention is a support system for quickly and accurately resolving problems users have with their devices, utilizing voice recognition technology and natural language processing technology to analyze the user's speech and provide the optimal solution.

[0986] First, the user installs the supporting application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[0987] To use the app, a user launches the support app on their device and speaks to it about the problem they want to solve (for example, "I can't connect to Wi-Fi"). The device captures this speech as voice data and converts it into text data using voice recognition technology. The converted text data is then sent to the server.

[0988] The server uses natural language processing to analyze the received text data and identify the problem the user is having. For example, it extracts keywords such as "Wi-Fi" and "connection" to understand the nature of the problem. The server then searches a database along with basic device information to search for known solutions. The identified solution (e.g., "Open the Settings app and check your Wi-Fi settings. Then try reconnecting") is sent from the server to the device.

[0989] The device presents the received solution to the user. The presentation method can be either a text display or a voice playback. The user follows the instructions to implement the solution and reports the result in the app. For example, after attempting the presented solution, the device provides feedback such as "Solved" or "Not yet solved."

[0990] User feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it will perform further diagnostics and suggest another solution. By repeating this process, we aim to provide the best support for the user's device and resolve the problem as quickly as possible.

[0991] Specific examples

[0992] Scenario: A user is having internet connection issues

[0993] 1. Initial Setup

[0994] Device: When you install the support app on your iPhone and launch the app for the first time, basic device information (OS: iOS 16, Internet contract status: Wi-Fi only) will be collected.

[0995] Terminal: Sends collected information to the server.

[0996] Server: Stores the information in a database and assigns a unique identifier to the device.

[0997] 2. User Interaction

[0998] User: Say "I can't connect to Wi-Fi."

[0999] Terminal: Captures speech as voice data, converts it into text data using voice recognition technology, and sends that data to the server.

[1000] 3. Problem Analysis and Diagnosis

[1001] Server: Analyze text data containing the keywords "Wi-Fi" and "cannot connect" using natural language processing technology.

[1002] Server: View device information and past problems and solutions to find the best solution.

[1003] 4. Providing a solution

[1004] Server: Generated the solution: "Open the Settings app and check your Wi-Fi settings. Then try reconnecting."

[1005] Terminal: Presents the generated solution to the user in voice and text.

[1006] 5. Feedback and final confirmation

[1007] User: Attempts to reconnect and provides feedback through the app that it's still not working.

[1008] Device: Send feedback to the server.

[1009] Server: Generate the next solution, "Please restart your router," and present it again.

[1010] By using this system, users can receive prompt, comprehensive and appropriate support tailored to the status of their device.

[1011] The processing flow will be explained below.

[1012] Step 1:

[1013] The user installs a supporting app on the device.

[1014] Step 2:

[1015] When a device launches the app for the first time, basic device information (OS version, app installation status, internet connection status, and device settings status) is collected.

[1016] Step 3:

[1017] The device sends the collected basic information to the server.

[1018] Step 4:

[1019] The server stores the received device information in a database and assigns a unique identifier (Device ID) to each device.

[1020] Step 5:

[1021] The user launches the app and talks about the problem they want to solve, such as "Wi-Fi won't connect."

[1022] Step 6:

[1023] The terminal acquires the user's speech as voice data and converts it into text data using voice recognition technology.

[1024] Step 7:

[1025] The terminal transmits the converted text data to the server.

[1026] Step 8:

[1027] The server analyzes the received text data using natural language processing technology to identify the problem the user is facing.

[1028] Step 9:

[1029] The server references basic information about the device and the type of problem, then searches a database for past solutions.

[1030] Step 10:

[1031] The server identifies the best solution and generates a message with specific instructions for the user.

[1032] Step 11:

[1033] The server generates a message and sends it to the terminal.

[1034] Step 12:

[1035] The terminal presents the message received from the server to the user in voice and text.

[1036] Step 13:

[1037] The user implements the proposed solution and reports the results in the app.

[1038] Step 14:

[1039] The terminal sends the user's feedback to the server.

[1040] Step 15:

[1041] The server receives the feedback and, if the problem persists, performs further diagnostics and generates additional solutions.

[1042] Step 16:

[1043] The server sends the new solution to the terminal, which presents it to the user again.

[1044] This series of steps allows users to receive the best support for their device's situation.

[1045] Example 1

[1046] 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."

[1047] Conventional user support systems required multiple manual operations and complex settings for users to solve device problems, making it difficult to solve problems quickly and accurately. Furthermore, they lacked a mechanism for providing optimal support tailored to the user's device status, resulting in low accuracy and efficiency of support. This significantly impaired user convenience.

[1048] 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.

[1049] In this invention, the server includes means for accepting a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for collecting basic information about the user's device and sending it to the server, means for having a database for searching for an optimal solution based on the identified problem, means for presenting the solution searched for from the database to the user, and means for generating further solutions based on user feedback, thereby enabling the user to receive appropriate support for quickly and accurately solving device problems.

[1050] The "means for receiving user speech" is a function for collecting speech spoken by a user into the device.

[1051] "Means for acquiring as voice data" refers to a technology for converting a user's speech into digital voice data and collecting it.

[1052] The "means for converting into text data" is a function that analyzes the voice data and converts the content into text format.

[1053] "Means for analyzing text data to identify the problem the user wants to solve" refers to a technology that extracts problem-related keywords and phrases from text data and identifies the problem the user is facing.

[1054] "Means of collecting basic information about the user's device" refers to a function that obtains information such as the device's OS version, installed applications, and Internet connection status.

[1055] The "means for transmitting to the server" is a function for transferring the collected basic information of the device to the server via the network.

[1056] "Means having a database for searching for the optimum solution" refers to a function for searching for the optimum solution by referring to a database in which past problems and their solutions are recorded.

[1057] The "means for presenting the solutions retrieved from the database to the user" is a function for providing the retrieved solutions to the user in the form of text display or audio playback.

[1058] The "means for generating further solutions based on user feedback" is a technique for analyzing feedback information from users and presenting new solutions in response to the analysis.

[1059] This invention is a support system for quickly and accurately resolving problems users have with devices, utilizing voice recognition technology and natural language processing technology to analyze the user's speech and provide the optimal solution.

[1060] First, the user installs the support application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started up. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[1061] Next, the user launches the support app and speaks to the app about the problem they want to solve. The user's speech is captured as voice data and converted into text data using voice recognition technology. This text data is then sent to the server.

[1062] The server uses natural language processing (NLP) to analyze the received text data and identify the problem the user is having. For example, it extracts keywords such as "Wi-Fi" and "connection" to understand the nature of the problem. The server then references basic device information and a database of past problems and solutions to search for the most appropriate solution. The identified solution (e.g., "Open the Settings app and check your Wi-Fi settings. Then try reconnecting") is sent from the server to the device.

[1063] The device presents the received solution to the user. The presentation method can be either a text display or a voice playback. The user follows the instructions to implement the solution and reports the result in the app. For example, after attempting the presented solution, the device provides feedback such as "Solved" or "Not yet solved."

[1064] User feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it will perform further diagnostics and suggest another solution. By repeating this process, we aim to provide the best support for the user's device and resolve the problem as quickly as possible.

[1065] As a concrete example, consider a case where a user is using an iPhone and is having trouble connecting to Wi-Fi. In this case, the user speaks to a support app saying, "I can't connect to Wi-Fi." In response, the support system captures the voice data, converts it into text data, analyzes it, and presents the optimal solution. For example, it may give instructions such as, "Open the Settings app and check your Wi-Fi settings."

[1066] An example prompt is:

[1067] "My user has an iPhone and is having trouble connecting to Wi-Fi. How can I help them?"

[1068] "After collecting basic device information, the user said 'Wi-Fi is not working'. Please provide an appropriate solution."

[1069] The system leverages speech recognition and natural language processing technologies, particularly generative AI models, to analyze and generate solutions, enabling users to receive faster and more appropriate support.

[1070] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1071] Step 1: Install supporting applications

[1072] The user installs a support application on the device.

[1073] Input: Applications downloaded from the app store at the user's request

[1074] Output: Supporting applications installed on the device

[1075] Step 2: First boot and device information collection

[1076] When the device launches the support app for the first time, it automatically collects basic device information (e.g., OS version, app installation status, Internet connection status, and device settings).

[1077] Input: Basic device information collected by first launching the support app

[1078] Output: A dataset of collected device information

[1079] Specific behavior: The process of packaging collected information as structured data

[1080] Step 3: Send device information to the server

[1081] The terminal sends the collected basic device information to the server.

[1082] Input: A dataset of collected device information

[1083] Output: Device information stored on the server

[1084] What it does: Transforms the information into the appropriate format and sends it over a secure channel to the server.

[1085] Step 4: Assigning a unique identifier

[1086] The server stores the received device information in a database and assigns each device a unique identifier.

[1087] Input: Device information sent to the server

[1088] Output: Device information stored in the database and assigned unique identifier

[1089] Specific operation: Stores device information in a database and generates and assigns a UUID (Universally Unique Identifier).

[1090] Step 5: Receiving the problem statement

[1091] The user launches the support app on their device and speaks the problem they want to solve (e.g., "My Wi-Fi isn't working").

[1092] Input: User spoken utterance

[1093] Output: Speech content captured as audio data

[1094] Specific operation: Converts audio collected through a microphone into digital data

[1095] Step 6: Converting audio data to text data

[1096] The terminal converts the voice data into text data using voice recognition technology.

[1097] Input: Captured audio data

[1098] Output: Converted text data

[1099] Specific operation: Analyzes voice data using a speech recognition engine and outputs it as text.

[1100] Step 7: Sending text data to the server

[1101] The terminal transmits the converted text data to the server.

[1102] Input: Text data

[1103] Output: Text data sent to the server

[1104] Specific behavior: Encodes text data in the appropriate format and sends it to the server.

[1105] Step 8: Analyze the problem

[1106] The server analyzes the received text data using natural language processing (NLP) technology to identify the problem the user is having.

[1107] Input: Text data sent to the server

[1108] Output: Identified problem

[1109] Specific behavior: Analyzes text data, extracts keywords (e.g., "Wi-Fi," "connection"), and identifies issues

[1110] Step 9: Device Information and Database Reference

[1111] The server searches for the best solution by referring to the device's basic information and a database of past problems and solutions.

[1112] Input: Identified problem and device information

[1113] Output: The optimal solution found

[1114] Specific behavior: Run a database search algorithm to identify a solution

[1115] Step 10: Generate and present solutions

[1116] The server generates a solution to the identified problem and sends it to the terminal.

[1117] The terminal presents the generated solutions to the user in the form of a text display and audio playback.

