System

Abstract

An object of a system according to an embodiment is to immediately diagnose a trouble of a smart device and provide an appropriate solution.SOLUTION: A system according to an embodiment includes a diagnosis unit, a solution providing unit, and a conversation feedback unit. The diagnosis unit immediately diagnoses a problem of the device of the user. The solution provider provides an appropriate solution based on a result of the diagnosis by the diagnoser. The conversation feedback unit provides feedback through a conversation with the user based on the solution provided by the solution providing unit.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] Conventional technology has the problem of making it difficult to immediately diagnose problems in smart devices and provide appropriate solutions.

[0005] The system according to the embodiment aims to immediately diagnose problems in smart devices and provide appropriate solutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a diagnosis unit, a solution providing unit, and a conversation feedback unit. The diagnosis unit immediately diagnoses a problem with a user's device. The solution providing unit provides an appropriate solution based on the diagnosis result by the diagnosis unit. The conversation feedback unit provides feedback through a conversation with the user based on the solution provided by the solution providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can instantly diagnose problems in smart devices and provide appropriate solutions. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The QuickFix AI system according to an embodiment of the present invention is a system that instantly diagnoses device problems and provides appropriate solutions, thereby enabling the QuickFix AI system to quickly and efficiently resolve device problems.

[0029] A QuickFix AI system according to an embodiment includes a diagnosis unit, a solution providing unit, and a conversational feedback unit. The diagnosis unit instantly diagnoses device problems of a user. For example, the diagnosis unit analyzes device problems reported by the user and identifies the cause of the problem. The diagnosis unit can identify problems such as rapid battery drain and app crashes. The solution providing unit provides an appropriate solution based on the results of the diagnosis by the diagnosis unit. For example, in response to rapid battery drain, the solution providing unit provides specific advice such as "close unnecessary apps running in the background" or "enable battery saver mode." The solution providing unit provides step-by-step guidance to the user to support problem resolution. The conversational feedback unit provides feedback through conversation with the user based on the solution provided by the solution providing unit. For example, if a user reports that "apps frequently crash," the conversational feedback unit collects detailed information by asking questions such as "Which apps crash?" and "When do they crash?" Based on this information, the conversational feedback unit proposes an appropriate solution. As a result, the QuickFix AI system according to the embodiment reduces the burden on the user by quickly diagnosing problems with the user's device and providing appropriate solutions.

[0030] The diagnostic unit analyzes device usage history, learns past trouble patterns, and can perform predictive diagnosis. For example, the generation AI analyzes a user's device usage history and learns past trouble patterns. For example, if a specific app crashes frequently, the generation AI analyzes the app's usage and the timing of crashes to predict future crashes. The diagnostic unit also analyzes the frequency and trends of trouble based on the user's device usage history. For example, it identifies time periods when battery drain occurs rapidly and app usage patterns to perform predictive diagnosis. The generation AI also learns past trouble patterns, and when a user reports a new trouble, it quickly diagnoses it based on past data. For example, if a similar trouble has occurred in the past, it prioritizes proposing solutions. By learning past trouble patterns, future troubles can be predicted and addressed quickly.

[0031] The diagnostic unit collects sensor information in real time, detects abnormalities, and performs immediate diagnosis. For example, the diagnostic unit uses the generation AI to collect temperature sensor information from a device in real time and performs immediate diagnosis when it detects an abnormal temperature rise. For example, if the device is overheating, it will suggest a cooling method. The diagnostic unit also uses the generation AI to detect abnormal vibrations based on the device's vibration sensor information and performs immediate diagnosis. For example, it can detect vibrations when the device is dropped and check for hardware damage. The diagnostic unit also analyzes the device's light sensor information and performs immediate diagnosis when it detects an abnormal change in light. For example, if the device's screen suddenly goes dark, it can identify a backlight failure. This allows sensor information to be collected in real time and abnormalities to be diagnosed immediately, enabling rapid response.

[0032] The diagnostic unit can work in conjunction with other smart devices to perform comprehensive trouble diagnosis. For example, the generation AI in the diagnostic unit works in conjunction with a smartwatch to perform trouble diagnosis based on the user's health data. For example, if the user's heart rate is rising sharply, it will identify the trouble as being caused by stress. The diagnostic unit also works in conjunction with smart home devices, and the generation AI will perform trouble diagnosis based on household environmental data. For example, if the room temperature is abnormally high, it will identify the trouble as being caused by overheating of the device. The diagnostic unit also works in conjunction with other smart devices, and the generation AI will perform comprehensive trouble diagnosis. For example, it will analyze voice data from a smart speaker to complement the user's trouble report. This makes it possible to perform comprehensive trouble diagnosis by working in conjunction with other smart devices.

