system

Abstract

The system according to this embodiment aims to quickly and accurately communicate the diagnostic results of communication equipment to the inspector and to provide specific corrective procedures. [Solution] The system according to the embodiment comprises a diagnostic unit, an audio output unit, and a procedure generation unit. The diagnostic unit performs a self-diagnosis of the communication equipment. The audio output unit communicates the diagnostic results obtained by the diagnostic unit to the inspector by voice. The procedure generation unit generates specific countermeasures based on the diagnostic results obtained by the diagnostic unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to quickly and accurately convey the diagnosis result of communication equipment to the inspector, and it also takes time to generate countermeasure procedures.

[0005] The system according to the embodiment aims to quickly and accurately convey the diagnosis result of communication equipment to the inspector and provide specific countermeasure procedures.

Means for Solving the Problems

[0006] The system according to the embodiment includes a diagnosis unit, an audio output unit, and a procedure generation unit. The diagnosis unit performs self-diagnosis of communication equipment. The audio output unit conveys the diagnosis result obtained by the diagnosis unit to the inspector by voice. The procedure generation unit generates specific countermeasure procedures based on the diagnosis result obtained by the diagnosis unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and accurately convey the diagnostic results of communication equipment to the inspector and provide specific corrective procedures. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment. [[ID=​​​​​​​​​​​​​​The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The self-diagnosis system for communication equipment according to an embodiment of the present invention is a system in which the communication equipment performs a self-diagnosis and communicates the results to an inspector by voice. This system allows the communication equipment to perform a self-diagnosis and communicate the results to the inspector by voice. Next, if a malfunction is found as a result of the self-diagnosis, the system provides the inspector with specific corrective action procedures by voice. This system is expected to help maintain the skills of inspectors, operators, and maintenance personnel and reduce their workload. Furthermore, it includes a function to record the self-diagnosis results for later analysis and a function to provide feedback on the results of the inspector's execution of the corrective action procedures. This supports the normal operation of the communication equipment and improves the work efficiency of inspectors. For example, the communication equipment performs a self-diagnosis. During this process, it checks each part of the communication equipment in detail to confirm that there are no abnormalities. For example, it diagnoses the status of the communication line and the operating status of the equipment. Next, it communicates the results of the self-diagnosis to the inspector by voice. For example, it provides a specific message by voice, such as "There is an abnormality in the communication line." Furthermore, if a malfunction is found as a result of the self-diagnosis, the system provides the inspector with specific corrective action procedures by voice. For example, it provides specific instructions by voice, such as "Please check the connection of the communication line." This allows the inspector to take action quickly. Furthermore, it includes a function to record the results of self-diagnosis for later analysis. This allows users to refer to past diagnosis results and understand the status of the communication equipment. It also includes a function to provide feedback on the results of the inspector's corrective actions. For example, it can receive feedback such as, "I have confirmed the connection of the communication line." This allows for real-time monitoring of the communication equipment's status and appropriate action to be taken. In this way, the self-diagnosis system for communication equipment can help maintain the skills of inspectors, operators, and maintenance personnel, reduce their workload, and support the normal operation of the communication equipment.

[0029] The communication equipment self-diagnosis system according to this embodiment comprises a diagnostic unit, an audio output unit, and a procedure generation unit. The diagnostic unit performs a self-diagnosis of the communication equipment. The diagnostic unit, for example, checks each part of the communication equipment in detail to confirm whether there are any abnormalities. For example, the diagnostic unit can diagnose the status of the communication line and the operating status of the equipment. The diagnostic unit can also analyze the operation log of the communication equipment and detect abnormalities. For example, the diagnostic unit can detect delays and packet losses in the communication line and report the abnormalities. The audio output unit conveys the diagnostic results obtained by the diagnostic unit to the inspector by voice. The audio output unit conveys the diagnostic results by voice as a specific message. For example, the audio output unit can convey a message such as "There is an abnormality in the communication line" by voice. The audio output unit can also output the diagnostic results by voice in real time. For example, the audio output unit conveys the diagnostic results by voice immediately as soon as they are obtained. The procedure generation unit generates specific countermeasures based on the diagnostic results obtained by the diagnostic unit. The procedure generation unit generates specific countermeasures based on the diagnostic results. For example, the procedure generation unit can generate specific instructions such as, "Please check the connection of the communication line." The procedure generation unit can also generate multiple corrective procedures based on the diagnostic results and present them with priority. For example, the procedure generation unit can generate a procedure such as, "First, check the connection of the communication line, and then restart the equipment." As a result, the self-diagnosis system for communication equipment according to this embodiment can improve the work efficiency of inspectors by conveying the self-diagnosis results of the communication equipment by voice and generating specific corrective procedures.

[0030] The system includes a recording unit for recording diagnostic results. The recording unit records the diagnostic results. For example, the recording unit can record the diagnostic results as digital data. For example, the recording unit can record the diagnostic results in text format or CSV format. The recording unit can also save the diagnostic results to cloud storage. For example, the recording unit can upload the diagnostic results to cloud storage for later access. Furthermore, the recording unit can record the diagnostic results chronologically, allowing reference to past diagnostic results. For example, the recording unit can organize the diagnostic results by date, making it easy to search for past diagnostic results. This allows for later analysis by recording the diagnostic results. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the diagnostic results into AI, which can then automatically organize and record the data.

[0031] The system includes a feedback receiving unit that receives feedback from inspectors. The feedback receiving unit receives feedback from inspectors. For example, the feedback receiving unit can provide feedback on the results of the inspector's execution of corrective procedures. For example, the feedback receiving unit can receive feedback such as, "I have confirmed the connection of the communication line." The feedback receiving unit can also receive feedback in real time. For example, the feedback receiving unit can receive feedback immediately after the inspector has executed a corrective procedure. Furthermore, the feedback receiving unit can record the feedback as digital data. For example, the feedback receiving unit can record the feedback in text format or CSV format. This allows for real-time monitoring of the communication equipment status and appropriate action to be taken by receiving feedback from inspectors. Some or all of the above-described processing in the feedback receiving unit may be performed using AI, or without AI. For example, the feedback receiving unit can input the inspector's feedback into AI, which can then automatically organize and record the data.

[0032] The diagnostic unit can specifically check each part of the communication equipment to confirm that there are no abnormalities. For example, the diagnostic unit can check each part of the communication equipment in detail to confirm that there are no abnormalities. For example, the diagnostic unit can diagnose the status of communication lines and the operating status of equipment. The diagnostic unit can also analyze the operation logs of the communication equipment to detect abnormalities. For example, the diagnostic unit can detect delays and packet loss in communication lines and report the abnormalities. Furthermore, the diagnostic unit can periodically check each part of the communication equipment to confirm that there are no abnormalities. For example, the diagnostic unit can perform diagnoses on a regular schedule such as daily, weekly, or monthly. This allows for early detection of abnormalities by checking each part of the communication equipment in detail. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the operation logs of the communication equipment into the AI, which can automatically detect abnormalities.