[1118] Input: Solution data from the server

[1119] Output: The solution presented to the user

[1120] Specific Action: Format the solution in text and audio format and present it to the user

[1121] Step 11: Accepting user feedback

[1122] The user implements the proposed solution and reports the results in the support app (e.g., "Still not resolved").

[1123] Input: User feedback

[1124] Output: Feedback data

[1125] Specific operation: Enter the feedback content into the app and convert it into digital data

[1126] Step 12: Sending feedback to the server

[1127] The terminal transmits the user's feedback to the server.

[1128] Input: Feedback data

[1129] Output: Feedback sent to the server

[1130] Specific behavior: Encodes the feedback data in the appropriate format and sends it to the server

[1131] Step 13: Generate further solutions

[1132] The server performs further detailed diagnosis based on the user's feedback and generates new solutions, which are sent to the terminal.

[1133] Input: Feedback data

[1134] Output: New solution

[1135] Specific behavior: Run an algorithm that analyzes the feedback and generates a new solution based on it.

[1136] This series of steps allows users to receive support to quickly and accurately resolve issues with their devices.

[1137] (Application example 1)

[1138] 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."

[1139] When performing maintenance or troubleshooting on industrial equipment, delays in factory production lines can occur if technicians with specialized knowledge are unable to respond quickly. Furthermore, accurately understanding the complex state of equipment and proposing appropriate solutions requires advanced technology and experience. To resolve these issues, a system is needed that can instantly assess the status of the equipment and provide a fast, accurate solution.

[1140] 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.

[1141] In this invention, the server includes means for accepting user speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for having a database for searching for an optimal solution based on the identified problem, means for presenting the solution searched for from the database to the user, means for acquiring basic information about the industrial equipment, means for transmitting the basic information about the industrial equipment to the server, and means for receiving and presenting a solution to the problem of the industrial equipment from the server. This enables maintenance and troubleshooting of the industrial equipment to be performed quickly and accurately.

[1142] "User utterance" refers to the words and sentences that are the source of the voice data input to the system.

[1143] "Voice data" refers to data that is a digital recording of a user's speech.

[1144] "Text data" refers to data in the form of a string of characters converted from audio data.

[1145] "Analysis" refers to the process of understanding text data and identifying the problem the user wants to solve.

[1146] The "database" refers to a collection of information that stores basic information about users' devices and industrial equipment, as well as past problems and their solutions.

[1147] "Optimal solution" refers to the most effective and appropriate way to solve the user's problem.

[1148] "Presenting to the user" refers to visually or audibly conveying the found solution to the user.

[1149] "Industrial equipment" refers to various machines and devices used in factories and production lines.

[1150] "Basic information" refers to important data about industrial equipment, such as the model name, OS version, and last maintenance date.

[1151] "Server" refers to the central management system that analyzes voice and text data and searches for and provides optimal solutions.

[1152] "Receiving" refers to the user's device retrieving the solution provided by the server.

[1153] This invention is a support system for quickly and accurately resolving problems users have with industrial machinery. Specifically, it utilizes voice recognition technology and natural language processing technology to analyze user utterances and provide optimal solutions.

[1154] First, the user installs a support application related to the industrial equipment on the device. After installation, the device collects basic information about the industrial equipment (e.g., OS version, equipment model, and last maintenance date) when it is first started up. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[1155] When a user uses a support application to solve a problem with industrial equipment, they first launch the app on their device and then speak to the application about the problem they want to solve (for example, "The robot arm is moving slowly"). The device captures this speech as audio data and converts it into text data using speech recognition technology. The Python speech_recognition library is used for speech recognition.

[1156] The converted text data is then sent to a server. The server uses natural language processing technology to analyze the received text data and identify the problem the user is having. A natural language processing library is used for the analysis. For example, keywords such as "robot arm" and "slow movement" are extracted to understand the nature of the problem. The server then references a database along with basic information about the industrial equipment to search for known solutions. The identified solution (e.g., "Please readjust the axis of the robot arm") is then sent from the server to the device.

[1157] The device presents the received solution to the user. The presentation method can be either a text display or audio playback. For audio playback, the Python pyttsx3 library is used. The user follows the instructions to implement the solution and reports the results in the app. For example, after attempting the presented solution, the device can provide feedback such as "Solved" or "Not yet solved."

[1158] The user's feedback is sent via the device to the server. The server receives the feedback, and if the problem is not resolved, it performs a more detailed diagnosis and suggests another solution. By repeating this process, the aim is to provide optimal support for the user's industrial equipment and quickly resolve the problem.

[1159] Specific examples

[1160] If an industrial device is not working properly, for example, by saying "The robot arm has stopped working," the system will immediately suggest a solution. Examples of prompts to input to the generative AI model are as follows:

[1161] Please provide a solution for a robot that is experiencing the following issue: "The robot arm has stopped moving." The robot's basic information is as follows: OS version: FactoryOS 1.2, robot model: XJ320, last maintenance date: 2023-01-25.

[1162] By using this system, factory maintenance personnel can quickly receive comprehensive and appropriate support tailored to the status of their industrial equipment.

[1163] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1164] Step 1:

[1165] The terminal installs the supporting application and collects basic information about the industrial equipment (e.g., OS version, equipment model, and last maintenance date) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device. The input is the basic device information, and the output is the device information with the identifier stored on the server.

[1166] Step 2:

[1167] A user launches a support application to solve a problem with industrial equipment and speaks out the problem they want to solve (for example, "The robot arm is moving slowly"). The device receives this speech as voice data. The input is the user's speech, and the output is voice data.

[1168] Step 3:

[1169] The device converts the acquired voice data into text data using voice recognition technology (Python's speech_recognition library). The input is voice data, and the output is text data.

[1170] Step 4:

[1171] The terminal sends the converted text data to the server. The input is the text data, and the output is the text data sent to the server.

[1172] Step 5:

[1173] The server analyzes the received text data using natural language processing technology (natural language processing library) and identifies the problem the user is facing. The input is the text data, and the output is the identified problem.

[1174] Step 6:

[1175] The server references the database along with basic information about the industrial equipment and searches for the best solution based on the identified problem. The input is the identified problem and basic information about the industrial equipment, and the output is the solution.

[1176] Step 7:

[1177] The server sends the searched solutions to the terminal. The input is the solution and the output is the solution sent to the terminal.

[1178] Step 8:

[1179] The terminal presents the received solution to the user, either as a text display or as audio (using the Python pyttsx3 library for audio playback). The input is the solution, and the output is the solution presented to the user.

[1180] Step 9:

[1181] The user follows the instructions, implements the solution, and reports the result in the app, e.g., feedback such as "solved" or "not yet solved." The input is the user feedback, and the output is the feedback data.

[1182] Step 10:

[1183] The terminal sends the user's feedback to the server, where the input is the feedback data and the output is the feedback data sent to the server.

[1184] Step 11:

[1185] The server receives the feedback, and if the problem is not resolved, it performs further diagnostics and suggests a different solution. This process is repeated to provide optimal support. The input is the feedback data, and the output is the new solution.

[1186] 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.

[1187] This invention is a support system for quickly and accurately resolving device-related problems that users encounter, utilizing speech recognition technology, natural language processing technology, and an emotion engine for recognizing the user's emotions. This system analyzes the user's speech and provides optimal solutions, while also providing support that takes the user's emotional state into consideration.

[1188] First, the user installs the supporting application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[1189] To use the app, a user launches the support app on their device and speaks to it about the problem they want to solve (for example, "I can't connect to Wi-Fi"). The device captures this speech as voice data and converts it into text data using voice recognition technology. The converted text data is then sent to the server.

[1190] The server analyzes the received text data using natural language processing technology to identify the problem the user is experiencing. It also uses an emotion engine to extract the user's emotional state (e.g., dissatisfaction, impatience, anger, etc.) from the text and voice data. For example, it can recognize that the user is dissatisfied based on the text data "Wi-Fi is not connecting" and the tone of the voice.

[1191] The server then searches the database for the optimal solution, taking into account the user's emotional state, basic device information, and the type of problem. For example, if the user is frustrated, a solution containing kind words and encouraging messages will be generated in addition to the usual solution. A specific solution might be something like, "Open the Settings app and check your Wi-Fi settings. Then try reconnecting," with an additional message depending on the user's condition.

[1192] The solution sent from the server is presented to the user via the device. The presentation method can be either text display or audio playback. The user attempts to solve the problem according to the presented solution and reports the result in the app (for example, feedback such as "Solved" or "Not yet solved").

[1193] The feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it performs a more detailed diagnosis and proposes a new solution. At this time, it again utilizes the emotion engine to recognize the user's emotional state and adjust the appropriate support method.

[1194] Specific examples

[1195] Scenario: A user is having internet connection issues

[1196] 1. Initial Setup

[1197] Device: When you install the support app on your iPhone and launch the app for the first time, basic device information (OS: iOS 16, Internet contract status: Wi-Fi only) will be collected.

[1198] Terminal: Sends collected information to the server.

[1199] Server: Stores the information in a database and assigns a unique identifier to the device.

[1200] 2. User Interaction

[1201] User: Say "I can't connect to Wi-Fi."

[1202] Terminal: Captures speech as voice data, converts it into text data using voice recognition technology, and sends that data to the server.

[1203] 3. Problem analysis and emotion recognition

[1204] Server: Analyzes text data containing the keywords "Wi-Fi" and "cannot connect" using natural language processing technology to identify the problem.

[1205] Server: Using the emotion engine, recognize the user's emotion from the text data and voice data. For example, recognize that the user is dissatisfied.

[1206] 4. Solution Search and Generation

[1207] Server: Generates the optimal solution by taking into account the user's emotional state and device information. Example: "Open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[1208] 5. Providing a solution

[1209] Server: Sends the generated solution to the device.

[1210] Terminal: Presents audio and text to the user.

[1211] 6. Feedback and Final Review

[1212] User: Try the proposed solution and provide feedback on the results. Example: "Still not solved."

[1213] Device: Sends feedback to the server.

[1214] Server: Perform additional diagnostics and generate a different solution: "Reboot your router" and provide the appropriate support message again.

[1215] This system allows users to quickly receive comprehensive and appropriate support tailored to the situation of their device, and provides more customized support by taking into account the user's emotional state.

[1216] The processing flow will be explained below.

[1217] Step 1:

[1218] The user installs a supporting app on the device.

[1219] Step 2:

[1220] When a device launches the app for the first time, basic device information (OS version, app installation status, internet connection status, and device settings status) is collected.

[1221] Step 3:

[1222] The device sends the collected basic information to the server.

[1223] Step 4:

[1224] The server stores the received device information in a database and assigns a unique identifier (Device ID) to each device.

