system

Abstract

The system according to this embodiment aims to automatically identify the cause of an inquiry and provide a solution. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects past inquiry data and Confluence information. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies the cause based on the analysis results obtained by the analysis unit. The provision unit provides a solution based on the cause identified by the identification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of identifying the cause of an inquiry and providing a solution is performed manually, resulting in low efficiency.

[0005] The system according to the embodiment aims to automatically identify the cause of an inquiry and provide a solution.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects past inquiry data and Confluence information. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies the cause based on the analysis results obtained by the analysis unit. The provision unit provides a solution based on the cause identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically identify the cause of an inquiry and provide a solution. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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), etc.

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) An AI agent system according to an embodiment of the present invention is a system that automates the process of investigating the cause of internal inquiries. This AI agent system analyzes past inquiry data and Confluence information to automate the process of investigating the cause of an inquiry. Based on the content of the inquiry, the AI ​​agent system proposes the cause and provides a solution. Furthermore, the AI ​​agent system automatically generates FAQs and forms an inquiry improvement cycle. This mechanism makes it possible to improve business efficiency by quickly identifying the cause of an inquiry and preventing its recurrence. For example, the AI ​​agent system collects and analyzes past inquiry data and Confluence information. In this process, the AI ​​agent system uses a large-scale language model to understand the content of the inquiry and identify the cause. For example, if there are many inquiries about a particular system error, the AI ​​agent system identifies the cause and proposes a solution. Next, the AI ​​agent system proposes the cause and provides a solution based on the content of the inquiry. For example, if a particular error occurs, the AI ​​agent system identifies the cause of the error and proposes a solution. In this process, the AI ​​agent system compares past inquiry data and Confluence information to provide the optimal solution. Furthermore, the AI ​​agent system automatically generates FAQs and forms an inquiry improvement cycle. For example, if there are many inquiries about a particular error, the AI ​​agent system automatically generates an FAQ about that error and provides it to internal users. This can reduce similar inquiries. This mechanism improves operational efficiency by quickly identifying the cause of inquiries and preventing recurrence. For example, if there are many inquiries about a particular error, the AI ​​agent system can identify the cause of that error and suggest a solution, thereby reducing similar inquiries. Furthermore, the AI ​​agent system can provide optimal answers by continuously learning and updating information in real time. This allows the AI ​​agent system to improve operational efficiency by quickly identifying the cause of inquiries and preventing recurrence.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects past inquiry data and Confluence information. The collection unit can collect inquiry data such as customer feedback and support requests. The collection unit can also collect information such as Confluence project documents and meeting notes. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input inquiry data and Confluence information into an AI model and have the AI ​​perform data collection. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as data mining and statistical analysis. The analysis unit can also analyze the data using AI and identify the cause of the inquiry. For example, the analysis unit can input the collected data into an AI model and have the AI ​​perform data analysis. The identification unit identifies the cause based on the analysis results obtained by the analysis unit. The identification unit can identify the cause using methods such as root cause analysis and causal relationship identification. The identification unit can also identify the cause using AI. For example, the identification unit can input analysis results into an AI model and have the AI ​​identify the cause. The provision unit provides a solution based on the cause identified by the identification unit. The provision unit can provide a solution using methods such as providing instructions or escalating to a support team. The provision unit can also provide a solution using AI. For example, the provision unit can input the identified cause into an AI model and have the AI ​​provide a solution. As a result, the AI ​​agent system according to this embodiment can automate everything from collecting inquiry data to identifying the cause and providing a solution, thereby improving operational efficiency.

