system

Abstract

An object of a system according to an embodiment is to simplify an Internet connection method and to facilitate handling when a failure occurs.SOLUTION: A system includes a connection diagnostic part, a wire setting part, and a AI chat part. The connection diagnosis unit selects an optimum Internet connection method based on the contractor information, the contract plan, and the photograph of the router, and automatically proposes a setting method. The wiring setting unit detects the cause of the failure of the device based on the lighting state of the light of the router or the lighting situation of the ONU. The AI Chat component provides an online agent to provide immediate support for Internet connection problems and questions.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 technologies have had the problem that the Internet connection method is complicated and it is difficult to deal with problems when they occur.

[0005] The system according to the embodiment aims to simplify the Internet connection method and to facilitate the response when a problem occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes a connection diagnostic unit, a wiring setup unit, and an AI chat unit. The connection diagnostic unit selects the optimal Internet connection method based on subscriber information, contract plan, and a photo of the router, and automatically suggests a setup method. The wiring setup unit detects the cause of equipment malfunctions based on the lighting status of the router light or the ONU light status. The AI ​​chat unit provides an online agent to provide immediate support for Internet connection problems and questions. [Effects of the Invention]

[0007] The system according to the embodiment simplifies the Internet connection method and makes it easy to deal with problems that may occur. [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) A connection assistance system according to an embodiment of the present invention is a system that utilizes generative AI to provide connection diagnosis, wiring configuration, and AI chat functions in order to solve multiple problems in Internet connections. As a result, the connection assistance system can efficiently solve multiple problems in Internet connections.

[0029] The connection assistance system according to the embodiment includes a connection diagnosis unit, a wiring setup unit, and an AI chat unit. The connection diagnosis unit selects the optimal Internet connection method based on subscriber information, a contract plan, and a photo of the router, and automatically proposes a setup method. For example, the generation AI analyzes subscriber information, a contract plan, and a photo of the router to present specific connection procedures. The generation AI receives subscriber information, a contract plan, and a photo of the router as input and proposes a connection method based on the input. The wiring setup unit detects the cause of a device malfunction based on the lighting status of the router or the lighting status of the ONU. For example, the generation AI analyzes the lighting pattern of the router's lights to identify the problem area. The generation AI also analyzes error messages on a PC screen and proposes specific solutions. The generation AI receives input from the lighting status of the router's lights, the lighting status of the ONU, and error messages on a PC screen, and uses them to identify the cause of the malfunction. The AI ​​chat unit provides an online agent to provide immediate support for Internet connection problems and questions. For example, the generation AI analyzes user questions and presents appropriate solutions. The generation AI receives input from users, such as questions and details of problems, and generates answers based on those inputs. This allows the connection assistance system according to the embodiment to efficiently solve multiple issues related to Internet connection. For example, even if a user does not know how to connect, the generation AI can suggest specific steps, allowing the user to easily connect. Furthermore, even if a malfunction occurs, the generation AI can identify the cause and present specific solutions, making recovery easier. Furthermore, support is available 24 hours a day, 365 days a year, providing users with the peace of mind that there is someone they can consult with at any time.

[0030] The connection diagnostic unit can analyze the subscriber's past connection history and suggest the connection method with the highest success rate. For example, the generation AI in the connection diagnostic unit analyzes the subscriber's past connection history and identifies the connection method with the highest success rate. For example, it suggests a similar procedure based on connection procedures that have been successful in the past. The connection diagnostic unit also analyzes the frequency with which a specific connection method has been successful based on the subscriber's past connection history and suggests the method with the highest success rate. For example, it prioritizes the presentation of connection methods that have been successful in the past. The connection diagnostic unit also analyzes the subscriber's past connection history and learns connection methods with the highest success rate. For example, it extracts successful procedures from the past connection history and suggests similar procedures. This makes it possible to suggest the optimal connection method based on the past connection history.

[0031] The connection diagnostic unit can propose the optimal connection method based on the subscriber's usage environment. For example, the generation AI in the connection diagnostic unit analyzes the layout of the subscriber's room and the arrangement of other electronic devices to propose the optimal connection method. For example, it proposes the optimal location to place a Wi-Fi router. The connection diagnostic unit also proposes the optimal connection method based on the subscriber's usage environment. For example, it proposes a connection method with minimal interference, taking into account the room layout and the arrangement of other electronic devices. The connection diagnostic unit also analyzes the subscriber's usage environment and learns the optimal connection method. For example, it proposes the optimal connection procedure based on the room layout and the arrangement of other electronic devices. This makes it possible to propose the optimal connection method taking into account the usage environment.

[0032] The connection diagnosis unit can provide a guide to the connection procedure using AR, using the subscriber's smartphone or tablet. For example, the generation AI in the connection diagnosis unit uses the subscriber's smartphone or tablet to provide a guide to the connection procedure using AR. For example, the connection procedure is visually displayed through a camera. The connection diagnosis unit also uses the subscriber's smartphone or tablet to provide the AR guide. For example, the connection procedure is displayed as a 3D model to make it easier to understand visually. The connection diagnosis unit also uses the generation AI in the connection diagnosis unit to learn the guide to the connection procedure using AR, using the subscriber's smartphone or tablet. For example, the connection procedure is displayed in real time using AR technology. This makes it possible to provide a guide to the connection procedure using AR.