[0033] The voice output unit can communicate diagnostic results as specific messages in voice. For example, the voice output unit can communicate a message such as, "There is an abnormality in the communication line." The voice output unit can also output diagnostic results in real time. For example, the voice output unit can communicate the diagnostic results immediately as soon as they are obtained. Furthermore, the voice output unit can output diagnostic results in multiple languages. For example, the voice output unit can communicate diagnostic results in multiple languages ​​such as Japanese, English, and French. This allows inspectors to respond quickly by communicating diagnostic results as specific messages in voice. Some or all of the above processing in the voice output unit may be performed using AI, or not. For example, the voice output unit can input diagnostic results into AI, which can then automatically generate and output voice messages.

[0034] The procedure generation unit can generate specific corrective actions based on the diagnostic results. For example, the procedure generation unit can generate specific instructions such as, "Please check the connection of the communication line." The procedure generation unit can also generate multiple corrective actions based on the diagnostic results and present them with priority. For example, the procedure generation unit can generate instructions such as, "First, check the connection of the communication line, and then restart the equipment." Furthermore, the procedure generation unit can record the corrective actions as digital data. For example, the procedure generation unit can record the corrective actions in text format or CSV format. This allows inspectors to take appropriate action by generating specific corrective actions based on the diagnostic results. Some or all of the above-described processes in the procedure generation unit may be performed using AI, for example, or without AI. For example, the procedure generation unit can input the diagnostic results into AI, and the AI ​​can automatically generate corrective actions.

[0035] The diagnostic unit can analyze the past diagnostic history of communication equipment and select an appropriate diagnostic method. For example, the diagnostic unit can analyze the past diagnostic history of communication equipment and select the optimal diagnostic method. For example, the diagnostic unit can identify frequently occurring problems from the past diagnostic history and prioritize diagnostic methods for those problems. The diagnostic unit can also predict problems that are likely to occur during specific time periods based on the past diagnostic history and select a diagnostic method suitable for that time period. Furthermore, the diagnostic unit can analyze the past diagnostic history and select a diagnostic method for problems that are likely to occur under specific environmental conditions. In this way, the optimal diagnostic method can be selected by analyzing the past diagnostic history. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input past diagnostic history data into a generating AI, which can automatically analyze the data and select the optimal diagnostic method.

[0036] The diagnostic unit can customize diagnostic items during diagnosis based on the usage status and environmental conditions of the communication equipment. For example, if the communication equipment is used frequently, the diagnostic unit will focus on diagnosing critical parts. Furthermore, if the communication equipment is in a high-temperature environment, the diagnostic unit can prioritize diagnosing temperature-related parts. Additionally, if the communication equipment is in a high-humidity environment, the diagnostic unit can focus on diagnosing humidity-related parts. This allows for more appropriate diagnosis by customizing diagnostic items based on usage status and environmental conditions. Some or all of the above-described processes in the diagnostic unit may be performed using AI, or without AI. For example, the diagnostic unit can input data on the communication equipment's usage status and environmental conditions into a generating AI, which can then automatically analyze the data and customize the diagnostic items.

[0037] The diagnostic unit can prioritize checking highly relevant diagnostic items based on the geographical location information of the communication equipment during diagnosis. For example, if the communication equipment is located in an urban area, the diagnostic unit will prioritize checking diagnostic items related to radio interference. If the communication equipment is located in a mountainous area, the diagnostic unit can also prioritize checking diagnostic items related to power supply. Furthermore, if the communication equipment is located near the coast, the diagnostic unit can also prioritize checking diagnostic items related to salt damage. This allows for more appropriate diagnosis by prioritizing diagnostic items based on geographical location information. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the geographical location information of the communication equipment into a generating AI, which can automatically analyze the data and prioritize checking highly relevant diagnostic items.

[0038] The diagnostic unit can analyze the social media activity of communication equipment during diagnosis and add relevant diagnostic items. For example, the diagnostic unit can analyze social media posts about communication equipment and add diagnostic items related to frequently reported problems. The diagnostic unit can also add diagnostic items for specific problems based on user feedback on social media. Furthermore, the diagnostic unit can analyze trends on social media and add relevant diagnostic items. This allows for the addition of relevant diagnostic items by analyzing social media activity. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input social media data into a generating AI, which can automatically analyze the data and add relevant diagnostic items.

[0039] The voice output unit can adjust the level of detail in the message based on the importance of the diagnostic result when outputting voice. For example, the voice output unit can deliver a message with a detailed explanation if the diagnostic result is important. It can also deliver a concise message if the diagnostic result is minor. Furthermore, it can deliver a message containing the necessary information if the diagnostic result is moderate. This allows for the appropriate delivery of necessary information by adjusting the level of detail in the message based on the importance of the diagnostic result. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, the voice output unit can input diagnostic result importance data into a generating AI, which can then automatically analyze the data and adjust the level of detail in the message.

[0040] The voice output unit can customize the content of messages based on the inspector's past feedback history when outputting voice messages. For example, the voice output unit can customize the content of messages based on the inspector's past feedback history when outputting voice messages. For example, the voice output unit can prioritize messages related to problems previously pointed out by the inspector. The voice output unit can also customize the content of messages based on the message format preferred by the inspector in the past. Furthermore, the voice output unit can adjust the content of messages based on feedback previously provided by the inspector. By customizing the content of messages based on past feedback history, more useful information can be provided to the inspector. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, the voice output unit can input the inspector's past feedback history data into a generating AI, which can then automatically analyze the data and customize the content of the messages.

[0041] The voice output unit can determine message priorities based on the submission date of the diagnostic results when outputting voice messages. For example, the voice output unit can prioritize messages based on the submission date of the diagnostic results when outputting voice messages. For example, in the case of urgent diagnostic results, the voice output unit can deliver the message with the highest priority. The voice output unit can also deliver messages with normal priority in the case of regular diagnostic results. Furthermore, the voice output unit can deliver messages with lower priority in the case of past diagnostic results. This allows important information to be conveyed quickly by determining message priorities based on the submission date of the diagnostic results. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, the voice output unit can input diagnostic result submission date data into a generating AI, which can automatically analyze the data and determine message priorities.

[0042] The voice output unit can adjust the order of messages based on the relevance of the diagnostic results when outputting voice. For example, the voice output unit can deliver the most relevant diagnostic results first. It can also deliver less relevant diagnostic results later. Furthermore, it can deliver moderately relevant diagnostic results in the middle. By adjusting the order of messages based on the relevance of the diagnostic results, important information can be delivered preferentially. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, the voice output unit can input the relevance data of the diagnostic results into a generating AI, which can automatically analyze the data and adjust the order of the messages.

