system

Abstract

The system according to this embodiment aims to analyze call conversations and quickly and accurately identify and respond to problems. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and an alert unit. The analysis unit analyzes the call conversation. The generation unit generates a fault report based on the information analyzed by the analysis unit. The alert unit detects new faults based on the report generated by the generation unit and sends an alert.

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 character of the chatbot, 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 identify a failure from a call conversation and respond promptly.

[0005] The system according to the embodiment aims to analyze a call conversation and identify and respond to a failure quickly and accurately.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and an alert unit. The analysis unit analyzes a call conversation. The generation unit generates a failure report based on the information analyzed by the analysis unit. The alert unit detects a new failure based on the report generated by the generation unit and sends an alert. [Effects of the Invention]

[0007] The system according to this embodiment can analyze call conversations and quickly and accurately identify and respond to problems. [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.

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

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

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

[0020] 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 automated fault response system according to an embodiment of the present invention is a system that automates fault response using AI. This automated fault response system analyzes call conversations and generates a concise and easy-to-understand fault report using a generation AI, with fault response personnel using predefined prompts. Next, it analyzes past fault reports and response history data and creates patterns. This allows for the analysis and presentation of risks and solutions for each fault pattern. Furthermore, it has a mechanism to immediately raise alerts for new faults that do not fit into any pattern. For example, in the step of analyzing call conversations, the generation AI analyzes the conversation content and extracts important information. For example, it extracts information such as the date and time of the fault, the scope of impact, and specific symptoms. Next, fault response personnel use predefined prompts, and the generation AI creates a concise and easy-to-understand fault report. This report includes the extracted important information, enabling personnel to respond quickly. Next, in the step of analyzing past fault reports and response history data, the generation AI analyzes this data and identifies fault patterns. For example, it identifies patterns such as faults occurring during specific time periods or faults related to specific systems. This allows for the analysis and presentation of risks and solutions for each fault pattern to personnel. Furthermore, the system includes a mechanism to immediately raise alerts for new failures that do not fit into existing patterns. When the generating AI detects a new failure, it immediately sends an alert to the relevant parties to encourage a swift response. For example, if a new type of failure occurs, the generating AI will detect it and notify the relevant parties. This system improves the efficiency of failure response and enables a rapid response. Relevant parties can take appropriate action based on the failure report generated by the generating AI. In addition, by analyzing past failure data, it is possible to predict the risk of future failures and take countermeasures in advance. Moreover, because it can respond to new failures immediately, the system stability is improved. In this way, the automated failure response system can improve the efficiency of failure response and enable a rapid response.

[0029] The automated fault response system according to this embodiment comprises an analysis unit, a generation unit, and an alert unit. The analysis unit analyzes call conversations. Call conversations include, but are not limited to, telephone conversations, video calls, and chats. The analysis unit converts the content of the call conversation into text data using, for example, speech recognition technology. The analysis unit can also extract important information from the conversation content using text analysis technology. For example, the analysis unit extracts information such as the date and time of the fault, the scope of impact, and specific symptoms from the conversation content. The generation unit generates a fault report based on the information analyzed by the analysis unit. The fault report includes, but is not limited to, information such as the type of fault, the time of occurrence, and the scope of impact. The generation unit creates a fault report based on the extracted information using, for example, generation AI. The generation unit can also analyze past fault reports and response history data to identify fault patterns. For example, the generation unit can identify patterns such as faults occurring during specific time periods or faults related to specific systems. The alert unit detects new faults based on the reports generated by the generation unit and sends immediate alerts. Alerts include, but are not limited to, real-time notifications and notifications without delay. The alerting unit, for example, uses a generation AI to detect new failures and immediately sends alerts to relevant parties. As a result, the automated failure response system according to the embodiment can improve the efficiency of failure response by analyzing call conversations, generating failure reports, and immediately detecting and sending alerts for new failures.

[0030] The analysis unit can analyze the content of a call and extract important information. For example, the analysis unit can convert the content of a call into text data using speech recognition technology. For example, the analysis unit can use speech recognition software to automatically analyze the speech and save it as text. The analysis unit can also extract important information from the conversation content using text analysis technology. For example, the analysis unit can extract information such as the date and time of the failure, the scope of the impact, and specific symptoms from the conversation content. This improves the accuracy of fault response by extracting important information from the content of the call. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the content of the call into a generative AI and have the generative AI perform the extraction of important information.

[0031] The generation unit can create an incident report based on the extracted information. For example, the generation unit can use a generation AI to create an incident report based on the extracted information. For example, the generation unit can use the generation AI to generate a clear and concise incident report based on the extracted information. The generation unit can also analyze past incident reports and response history data to identify incident patterns. For example, the generation unit can identify patterns such as incidents occurring during specific time periods or incidents related to specific systems. This allows stakeholders to respond quickly by creating incident reports based on the extracted information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the extracted information into a generation AI and have the generation AI create an incident report.

[0032] The alert unit can detect new failures and send immediate alerts. For example, the alert unit can use a generation AI to detect new failures and send immediate alerts to relevant parties. For instance, the alert unit can use a generation AI to detect new failures and notify relevant parties in real time. The alert unit can also adjust the method of sending alerts. For example, the alert unit can send alerts using methods such as real-time notification or no-delay notification. This enables rapid response by immediately detecting new failures and sending alerts. Some or all of the above-described processes in the alert unit may be performed using a generation AI, or without one. For example, the alert unit can have the generation AI detect new failures and send alerts.