[0033] The connection diagnosis unit can analyze the voice input of the subscriber and provide voice-guided connection procedures. In the connection diagnosis unit, for example, a generation AI analyzes the voice input of the subscriber and provides voice-guided connection procedures. For example, it analyzes voice commands and provides voice guidance on appropriate connection procedures. In addition, the connection diagnosis unit uses a generation AI to provide voice guidance based on the voice input of the subscriber. For example, it provides voice guidance on connection procedures using voice recognition technology. In addition, the connection diagnosis unit uses a generation AI to analyze the voice input of the subscriber and learn voice-guided connection procedures. For example, it provides voice guidance on connection procedures based on voice commands. In this way, it is possible to provide voice-guided connection procedures.

[0034] The wiring setting unit can analyze the internal logs of the router or ONU and identify the cause of the malfunction from the past error history. In the wiring setting unit, for example, the generation AI analyzes the internal logs of the router or ONU and identifies the cause of the malfunction from the past error history. For example, it analyzes the error log and identifies the cause based on a specific error code. In addition, the wiring setting unit can analyze the past error history based on the internal logs of the router or ONU and identify the cause of the malfunction. For example, it analyzes the error log and identifies the cause based on a specific error code. In addition, the generation AI analyzes the internal logs of the router or ONU and learns the cause of the malfunction from the past error history. For example, it identifies the cause based on the error log and proposes a countermeasure. This makes it possible to identify the cause of the malfunction based on the past error history.

[0035] The wiring setting unit can automatically map the customer's network topology and propose optimal wiring settings. In the wiring setting unit, for example, the generation AI automatically maps the customer's network topology and proposes optimal wiring settings. For example, it analyzes the placement of network devices and presents the optimal wiring route. In addition, in the wiring setting unit, the generation AI proposes optimal wiring settings based on the customer's network topology. For example, it analyzes the placement of network devices and presents a wiring route with minimal interference. In addition, in the wiring setting unit, the generation AI automatically maps the customer's network topology and learns optimal wiring settings. For example, it proposes the optimal wiring route based on the placement of network devices. In this way, the network topology can be automatically mapped and optimal wiring settings can be proposed.

[0036] The wiring setting unit works in cooperation with the subscriber's smart home devices and can perform wiring settings while monitoring the overall network status. In the wiring setting unit, for example, the generation AI works in cooperation with the subscriber's smart home devices and performs wiring settings while monitoring the overall network status. For example, it analyzes the status of the smart home devices and presents the optimal wiring route. In addition, the wiring setting unit works in cooperation with the subscriber's smart home devices and performs wiring settings while the generation AI monitors the overall network status. For example, it analyzes the status of the smart home devices and presents a wiring route with minimal interference. In addition, the wiring setting unit works in cooperation with the subscriber's smart home devices and learns wiring settings while monitoring the overall network status. For example, it proposes the optimal wiring route based on the status of the smart home devices. This allows wiring settings to be performed while working in cooperation with the smart home devices and monitoring the network status.

[0037] The wiring setting unit can analyze the wiring status in real time using the camera on the contractor's PC or smartphone and propose an appropriate wiring method. In the wiring setting unit, for example, the generation AI uses the camera on the contractor's PC or smartphone to analyze the wiring status in real time and propose an appropriate wiring method. For example, the camera image is analyzed and wiring problems are identified. In addition, the wiring setting unit can analyze the wiring status in real time based on the camera on the contractor's PC or smartphone and propose an appropriate wiring method. For example, the camera image is analyzed and wiring problems are identified. In addition, the wiring setting unit can analyze the wiring status in real time based on the camera image on the contractor's PC or smartphone and learn an appropriate wiring method. For example, the camera image is analyzed and wiring problems are identified based on the camera image and solutions are proposed. In this way, the wiring status can be analyzed in real time and an appropriate wiring method can be proposed.

[0038] The AI ​​chat unit can analyze the subscriber's past chat history and learn and provide the most effective support method. In the AI ​​chat unit, for example, the generation AI analyzes the subscriber's past chat history and learns and provides the most effective support method. For example, it proposes effective support procedures based on the content of past chats. In addition, the AI ​​chat unit can learn effective support methods based on the subscriber's past chat history. For example, it analyzes the content of past chats and proposes effective support procedures. In addition, the AI ​​chat unit can analyze the subscriber's past chat history and learn and provide the most effective support method. For example, it proposes effective support procedures based on the content of past chats. In this way, the most effective support method can be provided based on the past chat history.

[0039] The AI ​​chat unit monitors the subscriber's network environment in real time and can provide preventative alerts before problems occur. For example, the generation AI in the AI ​​chat unit monitors the subscriber's network environment in real time and can provide preventative alerts before problems occur. For example, it detects network abnormalities and sends alerts in advance. The AI ​​chat unit also monitors the subscriber's network environment in real time and can provide preventative alerts. For example, it detects network abnormalities and sends alerts in advance. The AI ​​chat unit also monitors the subscriber's network environment in real time and can provide preventative alerts before problems occur. For example, it detects network abnormalities and sends alerts in advance. This allows the network environment to be monitored in real time and can provide preventative alerts before problems occur.