[0043] The procedure generation unit can adjust the level of detail of the procedure based on the importance of the diagnostic result when generating the procedure. For example, the procedure generation unit can provide a detailed procedure for important diagnostic results. It can also provide a concise procedure for minor diagnostic results. Furthermore, it can provide a procedure containing the necessary information for moderate diagnostic results. By adjusting the level of detail of the procedure based on the importance of the diagnostic result, the necessary information can be appropriately provided. Some or all of the above processing in the procedure generation unit may be performed using AI, for example, or without AI. For example, the procedure generation unit can input diagnostic result importance data into a generation AI, which can automatically analyze the data and adjust the level of detail of the procedure.

[0044] The procedure generation unit can apply different procedure generation algorithms depending on the category of communication equipment when generating procedures. For example, the procedure generation unit can apply a procedure generation algorithm specifically for communication lines in the case of a problem related to communication lines. It can also apply a procedure generation algorithm specifically for equipment in the case of a problem related to equipment operation. Furthermore, it can apply a procedure generation algorithm specifically for software in the case of a problem related to software. This allows for the application of the most appropriate procedure generation algorithm for each category of communication equipment, thereby providing more appropriate troubleshooting procedures. Some or all of the above-described processes in the procedure generation unit may be performed using AI, for example, or without AI. For example, the procedure generation unit can input communication equipment category data into a generation AI, which can automatically analyze the data and apply the most appropriate procedure generation algorithm.

[0045] The procedure generation unit can determine the priority of procedures based on the submission timing of diagnostic results when generating procedures. For example, the procedure generation unit can provide the highest priority action procedure in the case of an urgent diagnostic result. It can also provide the normal priority action procedure in the case of a regular diagnostic result. Furthermore, it can provide the lower priority action procedure in the case of a past diagnostic result. This allows for the rapid provision of important action procedures by determining the priority of procedures based on the submission timing of diagnostic results. Some or all of the above processing in the procedure generation unit may be performed using AI, for example, or without AI. For example, the procedure generation unit can input diagnostic result submission timing data into a generation AI, which can automatically analyze the data and determine the priority of procedures.

[0046] The procedure generation unit can adjust the order of procedures based on the relevance of the diagnostic results during procedure generation. For example, the procedure generation unit can provide the procedures for the most relevant diagnostic results first. It can also postpone procedures for less relevant diagnostic results. Furthermore, it can provide procedures for moderately relevant diagnostic results in the middle. By adjusting the order of procedures based on the relevance of the diagnostic results, important countermeasures can be provided preferentially. Some or all of the above processing in the procedure generation unit may be performed using AI, for example, or not using AI. For example, the procedure generation unit can input the relevance data of the diagnostic results into a generating AI, which can automatically analyze the data and adjust the order of the procedures.

[0047] The recording unit can optimize the recording algorithm by referring to past recording data during recording. For example, the recording unit can optimize the recording algorithm by referring to past recording data during recording. For example, the recording unit can select the optimal recording algorithm based on past recording data. The recording unit can also analyze past recording data and improve the recording algorithm. Furthermore, the recording unit can optimize the recording algorithm by referring to past recording data. This makes more efficient recording possible by optimizing the recording algorithm by referring to past recording data. Some or all of the above processes in the recording unit may be performed using AI, for example, or without using AI. For example, the recording unit can input past recording data into a generating AI, which can then automatically analyze the data and optimize the recording algorithm.

[0048] The recording unit can weight the recorded data based on the submission date of the diagnostic results during recording. For example, the recording unit can weight the recorded data based on the submission date of the diagnostic results during recording. For example, the recording unit can give a high weight to the recorded data in the case of an urgent diagnostic result. The recording unit can also give a normal weight to the recorded data in the case of a regular diagnostic result. Furthermore, the recording unit can give a low weight to the recorded data in the case of a past diagnostic result. This allows important data to be recorded preferentially by weighting the recorded data based on the submission date of the diagnostic results. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the data on the submission date of the diagnostic results into a generating AI, and the generating AI can automatically analyze the data and weight the recorded data.

[0049] The feedback receiving unit can select the optimal receiving method when receiving feedback by referring to the inspector's past feedback history. For example, the feedback receiving unit can select the optimal receiving method by referring to the inspector's past feedback history when receiving feedback. For example, the feedback receiving unit can prioritize providing feedback methods previously used by the inspector. The feedback receiving unit can also propose the optimal feedback method based on the inspector's past feedback history. Furthermore, the feedback receiving unit can customize the feedback method based on the feedback format preferred by the inspector in the past. This allows inspectors to provide feedback more efficiently by selecting the optimal receiving method by referring to past feedback history. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input the inspector's past feedback history data into a generating AI, which can automatically analyze the data and select the optimal receiving method.

[0050] The feedback reception unit can select the optimal reception method based on the inspector's device information when receiving feedback. For example, if the inspector is using a smartphone, the feedback reception unit can provide a feedback method adapted to the screen size. Furthermore, if the inspector is using a tablet, the feedback reception unit can provide a feedback method optimized for a larger screen. Additionally, if the inspector is using a smartwatch, the feedback reception unit can provide a concise and highly visible feedback method. This allows inspectors to provide feedback more efficiently by selecting the optimal reception method based on their device information. Some or all of the above processing in the feedback reception unit may be performed using AI, or without AI. For example, the feedback reception unit can input the inspector's device information into a generating AI, which can automatically analyze the data and select the optimal reception method.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The diagnostic unit can upload diagnostic results to the cloud in real time when performing self-diagnosis of communication equipment, and can integrate with other related systems. For example, if the diagnostic unit detects an anomaly in communication equipment, it uploads that information to the cloud, and other systems automatically generate corrective procedures based on that information. The diagnostic unit can also integrate with a database on the cloud and improve diagnostic accuracy by referring to past diagnostic results. Furthermore, the diagnostic unit can use AI models on the cloud to perform more advanced anomaly detection. As a result, the diagnostic unit can achieve faster and more accurate diagnoses through integration with the cloud.

[0053] The recording unit can adjust the data retention period based on the importance of the diagnostic results when recording them. For example, if a serious abnormality is detected, the recording unit will retain the data for a long period. In the case of minor abnormalities, it can retain the data for only a short period. Furthermore, the recording unit can also adjust the data backup frequency according to the importance of the diagnostic results. This allows the recording unit to prioritize the storage of important data and achieve efficient data management.

[0054] The feedback reception unit can automatically categorize feedback received from inspectors and notify the relevant departments. For example, it can notify the network department of feedback regarding communication lines, or the hardware department of feedback regarding equipment operation. Furthermore, the feedback reception unit can analyze the feedback, identify common problems, and generate improvement suggestions. This allows the feedback reception unit to support efficient problem solving.