[0033] The diagnostic unit can analyze the user's voice input and automatically extract details of the problem from the voice to perform a diagnosis. For example, in the diagnostic unit, the generation AI analyzes the user's voice input and automatically extracts details of the problem. For example, if the user says, "My battery is running low quickly," the diagnostic unit identifies a battery-related problem. The diagnostic unit also uses voice recognition technology to convert the user's voice input into text and analyze the details of the problem. For example, if the user says, "My app keeps crashing," the diagnostic unit identifies the cause of the crash. The diagnostic unit also analyzes the user's voice input and automatically extracts details of the problem to perform a diagnosis. For example, if the user says, "My Wi-Fi won't connect," the diagnostic unit identifies a Wi-Fi-related problem. This allows the diagnostic unit to quickly grasp the details of the problem and perform a diagnosis by analyzing the voice input.

[0034] The solution providing unit can learn the user's past solution history and prioritize proposing the most effective solution. For example, the solution providing unit uses a generation AI to learn the user's past solution history and propose the most effective solution for a similar problem. For example, it prioritizes presenting solutions that have been successful in the past. The solution providing unit also builds a system in which the generation AI proposes the optimal solution based on the user's solution history. For example, it analyzes past data and selects solutions with a high success rate. The solution providing unit also uses a generation AI to learn the user's past solution history and propose the most effective solution depending on the type of problem. For example, it presents solutions that have been effective in the past for battery-related problems. In this way, by learning the past solution history, the most effective solution can be provided quickly.

[0035] The solution providing unit can collect feedback in real time while a solution is being executed and dynamically adjust the solution as needed. For example, the solution providing unit collects feedback from the user in real time while the generation AI is executing a solution and adjusts the solution as needed. For example, if the user is not satisfied with the solution, the solution providing unit proposes an alternative solution. The solution providing unit also builds a system in which the generation AI collects feedback in real time while the solution is being executed and dynamically adjusts the solution. For example, the solution is changed based on the user's reaction. The solution providing unit also collects feedback in real time while the generation AI is executing the solution and dynamically adjusts the solution. For example, if a problem occurs when the user executes the solution, it responds immediately. In this way, by collecting feedback in real time and dynamically adjusting the solution, user satisfaction is improved.

[0036] The solution providing unit can refer to the success stories of other users and propose solutions to similar problems. For example, the generation AI in the solution providing unit refers to the success stories of other users and proposes solutions to similar problems. For example, solutions to problems that occurred on the same device are shared. The solution providing unit also builds a system in which the generation AI proposes optimal solutions based on the success stories of other users. For example, it prioritizes presenting solutions with a high success rate. The solution providing unit also refers to the success stories of other users and proposes solutions according to the type of problem. For example, it presents solutions that other users have used successfully for battery-related problems. In this way, effective solutions can be provided by referring to the success stories of other users.

[0037] The solution providing unit can visualize the solution, allowing the user to intuitively understand it. For example, the solution providing unit allows the generation AI to visualize the solution, allowing the user to intuitively understand it. For example, the solution steps are shown using diagrams and icons. The solution providing unit also provides a visualized solution, allowing the user to easily understand the solution. For example, the solution is explained using videos and animations. The solution providing unit also builds a system in which the generation AI visualizes the solution, allowing the user to intuitively understand it. For example, the solution steps are displayed step by step. In this way, visualizing the solution makes it easier for the user to understand intuitively.

[0038] The conversation feedback unit can analyze a user's conversation history and provide feedback based on the user's preferences and tendencies. For example, the generation AI in the conversation feedback unit analyzes a user's conversation history and provides feedback based on the user's preferences and tendencies. For example, the conversation feedback unit prioritizes presenting solutions to problems related to apps that the user has used frequently in the past. The conversation feedback unit also builds a system in which the generation AI learns the user's preferences and tendencies based on the conversation history and customizes feedback. For example, the conversation feedback unit provides advice on functions that the user uses frequently. The conversation feedback unit also analyzes a user's conversation history and provides feedback based on the user's preferences and tendencies. For example, if a user prefers a certain style of language, the system provides feedback using that style of language. This improves user satisfaction by providing feedback based on the user's preferences and tendencies.

[0039] The conversation feedback unit can propose multiple solutions to a user's question and allow the user to choose from them. For example, the conversation feedback unit proposes multiple solutions to a user's question using a generation AI, allowing the user to choose from them. For example, in response to battery consumption, the conversation feedback unit presents options such as "close the app" or "enable battery saver mode." The conversation feedback unit also builds a system in which the generation AI proposes multiple solutions to a user's question. For example, in response to a Wi-Fi connection problem, the conversation feedback unit provides options such as "restart the router" or "reset Wi-Fi settings." The conversation feedback unit also proposes multiple solutions to a user's question and allows the user to choose from them. For example, in response to an app crash, the conversation feedback unit presents options such as "reinstall the app" or "clear the cache." By proposing multiple solutions, the user can select the best solution.