[0040] The AI ​​chat unit can provide voice support in cooperation with the subscriber's smart speaker. For example, the generation AI in the AI ​​chat unit can provide voice support in cooperation with the subscriber's smart speaker. For example, it can provide voice guidance on the connection procedure through the smart speaker. The generation AI in the AI ​​chat unit can also provide voice support based on the subscriber's smart speaker. For example, it can provide voice guidance on the connection procedure through the smart speaker. The generation AI in the AI ​​chat unit can also learn voice support in cooperation with the subscriber's smart speaker. For example, it can provide voice guidance on the connection procedure through the smart speaker. This allows it to provide voice support in cooperation with the smart speaker.

[0041] The AI ​​chat unit can provide support on SNS in cooperation with the contractor's SNS account. In the AI ​​chat unit, for example, the generation AI can link with the contractor's SNS account and provide support on SNS. For example, it can guide the contractor through the SNS. In addition, the AI ​​chat unit can provide support on SNS based on the contractor's SNS account. For example, it can guide the contractor through the SNS. In addition, the generation AI can link with the contractor's SNS account and learn support on SNS. For example, it can guide the contractor through the SNS. In this way, it can link with the SNS account and provide support on SNS.

[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 connection diagnostic unit can analyze the subscriber's past connection history and suggest the connection method with the highest success rate. For example, it can suggest a similar procedure based on connection procedures that have been successful in the past. The connection diagnostic unit can also analyze the frequency with which a specific connection method has been successful based on the subscriber's past connection history and suggest the method with the highest success rate. For example, it can prioritize the presentation of connection methods that have been successful in the past. The connection diagnostic unit also uses a generation AI to analyze the subscriber's past connection history and learn connection methods with a high success rate. For example, it can extract successful procedures from past connection history and suggest similar procedures. This makes it possible to suggest the optimal connection method based on past connection history.

[0044] The connection diagnostic unit can propose the optimal connection method based on the subscriber's usage environment. For example, the generation AI analyzes the layout of the subscriber's room and the arrangement of other electronic devices to propose the optimal connection method. For example, it proposes the optimal location to place a Wi-Fi router. The connection diagnostic unit also proposes the optimal connection method based on the subscriber's usage environment. For example, it proposes a connection method with minimal interference, taking into account the room layout and the arrangement of other electronic devices. The connection diagnostic unit also analyzes the subscriber's usage environment and learns the optimal connection method. For example, it proposes the optimal connection procedure based on the room layout and the arrangement of other electronic devices. This makes it possible to propose the optimal connection method taking into account the usage environment.

[0045] The connection diagnosis unit can provide a guide to the connection procedure using AR, using the subscriber's smartphone or tablet. For example, the generation AI can use the subscriber's smartphone or tablet to provide a guide to the connection procedure using AR. For example, the connection procedure can be visually displayed through a camera. The connection diagnosis unit can also use the subscriber's smartphone or tablet to provide the AR guide. For example, the connection procedure can be displayed as a 3D model to make it easier to understand visually. The connection diagnosis unit can also use the generation AI to learn the guide to the connection procedure using AR, using the subscriber's smartphone or tablet. For example, the connection procedure can be displayed in real time using AR technology. This makes it possible to provide a guide to the connection procedure using AR.

[0046] The connection diagnosis unit can analyze the voice input of the subscriber and provide voice-guided connection procedures. For example, the generation AI analyzes the voice input of the subscriber and provides voice-guided connection procedures. For example, it analyzes voice commands and provides voice guidance on appropriate connection procedures. The connection diagnosis unit also has the generation AI provide voice guidance based on the voice input of the subscriber. For example, it provides voice guidance on connection procedures using voice recognition technology. The connection diagnosis unit also has the generation AI analyze the voice input of the subscriber and learn voice-guided connection procedures. For example, it provides voice guidance on connection procedures based on voice commands. This makes it possible to provide voice-guided connection procedures.

[0047] The wiring setting unit can analyze the internal logs of routers and ONUs and identify the cause of a malfunction from past error history. For example, the generation AI analyzes the internal logs of routers and ONUs and identifies the cause of a malfunction from past error history. For example, it analyzes the error log and identifies the cause based on a specific error code. The wiring setting unit also analyzes the internal logs of routers and ONUs and identifies the cause of a malfunction. For example, it analyzes the error log and identifies the cause based on a specific error code. The wiring setting unit also analyzes the internal logs of routers and ONUs and learns the cause of a malfunction from past error history. For example, it identifies the cause based on the error log and proposes a solution. This makes it possible to identify the cause of a malfunction based on past error history.

[0048] The wiring setting unit can automatically map the subscriber's network topology and propose the optimal wiring setting. For example, the generation AI automatically maps the subscriber's network topology and proposes the optimal wiring setting. For example, it analyzes the placement of network devices and presents the optimal wiring route. Furthermore, the wiring setting unit has the generation AI propose the optimal wiring setting based on the subscriber's network topology. For example, it analyzes the placement of network devices and presents a wiring route with minimal interference. Furthermore, the wiring setting unit has the generation AI automatically map the subscriber's network topology and learn the optimal wiring setting. For example, it proposes the optimal wiring route based on the placement of network devices. In this way, the network topology can be automatically mapped and the optimal wiring setting can be proposed.

