System

Abstract

To provide a system according to an embodiment that reduces the time for a call center to respond to a user in responding to a failure in a network apparatus.SOLUTION: A system according to an embodiment comprises a receiving unit, a fill-in unit, an acquisition unit, and a presentation unit. The receiving unit reads a two-dimensional code applied to a network apparatus. The fill-in unit that fills in an interview sheet according to the network apparatus corresponding to the two-dimensional code. The acquisition unit acquires a lamp image obtained by photographing a lamp of the network apparatus corresponding to the two-dimensional code. The presentation unit inputs the interview sheet and the lamp image to a learning model to acquire a method for responding to and coping with a failure in the network apparatus, and presents the acquired method for responding to and coping with the failure.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there is a risk that the response time for users and call centers when dealing with a network device failure may be long.

[0005] The system according to the embodiment aims to reduce the response time between users and call centers when dealing with failures in network devices. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an entry unit, an acquisition unit, and a presentation unit. The reception unit reads a two-dimensional code attached to a network device. The entry unit enters a questionnaire corresponding to the network device corresponding to the two-dimensional code. The acquisition unit acquires a lamp image by capturing an image of a lamp of the network device corresponding to the two-dimensional code. The presentation unit inputs the questionnaire and the lamp image into a learning model to acquire a fault response and a countermeasure method for the network device, and presents the acquired fault response and countermeasure method. Effect of the Invention

[0007] The system according to the embodiment can reduce the response time of users and call centers when dealing with failures of network devices. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that uses a smartphone to diagnose when receiving and responding to malfunctions at a call center, thereby shortening the counter work and the user's response time. This system starts when a user reads a two-dimensional code attached to a network device with a smartphone. Next, a questionnaire corresponding to the network device corresponding to the two-dimensional code is displayed, and the user fills it out. The questionnaire includes questions about the details of the malfunction experienced by the user and the condition of the device. Next, the user takes a picture of the lamp of the network device with a smartphone and uploads the image to the system. The system inputs the questionnaire and the lamp image into the AI ​​learning model to obtain the malfunction response and the countermeasure. The obtained malfunction response and the countermeasure are presented to the user. For example, if the lamp of the network device is lit red, the AI ​​analyzes the state and presents countermeasures such as restarting the power supply and checking the cable connection. This allows the user to perform simple countermeasures by himself, and reduces inquiries to the call center. This system makes the counter work of the call center more efficient and shortens the user's response time. Furthermore, since the user can solve the problem by himself, the satisfaction level is also improved. This allows the system to use smartphones to diagnose problems when they are received at the call center, shortening counter operations and user response times.

[0029] The system according to the embodiment includes a reception unit, an entry unit, an acquisition unit, and a presentation unit. The reception unit reads a two-dimensional code attached to a network device. The two-dimensional code includes, for example, a QR code (registered trademark) and a Data Matrix code, but is not limited to such examples. The reception unit can read the two-dimensional code using, for example, a smartphone camera. The reception unit can also improve the reading accuracy of the two-dimensional code using AI. The entry unit fills in a questionnaire according to a network device corresponding to the two-dimensional code. The questionnaire includes, for example, details of a malfunction experienced by a user and questions regarding the state of the device, but is not limited to such examples. The entry unit allows, for example, a user to fill in the questionnaire on a smartphone screen. The entry unit can also analyze the contents of the questionnaire using AI and present appropriate questions. The acquisition unit acquires a lamp image obtained by photographing a lamp of a network device corresponding to the two-dimensional code. The lamp image includes, for example, a shooting angle and a resolution, but is not limited to such examples. The acquisition unit can photograph the lamp image using, for example, a smartphone camera. The acquisition unit can also improve the acquisition accuracy of the lamp image by using AI. The presentation unit acquires the fault response or countermeasure method of the network device by inputting the questionnaire and the lamp image into a learning model, and presents the acquired fault response or countermeasure method. The learning model includes, for example, a deep learning model and a support vector machine, but is not limited to such examples. The presentation unit can acquire the fault response or countermeasure method by using, for example, a generation AI. As a result, the system according to the embodiment can efficiently present the fault response or countermeasure method of the network device.

[0030] The reception unit reads the two-dimensional code attached to the network device. Examples of the two-dimensional code include, but are not limited to, QR codes and DataMatrix codes. The reception unit can read the two-dimensional code using, for example, a smartphone camera. Specifically, the smartphone camera app is started and the two-dimensional code is placed within the camera frame, so that the content of the code is automatically analyzed and the necessary information is obtained. The reception unit can also improve the reading accuracy of the two-dimensional code using AI. For example, AI can accurately read blurred images and partially missing codes using image recognition technology. Furthermore, AI can analyze codes photographed under different lighting conditions and from different angles with high accuracy. This allows the reception unit to allow the user to easily and quickly read the two-dimensional code and efficiently obtain information about the network device. Furthermore, the reception unit can automatically register the information of the read two-dimensional code in a database and link with other departments and systems. For example, the read information can be sent to a cloud server so that other systems can access it, thereby improving the overall business efficiency.

[0031] The entry unit fills in a questionnaire corresponding to the network device corresponding to the two-dimensional code. The questionnaire includes, for example, details of the malfunction experienced by the user and questions regarding the state of the device, but is not limited to such examples. The entry unit allows, for example, the user to fill in the questionnaire on the screen of a smartphone. Specifically, the user accesses the questionnaire through a smartphone app and answers the questions using a touch screen. The entry unit can also analyze the contents of the questionnaire by using AI and present appropriate questions. For example, the AI ​​can analyze the contents of the user's answers in real time and dynamically change the questions to be presented next, thereby collecting more detailed and relevant information. Furthermore, the AI ​​can automatically add important questions that the user is likely to overlook based on past data and similar cases. This allows the entry unit to allow the user to fill in the questionnaire efficiently and accurately, and to collect detailed information regarding malfunctions of the network device. Furthermore, the entry unit can reduce the burden on the user by automatically saving the contents entered by the user and making them available for later reference. For example, even if the user interrupts input midway, the previous input contents are retained when the user accesses the device again, so the user can smoothly continue input.

[0032] The acquisition unit acquires a lamp image of a lamp of a network device corresponding to a two-dimensional code. The lamp image includes, for example, a shooting angle and a resolution, but is not limited to such examples. The acquisition unit can, for example, shoot a lamp image using a smartphone camera. Specifically, a user aims the smartphone camera at a lamp of a network device and shoots it, and the acquisition unit automatically analyzes the image. The acquisition unit can also improve the acquisition accuracy of the lamp image using AI. For example, the AI ​​can remove noise from the captured image using image processing technology and accurately recognize the state of the lamp. Furthermore, the AI ​​can analyze the state of the lamp with high accuracy even in images taken under different lighting conditions and from different angles. This allows the acquisition unit to allow a user to easily and quickly acquire a lamp image and accurately grasp the state of the network device. Furthermore, the acquisition unit can automatically register the acquired lamp image in a database and link with other departments and systems. For example, the acquired image can be sent to a cloud server and made accessible to other systems, thereby improving the overall business efficiency.

[0033] The presentation unit obtains the fault response or countermeasure method of the network device by inputting the questionnaire and the lamp image into the learning model, and presents the obtained fault response or countermeasure method. The learning model includes, for example, a deep learning model and a support vector machine, but is not limited to such examples. The presentation unit can obtain the fault response or countermeasure method, for example, by using a generation AI. Specifically, the generation AI analyzes the contents of the questionnaire and the lamp image, and generates an optimal fault response method based on past data and similar cases. For example, a deep learning model can learn based on a huge data set and predict the fault pattern of the network device with high accuracy. In addition, a support vector machine can combine the answers to the questionnaire and the features of the lamp image to classify the optimal countermeasure method. This allows the presentation unit to provide the user with a quick and accurate fault response method. Furthermore, the presentation unit can use the generation AI to explain the countermeasure method to the user in an easy-to-understand manner. For example, by showing specific steps using not only text format but also images and videos, the user can easily understand and execute them. In addition, the presentation unit can collect user feedback and continuously improve the accuracy of the learning model. This allows the presentation unit to always provide highly accurate fault handling methods based on the latest information, thereby improving user satisfaction.