[0055] The diagnostic unit can visualize the diagnostic results when checking each part of the communication equipment and provide them to the inspector. For example, the diagnostic unit can display abnormal areas in graphs and charts. It can also display the diagnostic results in a 3D model, allowing the inspector to intuitively understand the abnormal areas. Furthermore, the diagnostic unit can display the diagnostic results on a map, indicating the location of the abnormal areas. In this way, the diagnostic unit can support the inspector in quickly identifying and addressing the abnormal areas.

[0056] The voice output unit can customize the content of messages when conveying diagnostic results by voice, based on the inspector's past behavioral history. For example, the voice output unit prioritizes messages related to problems the inspector has dealt with in the past. It can also adjust the message content based on the inspector's preferred message format in the past. Furthermore, the voice output unit can analyze the inspector's past behavioral history and generate the most appropriate message. As a result, the voice output unit can provide more useful information to the inspector.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The diagnostic unit performs a self-diagnosis of the communication equipment. The diagnostic unit checks each part of the communication equipment in detail to confirm that there are no abnormalities. For example, it diagnoses the status of the communication lines and the operating status of the equipment, and analyzes the operation logs to detect abnormalities. Specifically, it detects delays and packet loss in the communication lines and reports the abnormalities. Step 2: The voice output unit communicates the diagnostic results obtained by the diagnostic unit to the inspector via voice. The voice output unit communicates the diagnostic results as specific messages via voice. For example, it can communicate a message such as "There is an abnormality in the communication line." Furthermore, it communicates the diagnostic results via voice immediately as soon as they are obtained. Step 3: The procedure generation unit generates specific corrective actions based on the diagnostic results obtained by the diagnostic unit. The procedure generation unit generates specific corrective actions based on the diagnostic results, such as specific instructions like "Please check the connection of the communication line." It can also generate multiple corrective actions and present them with priority. For example, it can generate an action such as "First, check the connection of the communication line, and then restart the equipment."

[0059] (Example of form 2) The self-diagnosis system for communication equipment according to an embodiment of the present invention is a system in which the communication equipment performs a self-diagnosis and communicates the results to an inspector by voice. This system allows the communication equipment to perform a self-diagnosis and communicate the results to the inspector by voice. Next, if a malfunction is found as a result of the self-diagnosis, the system provides the inspector with specific corrective action procedures by voice. This system is expected to help maintain the skills of inspectors, operators, and maintenance personnel and reduce their workload. Furthermore, it includes a function to record the self-diagnosis results for later analysis and a function to provide feedback on the results of the inspector's execution of the corrective action procedures. This supports the normal operation of the communication equipment and improves the work efficiency of inspectors. For example, the communication equipment performs a self-diagnosis. During this process, it checks each part of the communication equipment in detail to confirm that there are no abnormalities. For example, it diagnoses the status of the communication line and the operating status of the equipment. Next, it communicates the results of the self-diagnosis to the inspector by voice. For example, it provides a specific message by voice, such as "There is an abnormality in the communication line." Furthermore, if a malfunction is found as a result of the self-diagnosis, the system provides the inspector with specific corrective action procedures by voice. For example, it provides specific instructions by voice, such as "Please check the connection of the communication line." This allows the inspector to take action quickly. Furthermore, it includes a function to record the results of self-diagnosis for later analysis. This allows users to refer to past diagnosis results and understand the status of the communication equipment. It also includes a function to provide feedback on the results of the inspector's corrective actions. For example, it can receive feedback such as, "I have confirmed the connection of the communication line." This allows for real-time monitoring of the communication equipment's status and appropriate action to be taken. In this way, the self-diagnosis system for communication equipment can help maintain the skills of inspectors, operators, and maintenance personnel, reduce their workload, and support the normal operation of the communication equipment.

[0060] The communication equipment self-diagnosis system according to this embodiment comprises a diagnostic unit, an audio output unit, and a procedure generation unit. The diagnostic unit performs a self-diagnosis of the communication equipment. The diagnostic unit, for example, checks each part of the communication equipment in detail to confirm whether there are any abnormalities. For example, the diagnostic unit can diagnose the status of the communication line and the operating status of the equipment. The diagnostic unit can also analyze the operation log of the communication equipment and detect abnormalities. For example, the diagnostic unit can detect delays and packet losses in the communication line and report the abnormalities. The audio output unit conveys the diagnostic results obtained by the diagnostic unit to the inspector by voice. The audio output unit conveys the diagnostic results by voice as a specific message. For example, the audio output unit can convey a message such as "There is an abnormality in the communication line" by voice. The audio output unit can also output the diagnostic results by voice in real time. For example, the audio output unit conveys the diagnostic results by voice immediately as soon as they are obtained. The procedure generation unit generates specific countermeasures based on the diagnostic results obtained by the diagnostic unit. The procedure generation unit generates specific countermeasures based on the diagnostic results. For example, the procedure generation unit can generate specific instructions such as, "Please check the connection of the communication line." The procedure generation unit can also generate multiple corrective procedures based on the diagnostic results and present them with priority. For example, the procedure generation unit can generate a procedure such as, "First, check the connection of the communication line, and then restart the equipment." As a result, the self-diagnosis system for communication equipment according to this embodiment can improve the work efficiency of inspectors by conveying the self-diagnosis results of the communication equipment by voice and generating specific corrective procedures.

[0061] The system includes a recording unit for recording diagnostic results. The recording unit records the diagnostic results. For example, the recording unit can record the diagnostic results as digital data. For example, the recording unit can record the diagnostic results in text format or CSV format. The recording unit can also save the diagnostic results to cloud storage. For example, the recording unit can upload the diagnostic results to cloud storage for later access. Furthermore, the recording unit can record the diagnostic results chronologically, allowing reference to past diagnostic results. For example, the recording unit can organize the diagnostic results by date, making it easy to search for past diagnostic results. This allows for later analysis by recording the diagnostic results. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the diagnostic results into AI, which can then automatically organize and record the data.

[0062] The system includes a feedback receiving unit that receives feedback from inspectors. The feedback receiving unit receives feedback from inspectors. For example, the feedback receiving unit can provide feedback on the results of the inspector's execution of corrective procedures. For example, the feedback receiving unit can receive feedback such as, "I have confirmed the connection of the communication line." The feedback receiving unit can also receive feedback in real time. For example, the feedback receiving unit can receive feedback immediately after the inspector has executed a corrective procedure. Furthermore, the feedback receiving unit can record the feedback as digital data. For example, the feedback receiving unit can record the feedback in text format or CSV format. This allows for real-time monitoring of the communication equipment status and appropriate action to be taken by receiving feedback from inspectors. Some or all of the above-described processing in the feedback receiving unit may be performed using AI, or without AI. For example, the feedback receiving unit can input the inspector's feedback into AI, which can then automatically organize and record the data.