[0040] The conversational feedback unit can also support other languages ​​and provide conversational feedback in multiple languages. The conversational feedback unit, for example, builds a system in which a generation AI provides conversational feedback in multiple languages. For example, feedback is provided in multiple languages, such as English, French, and Chinese. The conversational feedback unit also uses a multilingual generation AI to provide conversational feedback in a language selected by the user. For example, if the user selects Japanese, feedback is provided in Japanese. The conversational feedback unit also supports other languages ​​and provides conversational feedback in multiple languages. For example, if the user selects Spanish, feedback is provided in Spanish. This multilingual support makes it possible to accommodate users who speak different languages.

[0041] The conversation feedback unit can analyze the user's gestures and facial expressions and also take non-verbal feedback into consideration. For example, the conversation feedback unit constructs a system in which the generation AI analyzes the user's gestures and facial expressions and takes non-verbal feedback into consideration. For example, if the user tilts their head, additional explanation is provided. The conversation feedback unit also uses the generation AI that analyzes gestures and facial expressions to take the user's non-verbal feedback into consideration. For example, if the user is smiling, positive feedback is provided. The conversation feedback unit also uses the generation AI to analyze the user's gestures and facial expressions and take non-verbal feedback into consideration. For example, if the user has a confused expression, detailed explanation is provided. This makes it possible to take more appropriate action by taking non-verbal feedback into consideration.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The QuickFix AI system can further include a preventive measure providing unit to prevent problems with the user's device. For example, the preventive measure providing unit analyzes the user's device usage patterns and suggests preventive measures before problems occur. For example, if the battery is draining quickly, it suggests ways to extend the battery life. The preventive measure providing unit also monitors the status of the user's device and provides preventive measures when an abnormality is detected. For example, if the device temperature becomes too high, it suggests cooling methods. The preventive measure providing unit also periodically checks the software of the user's device and notifies the user when an update is required. This allows the user to take preventive measures before problems occur.

[0044] The QuickFix AI system can further include a trouble sharing unit for sharing a user's device troubles with other users. In the trouble sharing unit, for example, the generation AI anonymizes the user's trouble information and shares it with other users. For example, if other users using the same device have experienced a similar trouble, they can share the solution. The trouble sharing unit also refers to the success stories of other users based on the user's trouble information and proposes the optimal solution. For example, it prioritizes the presentation of successful solutions found by other users who have experienced the same trouble. The trouble sharing unit also uses the generation AI to collect the user's trouble information and analyze trouble trends. This allows the user to refer to the experiences of other users and find effective solutions.

[0045] The QuickFix AI system can further include a remote diagnosis unit for remotely diagnosing problems with a user's device. For example, the remote diagnosis unit allows the generating AI to remotely access the user's device and diagnose the problem. For example, if the user is unable to change the device settings, the generating AI can remotely check the settings and make appropriate changes. The remote diagnosis unit also allows the generating AI to collect logs from the user's device and identify the cause of the problem. For example, the generating AI can analyze the device's crash log and identify the cause of the crash. The remote diagnosis unit also allows the generating AI to monitor the status of the user's device in real time and respond immediately if an abnormality is detected. This allows users to receive professional diagnosis from the comfort of their own home.

[0046] The QuickFix AI system can further include a community support unit to resolve user device problems. In the community support unit, for example, the generation AI shares user trouble information with the community and collects advice from other users. For example, users who have experienced the same problem can provide solutions. The community support unit also builds a system in which the generation AI consults with experts within the community based on the user's trouble information. For example, a user who is knowledgeable about a particular device can propose a solution. In addition, the community support unit uses the generation AI to collect user trouble information and analyze trouble trends within the community. This allows users to utilize the community's knowledge to find effective solutions.