[0033] The generation unit can analyze past failure reports and response history data to identify failure patterns. For example, the generation unit uses a generation AI to analyze past failure reports and response history data and identify failure patterns. For example, the generation unit can identify patterns such as failures occurring during specific time periods or failures related to specific systems. The generation unit can also analyze risks and solutions based on failure patterns. For example, the generation unit analyzes risk factors and solutions based on specific patterns and presents them to stakeholders. In this way, by analyzing past failure data, failure patterns can be identified and risks and solutions can be analyzed. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past failure reports and response history data into a generation AI and have the generation AI perform the identification of failure patterns.

[0034] The generation unit can analyze risk factors and solutions for each failure pattern and present them to stakeholders. For example, the generation unit can use a generation AI to analyze risk factors and solutions for each failure pattern. For example, the generation unit can analyze risk factors and solutions based on a specific pattern and present them to stakeholders. The generation unit can also present appropriate countermeasures to stakeholders based on the risk factors and solutions. For example, the generation unit can present countermeasures and procedures to stakeholders based on the risk factors and solutions. This enables appropriate responses by analyzing and presenting risks and solutions for each failure pattern to stakeholders. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have the generation AI perform the analysis of risk factors and solutions for each failure pattern, and then have the generation AI perform the presentation to stakeholders.

[0035] The analysis unit can analyze the content of call conversations in real time and immediately extract important information. For example, the analysis unit uses a generative AI to analyze the content of call conversations in real time. For example, the analysis unit can use the generative AI to extract the date and time of the failure in real time during the conversation. The analysis unit can also use the generative AI to immediately identify and extract the scope of the impact during the conversation. Furthermore, the analysis unit can use the generative AI to analyze and extract specific symptoms in real time during the conversation. This enables a rapid response by extracting important information in real time. The definition and criteria of real time need to be clearly defined, for example, by specifying delay time and processing speed. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the content of the call conversation into a generative AI and have the generative AI perform the extraction of important information in real time.

[0036] The analysis unit can evaluate the importance of specific keywords and phrases when analyzing conversation content. For example, the analysis unit can use a generative AI to detect specific keywords and phrases when analyzing conversation content. For example, the analysis unit can have the generative AI detect keywords such as "urgent" and "critical" and rate them as highly important. The analysis unit can also have the generative AI detect phrases such as "system down" and "data loss" and rate them as highly important. Furthermore, the analysis unit can have the generative AI detect phrases such as "minor problem" and "temporary outage" and rate them as less important. This allows important information to be processed preferentially by evaluating importance based on specific keywords and phrases. The selection criteria and extraction methods for specific keywords and phrases need to be clearly defined, for example, by frequency and importance. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input conversation content into a generative AI and have the generative AI perform the detection of specific keywords and phrases and the evaluation of their importance.

[0037] The analysis unit can handle different languages ​​and dialects when analyzing the content of call conversations. For example, the analysis unit can use generative AI to handle different languages ​​and dialects. For example, the analysis unit can use a multilingual analysis algorithm with the generative AI to analyze conversations in different languages. The analysis unit can also use the generative AI to recognize and accurately analyze dialects and regional expressions. Furthermore, the analysis unit can use the generative AI with an automatic language detection function to identify and analyze the language of the conversation. This allows the system to support a wide range of users by handling different languages ​​and dialects. The methods and criteria for handling different languages ​​and dialects need to be clearly defined, for example, by using a multilingual system or a dialect dictionary. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the content of call conversations into the generative AI and have the generative AI perform analysis of different languages ​​and dialects.

[0038] The analysis unit can improve analysis accuracy by removing background noise and other noises when analyzing conversation content. For example, the analysis unit can use a generative AI to remove background noise and other noises. For instance, the generative AI can use noise filtering technology to remove background noise and analyze the conversation content. The analysis unit can also use speech recognition technology with the generative AI to extract important information while removing noise. Furthermore, the analysis unit can have the generative AI analyze multiple audio channels and remove noise to improve accuracy. This improves analysis accuracy by removing background noise and other noises. The methods and criteria for removing background noise and other noises need to be clearly defined, for example, noise filtering technology and speech cleanup technology. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input conversation content into a generative AI and have the generative AI perform background noise and other noise removal.

[0039] The generation unit can include detailed solutions by referencing data on similar past failures when generating failure reports. For example, the generation unit can use a generation AI to reference data on similar past failures. For example, the generation unit can have the generation AI reference a database of past failures and include solutions for similar failures in the report. The generation unit can also have the generation AI analyze past response history and include the optimal solution in the report. Furthermore, the generation unit can have the generation AI propose detailed solutions based on past failure patterns. This allows for the provision of detailed solutions by referencing data on similar past failures. The methods and criteria for referencing data on similar past failures need to be clearly defined, for example, database searches and historical data analysis. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input data on similar past failures into the generation AI and have the generation AI propose detailed solutions.

[0040] The generation unit can adjust the level of detail in a report based on the scope and priority of the failure when generating the report. For example, the generation unit can use a generation AI to evaluate the scope and priority of the failure. For example, the generation unit can have the generation AI evaluate the scope of the failure and generate a report that highlights important information. The generation unit can also have the generation AI consider the priority of the failure and generate a report that includes detailed information. Furthermore, the generation unit can have the generation AI adjust the level of detail in the report appropriately based on the scope and priority of the failure. This makes it possible to provide appropriate information by adjusting the level of detail in the report based on the scope and priority of the failure. The evaluation criteria and adjustment methods for the scope and priority of the failure need to be clearly defined, for example, the number of affected systems and users, the urgency and importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can have the generation AI perform the evaluation of the scope and priority of the failure, and have the generation AI perform the adjustment of the level of detail in the report.