[0034] The entry unit can enter a questionnaire including details of the malfunction experienced by the user and questions regarding the state of the device. For example, the entry unit provides a questionnaire including questions such as error messages and occurrence frequency in order to efficiently collect details of the malfunction experienced by the user. The entry unit can also provide a questionnaire including questions regarding the state of the device. For example, the entry unit provides a questionnaire including questions such as operation status and connection status. This makes it possible to efficiently collect details of the malfunction experienced by the user. A part or all of the above-mentioned processing in the entry unit may be performed using, for example, AI, or may be performed without using AI. For example, the entry unit can input the contents of the questionnaire filled in by the user to AI, and the AI ​​can analyze the details of the malfunction.

[0035] The presentation unit can obtain the fault response or the countermeasure method by using the generation AI. The presentation unit can obtain the fault response or the countermeasure method by using, for example, the generation AI. The generation AI includes, for example, GPT and Gemini, but is not limited to such examples. The presentation unit can input a questionnaire and a lamp image to the generation AI and obtain the fault response or the countermeasure method. For example, the presentation unit inputs a prompt to the generation AI, "Please tell me what to do when the lamp of the network device is lit red," and the generation AI outputs a countermeasure method such as restarting the power supply or checking the cable connection. As a result, the accuracy of the fault response or the countermeasure method is improved by using the generation AI. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, by using an AI, or may be performed without using an AI. For example, the presentation unit can input a questionnaire and a lamp image to the generation AI and obtain the fault response or the countermeasure method.

[0036] The reception unit can analyze the user's past reading history and select an appropriate reading method. The reception unit, for example, analyzes the user's past reading history and selects the optimal reading method. The reading history includes, for example, past success rates and frequency of use, but is not limited to such examples. For example, the reception unit proposes an optimal method based on the reading methods (camera angle, distance, etc.) used by the user in the past. The reception unit can also preferentially propose reading methods that the user has been successful in the past. Furthermore, the reception unit can also propose an optimal reading method for a specific time period from the user's past reading history. As a result, the success rate of reading is improved by proposing the optimal method based on the user's past reading history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past reading history into AI and cause AI to select the optimal reading method.

[0037] The reception unit can perform filtering based on the user's current network environment when reading the two-dimensional code. The reception unit performs filtering based on the user's current network environment when reading the two-dimensional code, for example. The network environment includes, but is not limited to, for example, connection speed and signal strength. For example, when the user's network environment is unstable, the reception unit suspends reading and waits until it stabilizes. In addition, when the user's network environment is high-speed, the reception unit can also perform reading quickly. Furthermore, the reception unit can automatically adjust optimal reading settings based on the user's network environment. This improves the accuracy of reading by adjusting the reading according to the user's network environment. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's network environment data to AI and have the AI ​​perform filtering.

[0038] The reception unit can preferentially read highly relevant codes in consideration of the geographical location information of the user when reading the two-dimensional code. For example, the reception unit preferentially reads highly relevant codes in consideration of the geographical location information of the user when reading the two-dimensional code. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the reception unit preferentially reads codes related to the area. In addition, when the user is moving, the reception unit can also preferentially read codes closest to the current location. Furthermore, when the user is in a specific facility, the reception unit can also preferentially read codes related to the facility. This enables efficient response by preferentially reading highly relevant codes based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to select highly relevant codes.

[0039] The reception unit can analyze the social media activity of the user and read the related code when reading the two-dimensional code. For example, the reception unit analyzes the social media activity of the user and reads the related code when reading the two-dimensional code. The social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the reception unit preferentially reads the related code based on the information shared by the user on social media. The reception unit can also preferentially read the code related to the account that the user follows on social media. Furthermore, the reception unit can also preferentially read the code related to the topic in which the user showed interest on social media. As a result, it is possible to respond according to the needs of the user by preferentially reading the related code based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and cause the AI ​​to select the related code.

[0040] The entry unit can select the optimal question by referring to the user's past malfunction history when entering. For example, the entry unit selects the optimal question by referring to the user's past malfunction history when entering. The malfunction history includes, for example, past error logs and repair history, but is not limited to such examples. For example, the entry unit provides a related question based on a malfunction experienced by the user in the past. The entry unit can also provide a question related to the most frequently occurring problem from the user's past malfunction history. Furthermore, the entry unit can analyze the user's past malfunction history and select the optimal question. As a result, efficient information collection is possible by providing the optimal question based on the user's past malfunction history. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using AI, or may be performed without using AI. For example, the entry unit can input the user's past malfunction history into AI and cause the AI ​​to select the optimal question.

[0041] The entry unit can customize the question based on the current state of the network device of the user when entering. The entry unit, for example, customizes the question based on the current state of the network device of the user when entering. The state of the network device includes, for example, a connection state and an operation state, but is not limited to such examples. For example, the entry unit provides a general question when the network device of the user is operating normally. In addition, the entry unit can also provide a question regarding a specific problem when the network device of the user shows an abnormality. Furthermore, the entry unit can customize an optimal question based on the state of the network device of the user. This enables efficient information collection by customizing the question based on the state of the network device of the user. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using AI, or may be performed without using AI. For example, the entry unit can input state data of the network device of the user to AI and cause the AI ​​to execute customization of the question.

[0042] The entry unit can provide an optimal question in consideration of the geographical location information of the user when entering. The entry unit provides an optimal question in consideration of the geographical location information of the user when entering, for example. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the entry unit provides a question related to the area. In addition, when the user is moving, the entry unit can also provide a question based on the current location. Furthermore, when the user is in a specific facility, the entry unit can also provide a question related to the facility. This enables efficient information collection by providing an optimal question based on the geographical location information of the user. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using AI, or may be performed without using AI. For example, the entry unit can input the geographical location information of the user to AI and cause the AI ​​to select the optimal question.

[0043] The entry unit can analyze the user's social media activity at the time of entry and provide related questions. The entry unit can, for example, analyze the user's social media activity at the time of entry and provide related questions. Social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the entry unit can provide related questions based on information shared by the user on social media. The entry unit can also provide questions related to accounts that the user follows on social media. Furthermore, the entry unit can also provide questions related to topics in which the user has shown interest on social media. This enables efficient information collection by providing related questions based on the user's social media activity. A part or all of the above-mentioned processing in the entry unit can be performed, for example, using AI or without using AI. For example, the entry unit can input the user's social media activity data into AI and cause the AI ​​to select related questions.

[0044] The acquisition unit can analyze the user's past lamp image acquisition history and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past lamp image acquisition history and selects the optimal acquisition method. The lamp image acquisition history includes, for example, past success rates and frequency of use, but is not limited to such examples. For example, the acquisition unit proposes the optimal method based on the acquisition method (camera angle, distance, etc.) used by the user in the past. The acquisition unit can also preferentially propose an acquisition method that the user has been successful in the past. Furthermore, the acquisition unit can also propose the optimal acquisition method for a specific time period from the user's past acquisition history. As a result, the success rate of acquisition is improved by suggesting the optimal method based on the user's past acquisition history. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's past acquisition history to AI and cause the AI ​​to select the optimal acquisition method.