[0047] The QuickFix AI system can further include an automatic repair unit to resolve device problems for users. The automatic repair unit, for example, creates a system in which the generating AI automatically repairs device problems for users. For example, if the device settings are incorrect, it automatically changes them to the correct settings. The automatic repair unit also allows the generating AI to automatically update the software on the user's device to prevent problems before they occur. For example, it automatically applies security patches. The automatic repair unit also allows the generating AI to monitor the hardware of the user's device and automatically repair it if an abnormality is detected. This allows users to resolve device problems without any hassle.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The diagnostics unit immediately diagnoses device problems for users. For example, it analyzes device problems reported by users and identifies the cause of the problem. The diagnostics unit can identify issues such as rapid battery drain or app crashes. Step 2: The solution provider provides appropriate solutions based on the results of the diagnosis by the diagnostics unit. For example, in response to rapid battery drain, the solution provider provides specific advice such as "close unnecessary apps running in the background" or "enable battery saver mode." The solution provider provides step-by-step guidance to the user to help resolve the problem. Step 3: The conversation feedback unit provides feedback through conversation with the user based on the solution provided by the solution provider. For example, if a user reports that "my app crashes frequently," the conversation feedback unit asks questions such as "Which app crashes?" and "When do they crash?" to collect detailed information. Based on this information, the conversation feedback unit proposes an appropriate solution.

[0050] (Example 2) The QuickFix AI system according to an embodiment of the present invention is a system that instantly diagnoses device problems and provides appropriate solutions, thereby enabling the QuickFix AI system to quickly and efficiently resolve device problems.

[0051] A QuickFix AI system according to an embodiment includes a diagnosis unit, a solution providing unit, and a conversational feedback unit. The diagnosis unit instantly diagnoses device problems of a user. For example, the diagnosis unit analyzes device problems reported by the user and identifies the cause of the problem. The diagnosis unit can identify problems such as rapid battery drain and app crashes. The solution providing unit provides an appropriate solution based on the results of the diagnosis by the diagnosis unit. For example, in response to rapid battery drain, the solution providing unit provides specific advice such as "close unnecessary apps running in the background" or "enable battery saver mode." The solution providing unit provides step-by-step guidance to the user to support problem resolution. The conversational feedback unit provides feedback through conversation with the user based on the solution provided by the solution providing unit. For example, if a user reports that "apps frequently crash," the conversational feedback unit collects detailed information by asking questions such as "Which apps crash?" and "When do they crash?" Based on this information, the conversational feedback unit proposes an appropriate solution. As a result, the QuickFix AI system according to the embodiment reduces the burden on the user by quickly diagnosing problems with the user's device and providing appropriate solutions.

[0052] The diagnostic unit analyzes device usage history, learns past trouble patterns, and can perform predictive diagnosis. For example, the generation AI analyzes a user's device usage history and learns past trouble patterns. For example, if a specific app crashes frequently, the generation AI analyzes the app's usage and the timing of crashes to predict future crashes. The diagnostic unit also analyzes the frequency and trends of trouble based on the user's device usage history. For example, it identifies time periods when battery drain occurs rapidly and app usage patterns to perform predictive diagnosis. The generation AI also learns past trouble patterns, and when a user reports a new trouble, it quickly diagnoses it based on past data. For example, if a similar trouble has occurred in the past, it prioritizes proposing solutions. By learning past trouble patterns, future troubles can be predicted and addressed quickly.

[0053] The diagnostic unit collects sensor information in real time, detects abnormalities, and performs immediate diagnosis. For example, the diagnostic unit uses the generation AI to collect temperature sensor information from a device in real time and performs immediate diagnosis when it detects an abnormal temperature rise. For example, if the device is overheating, it will suggest a cooling method. The diagnostic unit also uses the generation AI to detect abnormal vibrations based on the device's vibration sensor information and performs immediate diagnosis. For example, it can detect vibrations when the device is dropped and check for hardware damage. The diagnostic unit also analyzes the device's light sensor information and performs immediate diagnosis when it detects an abnormal change in light. For example, if the device's screen suddenly goes dark, it can identify a backlight failure. This allows sensor information to be collected in real time and abnormalities to be diagnosed immediately, enabling rapid response.

[0054] The diagnosis unit uses the emotion estimation function to estimate the stress level the user is feeling when reporting a problem, and can prioritize a prompt response if the stress level is high. For example, the generation AI in the diagnosis unit analyzes the user's voice tone and input speed to estimate the stress level when reporting a problem. For example, if the user is typing quickly, it determines that the stress level is high and provides a prompt response. The diagnosis unit also uses the emotion estimation function to analyze the user's facial expression to estimate the stress level when reporting a problem. For example, if the user is frowning, it determines that the stress level is high and provides a prompt response. The diagnosis unit also uses the generation AI to analyze the user's input content to estimate the stress level when reporting a problem. For example, if the user frequently uses emotional words, it determines that the stress level is high and provides a prompt response. In this way, the user's stress level can be estimated and a prompt response can be provided, thereby improving user satisfaction.