[0041] The generation unit can make fault report reports visually easy to understand by including visual elements when generating them. For example, the generation unit can use a generation AI to include visual elements in the report. For example, the generation unit can use a generation AI to include a graph showing the scope of the fault's impact in the report. The generation unit can also use a generation AI to include a chart showing the frequency of fault occurrences in the report. Furthermore, the generation unit can use a generation AI to include visual elements showing the effectiveness of solutions in the report. This allows for the provision of visually easy-to-understand reports by including visual elements. The types and usage of visual elements, such as graphs, charts, and diagrams, need to be clearly defined. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have a generation AI generate visual elements and include them in the report.

[0042] The generation unit can generate customized reports according to the roles and permissions of stakeholders during report generation. For example, the generation unit can use a generation AI to customize reports according to the roles and permissions of stakeholders. For example, the generation unit can use the generation AI to generate reports containing detailed information for administrators. The generation unit can also use the generation AI to generate reports containing technical details for engineers. Furthermore, the generation unit can use the generation AI to generate concise and easy-to-understand reports for general users. This enables the provision of appropriate information by providing reports customized according to the roles and permissions of stakeholders. The definition and customization methods for stakeholders' roles and permissions need to be clearly defined, for example, administrators, engineers, and operators. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can have the generation AI perform the customization of reports according to the roles and permissions of stakeholders.

[0043] The alert unit can improve the accuracy of alerts by referring to past failure data when detecting new failures. For example, the alert unit can refer to past failure data using a generation AI. For example, the alert unit can have the generation AI refer to past failure data, identify patterns of new failures, and send alerts. The alert unit can also have the generation AI analyze past failure response history and generate appropriate alerts. Furthermore, the alert unit can have the generation AI evaluate the risk of new failures based on past failure data and send alerts. This improves the accuracy of new failure detection by referring to past failure data. The method and criteria for referring to past failure data need to be clearly defined, for example, database search or historical data analysis. Some or all of the above processing in the alert unit may be performed using a generation AI, or not using a generation AI. For example, the alert unit can input past failure data into a generation AI and have the generation AI perform new failure detection and alert generation.

[0044] The alert unit can adjust the level of detail of an alert based on the scope and priority of the failure when sending an alert. For example, the alert unit uses a generation AI to evaluate the scope and priority of the failure. For example, the alert unit can have the generation AI evaluate the scope of the failure and send an alert that highlights important information. The alert unit can also have the generation AI consider the priority of the failure and send an alert that includes detailed information. Furthermore, the alert unit can have the generation AI adjust the level of detail of the alert appropriately based on the scope and priority of the failure. This makes it possible to provide appropriate information by adjusting the level of detail of the alert based on the scope and priority of the failure. The method and criteria for adjusting the level of detail of the alert need to be clearly defined, for example, the level of detail in the notification content and the comprehensiveness of the information. Some or all of the above processing in the alert unit may be performed using a generation AI, or not using a generation AI. For example, the alert unit can have the generation AI perform the evaluation of the scope and priority of the failure, and have the generation AI perform the adjustment of the level of detail of the alert.

[0045] The alert unit can send alerts considering geographical information when detecting new failures. The alert unit considers geographical information, for example, using a generation AI. For example, the alert unit uses the generation AI to identify the location of the failure and send alerts to stakeholders within the affected area. The alert unit can also use the generation AI to send alerts that include optimal countermeasures based on geographical information. Furthermore, the alert unit can use the generation AI to consider geographical information, evaluate the scope of impact, and send alerts. This allows for the sending of appropriate alerts to stakeholders within the affected area by considering geographical information. The method and criteria for referencing geographical information need to be clearly defined, for example, GPS data or regional codes. Some or all of the above processing in the alert unit may be performed using a generation AI, for example, or without a generation AI. For example, the alert unit can input geographical information into a generation AI and have the generation AI execute the sending of alerts.

[0046] The alert unit can send customized alerts based on the roles and permissions of the stakeholders when sending alerts. For example, the alert unit can use a generation AI to customize alerts based on the roles and permissions of the stakeholders. For example, the alert unit can use the generation AI to send alerts containing detailed information to administrators. The alert unit can also use the generation AI to send alerts containing technical details to technicians. Furthermore, the alert unit can use the generation AI to send concise and easy-to-understand alerts to general users. This enables the provision of appropriate information by sending alerts customized according to the roles and permissions of the stakeholders. The content and adjustment methods of the customized alerts need to be clearly defined, for example, the customization of notification content and the customization of recipients. Some or all of the above processing in the alert unit may be performed using a generation AI, or not. For example, the alert unit can have the generation AI perform the customization of alerts according to the roles and permissions of the stakeholders.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The analysis unit can use specialized analysis algorithms for different industries and fields when analyzing call conversations. For example, it can perform analyses specialized in medical terminology and symptoms for the medical industry, and analyses specialized in financial terminology and transactions for the financial industry. It can also perform analyses specialized in educational terminology and learning content for the education sector. Furthermore, it can perform analyses specialized in manufacturing processes and machine failures for the manufacturing industry. By performing analyses specialized for different industries and fields, it becomes possible to extract information with higher accuracy.

[0049] The generation unit can customize the content of incident reports by referring to the user's past behavior history when generating them. For example, it can provide a report with detailed technical information to users who have previously dealt with similar incidents. Conversely, it can provide a report with basic information and simple steps to users who are encountering an incident for the first time. Furthermore, it can suggest the optimal solution based on the user's past response history to a specific incident. In this way, by customizing the report content based on the user's past behavior history, it becomes possible to provide more appropriate information.