[0030] The data collection unit collects past inquiry data and information from Confluence. Specifically, it can collect inquiry data such as customer feedback and support requests. This includes tickets submitted by customers, emails, chat logs, and phone recordings. This data is important for gaining a detailed understanding of customer issues and requests. The data collection unit can also collect information such as Confluence project documents and meeting notes. Confluence contains project progress, technical specifications, meeting minutes, and team member comments, and this information helps to understand the background of inquiries and related technical details. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can input inquiry data and Confluence information into an AI model and have the AI ​​perform the data collection. The AI ​​uses natural language processing (NLP) techniques to extract important information from text data and automatically collect relevant data. This allows the data collection unit to efficiently collect large amounts of data and quickly obtain the necessary information. Furthermore, the data collection unit can flexibly set the frequency and scope of data collection, and can focus data collection on specific periods or projects. This allows the data collection unit to improve the overall data collection capabilities of the system and provide more accurate and comprehensive data.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it can analyze the data using methods such as data mining and statistical analysis. Data mining extracts patterns and trends from the collected data, while statistical analysis reveals the distribution and correlations of the data. This allows the analysis department to gain important insights from inquiry data. The analysis department can also use AI to analyze the data and identify the root cause of inquiries. For example, the analysis department can input the collected data into an AI model and have the AI ​​perform the data analysis. The AI ​​uses machine learning algorithms to detect hidden patterns and anomalies in the data and identify the root cause of inquiries. For example, it can extract common problems from customer feedback or identify frequently occurring problems from the content of support requests. Furthermore, the analysis department can compare historical and current data to understand inquiry trends and changes. This allows the analysis department to quickly and accurately identify the root cause of inquiries and improve the overall system performance.

[0032] The identification unit identifies the cause based on the analysis results obtained by the analysis unit. Specifically, the cause can be identified using methods such as root cause analysis and causal relationship identification. In root cause analysis, the source of the problem is identified, and measures are taken to eliminate the cause. In causal relationship identification, the relationship between the cause and effect of the problem is clarified, and the mechanism of the problem's occurrence is understood. This allows the identification unit to accurately identify the cause of the problem and take appropriate measures. The identification unit can also identify the cause using AI. For example, the identification unit can input the analysis results into an AI model and have the AI ​​perform the cause identification. The AI ​​uses machine learning algorithms to identify causal relationships from the data and quickly identify the cause of the problem. This allows the identification unit to quickly and accurately identify the cause of the problem and improve the overall system performance. Furthermore, the identification unit can compare past data with current data to grasp the trends and changes in problem occurrence. This allows the identification unit to quickly and accurately identify the cause of the problem and improve the overall system performance.

[0033] The service provider will provide solutions based on the causes identified by the specific department. Specifically, solutions can be provided using methods such as providing instructions or escalating to the support team. Providing instructions involves providing a document that details how to solve the problem, enabling the user to solve the problem themselves. Escalating to the support team means that if the problem is difficult to solve, the issue will be handed over to a specialized support team for a quick resolution. The service provider can also provide solutions using AI. For example, the service provider can input the identified causes into an AI model and have the AI ​​perform the task of providing solutions. The AI ​​will refer to a database of past solutions and propose the optimal solution. This allows the service provider to provide solutions quickly and accurately, and to resolve user problems rapidly. Furthermore, the service provider can collect feedback from users and continuously improve the accuracy and effectiveness of the solutions. This allows the service provider to improve the overall system performance and increase user satisfaction.

[0034] The AI ​​agent system further includes a generation unit that automatically generates FAQs and forms an inquiry improvement cycle. The generation unit can automatically generate FAQs using methods such as natural language generation technology or template-based generation. The generation unit can, for example, input inquiry data into an AI model and have the AI ​​generate the FAQs. This prevents the recurrence of inquiries and improves operational efficiency through the automatic generation of FAQs. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input inquiry data into an AI model and have the AI ​​generate the FAQs.

[0035] The data collection unit can filter past inquiry data based on specific periods or events. For example, it can prioritize collecting inquiry data from specific campaign periods. It can also filter and collect inquiry data after system updates. Furthermore, it can collect inquiry data from specific event periods, such as the year-end and New Year holidays. This allows for the collection of highly relevant data by filtering data based on specific periods or events. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input inquiry data into an AI model and have the AI ​​perform the data filtering.