[0055] The diagnostic unit can work in conjunction with other smart devices to perform comprehensive trouble diagnosis. For example, the generation AI in the diagnostic unit works in conjunction with a smartwatch to perform trouble diagnosis based on the user's health data. For example, if the user's heart rate is rising sharply, it will identify the trouble as being caused by stress. The diagnostic unit also works in conjunction with smart home devices, and the generation AI will perform trouble diagnosis based on household environmental data. For example, if the room temperature is abnormally high, it will identify the trouble as being caused by overheating of the device. The diagnostic unit also works in conjunction with other smart devices, and the generation AI will perform comprehensive trouble diagnosis. For example, it will analyze voice data from a smart speaker to complement the user's trouble report. This makes it possible to perform comprehensive trouble diagnosis by working in conjunction with other smart devices.

[0056] The diagnostic unit can analyze the user's voice input and automatically extract details of the problem from the voice to perform a diagnosis. For example, in the diagnostic unit, the generation AI analyzes the user's voice input and automatically extracts details of the problem. For example, if the user says, "My battery is running low quickly," the diagnostic unit identifies a battery-related problem. The diagnostic unit also uses voice recognition technology to convert the user's voice input into text and analyze the details of the problem. For example, if the user says, "My app keeps crashing," the diagnostic unit identifies the cause of the crash. The diagnostic unit also analyzes the user's voice input and automatically extracts details of the problem to perform a diagnosis. For example, if the user says, "My Wi-Fi won't connect," the diagnostic unit identifies a Wi-Fi-related problem. This allows the diagnostic unit to quickly grasp the details of the problem and perform a diagnosis by analyzing the voice input.

[0057] The diagnosis unit can use the emotion estimation function to adjust the appropriate tone and wording of troubleshooting based on the emotions the user feels when reporting a problem. For example, the diagnosis unit uses the emotion estimation function to analyze the emotions the user feels when reporting a problem, and the generation AI responds with an appropriate tone and wording. For example, if the user is angry, the generation AI responds calmly and politely. The diagnosis unit also analyzes the user's emotions and adjusts the appropriate tone and wording when troubleshooting. For example, if the user is feeling anxious, the generation AI uses wording that gives a sense of security. The diagnosis unit also uses the emotion estimation function to troubleshoot according to the user's emotions. For example, if the user is sad, the generation AI responds using words of encouragement. In this way, troubleshooting according to the user's emotions reduces the user's stress.

[0058] The solution providing unit can learn the user's past solution history and prioritize proposing the most effective solution. For example, the solution providing unit uses a generation AI to learn the user's past solution history and propose the most effective solution for a similar problem. For example, it prioritizes presenting solutions that have been successful in the past. The solution providing unit also builds a system in which the generation AI proposes the optimal solution based on the user's solution history. For example, it analyzes past data and selects solutions with a high success rate. The solution providing unit also uses a generation AI to learn the user's past solution history and propose the most effective solution depending on the type of problem. For example, it presents solutions that have been effective in the past for battery-related problems. In this way, by learning the past solution history, the most effective solution can be provided quickly.

[0059] The solution providing unit can collect feedback in real time while a solution is being executed and dynamically adjust the solution as needed. For example, the solution providing unit collects feedback from the user in real time while the generation AI is executing a solution and adjusts the solution as needed. For example, if the user is not satisfied with the solution, the solution providing unit proposes an alternative solution. The solution providing unit also builds a system in which the generation AI collects feedback in real time while the solution is being executed and dynamically adjusts the solution. For example, the solution is changed based on the user's reaction. The solution providing unit also collects feedback in real time while the generation AI is executing the solution and dynamically adjusts the solution. For example, if a problem occurs when the user executes the solution, it responds immediately. In this way, by collecting feedback in real time and dynamically adjusting the solution, user satisfaction is improved.

[0060] The solution providing unit can use the emotion estimation function to monitor the user's emotions when implementing a solution and prioritize suggesting solutions that are low in stress. For example, the solution providing unit uses the emotion estimation function to monitor the user's emotions when implementing a solution and prioritize suggesting solutions that are low in stress. For example, if the user is relaxed, it presents a simple solution. The solution providing unit also builds a system in which the generation AI monitors the user's emotions and prioritizes suggesting solutions that are low in stress. For example, if the user is feeling stressed, it proposes a solution with fewer steps. The solution providing unit also uses the emotion estimation function to propose solutions that correspond to the user's emotions. For example, if the user is feeling impatient, it prioritizes solutions that can be implemented quickly. In this way, by monitoring the user's emotions and providing solutions that are low in stress, user satisfaction is improved.