[0050] The alerting unit can adjust the alert priority based on the frequency and impact of new failures. For example, frequently occurring failures can be given a low priority, while failures with a significant impact can be given a high priority. Furthermore, based on historical data, appropriate priorities can be set for failures that occur during specific time periods or under specific conditions. In addition, failures with a wide-ranging impact can be given a high priority to encourage a rapid response. By adjusting the alert priority based on the frequency and impact of failures, a more effective response becomes possible.

[0051] The generation unit can collect and analyze data in real time when generating incident reports, allowing it to reflect the latest information. For example, it can monitor the occurrence status and scope of impact of incidents in real time and reflect them in the report. It can also add progress updates and new discoveries to the report in real time. Furthermore, it can collect feedback from stakeholders in real time and reflect it in the report. As a result, by collecting and analyzing data in real time, it can provide reports that include the latest information.

[0052] The analysis unit can improve analysis accuracy by combining speech recognition technology and natural language processing technology when analyzing the content of call conversations. For example, speech recognition technology is used to convert the conversation content into text data, and that text data is then analyzed using natural language processing technology. Furthermore, by linking speech recognition technology and natural language processing technology, the context and intent of the conversation can be understood more accurately. In addition, by combining speech recognition technology and natural language processing technology, it is possible to handle complex conversation content and specialized terminology. In this way, by improving analysis accuracy by combining speech recognition technology and natural language processing technology, more accurate information extraction becomes possible.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The analysis unit analyzes the call conversation. Call conversations include telephone conversations, video calls, chats, etc. The analysis unit uses speech recognition technology to convert the call conversation content into text data and uses text analysis technology to extract important information from the conversation content. For example, it extracts information such as the date and time of the failure, the scope of the impact, and specific symptoms from the conversation content. Step 2: The generation unit generates a fault report based on the information analyzed by the analysis unit. The fault report includes information such as the type of fault, the time of occurrence, and the scope of impact. The generation unit can create a fault report based on the information extracted using the generation AI, and can also identify fault patterns by analyzing past fault reports and response history data. Step 3: The alerting unit detects new failures based on reports generated by the generation unit and sends immediate alerts. Alerts include real-time and no-delay notifications. The alerting unit uses generation AI to detect new failures and sends immediate alerts to relevant parties.

[0055] (Example of form 2) The automated fault response system according to an embodiment of the present invention is a system that automates fault response using AI. This automated fault response system analyzes call conversations and generates a concise and easy-to-understand fault report using a generation AI, with fault response personnel using predefined prompts. Next, it analyzes past fault reports and response history data and creates patterns. This allows for the analysis and presentation of risks and solutions for each fault pattern. Furthermore, it has a mechanism to immediately raise alerts for new faults that do not fit into any pattern. For example, in the step of analyzing call conversations, the generation AI analyzes the conversation content and extracts important information. For example, it extracts information such as the date and time of the fault, the scope of impact, and specific symptoms. Next, fault response personnel use predefined prompts, and the generation AI creates a concise and easy-to-understand fault report. This report includes the extracted important information, enabling personnel to respond quickly. Next, in the step of analyzing past fault reports and response history data, the generation AI analyzes this data and identifies fault patterns. For example, it identifies patterns such as faults occurring during specific time periods or faults related to specific systems. This allows for the analysis and presentation of risks and solutions for each fault pattern to personnel. Furthermore, the system includes a mechanism to immediately raise alerts for new failures that do not fit into existing patterns. When the generating AI detects a new failure, it immediately sends an alert to the relevant parties to encourage a swift response. For example, if a new type of failure occurs, the generating AI will detect it and notify the relevant parties. This system improves the efficiency of failure response and enables a rapid response. Relevant parties can take appropriate action based on the failure report generated by the generating AI. In addition, by analyzing past failure data, it is possible to predict the risk of future failures and take countermeasures in advance. Moreover, because it can respond to new failures immediately, the system stability is improved. In this way, the automated failure response system can improve the efficiency of failure response and enable a rapid response.

[0056] The automated fault response system according to this embodiment comprises an analysis unit, a generation unit, and an alert unit. The analysis unit analyzes call conversations. Call conversations include, but are not limited to, telephone conversations, video calls, and chats. The analysis unit converts the content of the call conversation into text data using, for example, speech recognition technology. The analysis unit can also extract important information from the conversation content using text analysis technology. For example, the analysis unit extracts information such as the date and time of the fault, the scope of impact, and specific symptoms from the conversation content. The generation unit generates a fault report based on the information analyzed by the analysis unit. The fault report includes, but is not limited to, information such as the type of fault, the time of occurrence, and the scope of impact. The generation unit creates a fault report based on the extracted information using, for example, generation AI. The generation unit can also analyze past fault reports and response history data to identify fault patterns. For example, the generation unit can identify patterns such as faults occurring during specific time periods or faults related to specific systems. The alert unit detects new faults based on the reports generated by the generation unit and sends immediate alerts. Alerts include, but are not limited to, real-time notifications and notifications without delay. The alerting unit, for example, uses a generation AI to detect new failures and immediately sends alerts to relevant parties. As a result, the automated failure response system according to the embodiment can improve the efficiency of failure response by analyzing call conversations, generating failure reports, and immediately detecting and sending alerts for new failures.

[0057] The analysis unit can analyze the content of a call and extract important information. For example, the analysis unit can convert the content of a call into text data using speech recognition technology. For example, the analysis unit can use speech recognition software to automatically analyze the speech and save it as text. The analysis unit can also extract important information from the conversation content using text analysis technology. For example, the analysis unit can extract information such as the date and time of the failure, the scope of the impact, and specific symptoms from the conversation content. This improves the accuracy of fault response by extracting important information from the content of the call. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the content of the call into a generative AI and have the generative AI perform the extraction of important information.