[0036] The data collection unit can prioritize collecting information related to specific projects or teams when gathering information from Confluence. For example, it can prioritize collecting pages related to a specific project. It can also prioritize collecting documents created by a specific team. Furthermore, it can prioritize collecting information that has been tagged with a specific tag. This allows for efficient collection of necessary information by prioritizing information related to specific projects or teams. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input Confluence information into an AI model and have the AI ​​perform the information collection.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of query data related to that region. Furthermore, if the user is on the move, the data collection unit can also collect highly relevant data based on their current location. Additionally, if the user is overseas, the data collection unit can prioritize the collection of query data related to that country. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location information into an AI model and have the AI ​​perform the data collection.

[0038] The data collection unit can analyze the user's social media activity and collect relevant inquiry data during the collection process. For example, if the user mentions a specific issue on social media, the data collection unit can collect data related to that issue. It can also collect data related to a specific product if the user mentions that product on social media. Furthermore, if the user mentions a specific event on social media, the data collection unit can collect data related to that event. This allows for the efficient collection of relevant inquiry data by analyzing the user's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI model and have the AI ​​perform the data collection.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the query data during the analysis. For example, the analysis unit can perform a detailed analysis on important query data. It can also perform a standard analysis on general query data. Furthermore, it can perform a simplified analysis on low-priority query data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the query data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input query data into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical inquiries. It can also apply a support-specific analysis algorithm to support-related inquiries. Furthermore, it can apply a general-purpose analysis algorithm to general inquiries. This allows for more appropriate analysis by applying different analysis algorithms depending on the inquiry category. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input inquiry data into an AI model and have the AI ​​perform the application of the analysis algorithm.

[0041] The analysis department can prioritize analyses based on when inquiries were submitted. For example, it might prioritize analyzing recently submitted inquiry data. It could also prioritize analyzing inquiry data submitted during a specific period. Furthermore, it could analyze past inquiry data to determine priorities for preventing recurrence. This allows for the prioritization of data requiring immediate attention by prioritizing analyses based on when inquiries were submitted. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department could input inquiry data into an AI model and have the AI ​​determine the analysis priorities.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the queries during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant query data. It can also postpone the analysis of less relevant query data. Furthermore, the analysis unit can group relevant query data for analysis. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the queries. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input query data into an AI model and have the AI ​​perform the adjustment of the analysis order.

[0043] The identification unit can improve the accuracy of cause identification by considering the interrelationships of queries during the identification process. For example, the identification unit can improve the accuracy of cause identification based on related query data. The identification unit can also identify causes by grouping related query data. Furthermore, the identification unit can improve the accuracy of cause identification by analyzing the interrelationships of query data. In this way, the accuracy of cause identification is improved by considering the interrelationships of queries. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input query data into an AI model and have the AI ​​perform the process of improving the accuracy of cause identification.

[0044] The identification unit can identify the cause by considering the attribute information of the person submitting the inquiry. For example, the identification unit can identify the cause based on the submitter's job title and department. The identification unit can also identify the cause by considering the submitter's past inquiry history. Furthermore, the identification unit can identify the cause based on the submitter's skill level. This allows for more appropriate cause identification by considering the attribute information of the person submitting the inquiry. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input the submitter's attribute information into an AI model and have the AI ​​perform the cause identification.

[0045] The identification unit can identify the cause by considering the geographical distribution of queries at the time of identification. For example, the identification unit can identify the cause based on query data that frequently occurs in a particular region. The identification unit can also identify the cause by grouping geographically related query data. Furthermore, the identification unit can analyze the geographical distribution to improve the accuracy of cause identification. This allows for more appropriate cause identification by considering the geographical distribution of queries. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input query data into an AI model and have the AI ​​perform the cause identification.