[0061] The solution providing unit can refer to the success stories of other users and propose solutions to similar problems. For example, the generation AI in the solution providing unit refers to the success stories of other users and proposes solutions to similar problems. For example, solutions to problems that occurred on the same device are shared. The solution providing unit also builds a system in which the generation AI proposes optimal solutions based on the success stories of other users. For example, it prioritizes presenting solutions with a high success rate. The solution providing unit also refers to the success stories of other users and proposes solutions according to the type of problem. For example, it presents solutions that other users have used successfully for battery-related problems. In this way, effective solutions can be provided by referring to the success stories of other users.

[0062] The solution providing unit can visualize the solution, allowing the user to intuitively understand it. For example, the solution providing unit allows the generation AI to visualize the solution, allowing the user to intuitively understand it. For example, the solution steps are shown using diagrams and icons. The solution providing unit also provides a visualized solution, allowing the user to easily understand the solution. For example, the solution is explained using videos and animations. The solution providing unit also builds a system in which the generation AI visualizes the solution, allowing the user to intuitively understand it. For example, the solution steps are displayed step by step. In this way, visualizing the solution makes it easier for the user to understand intuitively.

[0063] The solution providing unit can use the emotion estimation function to adjust the way the solution is explained based on the emotion the user feels when implementing the solution. For example, the solution providing unit uses the emotion estimation function to analyze the emotion the user feels when implementing the solution, and the generation AI responds with an appropriate explanation method. For example, if the user feels anxious, a detailed explanation is provided. The solution providing unit also builds a system in which the generation AI analyzes the user's emotions and adjusts the way the solution is explained. For example, if the user feels relaxed, a concise explanation is provided. The solution providing unit also uses the emotion estimation function to adjust the way the solution is explained according to the user's emotions. For example, if the user feels impatient, a quick and simple explanation is provided. In this way, an explanation method that suits the user's emotions is provided, thereby promoting the user's understanding.

[0064] The conversation feedback unit can analyze a user's conversation history and provide feedback based on the user's preferences and tendencies. For example, the generation AI in the conversation feedback unit analyzes a user's conversation history and provides feedback based on the user's preferences and tendencies. For example, the conversation feedback unit prioritizes presenting solutions to problems related to apps that the user has used frequently in the past. The conversation feedback unit also builds a system in which the generation AI learns the user's preferences and tendencies based on the conversation history and customizes feedback. For example, the conversation feedback unit provides advice on functions that the user uses frequently. The conversation feedback unit also analyzes a user's conversation history and provides feedback based on the user's preferences and tendencies. For example, if a user prefers a certain style of language, the system provides feedback using that style of language. This improves user satisfaction by providing feedback based on the user's preferences and tendencies.

[0065] The conversation feedback unit can propose multiple solutions to a user's question and allow the user to choose from them. For example, the conversation feedback unit proposes multiple solutions to a user's question using a generation AI, allowing the user to choose from them. For example, in response to battery consumption, the conversation feedback unit presents options such as "close the app" or "enable battery saver mode." The conversation feedback unit also builds a system in which the generation AI proposes multiple solutions to a user's question. For example, in response to a Wi-Fi connection problem, the conversation feedback unit provides options such as "restart the router" or "reset Wi-Fi settings." The conversation feedback unit also proposes multiple solutions to a user's question and allows the user to choose from them. For example, in response to an app crash, the conversation feedback unit presents options such as "reinstall the app" or "clear the cache." By proposing multiple solutions, the user can select the best solution.

[0066] The conversation feedback unit uses the emotion estimation function to provide feedback according to the user's emotional state, thereby reducing the user's stress. The conversation feedback unit, for example, uses the emotion estimation function to analyze the user's emotional state, and the generation AI provides appropriate feedback. For example, if the user is feeling stressed, the generation AI provides feedback using encouraging words. The conversation feedback unit also builds a system in which the generation AI monitors the user's emotional state and provides feedback to reduce stress. For example, it uses language that helps the user relax. The conversation feedback unit also uses the emotion estimation function to provide feedback according to the user's emotional state. For example, if the user is impatient, the feedback is provided in a calm and relaxed tone. In this way, the user's stress is reduced by providing feedback according to the user's emotional state.

[0067] The conversational feedback unit can also support other languages ​​and provide conversational feedback in multiple languages. The conversational feedback unit, for example, builds a system in which a generation AI provides conversational feedback in multiple languages. For example, feedback is provided in multiple languages, such as English, French, and Chinese. The conversational feedback unit also uses a multilingual generation AI to provide conversational feedback in a language selected by the user. For example, if the user selects Japanese, feedback is provided in Japanese. The conversational feedback unit also supports other languages ​​and provides conversational feedback in multiple languages. For example, if the user selects Spanish, feedback is provided in Spanish. This multilingual support makes it possible to accommodate users who speak different languages.