[0058] The generation unit can create an incident report based on the extracted information. For example, the generation unit can use a generation AI to create an incident report based on the extracted information. For example, the generation unit can use the generation AI to generate a clear and concise incident report based on the extracted information. The generation unit can also analyze past incident reports and response history data to identify incident patterns. For example, the generation unit can identify patterns such as incidents occurring during specific time periods or incidents related to specific systems. This allows stakeholders to respond quickly by creating incident reports based on the extracted information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the extracted information into a generation AI and have the generation AI create an incident report.

[0059] The alert unit can detect new failures and send immediate alerts. For example, the alert unit can use a generation AI to detect new failures and send immediate alerts to relevant parties. For instance, the alert unit can use a generation AI to detect new failures and notify relevant parties in real time. The alert unit can also adjust the method of sending alerts. For example, the alert unit can send alerts using methods such as real-time notification or no-delay notification. This enables rapid response by immediately detecting new failures and sending alerts. Some or all of the above-described processes in the alert unit may be performed using a generation AI, or without one. For example, the alert unit can have the generation AI detect new failures and send alerts.

[0060] The generation unit can analyze past failure reports and response history data to identify failure patterns. For example, the generation unit uses a generation AI to analyze past failure reports and response history data and identify failure patterns. For example, the generation unit can identify patterns such as failures occurring during specific time periods or failures related to specific systems. The generation unit can also analyze risks and solutions based on failure patterns. For example, the generation unit analyzes risk factors and solutions based on specific patterns and presents them to stakeholders. In this way, by analyzing past failure data, failure patterns can be identified and risks and solutions can be analyzed. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past failure reports and response history data into a generation AI and have the generation AI perform the identification of failure patterns.

[0061] The generation unit can analyze risk factors and solutions for each failure pattern and present them to stakeholders. For example, the generation unit can use a generation AI to analyze risk factors and solutions for each failure pattern. For example, the generation unit can analyze risk factors and solutions based on a specific pattern and present them to stakeholders. The generation unit can also present appropriate countermeasures to stakeholders based on the risk factors and solutions. For example, the generation unit can present countermeasures and procedures to stakeholders based on the risk factors and solutions. This enables appropriate responses by analyzing and presenting risks and solutions for each failure pattern to stakeholders. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have the generation AI perform the analysis of risk factors and solutions for each failure pattern, and then have the generation AI perform the presentation to stakeholders.

[0062] The analysis unit can estimate the user's emotions and adjust the accuracy of the conversation analysis based on the estimated user emotions. The analysis unit estimates the user's emotions, for example, using generative AI. For example, the analysis unit estimates the user's emotions using voice tone analysis or text sentiment analysis. Furthermore, the analysis unit can adjust the accuracy of the conversation analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit adjusts the analysis accuracy by considering the tone and speed of the conversation. Also, if the user is relaxed, the analysis unit can perform a more detailed analysis and extract more information. Furthermore, if the user is in a hurry, the analysis unit can focus on important information and perform a rapid analysis. This allows for the extraction of more appropriate information by adjusting the accuracy of the conversation analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed, for example, using generative AI, or without using generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI perform adjustments to the accuracy of the conversation analysis.

[0063] The analysis unit can analyze the content of call conversations in real time and immediately extract important information. For example, the analysis unit uses a generative AI to analyze the content of call conversations in real time. For example, the analysis unit can use the generative AI to extract the date and time of the failure in real time during the conversation. The analysis unit can also use the generative AI to immediately identify and extract the scope of the impact during the conversation. Furthermore, the analysis unit can use the generative AI to analyze and extract specific symptoms in real time during the conversation. This enables a rapid response by extracting important information in real time. The definition and criteria of real time need to be clearly defined, for example, by specifying delay time and processing speed. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the content of the call conversation into a generative AI and have the generative AI perform the extraction of important information in real time.

[0064] The analysis unit can evaluate the importance of specific keywords and phrases when analyzing conversation content. For example, the analysis unit can use a generative AI to detect specific keywords and phrases when analyzing conversation content. For example, the analysis unit can have the generative AI detect keywords such as "urgent" and "critical" and rate them as highly important. The analysis unit can also have the generative AI detect phrases such as "system down" and "data loss" and rate them as highly important. Furthermore, the analysis unit can have the generative AI detect phrases such as "minor problem" and "temporary outage" and rate them as less important. This allows important information to be processed preferentially by evaluating importance based on specific keywords and phrases. The selection criteria and extraction methods for specific keywords and phrases need to be clearly defined, for example, by frequency and importance. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input conversation content into a generative AI and have the generative AI perform the detection of specific keywords and phrases and the evaluation of their importance.

[0065] The analysis unit can estimate the user's emotions and adjust the display format of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions, for example, using generative AI. For example, the analysis unit estimates the user's emotions using voice tone analysis or text sentiment analysis. The analysis unit can also adjust the display format of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the display format of the analysis results.

[0066] The analysis unit can handle different languages ​​and dialects when analyzing the content of call conversations. For example, the analysis unit can use generative AI to handle different languages ​​and dialects. For example, the analysis unit can use a multilingual analysis algorithm with the generative AI to analyze conversations in different languages. The analysis unit can also use the generative AI to recognize and accurately analyze dialects and regional expressions. Furthermore, the analysis unit can use the generative AI with an automatic language detection function to identify and analyze the language of the conversation. This allows the system to support a wide range of users by handling different languages ​​and dialects. The methods and criteria for handling different languages ​​and dialects need to be clearly defined, for example, by using a multilingual system or a dialect dictionary. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the content of call conversations into the generative AI and have the generative AI perform analysis of different languages ​​and dialects.