[1225] Step 5:

[1226] The user launches the app and talks about the problem they want to solve, such as "Wi-Fi won't connect."

[1227] Step 6:

[1228] The terminal acquires the user's speech as voice data and converts it into text data using voice recognition technology.

[1229] Step 7:

[1230] The terminal transmits the converted text data to the server.

[1231] Step 8:

[1232] The server analyzes the received text data using natural language processing technology to identify the problem the user is facing.

[1233] Step 9:

[1234] The server uses an emotion engine to recognize the user's emotion from the text data and voice data, for example, to detect that the user is dissatisfied.

[1235] Step 10:

[1236] The server takes into account the device's basic information and emotional state to search for past solutions from a database.

[1237] Step 11:

[1238] The server identifies the best solution and generates a message for the user that takes into account specific steps and emotions, such as "Open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[1239] Step 12:

[1240] The server generates a message and sends it to the terminal.

[1241] Step 13:

[1242] The terminal presents the message received from the server to the user in voice and text.

[1243] Step 14:

[1244] The user implements the proposed solution and reports the results in the app, for example, by providing feedback such as "still not solved."

[1245] Step 15:

[1246] The terminal sends the user's feedback to the server.

[1247] Step 16:

[1248] The server receives the feedback, and if the problem is not resolved, it performs a more detailed diagnosis and generates a new solution, again leveraging the emotion engine to recognize the user's emotional state.

[1249] Step 17:

[1250] The server sends a new solution to the device, which then presents it to the user again, for example, "Please restart your router. Please wait a moment."

[1251] This series of steps allows users to receive optimal support based on the context of their device. Combined with the emotion engine, customized support is provided that takes into account the user's emotional state.

[1252] Example 2

[1253] 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."

[1254] In conventional support systems, when a user reports a device problem, it is difficult to accurately and quickly identify the problem and provide the optimal solution. Furthermore, the quality of support can be compromised by not taking the user's emotional state into consideration. This often leads to stress for users and delays in resolving the problem.

[1255] 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.

[1256] In this invention, the server includes means for receiving a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for having a database for searching for an optimal solution based on the identified problem, means for recognizing the user's emotional state, and means for presenting the solution searched for in the database to the user. This makes it possible to quickly and accurately solve device problems while taking the user's emotional state into consideration.

[1257] "Means for accepting user speech and obtaining it as voice data" refers to a device or software that captures information that a user inputs into a device by voice and converts it into a format that can be stored electronically.

[1258] The "means for converting voice data into text data" refers to a device or program that analyzes voice data and converts it into corresponding text format information.

[1259] "Means for analyzing text data to identify the problem the user wants to solve" refers to a method or technology for analyzing the converted text data and extracting the user's problem or inquiry from its contents.

[1260] "Means having a database for searching for an optimal solution based on an identified problem" refers to a device or system that has the function of searching for an optimal response from a database that stores related solutions based on analyzed text data.

[1261] The "means for recognizing the user's emotional state" is a technology for analyzing the user's emotions from the tone of their voice and the contents of their text data, and recognizing their state.

[1262] The "means for presenting the solutions retrieved from the database to the user" is a device or software for visually or audibly presenting the solutions retrieved to the user.

[1263] The present invention is a support system for quickly and accurately resolving device-related problems that users encounter, utilizing speech recognition technology, natural language processing technology, and an emotion engine for recognizing the user's emotions. This system analyzes the user's speech and provides optimal solutions while also providing support that takes the user's emotional state into consideration.

[1264] First, the user installs the supporting application on their device. After installation, when the application is launched, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) at first launch. This information is sent to the server and stored in a database, and a unique identifier is assigned to each device.

[1265] The user then launches the support app and speaks out the problem they want to solve. For example, if the user says, "My Wi-Fi isn't connecting," the device captures this speech as voice data. Next, it uses voice recognition technology to convert this voice data into text data. Specifically, it uses the Google Cloud Speech-to-Text API or similar to convert the voice data into text format. This converted text data is then sent to the server.

[1266] The server analyzes the received text data using natural language processing technology (e.g., Amazon Comprehend) to identify the problem the user is experiencing. At the same time, it uses an emotion engine to extract the user's emotional state from the text and voice data. For example, it recognizes that the user is dissatisfied based on the utterance "My Wi-Fi isn't connecting" and the tone of the voice.

[1267] Next, the server searches for the optimal solution from a database, taking into account the user's emotional state, basic device information, and the type of problem. For example, if the user is frustrated, the server generates a solution that includes kind words and an encouraging message in addition to the usual solution. A specific solution might be, "Open the Settings app and check your Wi-Fi settings. Then, try reconnecting. We apologize for the inconvenience." This solution is sent from the server to the device and presented to the user via the device. It can be presented as text or audio.

[1268] The user attempts to solve the problem using the proposed solutions and reports the results in the app. For example, they can provide feedback such as "Solved" or "Not yet solved." This feedback is sent to the server via the device. The server receives the feedback, and if the problem is not solved, it performs a more detailed diagnosis and suggests a new solution. At this time, the emotion engine is again utilized to recognize the user's emotional state and adjust the appropriate support method.

[1269] Specific examples

[1270] Scenario: A user is having internet connection issues

[1271] Initial Setup

[1272] 1. Device: When you install the support app on your smartphone and launch the app for the first time, basic device information (OS version, internet connection status, etc.) will be collected.

[1273] 2. Terminal: Sends collected information to the server.

[1274] 3. Server: Stores the information in a database and assigns a unique identifier to the device.

[1275] User Interaction

[1276] 1. User: Says, "Wi-Fi is not connecting."

[1277] 2. Terminal: The speech is acquired as audio data, converted into text data using the Google Cloud Speech-to-Text API, and sent to the server.

[1278] Problem analysis and emotion recognition

[1279] 1. Server: Analyze text data containing the keywords "Wi-Fi" and "unable to connect" using Amazon Comprehend to identify problems.

[1280] 2. Server: Using the emotion engine, recognize the user's dissatisfaction from text and voice data.

[1281] Solution generation and delivery

[1282] 1. Server: Generates the optimal solution by taking into account the user's emotional state and device information. Example: "Please open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[1283] 2. Server: Sends the generated solution to the device.

[1284] 3. Terminal: Presents the user with voice and text.

[1285] Feedback and final confirmation

[1286] 1. User: Try the proposed solution and provide feedback on the results. Example: "Still not solved."

[1287] 2. Device: Sends feedback to the server.

[1288] 3. Server: Performs additional diagnostics and generates another solution, "Please restart the router," and sends it to the device again.

[1289] Using this system, users can quickly and accurately resolve problems with their devices and receive customized support tailored to their emotional state.

[1290] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1291] Step 1:

[1292] The user installs a supporting application on the device.

[1293] Input: Application installation request

[1294] Data processing / calculation: The installer copies the application files to the device and performs the necessary settings.

[1295] Output: Supporting applications installed on the device

[1296] Specific operations: Download the support app from the App Store or Google Play Store and complete the installation.

[1297] Step 2:

[1298] The device collects basic device information when the application is first launched.

[1299] Input: First launch of application

[1300] Data processing / calculation: Collects information such as the device's OS version, internet connection status, and installed apps, and generates data in JSON format.

[1301] Output: Basic device information collected

[1302] Specific operation: The device obtains device information and captures it in JSON format.

[1303] Step 3:

[1304] The device sends the collected information to the server.

[1305] Input: Collected device information (JSON format)

[1306] Data processing / calculation: Device information is encrypted using the HTTPS protocol and sent to the server.

[1307] Output: Basic device information sent to the server

[1308] Specific operation: The terminal sends JSON data using the HTTPS protocol.

[1309] Step 4:

[1310] The server stores the received information in a database and assigns each device a unique identifier.

[1311] Input: Sent device information (JSON format)

[1312] Data processing / calculation: Save device information in an RDBMS (e.g., MySQL), generate a UUID, and assign it to the device.

[1313] Output: A unique identifier assigned to the device.

[1314] Specific operation: The server generates a UUID and stores it in the database along with the information.

[1315] Step 5:

[1316] The user launches the support app and talks about the problem they want to solve.

[1317] Input: User utterance

[1318] Data processing / calculation: Acquire speech as audio data

[1319] Output: User's voice data

[1320] Specific action: The user talks about the problem into the device (e.g., "My Wi-Fi isn't working").

[1321] Step 6:

[1322] The device captures the user's speech as voice data.

[1323] Input: User utterance

[1324] Data processing / calculation: Capture audio data through a microphone and convert it into binary data format

[1325] Output: Captured audio data

[1326] Specific operation: The device records audio data.

[1327] Step 7:

[1328] The device uses voice recognition technology to convert the voice data into text data.

[1329] Input: Audio data

[1330] Data processing / calculation: Converting voice data into text data using the Google Cloud Speech-to-Text API

[1331] Output: Text data

[1332] Specific operation: Sends audio data to the Google Cloud Speech-to-Text API and receives the conversion result.

[1333] Step 8:

[1334] The terminal sends the converted text data to the server.

[1335] Input: Text data

[1336] Data processing / calculation: Encrypt the text data using the HTTPS protocol and send it to the server.

[1337] Output: Text data sent to the server

[1338] Specific operation: The terminal transmits the converted text data.

[1339] Step 9:

[1340] The server analyzes the received text data using natural language processing technology to identify problems.

[1341] Input: Text data

[1342] Data processing / calculation: Analyze text data using Amazon Comprehend and identify problems

[1343] Output: Identified issues

[1344] Specific operation: The server analyzes the text data and extracts the keywords "Wi-Fi" and "cannot connect."

[1345] Step 10:

[1346] The server uses an emotion engine to recognize the user's emotional state.

[1347] Input: Text data and audio data

[1348] Data processing / calculation: Analyze text and voice data using an emotion engine to determine emotional states

[1349] Output: Recognized emotional state of the user

[1350] What happens: The server analyzes the text and tone of voice and recognizes that the user is frustrated.

[1351] Step 11:

[1352] The server takes into account the user's emotional state, references the device's basic information and the type of problem, and generates the optimal solution.

[1353] Input: User's emotional state, basic device information, identified issues

[1354] Data processing / calculation: Searching the database and generating optimal solutions

[1355] Output: Generated solution

[1356] What happens: The server retrieves the solution from the database and generates a customized message.

[1357] Step 12:

[1358] The server sends the generated solution to the device.

[1359] Input: Generated solution

[1360] Data processing / calculation: Generate a solution in JSON format and send it to the terminal using the HTTPS protocol.

[1361] Output: Solution sent to terminal

[1362] Specific operation: The server sends the solution to the terminal.