[0068] The conversation feedback unit can analyze the user's gestures and facial expressions and also take non-verbal feedback into consideration. For example, the conversation feedback unit constructs a system in which the generation AI analyzes the user's gestures and facial expressions and takes non-verbal feedback into consideration. For example, if the user tilts their head, additional explanation is provided. The conversation feedback unit also uses the generation AI that analyzes gestures and facial expressions to take the user's non-verbal feedback into consideration. For example, if the user is smiling, positive feedback is provided. The conversation feedback unit also uses the generation AI to analyze the user's gestures and facial expressions and take non-verbal feedback into consideration. For example, if the user has a confused expression, detailed explanation is provided. This makes it possible to take more appropriate action by taking non-verbal feedback into consideration.

[0069] The conversation feedback unit can use the emotion estimation function to adjust the tone and content of the feedback based on the user's emotions. For example, the conversation feedback unit uses the emotion estimation function to analyze the user's emotions, and the generation AI provides feedback in an appropriate tone and content. For example, if the user is angry, a calm and polite tone is used. The conversation feedback unit also builds a system in which the generation AI analyzes the user's emotions and adjusts the tone and content of the feedback. For example, if the user is sad, words of encouragement are used. The conversation feedback unit also uses the emotion estimation function to provide feedback based on the user's emotions. For example, if the user is relaxed, feedback is provided in a casual tone. In this way, by providing feedback based on the user's emotions, user satisfaction is improved.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The QuickFix AI system can further include a preventive measure providing unit to prevent problems with the user's device. For example, the preventive measure providing unit analyzes the user's device usage patterns and suggests preventive measures before problems occur. For example, if the battery is draining quickly, it suggests ways to extend the battery life. The preventive measure providing unit also monitors the status of the user's device and provides preventive measures when an abnormality is detected. For example, if the device temperature becomes too high, it suggests cooling methods. The preventive measure providing unit also periodically checks the software of the user's device and notifies the user when an update is required. This allows the user to take preventive measures before problems occur.

[0072] The QuickFix AI system can further include a trouble sharing unit for sharing a user's device troubles with other users. In the trouble sharing unit, for example, the generation AI anonymizes the user's trouble information and shares it with other users. For example, if other users using the same device have experienced a similar trouble, they can share the solution. The trouble sharing unit also refers to the success stories of other users based on the user's trouble information and proposes the optimal solution. For example, it prioritizes the presentation of successful solutions found by other users who have experienced the same trouble. The trouble sharing unit also uses the generation AI to collect the user's trouble information and analyze trouble trends. This allows the user to refer to the experiences of other users and find effective solutions.

[0073] The QuickFix AI system can further include a remote diagnosis unit for remotely diagnosing problems with a user's device. For example, the remote diagnosis unit allows the generating AI to remotely access the user's device and diagnose the problem. For example, if the user is unable to change the device settings, the generating AI can remotely check the settings and make appropriate changes. The remote diagnosis unit also allows the generating AI to collect logs from the user's device and identify the cause of the problem. For example, the generating AI can analyze the device's crash log and identify the cause of the crash. The remote diagnosis unit also allows the generating AI to monitor the status of the user's device in real time and respond immediately if an abnormality is detected. This allows users to receive professional diagnosis from the comfort of their own home.

[0074] The QuickFix AI system can further include a community support unit to resolve user device problems. In the community support unit, for example, the generation AI shares user trouble information with the community and collects advice from other users. For example, users who have experienced the same problem can provide solutions. The community support unit also builds a system in which the generation AI consults with experts within the community based on the user's trouble information. For example, a user who is knowledgeable about a particular device can propose a solution. In addition, the community support unit uses the generation AI to collect user trouble information and analyze trouble trends within the community. This allows users to utilize the community's knowledge to find effective solutions.

[0075] The QuickFix AI system can further include an automatic repair unit to resolve device problems for users. The automatic repair unit, for example, creates a system in which the generating AI automatically repairs device problems for users. For example, if the device settings are incorrect, it automatically changes them to the correct settings. The automatic repair unit also allows the generating AI to automatically update the software on the user's device to prevent problems before they occur. For example, it automatically applies security patches. The automatic repair unit also allows the generating AI to monitor the hardware of the user's device and automatically repair it if an abnormality is detected. This allows users to resolve device problems without any hassle.

[0076] The QuickFix AI system can further include an emotion response unit that estimates the user's emotions and performs troubleshooting based on those emotions. For example, the generation AI analyzes the user's tone of voice and facial expressions to estimate their emotions. For example, if the user is feeling stressed, the emotion response unit responds quickly and courteously. The generation AI also adjusts the tone and language of troubleshooting based on the user's emotions. For example, if the user is feeling anxious, the emotion response unit uses language that conveys a sense of security. The generation AI also monitors the user's emotions and provides feedback according to those emotions. This enables troubleshooting that takes the user's emotions into consideration.