[0046] The identification unit can improve the accuracy of cause identification by referring to relevant literature for the inquiry during the identification process. For example, the identification unit can identify the cause by referring to relevant technical literature. The identification unit can also identify the cause based on literature related to past inquiries. Furthermore, the identification unit can analyze relevant literature to improve the accuracy of cause identification. In this way, the accuracy of cause identification is improved by referring to relevant literature for the inquiry. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input relevant literature into an AI model and have the AI ​​perform the cause identification.

[0047] The service provider can adjust the level of detail in the solution based on the importance of the inquiry at the time of delivery. For example, the service provider will provide a detailed solution for important inquiries. It can also provide a standard solution for general inquiries. Furthermore, it can provide a simplified solution for low-priority inquiries. This allows for the provision of detailed solutions for important inquiries by adjusting the level of detail based on the importance of the inquiry. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input inquiry data into an AI model and have the AI ​​perform the adjustment of the level of detail in the solution.

[0048] The service provider can apply different solution provision algorithms depending on the category of the inquiry at the time of provision. For example, the service provider can apply a technical solution provision algorithm to technical inquiries. It can also apply a support-specific solution provision algorithm to support-related inquiries. Furthermore, it can apply a general-purpose solution provision algorithm to general inquiries. This allows for the provision of more appropriate solutions by applying different solution provision algorithms depending on the category of the inquiry. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input inquiry data into an AI model and have the AI ​​perform the application of the solution provision algorithm.

[0049] The service provider can prioritize solutions based on when the inquiry was submitted. For example, it can provide quick solutions to recently submitted inquiries. It can also prioritize solutions to inquiries submitted within a specific period. Furthermore, it can provide solutions to prevent recurrence for past inquiries. By prioritizing solutions based on when the inquiry was submitted, it is possible to provide priority solutions to inquiries that require a quick response. Some or all of the above processes in the service provider may or may not be performed using AI. For example, the service provider can input inquiry data into an AI model and have the AI ​​determine the priority of solutions.

[0050] The service provider can adjust the order of solutions based on the relevance of the inquiries at the time of delivery. For example, the service provider can prioritize providing solutions to highly relevant inquiries. It can also postpone providing solutions to less relevant inquiries. Furthermore, the service provider can group relevant inquiries together and provide solutions accordingly. This allows for prioritizing solutions to highly relevant inquiries by adjusting the order of solutions based on the relevance of the inquiries. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input inquiry data into an AI model and have the AI ​​perform the adjustment of the order of solutions.

[0051] The generation unit can generate optimal FAQs by referring to past inquiry data during FAQ generation. For example, the generation unit can generate FAQs for frequently asked questions based on past inquiry data. The generation unit can also analyze past inquiry data and generate FAQs that include the most effective solutions. Furthermore, the generation unit can refer to past inquiry data to generate FAQs tailored to user needs. In this way, optimal FAQs can be generated by referring to past inquiry data. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input past inquiry data into an AI model and have the AI ​​perform the FAQ generation.

[0052] The generation unit can apply different FAQ generation methods to each category of inquiry when generating FAQs. For example, the generation unit can apply a technical FAQ generation method to technical inquiries. It can also apply a support-specific FAQ generation method to support-related inquiries. Furthermore, it can apply a general-purpose FAQ generation method to general inquiries. By applying different FAQ generation methods to each category of inquiry, more appropriate FAQs can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input inquiry data into an AI model and have the AI ​​perform the application of FAQ generation methods.

[0053] The generation unit can adjust the content of FAQs based on when the inquiry was submitted. For example, the generation unit can generate the latest FAQ for recently submitted inquiries. It can also generate FAQs relevant to a specific period for inquiries submitted during that period. Furthermore, it can generate FAQs for past inquiries to help prevent recurrence. By adjusting the content of FAQs based on when the inquiry was submitted, it is possible to provide more appropriate FAQs. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input inquiry data into an AI model and have the AI ​​perform the adjustment of the FAQ content.