[0063] The diagnostic unit can specifically check each part of the communication equipment to confirm that there are no abnormalities. For example, the diagnostic unit can check each part of the communication equipment in detail to confirm that there are no abnormalities. For example, the diagnostic unit can diagnose the status of communication lines and the operating status of equipment. The diagnostic unit can also analyze the operation logs of the communication equipment to detect abnormalities. For example, the diagnostic unit can detect delays and packet loss in communication lines and report the abnormalities. Furthermore, the diagnostic unit can periodically check each part of the communication equipment to confirm that there are no abnormalities. For example, the diagnostic unit can perform diagnoses on a regular schedule such as daily, weekly, or monthly. This allows for early detection of abnormalities by checking each part of the communication equipment in detail. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the operation logs of the communication equipment into the AI, which can automatically detect abnormalities.

[0064] The voice output unit can communicate diagnostic results as specific messages in voice. For example, the voice output unit can communicate a message such as, "There is an abnormality in the communication line." The voice output unit can also output diagnostic results in real time. For example, the voice output unit can communicate the diagnostic results immediately as soon as they are obtained. Furthermore, the voice output unit can output diagnostic results in multiple languages. For example, the voice output unit can communicate diagnostic results in multiple languages ​​such as Japanese, English, and French. This allows inspectors to respond quickly by communicating diagnostic results as specific messages in voice. Some or all of the above processing in the voice output unit may be performed using AI, or not. For example, the voice output unit can input diagnostic results into AI, which can then automatically generate and output voice messages.

[0065] The procedure generation unit can generate specific corrective actions based on the diagnostic results. For example, the procedure generation unit can generate specific instructions such as, "Please check the connection of the communication line." The procedure generation unit can also generate multiple corrective actions based on the diagnostic results and present them with priority. For example, the procedure generation unit can generate instructions such as, "First, check the connection of the communication line, and then restart the equipment." Furthermore, the procedure generation unit can record the corrective actions as digital data. For example, the procedure generation unit can record the corrective actions in text format or CSV format. This allows inspectors to take appropriate action by generating specific corrective actions based on the diagnostic results. Some or all of the above-described processes in the procedure generation unit may be performed using AI, for example, or without AI. For example, the procedure generation unit can input the diagnostic results into AI, and the AI ​​can automatically generate corrective actions.

[0066] The diagnostic unit can estimate the inspector's emotions and adjust the frequency of diagnoses based on the estimated emotions. For example, if the inspector is stressed, the diagnostic unit can reduce the frequency of diagnoses to alleviate the inspector's burden. Conversely, if the inspector is relaxed, the diagnostic unit can increase the frequency of diagnoses to perform more detailed checks. Furthermore, if the inspector is in a hurry, the diagnostic unit can minimize the frequency of diagnoses to prioritize a quick response. This reduces the inspector's burden by adjusting the frequency of diagnoses according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can input the inspector's emotion data into a generative AI, which can automatically estimate the emotions and adjust the frequency of diagnoses.

[0067] The diagnostic unit can analyze the past diagnostic history of communication equipment and select an appropriate diagnostic method. For example, the diagnostic unit can analyze the past diagnostic history of communication equipment and select the optimal diagnostic method. For example, the diagnostic unit can identify frequently occurring problems from the past diagnostic history and prioritize diagnostic methods for those problems. The diagnostic unit can also predict problems that are likely to occur during specific time periods based on the past diagnostic history and select a diagnostic method suitable for that time period. Furthermore, the diagnostic unit can analyze the past diagnostic history and select a diagnostic method for problems that are likely to occur under specific environmental conditions. In this way, the optimal diagnostic method can be selected by analyzing the past diagnostic history. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input past diagnostic history data into a generating AI, which can automatically analyze the data and select the optimal diagnostic method.

[0068] The diagnostic unit can customize diagnostic items during diagnosis based on the usage status and environmental conditions of the communication equipment. For example, if the communication equipment is used frequently, the diagnostic unit will focus on diagnosing critical parts. Furthermore, if the communication equipment is in a high-temperature environment, the diagnostic unit can prioritize diagnosing temperature-related parts. Additionally, if the communication equipment is in a high-humidity environment, the diagnostic unit can focus on diagnosing humidity-related parts. This allows for more appropriate diagnosis by customizing diagnostic items based on usage status and environmental conditions. Some or all of the above-described processes in the diagnostic unit may be performed using AI, or without AI. For example, the diagnostic unit can input data on the communication equipment's usage status and environmental conditions into a generating AI, which can then automatically analyze the data and customize the diagnostic items.

[0069] The diagnostic unit can estimate the inspector's emotions and prioritize diagnostic results based on the estimated emotions. For example, if the inspector is stressed, the diagnostic unit will prioritize conveying only the most important diagnostic results. If the inspector is relaxed, the diagnostic unit can also convey detailed diagnostic results. Furthermore, if the inspector is in a hurry, the diagnostic unit can convey the most important diagnostic results first. This ensures that important information is conveyed preferentially by prioritizing diagnostic results according to the inspector's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can input the inspector's emotion data into a generative AI, which can automatically estimate emotions and determine the priority of diagnostic results.

[0070] The diagnostic unit can prioritize checking highly relevant diagnostic items based on the geographical location information of the communication equipment during diagnosis. For example, if the communication equipment is located in an urban area, the diagnostic unit will prioritize checking diagnostic items related to radio interference. If the communication equipment is located in a mountainous area, the diagnostic unit can also prioritize checking diagnostic items related to power supply. Furthermore, if the communication equipment is located near the coast, the diagnostic unit can also prioritize checking diagnostic items related to salt damage. This allows for more appropriate diagnosis by prioritizing diagnostic items based on geographical location information. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the geographical location information of the communication equipment into a generating AI, which can automatically analyze the data and prioritize checking highly relevant diagnostic items.

[0071] The diagnostic unit can analyze the social media activity of communication equipment during diagnosis and add relevant diagnostic items. For example, the diagnostic unit can analyze social media posts about communication equipment and add diagnostic items related to frequently reported problems. The diagnostic unit can also add diagnostic items for specific problems based on user feedback on social media. Furthermore, the diagnostic unit can analyze trends on social media and add relevant diagnostic items. This allows for the addition of relevant diagnostic items by analyzing social media activity. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input social media data into a generating AI, which can automatically analyze the data and add relevant diagnostic items.

[0072] The voice output unit can estimate the inspector's emotions and adjust the tone of the voice message based on the estimated emotions. For example, if the inspector is nervous, the voice output unit can deliver the message in a calm tone. If the inspector is relaxed, the voice output unit can deliver the message in a bright tone. Furthermore, if the inspector is in a hurry, the voice output unit can deliver the message in a quick and concise tone. This allows for more appropriate communication by adjusting the tone of the voice message according to the inspector's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice output unit may be performed using AI or not using AI. For example, the voice output unit can input the inspector's emotion data into the generative AI, which can automatically estimate the emotions and adjust the tone of the voice message.