[0049] The wiring configuration unit works in conjunction with the subscriber's smart home devices and can perform wiring configuration while monitoring the overall network status. For example, the generation AI works in conjunction with the subscriber's smart home devices and performs wiring configuration while monitoring the overall network status. For example, it analyzes the status of the smart home devices and presents the optimal wiring route. Furthermore, the wiring configuration unit performs wiring configuration while the generation AI monitors the overall network status based on the subscriber's smart home devices. For example, it analyzes the status of the smart home devices and presents a wiring route with minimal interference. Furthermore, the wiring configuration unit works in conjunction with the subscriber's smart home devices and learns wiring configuration while monitoring the overall network status. For example, it proposes the optimal wiring route based on the status of the smart home devices. This allows wiring configuration to be performed while working in conjunction with the smart home devices and monitoring the network status.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The connection diagnostic unit selects the optimal internet connection method based on subscriber information, contract plan, and router photos, and automatically suggests configuration methods. For example, the generation AI analyzes subscriber information, contract plan, and router photos, and presents specific connection procedures. Step 2: The wiring configuration unit detects the cause of the equipment malfunction based on the lighting status of the router or the ONU. For example, the generation AI analyzes the lighting pattern of the router's lights to identify the problem area. The generation AI also analyzes error messages on the PC screen and presents specific solutions. Step 3: The AI ​​chat section provides online agents to provide immediate support for internet connection issues and questions. For example, the generation AI analyzes the user's question and provides appropriate solutions.

[0052] (Example 2) A connection assistance system according to an embodiment of the present invention is a system that utilizes generative AI to provide connection diagnosis, wiring configuration, and AI chat functions in order to solve multiple problems in Internet connections. As a result, the connection assistance system can efficiently solve multiple problems in Internet connections.

[0053] The connection assistance system according to the embodiment includes a connection diagnosis unit, a wiring setup unit, and an AI chat unit. The connection diagnosis unit selects the optimal Internet connection method based on subscriber information, a contract plan, and a photo of the router, and automatically proposes a setup method. For example, the generation AI analyzes subscriber information, a contract plan, and a photo of the router to present specific connection procedures. The generation AI receives subscriber information, a contract plan, and a photo of the router as input and proposes a connection method based on the input. The wiring setup unit detects the cause of a device malfunction based on the lighting status of the router or the lighting status of the ONU. For example, the generation AI analyzes the lighting pattern of the router's lights to identify the problem area. The generation AI also analyzes error messages on a PC screen and proposes specific solutions. The generation AI receives input from the lighting status of the router's lights, the lighting status of the ONU, and error messages on a PC screen, and uses them to identify the cause of the malfunction. The AI ​​chat unit provides an online agent to provide immediate support for Internet connection problems and questions. For example, the generation AI analyzes user questions and presents appropriate solutions. The generation AI receives input from users, such as questions and details of problems, and generates answers based on those inputs. This allows the connection assistance system according to the embodiment to efficiently solve multiple issues related to Internet connection. For example, even if a user does not know how to connect, the generation AI can suggest specific steps, allowing the user to easily connect. Furthermore, even if a malfunction occurs, the generation AI can identify the cause and present specific solutions, making recovery easier. Furthermore, support is available 24 hours a day, 365 days a year, providing users with the peace of mind that there is someone they can consult with at any time.

[0054] The connection diagnostic unit can analyze the subscriber's past connection history and suggest the connection method with the highest success rate. For example, the generation AI in the connection diagnostic unit analyzes the subscriber's past connection history and identifies the connection method with the highest success rate. For example, it suggests a similar procedure based on connection procedures that have been successful in the past. The connection diagnostic unit also analyzes the frequency with which a specific connection method has been successful based on the subscriber's past connection history and suggests the method with the highest success rate. For example, it prioritizes the presentation of connection methods that have been successful in the past. The connection diagnostic unit also analyzes the subscriber's past connection history and learns connection methods with the highest success rate. For example, it extracts successful procedures from the past connection history and suggests similar procedures. This makes it possible to suggest the optimal connection method based on the past connection history.

[0055] The connection diagnostic unit can propose the optimal connection method based on the subscriber's usage environment. For example, the generation AI in the connection diagnostic unit analyzes the layout of the subscriber's room and the arrangement of other electronic devices to propose the optimal connection method. For example, it proposes the optimal location to place a Wi-Fi router. The connection diagnostic unit also proposes the optimal connection method based on the subscriber's usage environment. For example, it proposes a connection method with minimal interference, taking into account the room layout and the arrangement of other electronic devices. The connection diagnostic unit also analyzes the subscriber's usage environment and learns the optimal connection method. For example, it proposes the optimal connection procedure based on the room layout and the arrangement of other electronic devices. This makes it possible to propose the optimal connection method taking into account the usage environment.