[0054] The generation unit can generate FAQs by referring to relevant market data for inquiries. For example, the generation unit can generate FAQs that correspond to the latest trends based on relevant market data. The generation unit can also analyze market data and generate FAQs that meet user needs. Furthermore, the generation unit can refer to market data and generate FAQs that are based on those of competitors. This allows for the generation of more appropriate FAQs by referring to relevant market data for inquiries. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input relevant market data into an AI model and have the AI ​​perform the FAQ generation.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can filter past inquiry data based on specific periods or events. For example, it can prioritize the collection of inquiry data from a specific campaign period. The data collection unit can also filter and collect inquiry data after a system update. Furthermore, it can collect inquiry data during specific event periods, such as the year-end and New Year holidays. This allows for the collection of highly relevant data by filtering it based on specific periods or events. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input inquiry data into an AI model and have the AI ​​perform the data filtering.

[0057] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, it can prioritize the collection of query data related to that region. Furthermore, if the user is on the move, the data collection unit can also collect highly relevant data based on their current location. Additionally, if the user is overseas, the data collection unit can prioritize the collection of query data related to that country. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location information into an AI model and have the AI ​​perform the data collection.

[0058] The data collection unit can analyze the user's social media activity and collect relevant inquiry data during the collection process. For example, if a user mentions a specific issue on social media, it can collect data related to that issue. The data collection unit can also collect data related to a specific product if the user mentions it on social media. Furthermore, if the user mentions a specific event on social media, it can collect data related to that event. This allows for the efficient collection of relevant inquiry data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI model and have the AI ​​perform the data collection.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the query data. For example, it can perform a detailed analysis on important query data, a standard analysis on general query data, and a simplified analysis on low-priority query data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the query data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input query data into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0060] The identification unit can improve the accuracy of cause identification by considering the interrelationships of queries during the identification process. For example, it can improve the accuracy of cause identification based on related query data. It can also identify causes by grouping related query data. Furthermore, it can improve the accuracy of cause identification by analyzing the interrelationships of query data. In this way, the accuracy of cause identification is improved by considering the interrelationships of queries. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input query data into an AI model and have the AI ​​perform the task of improving the accuracy of cause identification.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects past inquiry data and Confluence information. For example, it collects inquiry data such as customer feedback and support requests, and information such as Confluence project documents and meeting notes. Processing in the collection unit may or may not be performed using AI. Step 2: The analysis department analyzes the data collected by the collection department. For example, they may use methods such as data mining or statistical analysis to analyze the data. The analysis department can also use AI to analyze the data and identify the cause of the inquiry. Step 3: The identification unit identifies the cause based on the analysis results obtained by the analysis unit. For example, it identifies the cause using methods such as root cause analysis or identification of causal relationships. The identification unit can also identify the cause using AI. Step 4: The service provider provides a solution based on the cause identified by the specific service provider. This solution may be provided by methods such as providing instructions or escalating the issue to the support team. The service provider can also use AI to provide solutions.

[0063] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that automates the process of investigating the cause of internal inquiries. This AI agent system analyzes past inquiry data and Confluence information to automate the process of investigating the cause of an inquiry. Based on the content of the inquiry, the AI ​​agent system proposes the cause and provides a solution. Furthermore, the AI ​​agent system automatically generates FAQs and forms an inquiry improvement cycle. This mechanism makes it possible to improve business efficiency by quickly identifying the cause of an inquiry and preventing its recurrence. For example, the AI ​​agent system collects and analyzes past inquiry data and Confluence information. In this process, the AI ​​agent system uses a large-scale language model to understand the content of the inquiry and identify the cause. For example, if there are many inquiries about a particular system error, the AI ​​agent system identifies the cause and proposes a solution. Next, the AI ​​agent system proposes the cause and provides a solution based on the content of the inquiry. For example, if a particular error occurs, the AI ​​agent system identifies the cause of the error and proposes a solution. In this process, the AI ​​agent system compares past inquiry data and Confluence information to provide the optimal solution. Furthermore, the AI ​​agent system automatically generates FAQs and forms an inquiry improvement cycle. For example, if there are many inquiries about a particular error, the AI ​​agent system automatically generates an FAQ about that error and provides it to internal users. This can reduce similar inquiries. This mechanism improves operational efficiency by quickly identifying the cause of inquiries and preventing recurrence. For example, if there are many inquiries about a particular error, the AI ​​agent system can identify the cause of that error and suggest a solution, thereby reducing similar inquiries. Furthermore, the AI ​​agent system can provide optimal answers by continuously learning and updating information in real time. This allows the AI ​​agent system to improve operational efficiency by quickly identifying the cause of inquiries and preventing recurrence.