[1363] Step 13:

[1364] The terminal presents the solution to the user by playing it aloud and displaying it as text.

[1365] Input: Solution sent by the server

[1366] Data processing / calculation: Converting the solution into a text display and audio playback format

[1367] Output: The solution presented to the user

[1368] Specific operation: The device displays the solution on the screen and plays it aloud using the text-to-speech function.

[1369] Step 14:

[1370] The user follows the suggested solution and reports the solution in the app.

[1371] Input: Solution attempt results

[1372] Data processing / calculation: Input the solution results into the terminal and generate feedback

[1373] Output: Feedback data

[1374] What happens: The user reports feedback in the app, such as "Resolved" or "Still not resolved."

[1375] Step 15:

[1376] The device sends the feedback to the server.

[1377] Input: User feedback

[1378] Data processing / calculation: Feedback data is converted into JSON format and sent to the server using the HTTPS protocol.

[1379] Output: Feedback data sent to the server

[1380] Specific operation: The terminal transmits feedback data.

[1381] Step 16:

[1382] The server receives the feedback and, if the problem persists, provides further diagnostics and a new solution.

[1383] Input: Feedback data

[1384] Data processing / computation: Analyze feedback, perform additional diagnostics, and generate new solutions

[1385] Output: New solution

[1386] Specific action: The server re-analyzes the problem, generates a different solution (e.g., reboot the router), and sends it again to the device.

[1387] This series of processes allows users to quickly and accurately resolve device problems and receive support that takes their emotional state into consideration.

[1388] (Application example 2)

[1389] 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."

[1390] Conventional user support systems only provide a uniform solution without considering the user's emotions or stress when reporting a problem. As a result, users are unable to receive appropriate support for their emotional state, which can leave them dissatisfied. This invention aims to provide more accurate and customized support by taking into account the user's emotional state when they encounter a problem in services such as food delivery.

[1391] 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.

[1392] In this invention, the server includes means for accepting a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data to identify a problem the user wants to solve, means having a database for searching for an optimal solution based on the identified problem, means having an emotion engine for recognizing the user's emotional state, means for adjusting a solution based on the emotional state, and means for presenting the solution searched for from the database to the user. This makes it possible to provide support for quickly and accurately solving a problem when the user is in a situation where the user has a problem, taking into account the user's emotional state.

[1393] "User utterance" refers to an expression of intent made by a user through speech directed at a device.

[1394] "Voice data" refers to data that is a digital recording of a user's speech.

[1395] "Text data" refers to data obtained by converting voice data into character information.

[1396] A "database" refers to a collection of data that is systematically organized and stored, and can be searched and used.

[1397] "Emotion engine" refers to technology for recognizing a user's emotional state from text and voice data.

[1398] "Solution" refers to the method or procedure offered to solve a user's problem.

[1399] "Presentation medium" refers to the technology or method used to convey information to a user.

[1400] "Feedback" refers to the user's evaluation and reaction to the presented solution.

[1401] "Diagnosis" refers to the process of analyzing a user's problem and identifying its cause and solution.

[1402] "Customized support" refers to assistance that is tailored to the user's specific situation and emotional state.

[1403] To implement this invention, a comprehensive support system is required to acquire user utterances as voice data, convert them into text data, and analyze the data to identify problems. The support system performs a series of processes, including initial device configuration, user interaction, utilization of an emotion recognition engine, generation and presentation of solutions to problems, and feedback processing. This process is described in detail below.

[1404] Hardware and software used

[1405] Hardware: Smartphones, smart glasses

[1406] Software: Google Cloud Speech-to-Text API (speech recognition engine), IBM Watson Natural Language Understanding (natural language processing engine), Microsoft Azure Emotion API (emotion engine), cloud server, database management system

[1407] Data processing and calculation

[1408] First, a user installs a supporting application on their smartphone or smart glasses. When the application is launched for the first time, it collects basic device information (e.g., account information, delivery address, and network connection status) and sends it to a server. This assigns a unique identifier to the device, which is then stored in a database.

[1409] User Interaction

[1410] When a user reports a problem, such as "My order hasn't arrived" or "My order hasn't arrived even after the estimated delivery time," the app captures the voice and converts it into text data using the Google Cloud Speech-to-Text API. This text data is then sent to the server.

[1411] Natural Language Processing and Emotion Recognition

[1412] The server uses IBM Watson Natural Language Understanding to analyze the text data and identify the user's problem, while at the same time using the Microsoft Azure Emotion API to recognize the user's emotional state (e.g., irritation, impatience) from the user's voice and text data.

[1413] Solution generation and presentation

[1414] Based on the identified problem and emotional state, the system searches for the best solution from a database and generates an emotionally sensitive message. The generated solution is presented to the user via text and voice, and may include a message such as, "Sorry for the wait. Your driver is currently on his way to your address and is expected to arrive in 15 minutes."

[1415] Feedback and re-solution

[1416] When the user attempts a solution and provides feedback on the results, the server receives that feedback, performs a detailed diagnosis again, and generates a new solution. If the problem is reported as "still not solved," new steps or other measures are suggested.

[1417] Specific examples

[1418] When a user reports that their order hasn't arrived even after the estimated delivery time, the app converts the voice to text and recognizes the user's emotional state (irritation, impatience). The server checks the delivery status from the database, generates a solution such as "We are currently on our way to deliver it. It should arrive in about 10 minutes. We apologize for the wait," and presents it to the user in text and voice.

[1419] Prompt Sentence Examples

[1420] "My order hasn't arrived even after the estimated delivery time. Please check the delivery status and generate a response that takes the user's emotions into consideration."

[1421] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1422] Step 1:

[1423] The device receives the problem reported by the user as voice data. For example, the user speaks into an application on a smartphone or smart glasses, saying, "My order hasn't arrived even though the scheduled delivery time has passed." The device records this speech as voice data. This inputs the voice data.

[1424] Step 2:

[1425] The device converts this voice data into text data. Specifically, it uses the Google Cloud Speech-to-Text API to analyze the voice data and convert it into text information. This text data is output in a form that is sent to the server.

[1426] Step 3:

[1427] The server analyzes the received text data using natural language processing technology. IBM Watson Natural Language Understanding is used to identify the problem the user is having from the text data. For example, the keywords "estimated delivery time" and "not delivered" are extracted to identify the nature of the problem. The results of this analysis are used as input for the next process.

[1428] Step 4:

[1429] The server recognizes the user's emotional state from the analyzed text and voice data. It uses the Microsoft Azure Emotion API to extract the user's emotions (e.g., frustration, impatience). It saves the analysis results along with the emotional state as integrated data and proceeds to the next step.

[1430] Step 5:

[1431] The server searches the database for the optimal solution based on the identified problem and the recognized emotional state. The server refers to the device information and order history and generates a solution such as "We are currently on our way to deliver your order. It should arrive in about 10 minutes. We apologize for the wait." The solution is output as text data.

[1432] Step 6:

[1433] The server sends the generated solution to the device. The device then presents this solution to the user. The device displays or plays a message to the user in text or voice, such as "We are currently on our way to deliver your item and will arrive in about 10 minutes. We apologize for the wait." This presents the solution to the user.

[1434] Step 7:

[1435] The user tries the proposed solution and provides feedback on the results, such as "It hasn't arrived yet." This feedback data is sent from the terminal to the server.

[1436] Step 8:

[1437] The server receives this feedback, analyzes it, and if necessary performs further diagnostics, e.g., recalculating the delivery progress and estimated arrival time, and generating a different solution (e.g., "Please check again. The estimated arrival time is about 15 minutes later."). The analysis results and the new solution are output.

[1438] Step 9:

[1439] The server sends the regenerated solution to the terminal, which then presents it to the user. The re-presented solution is again displayed or played back to the user in text or audio, allowing the user to continue working until they find the appropriate solution.

[1440] Some examples of actual examples and prompts include:

[1441] "My order hasn't arrived even after the estimated delivery time. Please check the delivery status and generate a response that takes the user's emotions into consideration."

[1442] 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.

[1443] 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.

[1444] 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.

[1445] [Fourth embodiment]

[1446] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1447] 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.

[1448] 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).

[1449] 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.

[1450] 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.

[1451] 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).

[1452] 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.

[1453] 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.

[1454] 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.

[1455] 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.

[1456] 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.

[1457] 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.

[1458] 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."

[1459] The present invention is a support system for quickly and accurately resolving problems users have with their devices, utilizing voice recognition technology and natural language processing technology to analyze the user's speech and provide the optimal solution.

[1460] First, the user installs the supporting application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[1461] To use the app, a user launches the support app on their device and speaks to it about the problem they want to solve (for example, "I can't connect to Wi-Fi"). The device captures this speech as voice data and converts it into text data using voice recognition technology. The converted text data is then sent to the server.

[1462] The server uses natural language processing to analyze the received text data and identify the problem the user is having. For example, it extracts keywords such as "Wi-Fi" and "connection" to understand the nature of the problem. The server then searches a database along with basic device information to search for known solutions. The identified solution (e.g., "Open the Settings app and check your Wi-Fi settings. Then try reconnecting") is sent from the server to the device.

[1463] The device presents the received solution to the user. The presentation method can be either a text display or a voice playback. The user follows the instructions to implement the solution and reports the result in the app. For example, after attempting the presented solution, the device provides feedback such as "Solved" or "Not yet solved."

[1464] User feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it will perform further diagnostics and suggest another solution. By repeating this process, we aim to provide the best support for the user's device and resolve the problem as quickly as possible.

[1465] Specific examples

[1466] Scenario: A user is having internet connection issues

[1467] 1. Initial Setup

[1468] Device: When you install the support app on your iPhone and launch the app for the first time, basic device information (OS: iOS 16, Internet contract status: Wi-Fi only) will be collected.

[1469] Terminal: Sends collected information to the server.

[1470] Server: Stores the information in a database and assigns a unique identifier to the device.

[1471] 2. User Interaction

[1472] User: Say "I can't connect to Wi-Fi."

[1473] Terminal: Captures speech as voice data, converts it into text data using voice recognition technology, and sends that data to the server.

[1474] 3. Problem Analysis and Diagnosis

[1475] Server: Analyze text data containing the keywords "Wi-Fi" and "cannot connect" using natural language processing technology.

[1476] Server: View device information and past problems and solutions to find the best solution.

[1477] 4. Providing a solution

[1478] Server: Generated the solution: "Open the Settings app and check your Wi-Fi settings. Then try reconnecting."

[1479] Terminal: Presents the generated solution to the user in voice and text.

[1480] 5. Feedback and final confirmation

[1481] User: Attempts to reconnect and provides feedback through the app that it's still not working.

[1482] Device: Send feedback to the server.