[0056] The connection diagnosis unit uses the emotion estimation function to monitor in real time the stress level felt by the subscriber during connection setup and can suggest a low-stress connection method. The connection diagnosis unit, for example, uses the emotion estimation function to monitor in real time the stress level felt by the subscriber during connection setup. For example, it measures the stress level by analyzing facial expressions and voice tone. The connection diagnosis unit also uses the generation AI to suggest a low-stress connection method based on the subscriber's stress level. For example, it prioritizes presenting procedures that result in a low stress level. The connection diagnosis unit also uses the emotion estimation function to analyze the subscriber's stress level in real time and learns low-stress connection methods. For example, it suggests procedures that result in a low stress level. In this way, it is possible to monitor stress levels and suggest low-stress connection methods.

[0057] The connection diagnosis unit can provide a guide to the connection procedure using AR, using the subscriber's smartphone or tablet. For example, the generation AI in the connection diagnosis unit uses the subscriber's smartphone or tablet to provide a guide to the connection procedure using AR. For example, the connection procedure is visually displayed through a camera. The connection diagnosis unit also uses the subscriber's smartphone or tablet to provide the AR guide. For example, the connection procedure is displayed as a 3D model to make it easier to understand visually. The connection diagnosis unit also uses the generation AI in the connection diagnosis unit to learn the guide to the connection procedure using AR, using the subscriber's smartphone or tablet. For example, the connection procedure is displayed in real time using AR technology. This makes it possible to provide a guide to the connection procedure using AR.

[0058] The connection diagnosis unit can analyze the voice input of the subscriber and provide voice-guided connection procedures. In the connection diagnosis unit, for example, a generation AI analyzes the voice input of the subscriber and provides voice-guided connection procedures. For example, it analyzes voice commands and provides voice guidance on appropriate connection procedures. In addition, the connection diagnosis unit uses a generation AI to provide voice guidance based on the voice input of the subscriber. For example, it provides voice guidance on connection procedures using voice recognition technology. In addition, the connection diagnosis unit uses a generation AI to analyze the voice input of the subscriber and learn voice-guided connection procedures. For example, it provides voice guidance on connection procedures based on voice commands. In this way, it is possible to provide voice-guided connection procedures.

[0059] The connection diagnosis unit can use the emotion estimation function to provide an interactive, game-style connection procedure to elicit positive emotions felt by the subscriber during connection setup. The connection diagnosis unit, for example, uses the emotion estimation function to provide an interactive, game-style connection procedure to elicit positive emotions felt by the subscriber during connection setup. For example, it presents a connection procedure that incorporates game elements. The connection diagnosis unit also provides a game-style connection procedure that uses a generation AI to elicit positive emotions based on the subscriber's emotions. For example, the connection is made while having fun by progressing through the connection procedure within a game. The connection diagnosis unit also uses the emotion estimation function to learn an interactive, game-style connection procedure to elicit positive emotions from the subscriber. For example, it proposes a connection procedure that incorporates game elements. This makes it possible to provide an interactive, game-style connection procedure to elicit positive emotions.

[0060] The wiring setting unit can analyze the internal logs of the router or ONU and identify the cause of the malfunction from the past error history. In the wiring setting unit, for example, the generation AI analyzes the internal logs of the router or ONU and identifies the cause of the malfunction from the past error history. For example, it analyzes the error log and identifies the cause based on a specific error code. In addition, the wiring setting unit can analyze the past error history based on the internal logs of the router or ONU and identify the cause of the malfunction. For example, it analyzes the error log and identifies the cause based on a specific error code. In addition, the generation AI analyzes the internal logs of the router or ONU and learns the cause of the malfunction from the past error history. For example, it identifies the cause based on the error log and proposes a countermeasure. This makes it possible to identify the cause of the malfunction based on the past error history.

[0061] The wiring setting unit can automatically map the customer's network topology and propose optimal wiring settings. In the wiring setting unit, for example, the generation AI automatically maps the customer's network topology and proposes optimal wiring settings. For example, it analyzes the placement of network devices and presents the optimal wiring route. In addition, in the wiring setting unit, the generation AI proposes optimal wiring settings based on the customer's network topology. For example, it analyzes the placement of network devices and presents a wiring route with minimal interference. In addition, in the wiring setting unit, the generation AI automatically maps the customer's network topology and learns optimal wiring settings. For example, it proposes the optimal wiring route based on the placement of network devices. In this way, the network topology can be automatically mapped and optimal wiring settings can be proposed.

[0062] The wiring setup unit can use the emotion estimation function to provide relaxing music or guidance to reduce the anxiety felt by the subscriber during wiring setup. The wiring setup unit, for example, uses the emotion estimation function to provide relaxing music or guidance to reduce the anxiety felt by the subscriber during wiring setup. For example, the wiring setup unit guides the subscriber through wiring setup while playing relaxing music. Furthermore, the wiring setup unit uses a generation AI to provide relaxing music or guidance to reduce anxiety based on the subscriber's emotions. For example, the wiring setup unit guides the subscriber through wiring setup while playing relaxing music. Furthermore, the wiring setup unit uses the emotion estimation function to learn relaxing music or guidance to reduce the subscriber's anxiety. For example, the wiring setup unit guides the subscriber through wiring setup while playing relaxing music. In this way, relaxing music or guidance to reduce anxiety can be provided.