[0067] The analysis unit can improve analysis accuracy by removing background noise and other noises when analyzing conversation content. For example, the analysis unit can use a generative AI to remove background noise and other noises. For instance, the generative AI can use noise filtering technology to remove background noise and analyze the conversation content. The analysis unit can also use speech recognition technology with the generative AI to extract important information while removing noise. Furthermore, the analysis unit can have the generative AI analyze multiple audio channels and remove noise to improve accuracy. This improves analysis accuracy by removing background noise and other noises. The methods and criteria for removing background noise and other noises need to be clearly defined, for example, noise filtering technology and speech cleanup technology. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input conversation content into a generative AI and have the generative AI perform background noise and other noise removal.

[0068] The generation unit can estimate the user's emotions and adjust the report's presentation based on the estimated emotions. For example, the generation unit might use a generative AI to estimate the user's emotions. For example, it might use voice tone analysis or text sentiment analysis to estimate the user's emotions. Furthermore, the generation unit can adjust the report's presentation based on the estimated emotions. For example, if the user is stressed, the generation unit might generate a simple, concise report. If the user is relaxed, it might generate a report with more detailed information. Additionally, if the user is in a hurry, it might generate a report in a format that can be quickly understood. This allows for more appropriate information to be provided by adjusting the report's presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the generation unit may be performed using a generative AI, or without one. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the format of the report.

[0069] The generation unit can include detailed solutions by referencing data on similar past failures when generating failure reports. For example, the generation unit can use a generation AI to reference data on similar past failures. For example, the generation unit can have the generation AI reference a database of past failures and include solutions for similar failures in the report. The generation unit can also have the generation AI analyze past response history and include the optimal solution in the report. Furthermore, the generation unit can have the generation AI propose detailed solutions based on past failure patterns. This allows for the provision of detailed solutions by referencing data on similar past failures. The methods and criteria for referencing data on similar past failures need to be clearly defined, for example, database searches and historical data analysis. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input data on similar past failures into the generation AI and have the generation AI propose detailed solutions.

[0070] The generation unit can adjust the level of detail in a report based on the scope and priority of the failure when generating the report. For example, the generation unit can use a generation AI to evaluate the scope and priority of the failure. For example, the generation unit can have the generation AI evaluate the scope of the failure and generate a report that highlights important information. The generation unit can also have the generation AI consider the priority of the failure and generate a report that includes detailed information. Furthermore, the generation unit can have the generation AI adjust the level of detail in the report appropriately based on the scope and priority of the failure. This makes it possible to provide appropriate information by adjusting the level of detail in the report based on the scope and priority of the failure. The evaluation criteria and adjustment methods for the scope and priority of the failure need to be clearly defined, for example, the number of affected systems and users, the urgency and importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can have the generation AI perform the evaluation of the scope and priority of the failure, and have the generation AI perform the adjustment of the level of detail in the report.

[0071] The generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. The generation unit can estimate the user's emotions using, for example, a generative AI. For example, the generation unit can estimate the user's emotions using voice tone analysis or text sentiment analysis. Furthermore, the generation unit can adjust the length of the report based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a short, concise report. If the user is relaxed, the generation unit can generate a longer report containing more detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a short, easily understandable report. This allows for more appropriate information to be provided by adjusting the report length according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the report.

[0072] The generation unit can make fault report reports visually easy to understand by including visual elements when generating them. For example, the generation unit can use a generation AI to include visual elements in the report. For example, the generation unit can use a generation AI to include a graph showing the scope of the fault's impact in the report. The generation unit can also use a generation AI to include a chart showing the frequency of fault occurrences in the report. Furthermore, the generation unit can use a generation AI to include visual elements showing the effectiveness of solutions in the report. This allows for the provision of visually easy-to-understand reports by including visual elements. The types and usage of visual elements, such as graphs, charts, and diagrams, need to be clearly defined. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have a generation AI generate visual elements and include them in the report.

[0073] The generation unit can generate customized reports according to the roles and permissions of stakeholders during report generation. For example, the generation unit can use a generation AI to customize reports according to the roles and permissions of stakeholders. For example, the generation unit can use the generation AI to generate reports containing detailed information for administrators. The generation unit can also use the generation AI to generate reports containing technical details for engineers. Furthermore, the generation unit can use the generation AI to generate concise and easy-to-understand reports for general users. This enables the provision of appropriate information by providing reports customized according to the roles and permissions of stakeholders. The definition and customization methods for stakeholders' roles and permissions need to be clearly defined, for example, administrators, engineers, and operators. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can have the generation AI perform the customization of reports according to the roles and permissions of stakeholders.

[0074] The alert unit can estimate the user's emotions and adjust the urgency of alerts based on the estimated emotions. The alert unit estimates the user's emotions, for example, using generative AI. For example, the alert unit estimates the user's emotions using voice tone analysis or text sentiment analysis. The alert unit can also adjust the urgency of alerts based on the estimated emotions. For example, if the user is stressed, the alert unit will prioritize sending high-urgency alerts. Conversely, if the user is relaxed, the alert unit can send low-urgency alerts. Furthermore, if the user is in a hurry, the alert unit can send alerts requiring immediate attention. This allows for a more appropriate response by adjusting the urgency of alerts according to the user'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 alert unit may be performed using, for example, generative AI, or without generative AI. For example, the alert unit can input user emotion data into a generating AI, which can then adjust the urgency level of the alert.