[0045] The acquisition unit can perform filtering based on the current state of the user's network device when acquiring the lamp image. The acquisition unit performs filtering based on the current state of the user's network device when acquiring the lamp image, for example. The state of the network device includes, for example, a connection state and an operation state, but is not limited to such examples. For example, the acquisition unit provides a general acquisition method when the user's network device is operating normally. In addition, the acquisition unit can also provide an acquisition method related to a specific problem when the user's network device shows an abnormality. Furthermore, the acquisition unit can customize an optimal acquisition method based on the state of the user's network device. This improves the accuracy of acquisition by adjusting the acquisition according to the state of the user's network device. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input state data of the user's network device to AI and cause the AI ​​to perform filtering.

[0046] The acquisition unit can preferentially acquire highly relevant images in consideration of the geographical location information of the user when acquiring a lamp image. For example, the acquisition unit preferentially acquires highly relevant images in consideration of the geographical location information of the user when acquiring a lamp image. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the acquisition unit preferentially acquires images related to the area. In addition, when the user is moving, the acquisition unit can also preferentially acquire images closest to the current location. Furthermore, when the user is in a specific facility, the acquisition unit can also preferentially acquire images related to the facility. This enables efficient response by preferentially acquiring highly relevant images based on the geographical location information of the user. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the user to AI and cause the AI ​​to select highly relevant images.

[0047] The acquisition unit can analyze the social media activity of the user and acquire related images when acquiring the lamp image. For example, the acquisition unit analyzes the social media activity of the user and acquires related images when acquiring the lamp image. The social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the acquisition unit preferentially acquires related images based on information shared by the user on social media. The acquisition unit can also preferentially acquire images related to accounts that the user follows on social media. Furthermore, the acquisition unit can also preferentially acquire images related to topics in which the user has shown interest on social media. This makes it possible to respond to the needs of the user by preferentially acquiring related images based on the user's social media activity. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data into AI and cause the AI ​​to select related images.

[0048] The presentation unit can select the optimal countermeasure by referring to the user's past failure response history at the time of presentation. For example, the presentation unit selects the optimal countermeasure by referring to the user's past failure response history at the time of presentation. The failure response history includes, for example, past repair history and troubleshooting log, but is not limited to such examples. For example, the presentation unit provides a related countermeasure based on a failure experienced by the user in the past. The presentation unit can also provide the most effective countermeasure from the user's past failure response history. Furthermore, the presentation unit can analyze the user's past failure response history and select the optimal countermeasure. As a result, efficient response is possible by providing the optimal countermeasure based on the user's past failure response history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the user's past failure response history into AI and cause the AI ​​to select the optimal countermeasure.

[0049] The presentation unit can customize the countermeasure based on the current state of the network device of the user at the time of presentation. The presentation unit customizes the countermeasure based on the current state of the network device of the user at the time of presentation, for example. The state of the network device includes, for example, a connection state and an operation state, but is not limited to such examples. For example, the presentation unit provides a general countermeasure when the network device of the user is operating normally. In addition, the presentation unit can provide a countermeasure for a specific problem when the network device of the user shows an abnormality. Furthermore, the presentation unit can customize an optimal countermeasure based on the state of the network device of the user. This enables efficient response by customizing the countermeasure based on the state of the network device of the user. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input state data of the network device of the user to AI and cause the AI ​​to execute customization of the countermeasure.

[0050] The presentation unit can provide an optimal solution in consideration of the geographical location information of the user at the time of presentation. The presentation unit, for example, provides an optimal solution in consideration of the geographical location information of the user at the time of presentation. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the presentation unit provides a solution related to the area. In addition, when the user is moving, the presentation unit can also provide a solution based on the current location. Furthermore, when the user is in a specific facility, the presentation unit can also provide a solution related to the facility. This allows efficient response by providing an optimal solution based on the geographical location information of the user. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the geographical location information of the user to AI and cause the AI ​​to select the optimal solution.

[0051] The presentation unit can analyze the user's social media activity at the time of presentation and provide a related countermeasure. The presentation unit can, for example, analyze the user's social media activity at the time of presentation and provide a related countermeasure. The social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the presentation unit provides a related countermeasure based on information shared by the user on social media. The presentation unit can also provide a countermeasure related to an account that the user follows on social media. Furthermore, the presentation unit can also provide a countermeasure related to a topic in which the user has shown interest on social media. This makes it possible to respond according to the user's needs by providing a related countermeasure based on the user's social media activity. A part or all of the above-mentioned processing in the presentation unit can be performed, for example, using AI or without using AI. For example, the presentation unit can input the user's social media activity data into AI and cause the AI ​​to select a related countermeasure.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The reception unit can analyze the user's past inquiry history and select the optimal response method. For example, the reception unit can analyze what kind of problem the user has made an inquiry about in the past and propose a method for quickly responding when a similar problem recurs. The reception unit can also preferentially propose a response method that the user has been successful in the past. Furthermore, the reception unit can also propose the optimal response method for a specific time period from the user's past inquiry history. This improves the success rate of the response by suggesting the optimal method based on the user's past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past inquiry history into AI and have the AI ​​select the optimal response method.

[0054] The entry unit can customize the questions based on the current state of the user's network device. For example, the entry unit provides a general question when the user's network device is operating normally. In addition, the entry unit can provide a question related to a specific problem when the user's network device shows an abnormality. Furthermore, the entry unit can customize an optimal question based on the state of the user's network device. This enables efficient information collection by customizing the question based on the state of the user's network device. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using AI or may be performed without using AI. For example, the entry unit can input state data of the user's network device to AI and cause AI to customize the question.

[0055] The acquisition unit can analyze the user's past lamp image acquisition history and select the optimal acquisition method. For example, the acquisition unit suggests the optimal method based on the acquisition method (camera angle, distance, etc.) used by the user in the past. The acquisition unit can also preferentially suggest an acquisition method that the user has been successful in the past. Furthermore, the acquisition unit can also suggest the optimal acquisition method for a specific time period from the user's past acquisition history. This improves the success rate of acquisition by suggesting the optimal method based on the user's past acquisition history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the user's past acquisition history into AI and cause AI to select the optimal acquisition method.

[0056] The entry unit can select the most appropriate question by referring to the user's past malfunction history when entering the information. For example, the entry unit provides a related question based on a malfunction that the user has experienced in the past. The entry unit can also provide a question related to the most frequently occurring problem from the user's past malfunction history. Furthermore, the entry unit can analyze the user's past malfunction history and select the most appropriate question. This enables efficient information collection by providing the most appropriate question based on the user's past malfunction history. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using AI or may be performed without using AI. For example, the entry unit can input the user's past malfunction history into AI and cause AI to select the most appropriate question.

[0057] The acquisition unit can perform filtering based on the current state of the user's network device when acquiring the lamp image. For example, the acquisition unit provides a general acquisition method when the user's network device is operating normally. In addition, the acquisition unit can provide an acquisition method related to a specific problem when the user's network device shows an abnormality. Furthermore, the acquisition unit can customize an optimal acquisition method based on the state of the user's network device. This improves the accuracy of acquisition by adjusting the acquisition according to the state of the user's network device. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input state data of the user's network device to AI and have the AI ​​perform filtering.