[0073] The voice output unit can adjust the level of detail in the message based on the importance of the diagnostic result when outputting voice. For example, the voice output unit can deliver a message with a detailed explanation if the diagnostic result is important. It can also deliver a concise message if the diagnostic result is minor. Furthermore, it can deliver a message containing the necessary information if the diagnostic result is moderate. This allows for the appropriate delivery of necessary information by adjusting the level of detail in the message based on the importance of the diagnostic result. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, the voice output unit can input diagnostic result importance data into a generating AI, which can then automatically analyze the data and adjust the level of detail in the message.

[0074] The voice output unit can customize the content of messages based on the inspector's past feedback history when outputting voice messages. For example, the voice output unit can customize the content of messages based on the inspector's past feedback history when outputting voice messages. For example, the voice output unit can prioritize messages related to problems previously pointed out by the inspector. The voice output unit can also customize the content of messages based on the message format preferred by the inspector in the past. Furthermore, the voice output unit can adjust the content of messages based on feedback previously provided by the inspector. By customizing the content of messages based on past feedback history, more useful information can be provided to the inspector. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, the voice output unit can input the inspector's past feedback history data into a generating AI, which can then automatically analyze the data and customize the content of the messages.

[0075] The voice output unit can estimate the inspector's emotions and adjust the length of the voice message based on the estimated emotions. For example, if the inspector is nervous, the voice output unit can deliver a short, concise message. If the inspector is relaxed, the voice output unit can deliver a longer message with more detailed explanations. Furthermore, if the inspector is in a hurry, the voice output unit can deliver a quick and concise message. By adjusting the length of the voice message according to the inspector's emotions, more appropriate information can be conveyed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice output unit may be performed using AI, or not using AI. For example, the voice output unit can input the inspector's emotion data into the generative AI, which can automatically estimate the emotions and adjust the length of the voice message.

[0076] The voice output unit can determine message priorities based on the submission date of the diagnostic results when outputting voice messages. For example, the voice output unit can prioritize messages based on the submission date of the diagnostic results when outputting voice messages. For example, in the case of urgent diagnostic results, the voice output unit can deliver the message with the highest priority. The voice output unit can also deliver messages with normal priority in the case of regular diagnostic results. Furthermore, the voice output unit can deliver messages with lower priority in the case of past diagnostic results. This allows important information to be conveyed quickly by determining message priorities based on the submission date of the diagnostic results. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, the voice output unit can input diagnostic result submission date data into a generating AI, which can automatically analyze the data and determine message priorities.

[0077] The voice output unit can adjust the order of messages based on the relevance of the diagnostic results when outputting voice. For example, the voice output unit can deliver the most relevant diagnostic results first. It can also deliver less relevant diagnostic results later. Furthermore, it can deliver moderately relevant diagnostic results in the middle. By adjusting the order of messages based on the relevance of the diagnostic results, important information can be delivered preferentially. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, the voice output unit can input the relevance data of the diagnostic results into a generating AI, which can automatically analyze the data and adjust the order of the messages.

[0078] The procedure generation unit can estimate the inspector's emotions and adjust the way the procedure is presented based on the estimated emotions. For example, if the inspector is tense, the procedure generation unit can provide a simple and easily understandable presentation. If the inspector is relaxed, the procedure generation unit can also provide a presentation that includes detailed information. Furthermore, if the inspector is in a hurry, the procedure generation unit can provide a concise presentation. By adjusting the presentation of the procedure according to the inspector's emotions, procedures that are easier for the inspector to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the procedure generation unit may be performed using AI, for example, or without AI. For example, the procedure generation unit can input the inspector's emotional data into the generating AI, which can then automatically estimate the emotions and adjust the way the response procedure is expressed.

[0079] The procedure generation unit can adjust the level of detail of the procedure based on the importance of the diagnostic result when generating the procedure. For example, the procedure generation unit can provide a detailed procedure for important diagnostic results. It can also provide a concise procedure for minor diagnostic results. Furthermore, it can provide a procedure containing the necessary information for moderate diagnostic results. By adjusting the level of detail of the procedure based on the importance of the diagnostic result, the necessary information can be appropriately provided. Some or all of the above processing in the procedure generation unit may be performed using AI, for example, or without AI. For example, the procedure generation unit can input diagnostic result importance data into a generation AI, which can automatically analyze the data and adjust the level of detail of the procedure.

[0080] The procedure generation unit can apply different procedure generation algorithms depending on the category of communication equipment when generating procedures. For example, the procedure generation unit can apply a procedure generation algorithm specifically for communication lines in the case of a problem related to communication lines. It can also apply a procedure generation algorithm specifically for equipment in the case of a problem related to equipment operation. Furthermore, it can apply a procedure generation algorithm specifically for software in the case of a problem related to software. This allows for the application of the most appropriate procedure generation algorithm for each category of communication equipment, thereby providing more appropriate troubleshooting procedures. Some or all of the above-described processes in the procedure generation unit may be performed using AI, for example, or without AI. For example, the procedure generation unit can input communication equipment category data into a generation AI, which can automatically analyze the data and apply the most appropriate procedure generation algorithm.

[0081] The procedure generation unit can estimate the inspector's emotions and adjust the length of the procedure based on the estimated emotions. For example, if the inspector is tense, the procedure generation unit can provide a short, concise procedure. If the inspector is relaxed, the procedure generation unit can also provide a longer procedure with more detailed explanations. Furthermore, if the inspector is in a hurry, the procedure generation unit can provide a quick and concise procedure. By adjusting the length of the procedure according to the inspector's emotions, the procedure can be made easier for the inspector to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the procedure generation unit may be performed using AI, for example, or without AI. For example, the procedure generation unit can input the inspector's emotional data into the generating AI, which can then automatically estimate the emotion and adjust the length of the response procedure.

[0082] The procedure generation unit can determine the priority of procedures based on the submission timing of diagnostic results when generating procedures. For example, the procedure generation unit can provide the highest priority action procedure in the case of an urgent diagnostic result. It can also provide the normal priority action procedure in the case of a regular diagnostic result. Furthermore, it can provide the lower priority action procedure in the case of a past diagnostic result. This allows for the rapid provision of important action procedures by determining the priority of procedures based on the submission timing of diagnostic results. Some or all of the above processing in the procedure generation unit may be performed using AI, for example, or without AI. For example, the procedure generation unit can input diagnostic result submission timing data into a generation AI, which can automatically analyze the data and determine the priority of procedures.

[0083] The procedure generation unit can adjust the order of procedures based on the relevance of the diagnostic results during procedure generation. For example, the procedure generation unit can provide the procedures for the most relevant diagnostic results first. It can also postpone procedures for less relevant diagnostic results. Furthermore, it can provide procedures for moderately relevant diagnostic results in the middle. By adjusting the order of procedures based on the relevance of the diagnostic results, important countermeasures can be provided preferentially. Some or all of the above processing in the procedure generation unit may be performed using AI, for example, or not using AI. For example, the procedure generation unit can input the relevance data of the diagnostic results into a generating AI, which can automatically analyze the data and adjust the order of the procedures.