[0063] The wiring setting unit works in cooperation with the subscriber's smart home devices and can perform wiring settings while monitoring the overall network status. In the wiring setting unit, for example, the generation AI works in cooperation with the subscriber's smart home devices and performs wiring settings while monitoring the overall network status. For example, it analyzes the status of the smart home devices and presents the optimal wiring route. In addition, the wiring setting unit works in cooperation with the subscriber's smart home devices and performs wiring settings while the generation AI monitors the overall network status. For example, it analyzes the status of the smart home devices and presents a wiring route with minimal interference. In addition, the wiring setting unit works in cooperation with the subscriber's smart home devices and learns wiring settings while monitoring the overall network status. For example, it proposes the optimal wiring route based on the status of the smart home devices. This allows wiring settings to be performed while working in cooperation with the smart home devices and monitoring the network status.

[0064] The wiring setting unit can analyze the wiring status in real time using the camera on the contractor's PC or smartphone and propose an appropriate wiring method. In the wiring setting unit, for example, the generation AI uses the camera on the contractor's PC or smartphone to analyze the wiring status in real time and propose an appropriate wiring method. For example, the camera image is analyzed and wiring problems are identified. In addition, the wiring setting unit can analyze the wiring status in real time based on the camera on the contractor's PC or smartphone and propose an appropriate wiring method. For example, the camera image is analyzed and wiring problems are identified. In addition, the wiring setting unit can analyze the wiring status in real time based on the camera image on the contractor's PC or smartphone and learn an appropriate wiring method. For example, the camera image is analyzed and wiring problems are identified based on the camera image and solutions are proposed. In this way, the wiring status can be analyzed in real time and an appropriate wiring method can be proposed.

[0065] The wiring setting unit can use the emotion estimation function to provide an interactive visual guide to elicit positive emotions felt by the contractor during wiring setup. The wiring setting unit, for example, uses the emotion estimation function to provide an interactive visual guide to elicit positive emotions felt by the contractor during wiring setup. For example, the wiring setting unit uses a visual guide to guide the contractor through the wiring procedure. Further, the wiring setting unit provides a visual guide for the generation AI to elicit positive emotions based on the contractor's emotions. For example, the wiring setting unit uses a visual guide to guide the contractor through the wiring procedure. Further, the wiring setting unit uses the emotion estimation function to learn an interactive visual guide to elicit positive emotions from the contractor. For example, the wiring setting unit uses a visual guide to guide the contractor through the wiring procedure. In this way, an interactive visual guide to elicit positive emotions can be provided.

[0066] The AI ​​chat unit can analyze the subscriber's past chat history and learn and provide the most effective support method. In the AI ​​chat unit, for example, the generation AI analyzes the subscriber's past chat history and learns and provides the most effective support method. For example, it proposes effective support procedures based on the content of past chats. In addition, the AI ​​chat unit can learn effective support methods based on the subscriber's past chat history. For example, it analyzes the content of past chats and proposes effective support procedures. In addition, the AI ​​chat unit can analyze the subscriber's past chat history and learn and provide the most effective support method. For example, it proposes effective support procedures based on the content of past chats. In this way, the most effective support method can be provided based on the past chat history.

[0067] The AI ​​chat unit monitors the subscriber's network environment in real time and can provide preventative alerts before problems occur. For example, the generation AI in the AI ​​chat unit monitors the subscriber's network environment in real time and can provide preventative alerts before problems occur. For example, it detects network abnormalities and sends alerts in advance. The AI ​​chat unit also monitors the subscriber's network environment in real time and can provide preventative alerts. For example, it detects network abnormalities and sends alerts in advance. The AI ​​chat unit also monitors the subscriber's network environment in real time and can provide preventative alerts before problems occur. For example, it detects network abnormalities and sends alerts in advance. This allows the network environment to be monitored in real time and can provide preventative alerts before problems occur.

[0068] The AI ​​chat unit can use the emotion estimation function to provide kind language and encouraging messages to reduce the stress felt by the subscriber during chat. The AI ​​chat unit, for example, uses the emotion estimation function to provide kind language and encouraging messages to reduce the stress felt by the subscriber during chat. For example, an encouraging message is sent to a subscriber who is feeling stressed. Furthermore, the AI ​​chat unit uses the generation AI to provide kind language and encouraging messages to reduce stress based on the subscriber's emotions. For example, an encouraging message is sent to a subscriber who is feeling stressed. Furthermore, the AI ​​chat unit uses the emotion estimation function to learn kind language and encouraging messages to reduce the stress felt by the subscriber during chat. For example, an encouraging message is sent to a subscriber who is feeling stressed. This makes it possible to provide kind language and encouraging messages to reduce stress during chat.

[0069] The AI ​​chat unit can provide voice support in cooperation with the subscriber's smart speaker. For example, the generation AI in the AI ​​chat unit can provide voice support in cooperation with the subscriber's smart speaker. For example, it can provide voice guidance on the connection procedure through the smart speaker. The generation AI in the AI ​​chat unit can also provide voice support based on the subscriber's smart speaker. For example, it can provide voice guidance on the connection procedure through the smart speaker. The generation AI in the AI ​​chat unit can also learn voice support in cooperation with the subscriber's smart speaker. For example, it can provide voice guidance on the connection procedure through the smart speaker. This allows it to provide voice support in cooperation with the smart speaker.