[0058] The presentation unit can select the optimal countermeasure by referring to the user's past failure response history at the time of presentation. For example, the presentation unit provides a related countermeasure based on a failure experienced by the user in the past. The presentation unit can also provide the most effective countermeasure from the user's past failure response history. Furthermore, the presentation unit can analyze the user's past failure response history and select the optimal countermeasure. As a result, efficient response is possible by providing the optimal countermeasure based on the user's past failure response history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the presentation unit can input the user's past failure response history into AI and cause AI to select the optimal countermeasure.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The reception unit reads a two-dimensional code attached to a network device. Examples of two-dimensional codes include, but are not limited to, QR codes and Data Matrix codes. The reception unit can read the two-dimensional code using, for example, a smartphone camera. The reception unit can also improve the reading accuracy of the two-dimensional code using AI. Step 2: The entry unit fills in a questionnaire corresponding to the network device corresponding to the two-dimensional code. The questionnaire includes, for example, details of the malfunction experienced by the user and questions regarding the state of the device, but is not limited to such examples. The entry unit allows the user to fill in the questionnaire on the screen of a smartphone, for example. The entry unit can also use AI to analyze the contents of the questionnaire and present appropriate questions. Step 3: The acquisition unit acquires a lamp image obtained by capturing an image of a lamp of a network device corresponding to the two-dimensional code. The lamp image includes, for example, a capture angle and a resolution, but is not limited to such examples. The acquisition unit can capture the lamp image using, for example, a smartphone camera. The acquisition unit can also improve the capture accuracy of the lamp image using AI. Step 4: The presentation unit inputs the questionnaire and the lamp image into the learning model to obtain the fault response or countermeasure method for the network device, and presents the obtained fault response or countermeasure method. The learning model includes, but is not limited to, a deep learning model or a support vector machine. The presentation unit can obtain the fault response or countermeasure method by using, for example, a generation AI.

[0061] (Example 2) The system according to the embodiment of the present invention is a system that uses a smartphone to diagnose when receiving and responding to malfunctions at a call center, thereby shortening the counter work and the user's response time. This system starts when a user reads a two-dimensional code attached to a network device with a smartphone. Next, a questionnaire corresponding to the network device corresponding to the two-dimensional code is displayed, and the user fills it out. The questionnaire includes questions about the details of the malfunction experienced by the user and the condition of the device. Next, the user takes a picture of the lamp of the network device with a smartphone and uploads the image to the system. The system inputs the questionnaire and the lamp image into the AI ​​learning model to obtain the malfunction response and the countermeasure. The obtained malfunction response and the countermeasure are presented to the user. For example, if the lamp of the network device is lit red, the AI ​​analyzes the state and presents countermeasures such as restarting the power supply and checking the cable connection. This allows the user to perform simple countermeasures by himself, and reduces inquiries to the call center. This system makes the counter work of the call center more efficient and shortens the user's response time. Furthermore, since the user can solve the problem by himself, the satisfaction level is also improved. This allows the system to use smartphones to diagnose problems when they are received at the call center, shortening counter operations and user response times.

[0062] The system according to the embodiment includes a reception unit, an entry unit, an acquisition unit, and a presentation unit. The reception unit reads a two-dimensional code attached to a network device. The two-dimensional code includes, for example, a QR code and a Data Matrix code, but is not limited to such examples. The reception unit can read the two-dimensional code using, for example, a smartphone camera. The reception unit can also improve the reading accuracy of the two-dimensional code using AI. The entry unit fills in a questionnaire according to a network device corresponding to the two-dimensional code. The questionnaire includes, for example, details of a malfunction experienced by a user and questions regarding the state of the device, but is not limited to such examples. The entry unit allows, for example, a user to fill in the questionnaire on a smartphone screen. The entry unit can also analyze the contents of the questionnaire using AI and present appropriate questions. The acquisition unit acquires a lamp image obtained by photographing a lamp of a network device corresponding to the two-dimensional code. The lamp image includes, for example, a shooting angle and a resolution, but is not limited to such examples. The acquisition unit can photograph the lamp image using, for example, a smartphone camera. The acquisition unit can also improve the acquisition accuracy of the lamp image by using AI. The presentation unit acquires the fault response or countermeasure method of the network device by inputting the questionnaire and the lamp image into a learning model, and presents the acquired fault response or countermeasure method. The learning model includes, for example, a deep learning model and a support vector machine, but is not limited to such examples. The presentation unit can acquire the fault response or countermeasure method by using, for example, a generation AI. As a result, the system according to the embodiment can efficiently present the fault response or countermeasure method of the network device.

[0063] The reception unit reads the two-dimensional code attached to the network device. Examples of the two-dimensional code include, but are not limited to, QR codes and DataMatrix codes. The reception unit can read the two-dimensional code using, for example, a smartphone camera. Specifically, the smartphone camera app is started and the two-dimensional code is placed within the camera frame, so that the content of the code is automatically analyzed and the necessary information is obtained. The reception unit can also improve the reading accuracy of the two-dimensional code using AI. For example, AI can accurately read blurred images and partially missing codes using image recognition technology. Furthermore, AI can analyze codes photographed under different lighting conditions and from different angles with high accuracy. This allows the reception unit to allow the user to easily and quickly read the two-dimensional code and efficiently obtain information about the network device. Furthermore, the reception unit can automatically register the information of the read two-dimensional code in a database and link with other departments and systems. For example, the read information can be sent to a cloud server so that other systems can access it, thereby improving the overall business efficiency.

[0064] The entry unit fills in a questionnaire corresponding to the network device corresponding to the two-dimensional code. The questionnaire includes, for example, details of the malfunction experienced by the user and questions regarding the state of the device, but is not limited to such examples. The entry unit allows, for example, the user to fill in the questionnaire on the screen of a smartphone. Specifically, the user accesses the questionnaire through a smartphone app and answers the questions using a touch screen. The entry unit can also analyze the contents of the questionnaire by using AI and present appropriate questions. For example, the AI ​​can analyze the contents of the user's answers in real time and dynamically change the questions to be presented next, thereby collecting more detailed and relevant information. Furthermore, the AI ​​can automatically add important questions that the user is likely to overlook based on past data and similar cases. This allows the entry unit to allow the user to fill in the questionnaire efficiently and accurately, and to collect detailed information regarding malfunctions of the network device. Furthermore, the entry unit can reduce the burden on the user by automatically saving the contents entered by the user and making them available for later reference. For example, even if the user interrupts input midway, the previous input contents are retained when the user accesses the device again, so the user can smoothly continue input.

[0065] The acquisition unit acquires a lamp image of a lamp of a network device corresponding to a two-dimensional code. The lamp image includes, for example, a shooting angle and a resolution, but is not limited to such examples. The acquisition unit can, for example, shoot a lamp image using a smartphone camera. Specifically, a user aims the smartphone camera at a lamp of a network device and shoots it, and the acquisition unit automatically analyzes the image. The acquisition unit can also improve the acquisition accuracy of the lamp image using AI. For example, the AI ​​can remove noise from the captured image using image processing technology and accurately recognize the state of the lamp. Furthermore, the AI ​​can analyze the state of the lamp with high accuracy even in images taken under different lighting conditions and from different angles. This allows the acquisition unit to allow a user to easily and quickly acquire a lamp image and accurately grasp the state of the network device. Furthermore, the acquisition unit can automatically register the acquired lamp image in a database and link with other departments and systems. For example, the acquired image can be sent to a cloud server and made accessible to other systems, thereby improving the overall business efficiency.

[0066] The presentation unit obtains the fault response or countermeasure method of the network device by inputting the questionnaire and the lamp image into the learning model, and presents the obtained fault response or countermeasure method. The learning model includes, for example, a deep learning model and a support vector machine, but is not limited to such examples. The presentation unit can obtain the fault response or countermeasure method, for example, by using a generation AI. Specifically, the generation AI analyzes the contents of the questionnaire and the lamp image, and generates an optimal fault response method based on past data and similar cases. For example, a deep learning model can learn based on a huge data set and predict the fault pattern of the network device with high accuracy. In addition, a support vector machine can combine the answers to the questionnaire and the features of the lamp image to classify the optimal countermeasure method. This allows the presentation unit to provide the user with a quick and accurate fault response method. Furthermore, the presentation unit can use the generation AI to explain the countermeasure method to the user in an easy-to-understand manner. For example, by showing specific steps using not only text format but also images and videos, the user can easily understand and execute them. In addition, the presentation unit can collect user feedback and continuously improve the accuracy of the learning model. This allows the presentation unit to always provide highly accurate fault handling methods based on the latest information, thereby improving user satisfaction.