[0084] The recording unit can estimate the inspector's emotions and select recording data based on the estimated emotions. For example, if the inspector is tense, the recording unit will record only important data. If the inspector is relaxed, the recording unit can also record detailed data. Furthermore, if the inspector is in a hurry, the recording unit can prioritize recording the most important data. This allows for the priority recording of important data by selecting recording data according to the inspector's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not using AI. For example, the recording unit can input the inspector's emotion data into a generative AI, which can automatically estimate the emotions and select the recording data.

[0085] The recording unit can optimize the recording algorithm by referring to past recording data during recording. For example, the recording unit can optimize the recording algorithm by referring to past recording data during recording. For example, the recording unit can select the optimal recording algorithm based on past recording data. The recording unit can also analyze past recording data and improve the recording algorithm. Furthermore, the recording unit can optimize the recording algorithm by referring to past recording data. This makes more efficient recording possible by optimizing the recording algorithm by referring to past recording data. Some or all of the above processes in the recording unit may be performed using AI, for example, or without using AI. For example, the recording unit can input past recording data into a generating AI, which can then automatically analyze the data and optimize the recording algorithm.

[0086] The recording unit can estimate the inspector's emotions and adjust the recording frequency based on the estimated emotions. For example, if the inspector is tense, the recording unit can reduce the recording frequency to alleviate the inspector's burden. Conversely, if the inspector is relaxed, the recording unit can increase the recording frequency to collect more detailed data. Furthermore, if the inspector is in a hurry, the recording unit can minimize the recording frequency to prioritize a quick response. This reduces the inspector's burden by adjusting the recording frequency according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the inspector's emotion data into a generative AI, which can automatically estimate the emotions and adjust the recording frequency.

[0087] The recording unit can weight the recorded data based on the submission date of the diagnostic results during recording. For example, the recording unit can weight the recorded data based on the submission date of the diagnostic results during recording. For example, the recording unit can give a high weight to the recorded data in the case of an urgent diagnostic result. The recording unit can also give a normal weight to the recorded data in the case of a regular diagnostic result. Furthermore, the recording unit can give a low weight to the recorded data in the case of a past diagnostic result. This allows important data to be recorded preferentially by weighting the recorded data based on the submission date of the diagnostic results. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the data on the submission date of the diagnostic results into a generating AI, and the generating AI can automatically analyze the data and weight the recorded data.

[0088] The feedback receiving unit can estimate the inspector's emotions and adjust the feedback receiving method based on the estimated emotions. For example, if the inspector is nervous, the feedback receiving unit can provide a simple and intuitive feedback method. If the inspector is relaxed, the feedback receiving unit can also provide a detailed feedback method. Furthermore, if the inspector is in a hurry, the feedback receiving unit can provide a quick and concise feedback method. This makes it easier for inspectors to provide feedback by adjusting the feedback receiving method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback receiving unit may be performed using AI or not using AI. For example, the feedback receiving unit can input the inspector's emotion data into the generative AI, which can automatically estimate the emotions and adjust the feedback receiving method.

[0089] The feedback receiving unit can select the optimal receiving method when receiving feedback by referring to the inspector's past feedback history. For example, the feedback receiving unit can select the optimal receiving method by referring to the inspector's past feedback history when receiving feedback. For example, the feedback receiving unit can prioritize providing feedback methods previously used by the inspector. The feedback receiving unit can also propose the optimal feedback method based on the inspector's past feedback history. Furthermore, the feedback receiving unit can customize the feedback method based on the feedback format preferred by the inspector in the past. This allows inspectors to provide feedback more efficiently by selecting the optimal receiving method by referring to past feedback history. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input the inspector's past feedback history data into a generating AI, which can automatically analyze the data and select the optimal receiving method.

[0090] The feedback receiving unit can estimate the inspector's emotions and determine the priority of feedback based on the estimated emotions. For example, if the inspector is stressed, the feedback receiving unit will prioritize important feedback. If the inspector is relaxed, the feedback receiving unit can also prioritize detailed feedback. Furthermore, if the inspector is in a hurry, the feedback receiving unit can also prioritize quick feedback. This ensures that important feedback is prioritized by determining the priority of feedback according to the inspector's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit can input the inspector's emotion data into a generative AI, which can automatically estimate emotions and determine the priority of feedback.

[0091] The feedback reception unit can select the optimal reception method based on the inspector's device information when receiving feedback. For example, if the inspector is using a smartphone, the feedback reception unit can provide a feedback method adapted to the screen size. Furthermore, if the inspector is using a tablet, the feedback reception unit can provide a feedback method optimized for a larger screen. Additionally, if the inspector is using a smartwatch, the feedback reception unit can provide a concise and highly visible feedback method. This allows inspectors to provide feedback more efficiently by selecting the optimal reception method based on their device information. Some or all of the above processing in the feedback reception unit may be performed using AI, or without AI. For example, the feedback reception unit can input the inspector's device information into a generating AI, which can automatically analyze the data and select the optimal reception method. === Hard Collateral 1-1 === Each of the multiple elements described above, including the diagnostic unit, voice output unit, and procedure generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the diagnostic unit is implemented by the processor 46 of the smart device 14, which checks each part of the communication equipment in detail and confirms any abnormalities. The voice output unit is implemented by the speaker 40B of the smart device 14, which communicates the diagnostic results to the inspector by voice. The procedure generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates specific corrective procedures based on the diagnostic results. === Hard Collateral 1-2 === Each of the multiple elements described above, including the diagnostic unit, voice output unit, and procedure generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the diagnostic unit is implemented by the processor 46 of the smart glasses 214, which checks each part of the communication equipment in detail and identifies any abnormalities. The voice output unit is implemented by the speaker 240 of the smart glasses 214, which communicates the diagnostic results to the inspector by voice. The procedure generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates specific corrective procedures based on the diagnostic results. === Hard Collateral 1-3 === Each of the multiple elements described above, including the diagnostic unit, audio output unit, and procedure generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the diagnostic unit is implemented by the processor 46 of the headset terminal 314, which checks each part of the communication equipment in detail and confirms any abnormalities. The audio output unit is implemented by the speaker 240 of the headset terminal 314, which communicates the diagnostic results to the inspector by voice. The procedure generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates specific corrective procedures based on the diagnostic results. === Hard Collateral 1-4 === Each of the multiple elements described above, including the diagnostic unit, voice output unit, and procedure generation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the diagnostic unit is implemented by the processor 46 of the robot 414, which checks each part of the communication equipment in detail and identifies any abnormalities. The voice output unit is implemented by, for example, the speaker 240 of the robot 414, which communicates the diagnostic results to the inspector by voice. The procedure generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates specific corrective procedures based on the diagnostic results.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The diagnostic unit can upload diagnostic results to the cloud in real time when performing self-diagnosis of communication equipment, and can integrate with other related systems. For example, if the diagnostic unit detects an anomaly in communication equipment, it uploads that information to the cloud, and other systems automatically generate corrective procedures based on that information. The diagnostic unit can also integrate with a database on the cloud and improve diagnostic accuracy by referring to past diagnostic results. Furthermore, the diagnostic unit can use AI models on the cloud to perform more advanced anomaly detection. As a result, the diagnostic unit can achieve faster and more accurate diagnoses through integration with the cloud.