[0075] The alert unit can improve the accuracy of alerts by referring to past failure data when detecting new failures. For example, the alert unit can refer to past failure data using a generation AI. For example, the alert unit can have the generation AI refer to past failure data, identify patterns of new failures, and send alerts. The alert unit can also have the generation AI analyze past failure response history and generate appropriate alerts. Furthermore, the alert unit can have the generation AI evaluate the risk of new failures based on past failure data and send alerts. This improves the accuracy of new failure detection by referring to past failure data. The method and criteria for referring to past failure data need to be clearly defined, for example, database search or historical data analysis. Some or all of the above processing in the alert unit may be performed using a generation AI, or not using a generation AI. For example, the alert unit can input past failure data into a generation AI and have the generation AI perform new failure detection and alert generation.

[0076] The alert unit can adjust the level of detail of an alert based on the scope and priority of the failure when sending an alert. For example, the alert unit uses a generation AI to evaluate the scope and priority of the failure. For example, the alert unit can have the generation AI evaluate the scope of the failure and send an alert that highlights important information. The alert unit can also have the generation AI consider the priority of the failure and send an alert that includes detailed information. Furthermore, the alert unit can have the generation AI adjust the level of detail of the alert appropriately based on the scope and priority of the failure. This makes it possible to provide appropriate information by adjusting the level of detail of the alert based on the scope and priority of the failure. The method and criteria for adjusting the level of detail of the alert need to be clearly defined, for example, the level of detail in the notification content and the comprehensiveness of the information. Some or all of the above processing in the alert unit may be performed using a generation AI, or not using a generation AI. For example, the alert unit can have the generation AI perform the evaluation of the scope and priority of the failure, and have the generation AI perform the adjustment of the level of detail of the alert.

[0077] The alert unit can estimate the user's emotions and adjust the alert display format based on the estimated emotions. The alert unit estimates the user's emotions, for example, using generative AI. For example, the alert unit estimates the user's emotions using voice tone analysis or text sentiment analysis. Furthermore, the alert unit can adjust the alert display format based on the estimated emotions. For example, if the user is tense, the alert unit provides a simple and highly visible display method. If the user is relaxed, the alert unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the alert unit can provide a concise display method. This allows for more appropriate information to be provided by adjusting the alert display format according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the alert unit may be performed using, for example, generative AI, or without generative AI. For example, the alert unit can input user emotion data into a generating AI and have the AI ​​adjust the display format of the alerts.

[0078] The alert unit can send alerts considering geographical information when detecting new failures. The alert unit considers geographical information, for example, using a generation AI. For example, the alert unit uses the generation AI to identify the location of the failure and send alerts to stakeholders within the affected area. The alert unit can also use the generation AI to send alerts that include optimal countermeasures based on geographical information. Furthermore, the alert unit can use the generation AI to consider geographical information, evaluate the scope of impact, and send alerts. This allows for the sending of appropriate alerts to stakeholders within the affected area by considering geographical information. The method and criteria for referencing geographical information need to be clearly defined, for example, GPS data or regional codes. Some or all of the above processing in the alert unit may be performed using a generation AI, for example, or without a generation AI. For example, the alert unit can input geographical information into a generation AI and have the generation AI execute the sending of alerts.

[0079] The alert unit can send customized alerts based on the roles and permissions of the stakeholders when sending alerts. For example, the alert unit can use a generation AI to customize alerts based on the roles and permissions of the stakeholders. For example, the alert unit can use the generation AI to send alerts containing detailed information to administrators. The alert unit can also use the generation AI to send alerts containing technical details to technicians. Furthermore, the alert unit can use the generation AI to send concise and easy-to-understand alerts to general users. This enables the provision of appropriate information by sending alerts customized according to the roles and permissions of the stakeholders. The content and adjustment methods of the customized alerts need to be clearly defined, for example, the customization of notification content and the customization of recipients. Some or all of the above processing in the alert unit may be performed using a generation AI, or not. For example, the alert unit can have the generation AI perform the customization of alerts according to the roles and permissions of the stakeholders. === Hard Collateral 1-1 === Each of the multiple elements described above, including the analysis unit, generation unit, and alert unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14, which analyzes the call conversation and converts it into text data using speech recognition technology. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates a fault report based on the analyzed information. The alert unit is implemented by the control unit 46A of the smart device 14, which detects new faults and sends an immediate alert. === Hard Collateral 1-2 === Each of the multiple elements described above, including the analysis unit, generation unit, and alert unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214, which analyzes the call conversation and converts it into text data using speech recognition technology. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which generates a fault report based on the analyzed information. The alert unit is implemented by, for example, the control unit 46A of the smart glasses 214, which detects new faults and sends an immediate alert. === Hard Collateral 1-3 === Each of the multiple elements described above, including the analysis unit, generation unit, and alert unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314, which analyzes the call conversation and converts it into text data using speech recognition technology. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates a fault report based on the analyzed information. The alert unit is implemented by the control unit 46A of the headset terminal 314, which detects new faults and sends an immediate alert. === Hard Collateral 1-4 === Each of the multiple elements described above, including the analysis unit, generation unit, and alert unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414, which analyzes the call conversation and converts it into text data using speech recognition technology. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which generates a fault report based on the analyzed information. The alert unit is implemented by, for example, the control unit 46A of the robot 414, which detects new faults and sends an immediate alert.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The analysis unit can estimate the user's emotions and adjust the priority of conversation analysis based on those emotions. For example, if the user is feeling very stressed, the analysis unit will prioritize analyzing that conversation and respond quickly. If the user is relaxed, the analysis can be performed with normal priority. Furthermore, if the user is in a hurry, important information can be quickly extracted and an immediate response can be taken. In this way, adjusting the priority of conversation analysis according to the user's emotions enables a more appropriate response.