[0067] The entry unit can enter a questionnaire including details of the malfunction experienced by the user and questions regarding the state of the device. For example, the entry unit provides a questionnaire including questions such as error messages and occurrence frequency in order to efficiently collect details of the malfunction experienced by the user. The entry unit can also provide a questionnaire including questions regarding the state of the device. For example, the entry unit provides a questionnaire including questions such as operation status and connection status. This makes it possible to efficiently collect details of the malfunction experienced by the user. A part or all of the above-mentioned processing in the entry unit may be performed using, for example, AI, or may be performed without using AI. For example, the entry unit can input the contents of the questionnaire filled in by the user to AI, and the AI ​​can analyze the details of the malfunction.

[0068] The presentation unit can obtain the fault response or the countermeasure method by using the generation AI. The presentation unit can obtain the fault response or the countermeasure method by using, for example, the generation AI. The generation AI includes, for example, GPT and Gemini, but is not limited to such examples. The presentation unit can input a questionnaire and a lamp image to the generation AI and obtain the fault response or the countermeasure method. For example, the presentation unit inputs a prompt to the generation AI, "Please tell me what to do when the lamp of the network device is lit red," and the generation AI outputs a countermeasure method such as restarting the power supply or checking the cable connection. As a result, the accuracy of the fault response or the countermeasure method is improved by using the generation AI. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, by using an AI, or may be performed without using an AI. For example, the presentation unit can input a questionnaire and a lamp image to the generation AI and obtain the fault response or the countermeasure method.

[0069] The reception unit can estimate the user's emotion and adjust the timing of reading the two-dimensional code based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of reading the two-dimensional code based on the estimated user's emotion. For example, a technology such as facial expression recognition or voice analysis is used to estimate the user's emotion, but is not limited to such examples. For example, when the user is feeling stressed, the reception unit delays the timing of reading to provide the user with time to relax. In addition, when the user is in a hurry, the reception unit can also advance the timing of reading to quickly respond. Furthermore, when the user is feeling anxious, the reception unit can adjust the timing of reading to give the user a sense of security. In this way, the user's stress can be reduced by adjusting the timing of reading the two-dimensional code according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the user's emotions.

[0070] The reception unit can analyze the user's past reading history and select an appropriate reading method. The reception unit, for example, analyzes the user's past reading history and selects the optimal reading method. The reading history includes, for example, past success rates and frequency of use, but is not limited to such examples. For example, the reception unit proposes an optimal method based on the reading methods (camera angle, distance, etc.) used by the user in the past. The reception unit can also preferentially propose reading methods that the user has been successful in the past. Furthermore, the reception unit can also propose an optimal reading method for a specific time period from the user's past reading history. As a result, the success rate of reading is improved by proposing the optimal method based on the user's past reading history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past reading history into AI and cause AI to select the optimal reading method.

[0071] The reception unit can perform filtering based on the user's current network environment when reading the two-dimensional code. The reception unit performs filtering based on the user's current network environment when reading the two-dimensional code, for example. The network environment includes, but is not limited to, for example, connection speed and signal strength. For example, when the user's network environment is unstable, the reception unit suspends reading and waits until it stabilizes. In addition, when the user's network environment is high-speed, the reception unit can also perform reading quickly. Furthermore, the reception unit can automatically adjust optimal reading settings based on the user's network environment. This improves the accuracy of reading by adjusting the reading according to the user's network environment. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's network environment data to AI and have the AI ​​perform filtering.

[0072] The reception unit can estimate the user's emotion and determine the priority order of the two-dimensional code to be read based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority order of the two-dimensional code to be read based on the estimated user's emotion. For example, a technology such as facial expression recognition or voice analysis is used to estimate the user's emotion, but the present invention is not limited to such an example. For example, when the user is stressed, the reception unit postpones the code of low importance. In addition, when the user is relaxed, the reception unit can also preferentially read the code of high importance. Furthermore, when the user is in a hurry, the reception unit can also read the most important code first. In this way, the priority order of the two-dimensional code is determined according to the user's emotion, thereby reducing the user's stress. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the present invention is not limited to such an example. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the user's emotions.

[0073] The reception unit can preferentially read highly relevant codes in consideration of the geographical location information of the user when reading the two-dimensional code. For example, the reception unit preferentially reads highly relevant codes in consideration of the geographical location information of the user when reading the two-dimensional code. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the reception unit preferentially reads codes related to the area. In addition, when the user is moving, the reception unit can also preferentially read codes closest to the current location. Furthermore, when the user is in a specific facility, the reception unit can also preferentially read codes related to the facility. This enables efficient response by preferentially reading highly relevant codes based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to select highly relevant codes.

[0074] The reception unit can analyze the social media activity of the user and read the related code when reading the two-dimensional code. For example, the reception unit analyzes the social media activity of the user and reads the related code when reading the two-dimensional code. The social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the reception unit preferentially reads the related code based on the information shared by the user on social media. The reception unit can also preferentially read the code related to the account that the user follows on social media. Furthermore, the reception unit can also preferentially read the code related to the topic in which the user showed interest on social media. As a result, it is possible to respond according to the needs of the user by preferentially reading the related code based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and cause the AI ​​to select the related code.

[0075] The entry unit can estimate the user's emotion and adjust the question content of the questionnaire based on the estimated user's emotion. The entry unit, for example, estimates the user's emotion and adjusts the question content of the questionnaire based on the estimated user's emotion. For example, a technology such as facial expression recognition or voice analysis is used to estimate the user's emotion, but is not limited to such examples. For example, the entry unit provides simple and short questions when the user is stressed. In addition, the entry unit can also provide detailed questions when the user is relaxed. Furthermore, the entry unit can also provide the most important questions preferentially when the user is in a hurry. This makes it possible to reduce the user's stress by adjusting the question content of the questionnaire according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the entry unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the entry unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0076] The entry unit can select the optimal question by referring to the user's past malfunction history when entering. For example, the entry unit selects the optimal question by referring to the user's past malfunction history when entering. The malfunction history includes, for example, past error logs and repair history, but is not limited to such examples. For example, the entry unit provides a related question based on a malfunction experienced by the user in the past. The entry unit can also provide a question related to the most frequently occurring problem from the user's past malfunction history. Furthermore, the entry unit can analyze the user's past malfunction history and select the optimal question. As a result, efficient information collection is possible by providing the optimal question based on the user's past malfunction history. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using AI, or may be performed without using AI. For example, the entry unit can input the user's past malfunction history into AI and cause the AI ​​to select the optimal question.

[0077] The entry unit can customize the question based on the current state of the network device of the user when entering. The entry unit, for example, customizes the question based on the current state of the network device of the user when entering. The state of the network device includes, for example, a connection state and an operation state, but is not limited to such examples. For example, the entry unit provides a general question when the network device of the user is operating normally. In addition, the entry unit can also provide a question regarding a specific problem when the network device of the user shows an abnormality. Furthermore, the entry unit can customize an optimal question based on the state of the network device of the user. This enables efficient information collection by customizing the question based on the state of the network device of the user. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using AI, or may be performed without using AI. For example, the entry unit can input state data of the network device of the user to AI and cause the AI ​​to execute customization of the question.

[0078] The entry unit can estimate the user's emotion and adjust the entry order of the questionnaire based on the estimated user's emotion. The entry unit, for example, estimates the user's emotion and adjusts the entry order of the questionnaire based on the estimated user's emotion. For example, a technology such as facial expression recognition or voice analysis is used to estimate the user's emotion, but is not limited to such examples. For example, the entry unit starts with simple questions when the user is stressed. Also, the entry unit can start with detailed questions when the user is relaxed. Furthermore, the entry unit can provide the most important questions first when the user is in a hurry. In this way, the user's stress can be reduced by adjusting the entry order of the questionnaire according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the entry unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0079] The entry unit can provide an optimal question in consideration of the geographical location information of the user when entering. The entry unit provides an optimal question in consideration of the geographical location information of the user when entering, for example. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the entry unit provides a question related to the area. In addition, when the user is moving, the entry unit can also provide a question based on the current location. Furthermore, when the user is in a specific facility, the entry unit can also provide a question related to the facility. This enables efficient information collection by providing an optimal question based on the geographical location information of the user. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using AI, or may be performed without using AI. For example, the entry unit can input the geographical location information of the user to AI and cause the AI ​​to select the optimal question.