[0077] The QuickFix AI system can further include an emotion suggestion unit that estimates the user's emotions and proposes solutions based on those emotions. In the emotion suggestion unit, for example, the generation AI analyzes the user's emotions and proposes solutions that correspond to those emotions. For example, if the user is relaxed, it will propose solutions that include detailed steps. In addition, the emotion suggestion unit adjusts the priority of solutions based on the user's emotions. For example, if the user is in a hurry, it will prioritize solutions that can be implemented quickly. In addition, the emotion suggestion unit monitors the user's emotions and provides solutions that correspond to those emotions. This makes it possible to propose solutions that take the user's emotions into consideration.

[0078] The QuickFix AI system can further include an emotion feedback unit that estimates the user's emotions and provides feedback based on those emotions. For example, the generation AI analyzes the user's emotions and provides feedback according to those emotions. For example, if the user is angry, the generation AI provides calm and polite feedback. The emotion feedback unit also adjusts the content of the feedback based on the user's emotions. For example, if the user is sad, the generation AI uses encouraging words. The emotion feedback unit also monitors the user's emotions and provides feedback according to those emotions. This enables feedback that takes the user's emotions into consideration.

[0079] The QuickFix AI system can further include an emotion prevention unit that estimates the user's emotions and provides preventive measures based on the emotions. In the emotion prevention unit, for example, the generation AI analyzes the user's emotions and provides preventive measures according to the emotions. For example, if the user is feeling stressed, the emotion prevention unit suggests preventive measures to reduce stress. In addition, the generation AI adjusts the priority of preventive measures based on the user's emotions. For example, if the user is relaxed, the emotion prevention unit suggests detailed preventive measures. In addition, the generation AI monitors the user's emotions and provides preventive measures according to the emotions. This makes it possible to provide preventive measures that take the user's emotions into consideration.

[0080] The QuickFix AI system can further include an emotion communication unit that estimates the user's emotions and communicates based on those emotions. In the emotion communication unit, for example, the generation AI analyzes the user's emotions and communicates according to those emotions. For example, if the user is feeling anxious, the communication unit will provide a sense of security. In addition, the emotion communication unit adjusts the tone and language of communication based on the user's emotions. For example, if the user is angry, the tone will be calm and polite. In addition, the emotion communication unit monitors the user's emotions and communicates according to those emotions. This enables communication that takes the user's emotions into consideration.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The diagnostics unit immediately diagnoses device problems for users. For example, it analyzes device problems reported by users and identifies the cause of the problem. The diagnostics unit can identify issues such as rapid battery drain or app crashes. Step 2: The solution provider provides appropriate solutions based on the results of the diagnosis by the diagnostics unit. For example, in response to rapid battery drain, the solution provider provides specific advice such as "close unnecessary apps running in the background" or "enable battery saver mode." The solution provider provides step-by-step guidance to the user to help resolve the problem. Step 3: The conversation feedback unit provides feedback through conversation with the user based on the solution provided by the solution provider. For example, if a user reports that "my app crashes frequently," the conversation feedback unit asks questions such as "Which app crashes?" and "When do they crash?" to collect detailed information. Based on this information, the conversation feedback unit proposes an appropriate solution.

[0083] 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.

[0084] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0089] 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.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0104] 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0119] 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] 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.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0124] 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.

[0125] 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.

[0126] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] 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.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0134] 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.

[0135] 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).

[0136] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes 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.

[0144] The hardware resource that executes the specific process 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 process may be a single processor.

[0145] 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.

[0146] 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.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a diagnostics unit for instantly diagnosing problems with a user's device; a solution providing unit that provides an appropriate solution based on the result of the diagnosis by the diagnosis unit; a conversation feedback unit that provides feedback through a conversation with the user based on the solution provided by the solution providing unit; A system characterized by:

2. The diagnostic unit Analyzes device usage history, learns past trouble patterns, and performs predictive diagnosis 2. The system of claim 1.

3. The diagnostic unit Collects device sensor information in real time, detects abnormalities, and performs immediate diagnosis 2. The system of claim 1.

4. The diagnostic unit Estimate the stress level that users feel when reporting a problem, and prioritize a quick response if stress levels are high 2. The system of claim 1.

5. The diagnostic unit Link with other smart devices to perform comprehensive troubleshooting 2. The system of claim 1.

6. The diagnostic unit Analyzes user voice input, automatically extracts details of the problem from the voice, and diagnoses it 2. The system of claim 1.

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