[1483] Server: Generate the next solution, "Please restart your router," and present it again.

[1484] By using this system, users can receive prompt, comprehensive and appropriate support tailored to the status of their device.

[1485] The processing flow will be explained below.

[1486] Step 1:

[1487] The user installs a supporting app on the device.

[1488] Step 2:

[1489] When a device launches the app for the first time, basic device information (OS version, app installation status, internet connection status, and device settings status) is collected.

[1490] Step 3:

[1491] The device sends the collected basic information to the server.

[1492] Step 4:

[1493] The server stores the received device information in a database and assigns a unique identifier (Device ID) to each device.

[1494] Step 5:

[1495] The user launches the app and talks about the problem they want to solve, such as "Wi-Fi won't connect."

[1496] Step 6:

[1497] The terminal acquires the user's speech as voice data and converts it into text data using voice recognition technology.

[1498] Step 7:

[1499] The terminal transmits the converted text data to the server.

[1500] Step 8:

[1501] The server analyzes the received text data using natural language processing technology to identify the problem the user is facing.

[1502] Step 9:

[1503] The server references basic information about the device and the type of problem, then searches a database for past solutions.

[1504] Step 10:

[1505] The server identifies the best solution and generates a message with specific instructions for the user.

[1506] Step 11:

[1507] The server generates a message and sends it to the terminal.

[1508] Step 12:

[1509] The terminal presents the message received from the server to the user in voice and text.

[1510] Step 13:

[1511] The user implements the proposed solution and reports the results in the app.

[1512] Step 14:

[1513] The terminal sends the user's feedback to the server.

[1514] Step 15:

[1515] The server receives the feedback and, if the problem persists, performs further diagnostics and generates additional solutions.

[1516] Step 16:

[1517] The server sends the new solution to the terminal, which presents it to the user again.

[1518] This series of steps allows users to receive the best support for their device's situation.

[1519] Example 1

[1520] 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."

[1521] Conventional user support systems required multiple manual operations and complex settings for users to solve device problems, making it difficult to solve problems quickly and accurately. Furthermore, they lacked a mechanism for providing optimal support tailored to the user's device status, resulting in low accuracy and efficiency of support. This significantly impaired user convenience.

[1522] 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.

[1523] In this invention, the server includes means for accepting a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for collecting basic information about the user's device and sending it to the server, means for having a database for searching for an optimal solution based on the identified problem, means for presenting the solution searched for from the database to the user, and means for generating further solutions based on user feedback, thereby enabling the user to receive appropriate support for quickly and accurately solving device problems.

[1524] The "means for receiving user speech" is a function for collecting speech spoken by a user into the device.

[1525] "Means for acquiring as voice data" refers to a technology for converting a user's speech into digital voice data and collecting it.

[1526] The "means for converting into text data" is a function that analyzes the voice data and converts the content into text format.

[1527] "Means for analyzing text data to identify the problem the user wants to solve" refers to a technology that extracts problem-related keywords and phrases from text data and identifies the problem the user is facing.

[1528] "Means of collecting basic information about the user's device" refers to a function that obtains information such as the device's OS version, installed applications, and Internet connection status.

[1529] The "means for transmitting to the server" is a function for transferring the collected basic information of the device to the server via the network.

[1530] "Means having a database for searching for the optimum solution" refers to a function for searching for the optimum solution by referring to a database in which past problems and their solutions are recorded.

[1531] The "means for presenting the solutions retrieved from the database to the user" is a function for providing the retrieved solutions to the user in the form of text display or audio playback.

[1532] The "means for generating further solutions based on user feedback" is a technique for analyzing feedback information from users and presenting new solutions in response to the analysis.

[1533] This invention is a support system for quickly and accurately resolving problems users have with devices, utilizing voice recognition technology and natural language processing technology to analyze the user's speech and provide the optimal solution.

[1534] First, the user installs the support application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started up. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[1535] Next, the user launches the support app and speaks to the app about the problem they want to solve. The user's speech is captured as voice data and converted into text data using voice recognition technology. This text data is then sent to the server.

[1536] The server uses natural language processing (NLP) to analyze the received text data and identify the problem the user is having. For example, it extracts keywords such as "Wi-Fi" and "connection" to understand the nature of the problem. The server then references basic device information and a database of past problems and solutions to search for the most appropriate solution. The identified solution (e.g., "Open the Settings app and check your Wi-Fi settings. Then try reconnecting") is sent from the server to the device.

[1537] The device presents the received solution to the user. The presentation method can be either a text display or a voice playback. The user follows the instructions to implement the solution and reports the result in the app. For example, after attempting the presented solution, the device provides feedback such as "Solved" or "Not yet solved."

[1538] User feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it will perform further diagnostics and suggest another solution. By repeating this process, we aim to provide the best support for the user's device and resolve the problem as quickly as possible.

[1539] As a concrete example, consider a case where a user is using an iPhone and is having trouble connecting to Wi-Fi. In this case, the user speaks to a support app saying, "I can't connect to Wi-Fi." In response, the support system captures the voice data, converts it into text data, analyzes it, and presents the optimal solution. For example, it may give instructions such as, "Open the Settings app and check your Wi-Fi settings."

[1540] An example prompt is:

[1541] "My user has an iPhone and is having trouble connecting to Wi-Fi. How can I help them?"

[1542] "After collecting basic device information, the user said 'Wi-Fi is not working'. Please provide an appropriate solution."

[1543] The system leverages speech recognition and natural language processing technologies, particularly generative AI models, to analyze and generate solutions, enabling users to receive faster and more appropriate support.

[1544] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1545] Step 1: Install supporting applications

[1546] The user installs a support application on the device.

[1547] Input: Applications downloaded from the app store at the user's request

[1548] Output: Supporting applications installed on the device

[1549] Step 2: First boot and device information collection

[1550] When the device launches the support app for the first time, it automatically collects basic device information (e.g., OS version, app installation status, Internet connection status, and device settings).

[1551] Input: Basic device information collected by first launching the support app

[1552] Output: A dataset of collected device information

[1553] Specific behavior: The process of packaging collected information as structured data

[1554] Step 3: Send device information to the server

[1555] The terminal sends the collected basic device information to the server.

[1556] Input: A dataset of collected device information

[1557] Output: Device information stored on the server

[1558] What it does: Transforms the information into the appropriate format and sends it over a secure channel to the server.

[1559] Step 4: Assigning a unique identifier

[1560] The server stores the received device information in a database and assigns each device a unique identifier.

[1561] Input: Device information sent to the server

[1562] Output: Device information stored in the database and assigned unique identifier

[1563] Specific operation: Stores device information in a database and generates and assigns a UUID (Universally Unique Identifier).

[1564] Step 5: Receiving the problem statement

[1565] The user launches the support app on their device and speaks the problem they want to solve (e.g., "My Wi-Fi isn't working").

[1566] Input: User spoken utterance

[1567] Output: Speech content captured as audio data

[1568] Specific operation: Converts audio collected through a microphone into digital data

[1569] Step 6: Converting audio data to text data

[1570] The terminal converts the voice data into text data using voice recognition technology.

[1571] Input: Captured audio data

[1572] Output: Converted text data

[1573] Specific operation: Analyzes voice data using a speech recognition engine and outputs it as text.

[1574] Step 7: Sending text data to the server

[1575] The terminal transmits the converted text data to the server.

[1576] Input: Text data

[1577] Output: Text data sent to the server

[1578] Specific behavior: Encodes text data in the appropriate format and sends it to the server.

[1579] Step 8: Analyze the problem

[1580] The server analyzes the received text data using natural language processing (NLP) technology to identify the problem the user is having.

[1581] Input: Text data sent to the server

[1582] Output: Identified problem

[1583] Specific behavior: Analyzes text data, extracts keywords (e.g., "Wi-Fi," "connection"), and identifies issues

[1584] Step 9: Device Information and Database Reference

[1585] The server searches for the best solution by referring to the device's basic information and a database of past problems and solutions.

[1586] Input: Identified problem and device information

[1587] Output: The optimal solution found

[1588] Specific behavior: Run a database search algorithm to identify a solution

[1589] Step 10: Generate and present solutions

[1590] The server generates a solution to the identified problem and sends it to the terminal.

[1591] The terminal presents the generated solutions to the user in the form of a text display and audio playback.

[1592] Input: Solution data from the server

[1593] Output: The solution presented to the user

[1594] Specific Action: Format the solution in text and audio format and present it to the user

[1595] Step 11: Accepting user feedback

[1596] The user implements the proposed solution and reports the results in the support app (e.g., "Still not resolved").

[1597] Input: User feedback

[1598] Output: Feedback data

[1599] Specific operation: Enter the feedback content into the app and convert it into digital data

[1600] Step 12: Sending feedback to the server

[1601] The terminal transmits the user's feedback to the server.

[1602] Input: Feedback data

[1603] Output: Feedback sent to the server

[1604] Specific behavior: Encodes the feedback data in the appropriate format and sends it to the server

[1605] Step 13: Generate further solutions

[1606] The server performs further detailed diagnosis based on the user's feedback and generates new solutions, which are sent to the terminal.

[1607] Input: Feedback data

[1608] Output: New solution

[1609] Specific behavior: Run an algorithm that analyzes the feedback and generates a new solution based on it.

[1610] This series of steps allows users to receive support to quickly and accurately resolve issues with their devices.

[1611] (Application example 1)

[1612] 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."

[1613] When performing maintenance or troubleshooting on industrial equipment, delays in factory production lines can occur if technicians with specialized knowledge are unable to respond quickly. Furthermore, accurately understanding the complex state of equipment and proposing appropriate solutions requires advanced technology and experience. To resolve these issues, a system is needed that can instantly assess the status of the equipment and provide a fast, accurate solution.

[1614] 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.

[1615] In this invention, the server includes means for accepting user speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for having a database for searching for an optimal solution based on the identified problem, means for presenting the solution searched for from the database to the user, means for acquiring basic information about the industrial equipment, means for transmitting the basic information about the industrial equipment to the server, and means for receiving and presenting a solution to the problem of the industrial equipment from the server. This enables maintenance and troubleshooting of the industrial equipment to be performed quickly and accurately.

[1616] "User utterance" refers to the words and sentences that are the source of the voice data input to the system.

[1617] "Voice data" refers to data that is a digital recording of a user's speech.

[1618] "Text data" refers to data in the form of a string of characters converted from audio data.

[1619] "Analysis" refers to the process of understanding text data and identifying the problem the user wants to solve.

[1620] The "database" refers to a collection of information that stores basic information about users' devices and industrial equipment, as well as past problems and their solutions.

[1621] "Optimal solution" refers to the most effective and appropriate way to solve the user's problem.