[0080] The entry unit can analyze the user's social media activity at the time of entry and provide related questions. The entry unit can, for example, analyze the user's social media activity at the time of entry and provide related questions. Social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the entry unit can provide related questions based on information shared by the user on social media. The entry unit can also provide questions related to accounts that the user follows on social media. Furthermore, the entry unit can also provide questions related to topics in which the user has shown interest on social media. This enables efficient information collection by providing related questions based on the user's social media activity. A part or all of the above-mentioned processing in the entry unit can be performed, for example, using AI or without using AI. For example, the entry unit can input the user's social media activity data into AI and cause the AI ​​to select related questions.

[0081] The acquisition unit can estimate the user's emotion and adjust the acquisition timing of the lamp image based on the estimated user's emotion. The acquisition unit, for example, estimates the user's emotion and adjusts the acquisition timing of the lamp image based on the estimated user's emotion. For example, a technology such as facial expression recognition or voice analysis is used to estimate the user's emotion, but is not limited to such an example. For example, when the user is feeling stressed, the acquisition unit delays the acquisition timing to provide the user with time to relax. In addition, when the user is in a hurry, the acquisition unit can also advance the acquisition timing to respond quickly. Furthermore, when the user is feeling anxious, the acquisition unit can adjust the acquisition timing to give the user a sense of security. In this way, the user's stress can be reduced by adjusting the acquisition timing of the lamp image according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the user's emotions.

[0082] The acquisition unit can analyze the user's past lamp image acquisition history and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past lamp image acquisition history and selects the optimal acquisition method. The lamp image acquisition history includes, for example, past success rates and frequency of use, but is not limited to such examples. For example, the acquisition unit proposes the optimal method based on the acquisition method (camera angle, distance, etc.) used by the user in the past. The acquisition unit can also preferentially propose an acquisition method that the user has been successful in the past. Furthermore, the acquisition unit can also propose the optimal acquisition method for a specific time period from the user's past acquisition history. As a result, the success rate of acquisition is improved by suggesting the optimal method based on the user's past acquisition history. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's past acquisition history to AI and cause the AI ​​to select the optimal acquisition method.

[0083] The acquisition unit can perform filtering based on the current state of the user's network device when acquiring the lamp image. The acquisition unit performs filtering based on the current state of the user's network device when acquiring the lamp image, for example. The state of the network device includes, for example, a connection state and an operation state, but is not limited to such examples. For example, the acquisition unit provides a general acquisition method when the user's network device is operating normally. In addition, the acquisition unit can also provide an acquisition method related to a specific problem when the user's network device shows an abnormality. Furthermore, the acquisition unit can customize an optimal acquisition method based on the state of the user's network device. This improves the accuracy of acquisition by adjusting the acquisition according to the state of the user's network device. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input state data of the user's network device to AI and cause the AI ​​to perform filtering.

[0084] The acquisition unit can estimate the user's emotion and determine the priority order of the lamp images to be acquired based on the estimated user's emotion. The acquisition unit, for example, estimates the user's emotion and determines the priority order of the lamp images to be acquired based on the estimated user's emotion. For example, a technology such as facial expression recognition or voice analysis is used to estimate the user's emotion, but is not limited to such an example. For example, when the user is stressed, the acquisition unit postpones images of low importance. In addition, when the user is relaxed, the acquisition unit can also preferentially acquire images of high importance. Furthermore, when the user is in a hurry, the acquisition unit can also acquire the most important image first. In this way, the user's stress can be reduced by determining the priority order of the lamp images according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the user's emotions.

[0085] The acquisition unit can preferentially acquire highly relevant images in consideration of the geographical location information of the user when acquiring a lamp image. For example, the acquisition unit preferentially acquires highly relevant images in consideration of the geographical location information of the user when acquiring a lamp image. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the acquisition unit preferentially acquires images related to the area. In addition, when the user is moving, the acquisition unit can also preferentially acquire images closest to the current location. Furthermore, when the user is in a specific facility, the acquisition unit can also preferentially acquire images related to the facility. This enables efficient response by preferentially acquiring highly relevant images based on the geographical location information of the user. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the user to AI and cause the AI ​​to select highly relevant images.

[0086] The acquisition unit can analyze the social media activity of the user and acquire related images when acquiring the lamp image. For example, the acquisition unit analyzes the social media activity of the user and acquires related images when acquiring the lamp image. The social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the acquisition unit preferentially acquires related images based on information shared by the user on social media. The acquisition unit can also preferentially acquire images related to accounts that the user follows on social media. Furthermore, the acquisition unit can also preferentially acquire images related to topics in which the user has shown interest on social media. This makes it possible to respond to the needs of the user by preferentially acquiring related images based on the user's social media activity. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data into AI and cause the AI ​​to select related images.

[0087] The presentation unit can estimate the user's emotion and adjust the presentation method of the fault response or the countermeasure method based on the estimated user's emotion. The presentation unit, for example, estimates the user's emotion and adjusts the presentation method of the fault response or the countermeasure method based on the estimated user's emotion. For example, a technology such as facial expression recognition or voice analysis is used for estimating the user's emotion, but is not limited to such examples. For example, when the user is stressed, the presentation unit provides a presentation method that is simple and highly visible. In addition, when the user is relaxed, the presentation unit can also provide a presentation method that includes detailed information. Furthermore, when the user is in a hurry, the presentation unit can also provide a presentation method that focuses on the main points. In this way, the presentation method can be adjusted according to the user's emotion, thereby reducing the user's stress. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the presentation unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the user's emotions.

[0088] The presentation unit can select the optimal countermeasure by referring to the user's past failure response history at the time of presentation. For example, the presentation unit selects the optimal countermeasure by referring to the user's past failure response history at the time of presentation. The failure response history includes, for example, past repair history and troubleshooting log, but is not limited to such examples. For example, the presentation unit provides a related countermeasure based on a failure experienced by the user in the past. The presentation unit can also provide the most effective countermeasure from the user's past failure response history. Furthermore, the presentation unit can analyze the user's past failure response history and select the optimal countermeasure. As a result, efficient response is possible by providing the optimal countermeasure based on the user's past failure response history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the user's past failure response history into AI and cause the AI ​​to select the optimal countermeasure.

[0089] The presentation unit can customize the countermeasure based on the current state of the network device of the user at the time of presentation. The presentation unit customizes the countermeasure based on the current state of the network device of the user at the time of presentation, for example. The state of the network device includes, for example, a connection state and an operation state, but is not limited to such examples. For example, the presentation unit provides a general countermeasure when the network device of the user is operating normally. In addition, the presentation unit can provide a countermeasure for a specific problem when the network device of the user shows an abnormality. Furthermore, the presentation unit can customize an optimal countermeasure based on the state of the network device of the user. This enables efficient response by customizing the countermeasure based on the state of the network device of the user. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input state data of the network device of the user to AI and cause the AI ​​to execute customization of the countermeasure.

[0090] The presentation unit can estimate the user's emotion and adjust the presentation order of the fault response or the countermeasures based on the estimated user's emotion. The presentation unit, for example, estimates the user's emotion and adjusts the presentation order of the fault response or the countermeasures based on the estimated user's emotion. For example, a technology such as facial expression recognition or voice analysis is used for estimating the user's emotion, but is not limited to such examples. For example, when the user is feeling stressed, the presentation unit presents a simple countermeasure first. In addition, when the user is relaxed, the presentation unit can also present a detailed countermeasure first. Furthermore, when the user is in a hurry, the presentation unit can also present the most important countermeasure first. In this way, the user's stress can be reduced by adjusting the presentation order according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the user's emotions.