[0094] The recording unit can adjust the data retention period based on the importance of the diagnostic results when recording them. For example, if a serious abnormality is detected, the recording unit will retain the data for a long period. In the case of minor abnormalities, it can retain the data for only a short period. Furthermore, the recording unit can also adjust the data backup frequency according to the importance of the diagnostic results. This allows the recording unit to prioritize the storage of important data and achieve efficient data management.

[0095] The feedback reception unit can automatically categorize feedback received from inspectors and notify the relevant departments. For example, it can notify the network department of feedback regarding communication lines, or the hardware department of feedback regarding equipment operation. Furthermore, the feedback reception unit can analyze the feedback, identify common problems, and generate improvement suggestions. This allows the feedback reception unit to support efficient problem solving.

[0096] The diagnostic unit can visualize the diagnostic results when checking each part of the communication equipment and provide them to the inspector. For example, the diagnostic unit can display abnormal areas in graphs and charts. It can also display the diagnostic results in a 3D model, allowing the inspector to intuitively understand the abnormal areas. Furthermore, the diagnostic unit can display the diagnostic results on a map, indicating the location of the abnormal areas. In this way, the diagnostic unit can support the inspector in quickly identifying and addressing the abnormal areas.

[0097] The voice output unit can customize the content of messages when conveying diagnostic results by voice, based on the inspector's past behavioral history. For example, the voice output unit prioritizes messages related to problems the inspector has dealt with in the past. It can also adjust the message content based on the inspector's preferred message format in the past. Furthermore, the voice output unit can analyze the inspector's past behavioral history and generate the most appropriate message. As a result, the voice output unit can provide more useful information to the inspector.

[0098] The procedure generation unit can estimate the inspector's emotions when generating specific action procedures based on diagnostic results, and adjust the difficulty of the procedures based on the estimated emotions. For example, if the inspector is feeling stressed, the procedure generation unit can provide simple and intuitive procedures. If the inspector is relaxed, it can also provide detailed procedures. Furthermore, if the inspector is in a hurry, the procedure generation unit can provide procedures that can be executed quickly. In this way, the procedure generation unit can provide the optimal action procedures according to the inspector's emotions.

[0099] The diagnostic unit can estimate the inspector's emotions and adjust the method of notifying them of the diagnostic results based on those estimated emotions. For example, if the inspector is tense, the diagnostic unit can deliver the results in a calm tone. If the inspector is relaxed, it can deliver the results in a cheerful tone. Furthermore, if the inspector is in a hurry, it can deliver the results in a quick and concise tone. In this way, the diagnostic unit can provide the most appropriate notification method according to the inspector's emotions.

[0100] The voice output unit can estimate the inspector's emotions when conveying diagnostic results verbally, and adjust the speed of the voice message based on the estimated emotions. For example, if the inspector is tense, the voice output unit will deliver the message at a slower speed. If the inspector is relaxed, it can deliver the message at a normal speed. Furthermore, if the inspector is in a hurry, it can deliver the message at a faster speed. In this way, the voice output unit can provide information at the optimal speed according to the inspector's emotions.

[0101] The procedure generation unit can estimate the inspector's emotions when generating specific action procedures based on diagnostic results, and adjust the way the procedures are presented based on the estimated emotions. For example, if the inspector is tense, the procedure generation unit can provide a simple and highly visible presentation. If the inspector is relaxed, it can also provide a presentation that includes detailed information. Furthermore, if the inspector is in a hurry, the procedure generation unit can provide a concise presentation. In this way, the procedure generation unit can provide the most appropriate action procedures according to the inspector's emotions.

[0102] The feedback reception unit can estimate the inspector's emotions and adjust the feedback reception method based on those emotions. For example, if the inspector is tense, the feedback reception unit can provide a simple and intuitive feedback method. If the inspector is relaxed, it can provide a more detailed feedback method. Furthermore, if the inspector is in a hurry, the feedback reception unit can provide a quick and concise feedback method. In this way, the feedback reception unit can provide the most appropriate feedback method according to the inspector's emotions.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The diagnostic unit performs a self-diagnosis of the communication equipment. The diagnostic unit checks each part of the communication equipment in detail to confirm that there are no abnormalities. For example, it diagnoses the status of the communication lines and the operating status of the equipment, and analyzes the operation logs to detect abnormalities. Specifically, it detects delays and packet loss in the communication lines and reports the abnormalities. Step 2: The voice output unit communicates the diagnostic results obtained by the diagnostic unit to the inspector via voice. The voice output unit communicates the diagnostic results as specific messages via voice. For example, it can communicate a message such as "There is an abnormality in the communication line." Furthermore, it communicates the diagnostic results via voice immediately as soon as they are obtained. Step 3: The procedure generation unit generates specific corrective actions based on the diagnostic results obtained by the diagnostic unit. The procedure generation unit generates specific corrective actions based on the diagnostic results, such as specific instructions like "Please check the connection of the communication line." It can also generate multiple corrective actions and present them with priority. For example, it can generate an action such as "First, check the connection of the communication line, and then restart the equipment."

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A diagnostic unit that performs self-diagnosis of communication equipment, An audio output unit that transmits the diagnostic results obtained by the diagnostic unit to the inspector by voice, A procedure generation unit generates specific countermeasures based on the diagnostic results obtained by the diagnostic unit, Equipped with A system characterized by the following features.

2. It is equipped with a recording unit for recording diagnostic results. The system according to feature 1.

3. It is equipped with a feedback reception unit that receives feedback from inspectors. The system according to feature 1.

4. The aforementioned diagnostic unit, We will specifically check each part of the communication equipment to confirm that there are no abnormalities. The system according to feature 1.

5. The aforementioned audio output unit is The diagnostic results are conveyed as a specific message via voice. The system according to feature 1.

6. The aforementioned generation unit, Generate specific action plans based on the diagnostic results. The system according to feature 1.

7. The aforementioned diagnostic unit, The system estimates the inspector's emotions and adjusts the frequency of the diagnosis based on the estimated emotions. The system according to feature 1.

8. The aforementioned diagnostic unit, Analyze the past diagnostic history of the communication equipment and select the appropriate diagnostic method. The system according to feature 1.

9. The aforementioned diagnostic unit, During the diagnostic process, the diagnostic items are customized based on the usage status and environmental conditions of the communication equipment. The system according to feature 1.

Images