[1622] "Presenting to the user" refers to visually or audibly conveying the found solution to the user.

[1623] "Industrial equipment" refers to various machines and devices used in factories and production lines.

[1624] "Basic information" refers to important data about industrial equipment, such as the model name, OS version, and last maintenance date.

[1625] "Server" refers to the central management system that analyzes voice and text data and searches for and provides optimal solutions.

[1626] "Receiving" refers to the user's device retrieving the solution provided by the server.

[1627] This invention is a support system for quickly and accurately resolving problems users have with industrial machinery. Specifically, it utilizes voice recognition technology and natural language processing technology to analyze user utterances and provide optimal solutions.

[1628] First, the user installs a support application related to the industrial equipment on the device. After installation, the device collects basic information about the industrial equipment (e.g., OS version, equipment model, and last maintenance date) when it is first started up. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[1629] When a user uses a support application to solve a problem with industrial equipment, they first launch the app on their device and then speak to the application about the problem they want to solve (for example, "The robot arm is moving slowly"). The device captures this speech as audio data and converts it into text data using speech recognition technology. The Python speech_recognition library is used for speech recognition.

[1630] The converted text data is then sent to a server. The server uses natural language processing technology to analyze the received text data and identify the problem the user is having. A natural language processing library is used for the analysis. For example, keywords such as "robot arm" and "slow movement" are extracted to understand the nature of the problem. The server then references a database along with basic information about the industrial equipment to search for known solutions. The identified solution (e.g., "Please readjust the axis of the robot arm") is then sent from the server to the device.

[1631] The device presents the received solution to the user. The presentation method can be either a text display or audio playback. For audio playback, the Python pyttsx3 library is used. The user follows the instructions to implement the solution and reports the results in the app. For example, after attempting the presented solution, the device can provide feedback such as "Solved" or "Not yet solved."

[1632] The user's feedback is sent via the device to the server. The server receives the feedback, and if the problem is not resolved, it performs a more detailed diagnosis and suggests another solution. By repeating this process, the aim is to provide optimal support for the user's industrial equipment and quickly resolve the problem.

[1633] Specific examples

[1634] If an industrial device is not working properly, for example, by saying "The robot arm has stopped working," the system will immediately suggest a solution. Examples of prompts to input to the generative AI model are as follows:

[1635] Please provide a solution for a robot that is experiencing the following issue: "The robot arm has stopped moving." The robot's basic information is as follows: OS version: FactoryOS 1.2, robot model: XJ320, last maintenance date: 2023-01-25.

[1636] By using this system, factory maintenance personnel can quickly receive comprehensive and appropriate support tailored to the status of their industrial equipment.

[1637] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1638] Step 1:

[1639] The terminal installs the supporting application and collects basic information about the industrial equipment (e.g., OS version, equipment model, and last maintenance date) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device. The input is the basic device information, and the output is the device information with the identifier stored on the server.

[1640] Step 2:

[1641] A user launches a support application to solve a problem with industrial equipment and speaks out the problem they want to solve (for example, "The robot arm is moving slowly"). The device receives this speech as voice data. The input is the user's speech, and the output is voice data.

[1642] Step 3:

[1643] The device converts the acquired voice data into text data using voice recognition technology (Python's speech_recognition library). The input is voice data, and the output is text data.

[1644] Step 4:

[1645] The terminal sends the converted text data to the server. The input is the text data, and the output is the text data sent to the server.

[1646] Step 5:

[1647] The server analyzes the received text data using natural language processing technology (natural language processing library) and identifies the problem the user is facing. The input is the text data, and the output is the identified problem.

[1648] Step 6:

[1649] The server references the database along with basic information about the industrial equipment and searches for the best solution based on the identified problem. The input is the identified problem and basic information about the industrial equipment, and the output is the solution.

[1650] Step 7:

[1651] The server sends the searched solutions to the terminal. The input is the solution and the output is the solution sent to the terminal.

[1652] Step 8:

[1653] The terminal presents the received solution to the user, either as a text display or as audio (using the Python pyttsx3 library for audio playback). The input is the solution, and the output is the solution presented to the user.

[1654] Step 9:

[1655] The user follows the instructions, implements the solution, and reports the result in the app, e.g., feedback such as "solved" or "not yet solved." The input is the user feedback, and the output is the feedback data.

[1656] Step 10:

[1657] The terminal sends the user's feedback to the server, where the input is the feedback data and the output is the feedback data sent to the server.

[1658] Step 11:

[1659] The server receives the feedback, and if the problem is not resolved, it performs further diagnostics and suggests a different solution. This process is repeated to provide optimal support. The input is the feedback data, and the output is the new solution.

[1660] 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.

[1661] This invention is a support system for quickly and accurately resolving device-related problems that users encounter, utilizing speech recognition technology, natural language processing technology, and an emotion engine for recognizing the user's emotions. This system analyzes the user's speech and provides optimal solutions, while also providing support that takes the user's emotional state into consideration.

[1662] First, the user installs the supporting application on the device. After installation, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) when it is first started. This information is sent to the server, where it is stored in a database and a unique identifier is assigned to each device.

[1663] To use the app, a user launches the support app on their device and speaks to it about the problem they want to solve (for example, "I can't connect to Wi-Fi"). The device captures this speech as voice data and converts it into text data using voice recognition technology. The converted text data is then sent to the server.

[1664] The server analyzes the received text data using natural language processing technology to identify the problem the user is experiencing. It also uses an emotion engine to extract the user's emotional state (e.g., dissatisfaction, impatience, anger, etc.) from the text and voice data. For example, it can recognize that the user is dissatisfied based on the text data "Wi-Fi is not connecting" and the tone of the voice.

[1665] The server then searches the database for the optimal solution, taking into account the user's emotional state, basic device information, and the type of problem. For example, if the user is frustrated, a solution containing kind words and encouraging messages will be generated in addition to the usual solution. A specific solution might be something like, "Open the Settings app and check your Wi-Fi settings. Then try reconnecting," with an additional message depending on the user's condition.

[1666] The solution sent from the server is presented to the user via the device. The presentation method can be either text display or audio playback. The user attempts to solve the problem according to the presented solution and reports the result in the app (for example, feedback such as "Solved" or "Not yet solved").

[1667] The feedback is sent via the device to the server. The server receives this feedback, and if the problem is not resolved, it performs a more detailed diagnosis and proposes a new solution. At this time, it again utilizes the emotion engine to recognize the user's emotional state and adjust the appropriate support method.

[1668] Specific examples

[1669] Scenario: A user is having internet connection issues

[1670] 1. Initial Setup

[1671] Device: When you install the support app on your iPhone and launch the app for the first time, basic device information (OS: iOS 16, Internet contract status: Wi-Fi only) will be collected.

[1672] Terminal: Sends collected information to the server.

[1673] Server: Stores the information in a database and assigns a unique identifier to the device.

[1674] 2. User Interaction

[1675] User: Say "I can't connect to Wi-Fi."

[1676] Terminal: Captures speech as voice data, converts it into text data using voice recognition technology, and sends that data to the server.

[1677] 3. Problem analysis and emotion recognition

[1678] Server: Analyzes text data containing the keywords "Wi-Fi" and "cannot connect" using natural language processing technology to identify the problem.

[1679] Server: Using the emotion engine, recognize the user's emotion from the text data and voice data. For example, recognize that the user is dissatisfied.

[1680] 4. Solution Search and Generation

[1681] Server: Generates the optimal solution by taking into account the user's emotional state and device information. Example: "Open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[1682] 5. Providing a solution

[1683] Server: Sends the generated solution to the device.

[1684] Terminal: Presents audio and text to the user.

[1685] 6. Feedback and Final Review

[1686] User: Try the proposed solution and provide feedback on the results. Example: "Still not solved."

[1687] Device: Sends feedback to the server.

[1688] Server: Perform additional diagnostics and generate a different solution: "Reboot your router" and provide the appropriate support message again.

[1689] This system allows users to quickly receive comprehensive and appropriate support tailored to the situation of their device, and provides more customized support by taking into account the user's emotional state.

[1690] The processing flow will be explained below.

[1691] Step 1:

[1692] The user installs a supporting app on the device.

[1693] Step 2:

[1694] When a device launches the app for the first time, basic device information (OS version, app installation status, internet connection status, and device settings status) is collected.

[1695] Step 3:

[1696] The device sends the collected basic information to the server.

[1697] Step 4:

[1698] The server stores the received device information in a database and assigns a unique identifier (Device ID) to each device.

[1699] Step 5:

[1700] The user launches the app and talks about the problem they want to solve, such as "Wi-Fi won't connect."

[1701] Step 6:

[1702] The terminal acquires the user's speech as voice data and converts it into text data using voice recognition technology.

[1703] Step 7:

[1704] The terminal transmits the converted text data to the server.

[1705] Step 8:

[1706] The server analyzes the received text data using natural language processing technology to identify the problem the user is facing.

[1707] Step 9:

[1708] The server uses an emotion engine to recognize the user's emotion from the text data and voice data, for example, to detect that the user is dissatisfied.

[1709] Step 10:

[1710] The server takes into account the device's basic information and emotional state to search for past solutions from a database.

[1711] Step 11:

[1712] The server identifies the best solution and generates a message for the user that takes into account specific steps and emotions, such as "Open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[1713] Step 12:

[1714] The server generates a message and sends it to the terminal.

[1715] Step 13:

[1716] The terminal presents the message received from the server to the user in voice and text.

[1717] Step 14:

[1718] The user implements the proposed solution and reports the results in the app, for example, by providing feedback such as "still not solved."

[1719] Step 15:

[1720] The terminal sends the user's feedback to the server.

[1721] Step 16:

[1722] The server receives the feedback, and if the problem is not resolved, it performs a more detailed diagnosis and generates a new solution, again leveraging the emotion engine to recognize the user's emotional state.

[1723] Step 17:

[1724] The server sends a new solution to the device, which then presents it to the user again, for example, "Please restart your router. Please wait a moment."

[1725] This series of steps allows users to receive optimal support based on the context of their device. Combined with the emotion engine, customized support is provided that takes into account the user's emotional state.

[1726] Example 2

[1727] 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."

[1728] In conventional support systems, when a user reports a device problem, it is difficult to accurately and quickly identify the problem and provide the optimal solution. Furthermore, the quality of support can be compromised by not taking the user's emotional state into consideration. This often leads to stress for users and delays in resolving the problem.

[1729] 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.

[1730] In this invention, the server includes means for receiving a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data and identifying a problem the user wants to solve, means for having a database for searching for an optimal solution based on the identified problem, means for recognizing the user's emotional state, and means for presenting the solution searched for in the database to the user. This makes it possible to quickly and accurately solve device problems while taking the user's emotional state into consideration.