[0082] The generation unit can estimate the user's emotions when generating an incident report and adjust the tone of the report based on the estimated emotions. For example, if the user is stressed, the generation unit will create a report in a calm and reassuring tone. If the user is relaxed, it can create a report in a friendly and detailed tone. Furthermore, if the user is in a hurry, it can create a report in a concise and to-the-point tone. By adjusting the tone of the report according to the user's emotions, it becomes possible to provide more appropriate information.

[0083] The alerting unit can estimate the user's emotions and adjust how alerts are sent based on that estimation. For example, if the user is stressed, the alerting unit will prioritize sending high-priority alerts. If the user is relaxed, the normal alerting method can be used. Furthermore, if the user is in a hurry, alerts requiring immediate attention can be sent. This allows for more appropriate responses by adjusting the alerting method according to the user's emotions.

[0084] The generation unit can estimate the user's emotions when generating an incident report and adjust the report content based on the estimated emotions. For example, if the user is stressed, the generation unit will create a concise and to-the-point report. If the user is relaxed, it can create a report with detailed information. Furthermore, if the user is in a hurry, it can create a report in a format that can be quickly understood. By adjusting the report content according to the user's emotions, it becomes possible to provide more appropriate information.

[0085] The alert function can estimate the user's emotions and adjust the alert display format based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting the alert display format according to the user's emotions, it becomes possible to provide more appropriate information.

[0086] The analysis unit can use specialized analysis algorithms for different industries and fields when analyzing call conversations. For example, it can perform analyses specialized in medical terminology and symptoms for the medical industry, and analyses specialized in financial terminology and transactions for the financial industry. It can also perform analyses specialized in educational terminology and learning content for the education sector. Furthermore, it can perform analyses specialized in manufacturing processes and machine failures for the manufacturing industry. By performing analyses specialized for different industries and fields, it becomes possible to extract information with higher accuracy.

[0087] The generation unit can customize the content of incident reports by referring to the user's past behavior history when generating them. For example, it can provide a report with detailed technical information to users who have previously dealt with similar incidents. Conversely, it can provide a report with basic information and simple steps to users who are encountering an incident for the first time. Furthermore, it can suggest the optimal solution based on the user's past response history to a specific incident. In this way, by customizing the report content based on the user's past behavior history, it becomes possible to provide more appropriate information.

[0088] The alerting unit can adjust the alert priority based on the frequency and impact of new failures. For example, frequently occurring failures can be given a low priority, while failures with a significant impact can be given a high priority. Furthermore, based on historical data, appropriate priorities can be set for failures that occur during specific time periods or under specific conditions. In addition, failures with a wide-ranging impact can be given a high priority to encourage a rapid response. By adjusting the alert priority based on the frequency and impact of failures, a more effective response becomes possible.

[0089] The generation unit can collect and analyze data in real time when generating incident reports, allowing it to reflect the latest information. For example, it can monitor the occurrence status and scope of impact of incidents in real time and reflect them in the report. It can also add progress updates and new discoveries to the report in real time. Furthermore, it can collect feedback from stakeholders in real time and reflect it in the report. As a result, by collecting and analyzing data in real time, it can provide reports that include the latest information.

[0090] The analysis unit can improve analysis accuracy by combining speech recognition technology and natural language processing technology when analyzing the content of call conversations. For example, speech recognition technology is used to convert the conversation content into text data, and that text data is then analyzed using natural language processing technology. Furthermore, by linking speech recognition technology and natural language processing technology, the context and intent of the conversation can be understood more accurately. In addition, by combining speech recognition technology and natural language processing technology, it is possible to handle complex conversation content and specialized terminology. In this way, by improving analysis accuracy by combining speech recognition technology and natural language processing technology, more accurate information extraction becomes possible.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The analysis unit analyzes the call conversation. Call conversations include telephone conversations, video calls, chats, etc. The analysis unit uses speech recognition technology to convert the call conversation content into text data and uses text analysis technology to extract important information from the conversation content. For example, it extracts information such as the date and time of the failure, the scope of the impact, and specific symptoms from the conversation content. Step 2: The generation unit generates a fault report based on the information analyzed by the analysis unit. The fault report includes information such as the type of fault, the time of occurrence, and the scope of impact. The generation unit can create a fault report based on the information extracted using the generation AI, and can also identify fault patterns by analyzing past fault reports and response history data. Step 3: The alerting unit detects new failures based on reports generated by the generation unit and sends immediate alerts. Alerts include real-time and no-delay notifications. The alerting unit uses generation AI to detect new failures and sends immediate alerts to relevant parties.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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. The analysis unit analyzes the call conversation, A generation unit generates a failure report based on the information analyzed by the analysis unit, The system includes an alert unit that detects new failures based on reports generated by the generation unit and sends alerts. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the content of the call and extract important information. The system according to feature 1.

3. The generating unit is Create an incident report based on the extracted information. The system according to feature 1.

4. The alert unit is, Detects new failures and sends immediate alerts. The system according to feature 1.

5. The generating unit is By analyzing past incident reports and response history data, we identify patterns in the failures. The system according to feature 1.

6. The generating unit is Analyze the risk factors and solutions for each failure pattern and present them to stakeholders. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of conversation analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, The system analyzes call conversations in real time and instantly extracts important information. The system according to feature 1.

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