[0091] The presentation unit can provide an optimal solution in consideration of the geographical location information of the user at the time of presentation. The presentation unit, for example, provides an optimal solution in consideration of the geographical location information of the user at the time of presentation. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is in a specific area, the presentation unit provides a solution related to the area. In addition, when the user is moving, the presentation unit can also provide a solution based on the current location. Furthermore, when the user is in a specific facility, the presentation unit can also provide a solution related to the facility. This allows efficient response by providing an optimal solution based on the geographical location information of the user. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the geographical location information of the user to AI and cause the AI ​​to select the optimal solution.

[0092] The presentation unit can analyze the user's social media activity at the time of presentation and provide a related countermeasure. The presentation unit can, for example, analyze the user's social media activity at the time of presentation and provide a related countermeasure. The social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the presentation unit provides a related countermeasure based on information shared by the user on social media. The presentation unit can also provide a countermeasure related to an account that the user follows on social media. Furthermore, the presentation unit can also provide a countermeasure related to a topic in which the user has shown interest on social media. This makes it possible to respond according to the user's needs by providing a related countermeasure based on the user's social media activity. A part or all of the above-mentioned processing in the presentation unit can be performed, for example, using AI or without using AI. For example, the presentation unit can input the user's social media activity data into AI and cause the AI ​​to select a related countermeasure.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The reception unit can analyze the user's past inquiry history and select the optimal response method. For example, the reception unit can analyze what kind of problem the user has made an inquiry about in the past and propose a method for quickly responding when a similar problem recurs. The reception unit can also preferentially propose a response method that the user has been successful in the past. Furthermore, the reception unit can also propose the optimal response method for a specific time period from the user's past inquiry history. This improves the success rate of the response by suggesting the optimal method based on the user's past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past inquiry history into AI and have the AI ​​select the optimal response method.

[0095] The entry unit can customize the questions based on the current state of the user's network device. For example, the entry unit provides a general question when the user's network device is operating normally. In addition, the entry unit can provide a question related to a specific problem when the user's network device shows an abnormality. Furthermore, the entry unit can customize an optimal question based on the state of the user's network device. This enables efficient information collection by customizing the question based on the state of the user's network device. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using AI or may be performed without using AI. For example, the entry unit can input state data of the user's network device to AI and cause AI to customize the question.

[0096] The presentation unit can estimate the user's emotion and adjust the presentation method of the failure response or the countermeasure method based on the estimated user's emotion. For example, when the user is stressed, the presentation unit provides a simple and highly visible presentation method. Furthermore, when the user is relaxed, the presentation unit can also provide a presentation method including detailed information. Furthermore, when the user is in a hurry, the presentation unit can also provide a presentation method that focuses on the main points. This makes it possible to reduce the user's stress by adjusting the presentation method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0097] The acquisition unit can analyze the user's past lamp image acquisition history and select the optimal acquisition method. For example, the acquisition unit suggests the optimal method based on the acquisition method (camera angle, distance, etc.) used by the user in the past. The acquisition unit can also preferentially suggest an acquisition method that the user has been successful in the past. Furthermore, the acquisition unit can also suggest the optimal acquisition method for a specific time period from the user's past acquisition history. This improves the success rate of acquisition by suggesting the optimal method based on the user's past acquisition history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the user's past acquisition history into AI and cause AI to select the optimal acquisition method.

[0098] The reception unit can estimate the user's emotion and determine the priority of the two-dimensional code to be read based on the estimated user's emotion. For example, when the user is stressed, the reception unit postpones reading a code with a low importance. Also, when the user is relaxed, the reception unit can preferentially read a code with a high importance. Furthermore, when the user is in a hurry, the reception unit can read the most important code first. In this way, the priority of the two-dimensional code is determined according to the user's emotion, thereby reducing the user's stress. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0099] The entry unit can select the most appropriate question by referring to the user's past malfunction history when entering the information. For example, the entry unit provides a related question based on a malfunction that the user has experienced in the past. The entry unit can also provide a question related to the most frequently occurring problem from the user's past malfunction history. Furthermore, the entry unit can analyze the user's past malfunction history and select the most appropriate question. This enables efficient information collection by providing the most appropriate question based on the user's past malfunction history. A part or all of the above-mentioned processing in the entry unit may be performed, for example, using AI or may be performed without using AI. For example, the entry unit can input the user's past malfunction history into AI and cause AI to select the most appropriate question.

[0100] The presentation unit can estimate the user's emotion and adjust the presentation order of the fault response or the countermeasures based on the estimated user's emotion. For example, when the user is stressed, the presentation unit presents the simple countermeasures first. Also, when the user is relaxed, the presentation unit can present the detailed countermeasures first. Furthermore, when the user is in a hurry, the presentation unit can present the most important countermeasure first. This allows the user's stress to be reduced by adjusting the presentation order according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0101] The acquisition unit can perform filtering based on the current state of the user's network device when acquiring the lamp image. For example, the acquisition unit provides a general acquisition method when the user's network device is operating normally. In addition, the acquisition unit can provide an acquisition method related to a specific problem when the user's network device shows an abnormality. Furthermore, the acquisition unit can customize an optimal acquisition method based on the state of the user's network device. This improves the accuracy of acquisition by adjusting the acquisition according to the state of the user's network device. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input state data of the user's network device to AI and have the AI ​​perform filtering.

[0102] The entry unit can estimate the user's emotion and adjust the content of the questions in the questionnaire based on the estimated user's emotion. For example, the entry unit provides simple and short questions when the user is stressed. In addition, the entry unit can also provide detailed questions when the user is relaxed. Furthermore, the entry unit can provide the most important questions preferentially when the user is in a hurry. This allows the user's stress to be reduced by adjusting the content of the questions in the questionnaire according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the entry unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the entry unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0103] The presentation unit can select the optimal countermeasure by referring to the user's past failure response history at the time of presentation. For example, the presentation unit provides a related countermeasure based on a failure experienced by the user in the past. The presentation unit can also provide the most effective countermeasure from the user's past failure response history. Furthermore, the presentation unit can analyze the user's past failure response history and select the optimal countermeasure. As a result, efficient response is possible by providing the optimal countermeasure based on the user's past failure response history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the presentation unit can input the user's past failure response history into AI and cause AI to select the optimal countermeasure.

[0104] The process flow of the second embodiment will be briefly described below.

[0105] Step 1: The reception unit reads a two-dimensional code attached to a network device. Examples of two-dimensional codes include, but are not limited to, QR codes and Data Matrix codes. The reception unit can read the two-dimensional code using, for example, a smartphone camera. The reception unit can also improve the reading accuracy of the two-dimensional code using AI. Step 2: The entry unit fills in a questionnaire corresponding to the network device corresponding to the two-dimensional code. The questionnaire includes, for example, details of the malfunction experienced by the user and questions regarding the state of the device, but is not limited to such examples. The entry unit allows the user to fill in the questionnaire on the screen of a smartphone, for example. The entry unit can also use AI to analyze the contents of the questionnaire and present appropriate questions. Step 3: The acquisition unit acquires a lamp image obtained by capturing an image of a lamp of a network device corresponding to the two-dimensional code. The lamp image includes, for example, a capture angle and a resolution, but is not limited to such examples. The acquisition unit can capture the lamp image using, for example, a smartphone camera. The acquisition unit can also improve the capture accuracy of the lamp image using AI. Step 4: The presentation unit inputs the questionnaire and the lamp image into the learning model to obtain the fault response or countermeasure method for the network device, and presents the obtained fault response or countermeasure method. The learning model includes, but is not limited to, a deep learning model or a support vector machine. The presentation unit can obtain the fault response or countermeasure method by using, for example, a generation AI.