[1731] "Means for accepting user speech and obtaining it as voice data" refers to a device or software that captures information that a user inputs into a device by voice and converts it into a format that can be stored electronically.

[1732] The "means for converting voice data into text data" refers to a device or program that analyzes voice data and converts it into corresponding text format information.

[1733] "Means for analyzing text data to identify the problem the user wants to solve" refers to a method or technology for analyzing the converted text data and extracting the user's problem or inquiry from its contents.

[1734] "Means having a database for searching for an optimal solution based on an identified problem" refers to a device or system that has the function of searching for an optimal response from a database that stores related solutions based on analyzed text data.

[1735] The "means for recognizing the user's emotional state" is a technology for analyzing the user's emotions from the tone of their voice and the contents of their text data, and recognizing their state.

[1736] The "means for presenting the solutions retrieved from the database to the user" is a device or software for visually or audibly presenting the solutions retrieved to the user.

[1737] The present invention is a support system for quickly and accurately resolving device-related problems that users encounter, utilizing speech recognition technology, natural language processing technology, and an emotion engine for recognizing the user's emotions. This system analyzes the user's speech and provides optimal solutions while also providing support that takes the user's emotional state into consideration.

[1738] First, the user installs the supporting application on their device. After installation, when the application is launched, the device collects basic device information (e.g., OS version, app installation status, internet connection status, device settings) at first launch. This information is sent to the server and stored in a database, and a unique identifier is assigned to each device.

[1739] The user then launches the support app and speaks out the problem they want to solve. For example, if the user says, "My Wi-Fi isn't connecting," the device captures this speech as voice data. Next, it uses voice recognition technology to convert this voice data into text data. Specifically, it uses the Google Cloud Speech-to-Text API or similar to convert the voice data into text format. This converted text data is then sent to the server.

[1740] The server analyzes the received text data using natural language processing technology (e.g., Amazon Comprehend) to identify the problem the user is experiencing. At the same time, it uses an emotion engine to extract the user's emotional state from the text and voice data. For example, it recognizes that the user is dissatisfied based on the utterance "My Wi-Fi isn't connecting" and the tone of the voice.

[1741] Next, the server searches for the optimal solution from a database, taking into account the user's emotional state, basic device information, and the type of problem. For example, if the user is frustrated, the server generates a solution that includes kind words and an encouraging message in addition to the usual solution. A specific solution might be, "Open the Settings app and check your Wi-Fi settings. Then, try reconnecting. We apologize for the inconvenience." This solution is sent from the server to the device and presented to the user via the device. It can be presented as text or audio.

[1742] The user attempts to solve the problem using the proposed solutions and reports the results in the app. For example, they can provide feedback such as "Solved" or "Not yet solved." This feedback is sent to the server via the device. The server receives the feedback, and if the problem is not solved, it performs a more detailed diagnosis and suggests a new solution. At this time, the emotion engine is again utilized to recognize the user's emotional state and adjust the appropriate support method.

[1743] Specific examples

[1744] Scenario: A user is having internet connection issues

[1745] Initial Setup

[1746] 1. Device: When you install the support app on your smartphone and launch the app for the first time, basic device information (OS version, internet connection status, etc.) will be collected.

[1747] 2. Terminal: Sends collected information to the server.

[1748] 3. Server: Stores the information in a database and assigns a unique identifier to the device.

[1749] User Interaction

[1750] 1. User: Says, "Wi-Fi is not connecting."

[1751] 2. Terminal: The speech is acquired as audio data, converted into text data using the Google Cloud Speech-to-Text API, and sent to the server.

[1752] Problem analysis and emotion recognition

[1753] 1. Server: Analyze text data containing the keywords "Wi-Fi" and "unable to connect" using Amazon Comprehend to identify problems.

[1754] 2. Server: Using the emotion engine, recognize the user's dissatisfaction from text and voice data.

[1755] Solution generation and delivery

[1756] 1. Server: Generates the optimal solution by taking into account the user's emotional state and device information. Example: "Please open the Settings app and check your Wi-Fi settings. Then try reconnecting. We apologize for the inconvenience."

[1757] 2. Server: Sends the generated solution to the device.

[1758] 3. Terminal: Presents the user with voice and text.

[1759] Feedback and final confirmation

[1760] 1. User: Try the proposed solution and provide feedback on the results. Example: "Still not solved."

[1761] 2. Device: Sends feedback to the server.

[1762] 3. Server: Performs additional diagnostics and generates another solution, "Please restart the router," and sends it to the device again.

[1763] Using this system, users can quickly and accurately resolve problems with their devices and receive customized support tailored to their emotional state.

[1764] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1765] Step 1:

[1766] The user installs a supporting application on the device.

[1767] Input: Application installation request

[1768] Data processing / calculation: The installer copies the application files to the device and performs the necessary settings.

[1769] Output: Supporting applications installed on the device

[1770] Specific operations: Download the support app from the App Store or Google Play Store and complete the installation.

[1771] Step 2:

[1772] The device collects basic device information when the application is first launched.

[1773] Input: First launch of application

[1774] Data processing / calculation: Collects information such as the device's OS version, internet connection status, and installed apps, and generates data in JSON format.

[1775] Output: Basic device information collected

[1776] Specific operation: The device obtains device information and captures it in JSON format.

[1777] Step 3:

[1778] The device sends the collected information to the server.

[1779] Input: Collected device information (JSON format)

[1780] Data processing / calculation: Device information is encrypted using the HTTPS protocol and sent to the server.

[1781] Output: Basic device information sent to the server

[1782] Specific operation: The terminal sends JSON data using the HTTPS protocol.

[1783] Step 4:

[1784] The server stores the received information in a database and assigns each device a unique identifier.

[1785] Input: Sent device information (JSON format)

[1786] Data processing / calculation: Save device information in an RDBMS (e.g., MySQL), generate a UUID, and assign it to the device.

[1787] Output: A unique identifier assigned to the device.

[1788] Specific operation: The server generates a UUID and stores it in the database along with the information.

[1789] Step 5:

[1790] The user launches the support app and talks about the problem they want to solve.

[1791] Input: User utterance

[1792] Data processing / calculation: Acquire speech as audio data

[1793] Output: User's voice data

[1794] Specific action: The user talks about the problem into the device (e.g., "My Wi-Fi isn't working").

[1795] Step 6:

[1796] The device captures the user's speech as voice data.

[1797] Input: User utterance

[1798] Data processing / calculation: Capture audio data through a microphone and convert it into binary data format

[1799] Output: Captured audio data

[1800] Specific operation: The device records audio data.

[1801] Step 7:

[1802] The device uses voice recognition technology to convert the voice data into text data.

[1803] Input: Audio data

[1804] Data processing / calculation: Converting voice data into text data using the Google Cloud Speech-to-Text API

[1805] Output: Text data

[1806] Specific operation: Sends audio data to the Google Cloud Speech-to-Text API and receives the conversion result.

[1807] Step 8:

[1808] The terminal sends the converted text data to the server.

[1809] Input: Text data

[1810] Data processing / calculation: Encrypt the text data using the HTTPS protocol and send it to the server.

[1811] Output: Text data sent to the server

[1812] Specific operation: The terminal transmits the converted text data.

[1813] Step 9:

[1814] The server analyzes the received text data using natural language processing technology to identify problems.

[1815] Input: Text data

[1816] Data processing / calculation: Analyze text data using Amazon Comprehend and identify problems

[1817] Output: Identified issues

[1818] Specific operation: The server analyzes the text data and extracts the keywords "Wi-Fi" and "cannot connect."

[1819] Step 10:

[1820] The server uses an emotion engine to recognize the user's emotional state.

[1821] Input: Text data and audio data

[1822] Data processing / calculation: Analyze text and voice data using an emotion engine to determine emotional states

[1823] Output: Recognized emotional state of the user

[1824] What happens: The server analyzes the text and tone of voice and recognizes that the user is frustrated.

[1825] Step 11:

[1826] The server takes into account the user's emotional state, references the device's basic information and the type of problem, and generates the optimal solution.

[1827] Input: User's emotional state, basic device information, identified issues

[1828] Data processing / calculation: Searching the database and generating optimal solutions

[1829] Output: Generated solution

[1830] What happens: The server retrieves the solution from the database and generates a customized message.

[1831] Step 12:

[1832] The server sends the generated solution to the device.

[1833] Input: Generated solution

[1834] Data processing / calculation: Generate a solution in JSON format and send it to the terminal using the HTTPS protocol.

[1835] Output: Solution sent to terminal

[1836] Specific operation: The server sends the solution to the terminal.

[1837] Step 13:

[1838] The terminal presents the solution to the user by playing it aloud and displaying it as text.

[1839] Input: Solution sent by the server

[1840] Data processing / calculation: Converting the solution into a text display and audio playback format

[1841] Output: The solution presented to the user

[1842] Specific operation: The device displays the solution on the screen and plays it aloud using the text-to-speech function.

[1843] Step 14:

[1844] The user follows the suggested solution and reports the solution in the app.

[1845] Input: Solution attempt results

[1846] Data processing / calculation: Input the solution results into the terminal and generate feedback

[1847] Output: Feedback data

[1848] What happens: The user reports feedback in the app, such as "Resolved" or "Still not resolved."

[1849] Step 15:

[1850] The device sends the feedback to the server.

[1851] Input: User feedback

[1852] Data processing / calculation: Feedback data is converted into JSON format and sent to the server using the HTTPS protocol.

[1853] Output: Feedback data sent to the server

[1854] Specific operation: The terminal transmits feedback data.

[1855] Step 16:

[1856] The server receives the feedback and, if the problem persists, provides further diagnostics and a new solution.

[1857] Input: Feedback data

[1858] Data processing / computation: Analyze feedback, perform additional diagnostics, and generate new solutions

[1859] Output: New solution

[1860] Specific action: The server re-analyzes the problem, generates a different solution (e.g., reboot the router), and sends it again to the device.

[1861] This series of processes allows users to quickly and accurately resolve device problems and receive support that takes their emotional state into consideration.

[1862] (Application example 2)

[1863] 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."

[1864] Conventional user support systems only provide a uniform solution without considering the user's emotions or stress when reporting a problem. As a result, users are unable to receive appropriate support for their emotional state, which can leave them dissatisfied. This invention aims to provide more accurate and customized support by taking into account the user's emotional state when they encounter a problem in services such as food delivery.

[1865] 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.