[0070] The AI ​​chat unit can provide support on SNS in cooperation with the contractor's SNS account. In the AI ​​chat unit, for example, the generation AI can link with the contractor's SNS account and provide support on SNS. For example, it can guide the contractor through the SNS. In addition, the AI ​​chat unit can provide support on SNS based on the contractor's SNS account. For example, it can guide the contractor through the SNS. In addition, the generation AI can link with the contractor's SNS account and learn support on SNS. For example, it can guide the contractor through the SNS. In this way, it can link with the SNS account and provide support on SNS.

[0071] The AI ​​chat unit can use the emotion estimation function to provide support incorporating humor or entertainment elements to elicit positive emotions felt by the subscriber during chat. The AI ​​chat unit, for example, uses the emotion estimation function to provide support incorporating humor or entertainment elements to elicit positive emotions felt by the subscriber during chat. For example, it sends a humorous message. Furthermore, the AI ​​chat unit provides support incorporating humor or entertainment elements to elicit positive emotions based on the subscriber's emotions. For example, it sends a humorous message. Furthermore, the AI ​​chat unit uses the emotion estimation function to learn humor or entertainment elements to elicit positive emotions felt by the subscriber during chat. For example, it sends a humorous message. This makes it possible to provide support incorporating humor or entertainment elements to elicit positive emotions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The connection diagnostic unit can analyze the subscriber's past connection history and suggest the connection method with the highest success rate. For example, it can suggest a similar procedure based on connection procedures that have been successful in the past. The connection diagnostic unit can also analyze the frequency with which a specific connection method has been successful based on the subscriber's past connection history and suggest the method with the highest success rate. For example, it can prioritize the presentation of connection methods that have been successful in the past. The connection diagnostic unit also uses a generation AI to analyze the subscriber's past connection history and learn connection methods with a high success rate. For example, it can extract successful procedures from past connection history and suggest similar procedures. This makes it possible to suggest the optimal connection method based on past connection history.

[0074] The connection diagnostic unit can propose the optimal connection method based on the subscriber's usage environment. For example, the generation AI analyzes the layout of the subscriber's room and the arrangement of other electronic devices to propose the optimal connection method. For example, it proposes the optimal location to place a Wi-Fi router. The connection diagnostic unit also proposes the optimal connection method based on the subscriber's usage environment. For example, it proposes a connection method with minimal interference, taking into account the room layout and the arrangement of other electronic devices. The connection diagnostic unit also analyzes the subscriber's usage environment and learns the optimal connection method. For example, it proposes the optimal connection procedure based on the room layout and the arrangement of other electronic devices. This makes it possible to propose the optimal connection method taking into account the usage environment.

[0075] The connection diagnostic unit uses the emotion estimation function to monitor in real time the stress level felt by the subscriber during connection setup and can suggest a low-stress connection method. For example, it measures stress levels by analyzing facial expressions and voice tone. The connection diagnostic unit also uses the generation AI to suggest a low-stress connection method based on the subscriber's stress level. For example, it prioritizes presenting procedures that result in a low stress level. The connection diagnostic unit also uses the emotion estimation function to analyze the subscriber's stress level in real time and learns low-stress connection methods. For example, it suggests procedures that result in a low stress level. This makes it possible to monitor stress levels and suggest low-stress connection methods.

[0076] The connection diagnosis unit can provide a guide to the connection procedure using AR, using the subscriber's smartphone or tablet. For example, the generation AI can use the subscriber's smartphone or tablet to provide a guide to the connection procedure using AR. For example, the connection procedure can be visually displayed through a camera. The connection diagnosis unit can also use the subscriber's smartphone or tablet to provide the AR guide. For example, the connection procedure can be displayed as a 3D model to make it easier to understand visually. The connection diagnosis unit can also use the generation AI to learn the guide to the connection procedure using AR, using the subscriber's smartphone or tablet. For example, the connection procedure can be displayed in real time using AR technology. This makes it possible to provide a guide to the connection procedure using AR.

[0077] The connection diagnosis unit can analyze the voice input of the subscriber and provide voice-guided connection procedures. For example, the generation AI analyzes the voice input of the subscriber and provides voice-guided connection procedures. For example, it analyzes voice commands and provides voice guidance on appropriate connection procedures. The connection diagnosis unit also has the generation AI provide voice guidance based on the voice input of the subscriber. For example, it provides voice guidance on connection procedures using voice recognition technology. The connection diagnosis unit also has the generation AI analyze the voice input of the subscriber and learn voice-guided connection procedures. For example, it provides voice guidance on connection procedures based on voice commands. This makes it possible to provide voice-guided connection procedures.

[0078] The connection diagnosis unit can use the emotion estimation function to provide an interactive, game-style connection procedure to elicit positive emotions felt by the subscriber during connection setup. For example, the emotion estimation function can be used to provide an interactive, game-style connection procedure to elicit positive emotions felt by the subscriber during connection setup. For example, a connection procedure incorporating game elements can be presented. The connection diagnosis unit also provides a game-style connection procedure that uses a generation AI to elicit positive emotions based on the subscriber's emotions. For example, the connection can be made while having fun by progressing through the connection procedure within a game. The connection diagnosis unit also uses the emotion estimation function to learn an interactive, game-style connection procedure to elicit positive emotions from the subscriber. For example, a connection procedure incorporating game elements can be proposed. This makes it possible to provide an interactive, game-style connection procedure to elicit positive emotions.