[0106] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0107] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0108] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0109] For example, the reception unit can read a two-dimensional code using the camera 42 of the smart device 14. For example, the entry unit can fill in a questionnaire using the touch panel 38A of the smart device 14. For example, the acquisition unit can take a lamp image using the camera 42 of the smart device 14. For example, the presentation unit can acquire a fault response or a countermeasure method by the specific processing unit 290 of the data processing device 12 and present it using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] 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.

[0114] 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 the voice according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0116] 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 performed in a secure state.

[0117] Fig. 4 shows an example of 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.

[0118] 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.

[0119] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0122] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0123] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0124] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0125] For example, the reception unit can read a two-dimensional code using the camera 42 of the smart glasses 214. For example, the entry unit can fill out a questionnaire by voice input using the microphone 238 of the smart glasses 214. For example, the acquisition unit can capture a lamp image using the camera 42 of the smart glasses 214. For example, the presentation unit can acquire a failure response or a countermeasure method by the specific processing unit 290 of the data processing device 12, and present it by voice using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] 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.

[0130] 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 the voice according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0132] 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 performed in a secure state.

[0133] 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.

[0134] 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.

[0135] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0136] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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 identification processing unit 290 using these models.

[0137] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0138] 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 voice indicating a user input for 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.

[0139] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0140] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0141] For example, the reception unit can read a two-dimensional code using the camera 42 of the headset terminal 314. For example, the entry unit can enter a medical questionnaire by voice input using the microphone 238 of the headset terminal 314. For example, the acquisition unit can take a lamp image using the camera 42 of the headset terminal 314. For example, the presentation unit can acquire a fault response or a countermeasure method by the specific processing unit 290 of the data processing device 12, and present it using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] 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.

[0146] 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 the voice according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0148] 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 performed in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0150] 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.

[0151] 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.

[0152] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0154] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] 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 a voice indicating a user input for 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.

[0156] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0158] For example, the reception unit can read a two-dimensional code using the camera 42 of the robot 414. For example, the entry unit can enter a medical questionnaire by voice input using the microphone 238 of the robot 414. For example, the acquisition unit can take a lamp image using the camera 42 of the robot 414. For example, the presentation unit can acquire a fault response or a countermeasure method by the specific processing unit 290 of the data processing device 12, and present it by voice using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0160] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0162] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0163] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0164] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0166] 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.

[0167] 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 Universal Serial Bus (USB) 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.

[0168] In addition, 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 upon request from the data processing device 12.

[0169] 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.

[0170] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0171] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0172] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0173] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0175] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0177] (Appendix 1) A reception unit that reads a two-dimensional code attached to a network device; a filling-in section for filling in a questionnaire corresponding to a network device corresponding to the two-dimensional code; an acquisition unit that acquires a lamp image by capturing an image of a lamp of a network device corresponding to the two-dimensional code; a presentation unit that acquires a fault response and a countermeasure method for the network device by inputting the questionnaire and the lamp image into a learning model, and presents the acquired fault response and countermeasure method. A system characterized by: (Appendix 2) The entry section includes: Complete a questionnaire that includes details of the problem the user is experiencing and questions about the condition of the equipment 2. The system of claim 1, (Appendix 3) The presentation unit is Using generative AI to respond to malfunctions or obtain solutions 2. The system of claim 1, (Appendix 4) The reception unit is Estimate the user's emotions and adjust the timing of reading the 2D code based on the estimated user emotions. 2. The system of claim 1, (Appendix 5) The reception unit is Analyze the user's past reading history and select the appropriate reading method 2. The system of claim 1, (Appendix 6) The reception unit is When reading a 2D code, filtering is performed based on the user's current network environment. 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and determine the priority of the 2D codes to be read based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The reception unit is When reading 2D codes, the system takes into account the user's geographic location information and prioritizes reading codes that are more relevant to the user. 2. The system of claim 1, (Appendix 9) The reception unit is When a 2D code is read, the system analyzes the user's social media activity and reads related codes. 2. The system of claim 1, (Appendix 10) The entry section includes: To estimate a user's emotion and adjust the questions in a questionnaire based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The entry section includes: When filling out the form, the most appropriate questions are selected by referring to the user's past defect history. 2. The system of claim 1, (Appendix 12) The entry section includes: When filling out, the questions are tailored based on the user's current network equipment state 2. The system of claim 1, (Appendix 13) The entry section includes: The user's emotions are estimated, and the order in which the questionnaire is filled out is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 14) The entry section includes: When filling out a form, it takes into account the user's geographic location to provide the most appropriate questions 2. The system of claim 1, (Appendix 15) The entry section includes: As you fill out the form, we analyze your social media activity and provide you with relevant questions. 2. The system of claim 1, (Appendix 16) The acquisition unit is To estimate a user's emotion and adjust a timing of acquiring a lamp image based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The acquisition unit is Analyze the user's past lamp image acquisition history and select the optimal acquisition method 2. The system of claim 1, (Appendix 18) The acquisition unit is When acquiring lamp images, filtering is performed based on the current state of the user's network equipment. 2. The system of claim 1, (Appendix 19) The acquisition unit is A user's emotion is estimated, and a priority order of lamp images to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The acquisition unit is When acquiring lamp images, the system prioritizes acquiring images that are more relevant to the user based on the user's geographic location information. 2. The system of claim 1, (Appendix 21) The acquisition unit is When a lamp image is captured, the user's social media activity is analyzed to capture related images. 2. The system of claim 1, (Appendix 22) The presentation unit is Inferring the user's emotions and adjusting the way in which a fault is handled or a solution is presented based on the inferred user's emotions 2. The system of claim 1, (Appendix 23) The presentation unit is When presenting the problem, the system selects the optimal solution by referring to the user's past troubleshooting history. 2. The system of claim 1, (Appendix 24) The presentation unit is When presented, customize the response based on the user's current network device status 2. The system of claim 1, (Appendix 25) The presentation unit is Inferring the user's emotions and adjusting the order of presenting troubleshooting or solutions based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The presentation unit is When presented, the system takes into account the user's geographic location to provide the best possible solution. 2. The system of claim 1, (Appendix 27) The presentation unit is When presented, analyze the user's social media activity and provide relevant actionable information 2. The system of claim 1, [Explanation of symbols]

[0178] 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 reception unit that reads a two-dimensional code attached to a network device; a filling-in section for filling in a questionnaire corresponding to a network device corresponding to the two-dimensional code; an acquisition unit that acquires a lamp image by capturing an image of a lamp of a network device corresponding to the two-dimensional code; a presentation unit that acquires a fault response and a countermeasure method for the network device by inputting the questionnaire and the lamp image into a learning model, and presents the acquired fault response and countermeasure method. A system characterized by:

2. The entry section includes: Complete a questionnaire that includes details of the problem the user is experiencing and questions about the condition of the equipment 2. The system of claim 1 .

3. The presentation unit is Use generative AI to respond to failures or obtain solutions 2. The system of claim 1 .

4. The reception unit is To estimate a user's emotion and adjust a timing of reading a two-dimensional code based on the estimated user's emotion.

2. The system of claim 1 .

5. The reception unit is Analyze the user's past reading history and select the appropriate reading method 2. The system of claim 1 .

6. The reception unit is When reading a 2D code, filtering is performed based on the user's current network environment.

2. The system of claim 1 .

7. The reception unit is A user's emotion is estimated, and a priority order of two-dimensional codes to be read is determined based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is When reading 2D codes, the system takes into account the user's geographic location information and prioritizes reading codes that are more relevant to the user.

2. The system of claim 1 .

9. The reception unit is When the 2D code is read, the user's social media activity is analyzed and related codes are read.

2. The system of claim 1 .

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