[1866] In this invention, the server includes means for accepting a user's speech and acquiring it as voice data, means for converting the voice data into text data, means for analyzing the text data to identify a problem the user wants to solve, means having a database for searching for an optimal solution based on the identified problem, means having an emotion engine for recognizing the user's emotional state, means for adjusting a solution based on the emotional state, and means for presenting the solution searched for from the database to the user. This makes it possible to provide support for quickly and accurately solving a problem when the user is in a situation where the user has a problem, taking into account the user's emotional state.

[1867] "User utterance" refers to an expression of intent made by a user through speech directed at a device.

[1868] "Voice data" refers to data that is a digital recording of a user's speech.

[1869] "Text data" refers to data obtained by converting voice data into character information.

[1870] A "database" refers to a collection of data that is systematically organized and stored, and can be searched and used.

[1871] "Emotion engine" refers to technology for recognizing a user's emotional state from text and voice data.

[1872] "Solution" refers to the method or procedure offered to solve a user's problem.

[1873] "Presentation medium" refers to the technology or method used to convey information to a user.

[1874] "Feedback" refers to the user's evaluation and reaction to the presented solution.

[1875] "Diagnosis" refers to the process of analyzing a user's problem and identifying its cause and solution.

[1876] "Customized support" refers to assistance that is tailored to the user's specific situation and emotional state.

[1877] To implement this invention, a comprehensive support system is required to acquire user utterances as voice data, convert them into text data, and analyze the data to identify problems. The support system performs a series of processes, including initial device configuration, user interaction, utilization of an emotion recognition engine, generation and presentation of solutions to problems, and feedback processing. This process is described in detail below.

[1878] Hardware and software used

[1879] Hardware: Smartphones, smart glasses

[1880] Software: Google Cloud Speech-to-Text API (speech recognition engine), IBM Watson Natural Language Understanding (natural language processing engine), Microsoft Azure Emotion API (emotion engine), cloud server, database management system

[1881] Data processing and calculation

[1882] First, a user installs a supporting application on their smartphone or smart glasses. When the application is launched for the first time, it collects basic device information (e.g., account information, delivery address, and network connection status) and sends it to a server. This assigns a unique identifier to the device, which is then stored in a database.

[1883] User Interaction

[1884] When a user reports a problem, such as "My order hasn't arrived" or "My order hasn't arrived even after the estimated delivery time," the app captures the voice and converts it into text data using the Google Cloud Speech-to-Text API. This text data is then sent to the server.

[1885] Natural Language Processing and Emotion Recognition

[1886] The server uses IBM Watson Natural Language Understanding to analyze the text data and identify the user's problem, while at the same time using the Microsoft Azure Emotion API to recognize the user's emotional state (e.g., irritation, impatience) from the user's voice and text data.

[1887] Solution generation and presentation

[1888] Based on the identified problem and emotional state, the system searches for the best solution from a database and generates an emotionally sensitive message. The generated solution is presented to the user via text and voice, and may include a message such as, "Sorry for the wait. Your driver is currently on his way to your address and is expected to arrive in 15 minutes."

[1889] Feedback and re-solution

[1890] When the user attempts a solution and provides feedback on the results, the server receives that feedback, performs a detailed diagnosis again, and generates a new solution. If the problem is reported as "still not solved," new steps or other measures are suggested.

[1891] Specific examples

[1892] When a user reports that their order hasn't arrived even after the estimated delivery time, the app converts the voice to text and recognizes the user's emotional state (irritation, impatience). The server checks the delivery status from the database, generates a solution such as "We are currently on our way to deliver it. It should arrive in about 10 minutes. We apologize for the wait," and presents it to the user in text and voice.

[1893] Prompt Sentence Examples

[1894] "My order hasn't arrived even after the estimated delivery time. Please check the delivery status and generate a response that takes the user's emotions into consideration."

[1895] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1896] Step 1:

[1897] The device receives the problem reported by the user as voice data. For example, the user speaks into an application on a smartphone or smart glasses, saying, "My order hasn't arrived even though the scheduled delivery time has passed." The device records this speech as voice data. This inputs the voice data.

[1898] Step 2:

[1899] The device converts this voice data into text data. Specifically, it uses the Google Cloud Speech-to-Text API to analyze the voice data and convert it into text information. This text data is output in a form that is sent to the server.

[1900] Step 3:

[1901] The server analyzes the received text data using natural language processing technology. IBM Watson Natural Language Understanding is used to identify the problem the user is having from the text data. For example, the keywords "estimated delivery time" and "not delivered" are extracted to identify the nature of the problem. The results of this analysis are used as input for the next process.

[1902] Step 4:

[1903] The server recognizes the user's emotional state from the analyzed text and voice data. It uses the Microsoft Azure Emotion API to extract the user's emotions (e.g., frustration, impatience). It saves the analysis results along with the emotional state as integrated data and proceeds to the next step.

[1904] Step 5:

[1905] The server searches the database for the optimal solution based on the identified problem and the recognized emotional state. The server refers to the device information and order history and generates a solution such as "We are currently on our way to deliver your order. It should arrive in about 10 minutes. We apologize for the wait." The solution is output as text data.

[1906] Step 6:

[1907] The server sends the generated solution to the device. The device then presents this solution to the user. The device displays or plays a message to the user in text or voice, such as "We are currently on our way to deliver your item and will arrive in about 10 minutes. We apologize for the wait." This presents the solution to the user.

[1908] Step 7:

[1909] The user tries the proposed solution and provides feedback on the results, such as "It hasn't arrived yet." This feedback data is sent from the terminal to the server.

[1910] Step 8:

[1911] The server receives this feedback, analyzes it, and if necessary performs further diagnostics, e.g., recalculating the delivery progress and estimated arrival time, and generating a different solution (e.g., "Please check again. The estimated arrival time is about 15 minutes later."). The analysis results and the new solution are output.

[1912] Step 9:

[1913] The server sends the regenerated solution to the terminal, which then presents it to the user. The re-presented solution is again displayed or played back to the user in text or audio, allowing the user to continue working until they find the appropriate solution.

[1914] Some examples of actual examples and prompts include:

[1915] "My order hasn't arrived even after the estimated delivery time. Please check the delivery status and generate a response that takes the user's emotions into consideration."

[1916] 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.

[1917] 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.

[1918] 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.

[1919] 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.

[1920] 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.

[1921] 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.

[1922] 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).

[1923] 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.

[1924] 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."

[1925] 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.

[1926] 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).

[1927] 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.

[1928] 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.

[1929] 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.

[1930] 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.

[1931] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1932] 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.

[1933] 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.

[1934] 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.

[1935] 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.

[1936] 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.

[1937] The following is further disclosed regarding the above embodiment.

[1938] (Claim 1)

[1939] means for receiving a user's speech and acquiring it as voice data;

[1940] means for converting the voice data into text data;

[1941] means for analyzing the text data to identify a problem that the user wants to solve;

[1942] means having a database for searching for an optimal solution based on the identified problem;

[1943] means for presenting solutions retrieved from said database to a user;

[1944] A system including:

[1945] (Claim 2)

[1946] 2. The system of claim 1, wherein the database comprises a record containing basic information about a user's device.

[1947] (Claim 3)

[1948] 10. The system of claim 1, wherein the presentation means comprises text and audio presentation means.

[1949] "Example 1"

[1950] (Claim 1)

[1951] means for receiving a user's speech and acquiring it as voice data;

[1952] means for converting the voice data into text data;

[1953] means for analyzing the text data to identify a problem that the user wants to solve;

[1954] means for collecting basic information about the user's device and transmitting it to a server;

[1955] means having a database for searching for an optimal solution based on the identified problem;

[1956] means for presenting solutions retrieved from said database to a user;

[1957] means for generating further solutions based on user feedback;

[1958] A system including:

[1959] (Claim 2)

[1960] 2. The system of claim 1, wherein the database comprises a record containing basic information about a user's device.

[1961] (Claim 3)

[1962] 10. The system of claim 1, wherein the presentation means comprises text and audio presentation means.

[1963] "Application Example 1"

[1964] (Claim 1)

[1965] means for receiving a user's speech and acquiring it as voice data;

[1966] means for converting the voice data into text data;

[1967] means for analyzing the text data to identify a problem that the user wants to solve;

[1968] means having a database for searching for an optimal solution based on the identified problem;

[1969] means for presenting solutions retrieved from said database to a user;

[1970] a means for obtaining basic information about industrial equipment;

[1971] means for transmitting basic information of the industrial equipment to a server;

[1972] means for receiving and presenting solutions to problems with the industrial equipment from a server;

[1973] A system including:

[1974] (Claim 2)

[1975] 2. The system of claim 1, wherein the database comprises a record containing basic information about a user's device.

[1976] (Claim 3)

[1977] 10. The system of claim 1, wherein the presentation means comprises text and audio presentation means.

[1978] "Example 2: Combining Emotion Engines"

[1979] (Claim 1)

[1980] means for receiving a user's speech and acquiring it as voice data;

[1981] means for converting the voice data into text data;

[1982] means for analyzing the text data to identify a problem that the user wants to solve;

[1983] means having a database for searching for an optimal solution based on the identified problem;

[1984] means for recognizing the emotional state of a user;

[1985] means for presenting solutions retrieved from said database to a user;

[1986] A system including:

[1987] (Claim 2)

[1988] 2. The system of claim 1, wherein the database comprises a record containing basic information about a user's device.

[1989] (Claim 3)

[1990] 10. The system of claim 1, wherein the presentation means comprises text and audio presentation means.

[1991] "Application example 2 when combining emotion engines"

[1992] (Claim 1)

[1993] means for receiving a user's speech and acquiring it as voice data;

[1994] means for converting the voice data into text data;

[1995] means for analyzing the text data to identify a problem that the user wants to solve;

[1996] means having a database for searching for an optimal solution based on the identified problem;

[1997] means having an emotion engine for recognizing an emotional state of a user;

[1998] means for adjusting a solution based on said emotional state;

[1999] means for presenting solutions retrieved from said database to a user;

[2000] A system including:

[2001] (Claim 2)

[2002] 2. The system of claim 1, wherein the database comprises a record containing basic information of the user's device and the user's emotional state.

[2003] (Claim 3)

[2004] 10. The system of claim 1, wherein the presentation means comprises text and audio presentation means, and further comprises means for receiving feedback and providing additional diagnostics. [Explanation of symbols]

[2005] 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. means for receiving a user's speech and acquiring it as voice data; means for converting the voice data into text data; means for analyzing the text data to identify a problem that the user wants to solve; means having a database for searching for an optimal solution based on the identified problem; means for presenting solutions retrieved from said database to a user; A system including:

2. 2. The system of claim 1, wherein said database comprises a record containing basic information about a user's device.

3. The system of claim 1 , wherein the presentation means comprises text and audio presentation means.

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