[0079] The wiring setting unit can analyze the internal logs of routers and ONUs and identify the cause of a malfunction from past error history. For example, the generation AI analyzes the internal logs of routers and ONUs and identifies the cause of a malfunction from past error history. For example, it analyzes the error log and identifies the cause based on a specific error code. The wiring setting unit also analyzes the internal logs of routers and ONUs and identifies the cause of a malfunction. For example, it analyzes the error log and identifies the cause based on a specific error code. The wiring setting unit also analyzes the internal logs of routers and ONUs and learns the cause of a malfunction from past error history. For example, it identifies the cause based on the error log and proposes a solution. This makes it possible to identify the cause of a malfunction based on past error history.

[0080] The wiring setting unit can automatically map the subscriber's network topology and propose the optimal wiring setting. For example, the generation AI automatically maps the subscriber's network topology and proposes the optimal wiring setting. For example, it analyzes the placement of network devices and presents the optimal wiring route. Furthermore, the wiring setting unit has the generation AI propose the optimal wiring setting based on the subscriber's network topology. For example, it analyzes the placement of network devices and presents a wiring route with minimal interference. Furthermore, the wiring setting unit has the generation AI automatically map the subscriber's network topology and learn the optimal wiring setting. For example, it proposes the optimal wiring route based on the placement of network devices. In this way, the network topology can be automatically mapped and the optimal wiring setting can be proposed.

[0081] The wiring setup unit can use the emotion estimation function to provide relaxing music or guidance to reduce the anxiety felt by the subscriber during wiring setup. For example, the emotion estimation function can be used to provide relaxing music or guidance to reduce the anxiety felt by the subscriber during wiring setup. For example, the wiring setup can be guided while playing relaxing music. Furthermore, the wiring setup unit uses a generation AI to provide relaxing music or guidance to reduce anxiety based on the subscriber's emotions. For example, the wiring setup can be guided while playing relaxing music. Furthermore, the wiring setup unit uses the emotion estimation function to learn relaxing music or guidance to reduce the subscriber's anxiety. For example, the wiring setup can be guided while playing relaxing music. In this way, relaxing music or guidance to reduce anxiety can be provided.

[0082] The wiring configuration unit works in conjunction with the subscriber's smart home devices and can perform wiring configuration while monitoring the overall network status. For example, the generation AI works in conjunction with the subscriber's smart home devices and performs wiring configuration while monitoring the overall network status. For example, it analyzes the status of the smart home devices and presents the optimal wiring route. Furthermore, the wiring configuration unit performs wiring configuration while the generation AI monitors the overall network status based on the subscriber's smart home devices. For example, it analyzes the status of the smart home devices and presents a wiring route with minimal interference. Furthermore, the wiring configuration unit works in conjunction with the subscriber's smart home devices and learns wiring configuration while monitoring the overall network status. For example, it proposes the optimal wiring route based on the status of the smart home devices. This allows wiring configuration to be performed while working in conjunction with the smart home devices and monitoring the network status.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The connection diagnostic unit selects the optimal internet connection method based on subscriber information, contract plan, and router photos, and automatically suggests configuration methods. For example, the generation AI analyzes subscriber information, contract plan, and router photos, and presents specific connection procedures. Step 2: The wiring configuration unit detects the cause of the equipment malfunction based on the lighting status of the router or the ONU. For example, the generation AI analyzes the lighting pattern of the router's lights to identify the problem area. The generation AI also analyzes error messages on the PC screen and presents specific solutions. Step 3: The AI ​​chat section provides online agents to provide immediate support for internet connection issues and questions. For example, the generation AI analyzes the user's question and provides appropriate solutions.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 AI 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 connection diagnostic section selects the optimal internet connection method based on subscriber information, contract plan, and router photos, and automatically suggests how to set it up. a wiring setting unit that detects the cause of equipment malfunction based on the lighting status of the router light or the lighting status of the ONU; It also has an AI chat section that provides online agents and provides immediate support for internet connection issues and questions. A system characterized by:

2. The connection diagnosis unit Analyzes subscribers' past connection history and suggests the most successful connection method The system of claim 1 .

3. The connection diagnosis unit Propose the optimal connection method based on the subscriber's usage environment The system of claim 1 .

4. The connection diagnosis unit Monitors the stress level felt by subscribers during connection setup in real time and suggests connection methods that cause less stress The system of claim 1 .

5. The connection diagnosis unit The subscriber's smartphone or tablet is used to provide a guide to the connection procedure using the AR. The system of claim 1 .

6. The connection diagnosis unit Analyzes the subscriber's voice input and provides voice-guided connection instructions The system of claim 1 .

7. The connection diagnosis unit Providing an interactive gamified connection process to elicit positive emotions felt by subscribers during the connection setup The system of claim 1 .

8. The wiring setting unit Analyze the internal logs of the router and the ONU, and identify the cause of the problem from past error history. The system of claim 1 .

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