[0064] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects past inquiry data and Confluence information. The collection unit can collect inquiry data such as customer feedback and support requests. The collection unit can also collect information such as Confluence project documents and meeting notes. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input inquiry data and Confluence information into an AI model and have the AI ​​perform data collection. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as data mining and statistical analysis. The analysis unit can also analyze the data using AI and identify the cause of the inquiry. For example, the analysis unit can input the collected data into an AI model and have the AI ​​perform data analysis. The identification unit identifies the cause based on the analysis results obtained by the analysis unit. The identification unit can identify the cause using methods such as root cause analysis and causal relationship identification. The identification unit can also identify the cause using AI. For example, the identification unit can input analysis results into an AI model and have the AI ​​identify the cause. The provision unit provides a solution based on the cause identified by the identification unit. The provision unit can provide a solution using methods such as providing instructions or escalating to a support team. The provision unit can also provide a solution using AI. For example, the provision unit can input the identified cause into an AI model and have the AI ​​provide a solution. As a result, the AI ​​agent system according to this embodiment can automate everything from collecting inquiry data to identifying the cause and providing a solution, thereby improving operational efficiency.

[0065] The data collection unit collects past inquiry data and information from Confluence. Specifically, it can collect inquiry data such as customer feedback and support requests. This includes tickets submitted by customers, emails, chat logs, and phone recordings. This data is important for gaining a detailed understanding of customer issues and requests. The data collection unit can also collect information such as Confluence project documents and meeting notes. Confluence contains project progress, technical specifications, meeting minutes, and team member comments, and this information helps to understand the background of inquiries and related technical details. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can input inquiry data and Confluence information into an AI model and have the AI ​​perform the data collection. The AI ​​uses natural language processing (NLP) techniques to extract important information from text data and automatically collect relevant data. This allows the data collection unit to efficiently collect large amounts of data and quickly obtain the necessary information. Furthermore, the data collection unit can flexibly set the frequency and scope of data collection, and can focus data collection on specific periods or projects. This allows the data collection unit to improve the overall data collection capabilities of the system and provide more accurate and comprehensive data.

[0066] The analysis department analyzes the data collected by the data collection department. Specifically, it can analyze the data using methods such as data mining and statistical analysis. Data mining extracts patterns and trends from the collected data, while statistical analysis reveals the distribution and correlations of the data. This allows the analysis department to gain important insights from inquiry data. The analysis department can also use AI to analyze the data and identify the root cause of inquiries. For example, the analysis department can input the collected data into an AI model and have the AI ​​perform the data analysis. The AI ​​uses machine learning algorithms to detect hidden patterns and anomalies in the data and identify the root cause of inquiries. For example, it can extract common problems from customer feedback or identify frequently occurring problems from the content of support requests. Furthermore, the analysis department can compare historical and current data to understand inquiry trends and changes. This allows the analysis department to quickly and accurately identify the root cause of inquiries and improve the overall system performance.

[0067] The identification unit identifies the cause based on the analysis results obtained by the analysis unit. Specifically, the cause can be identified using methods such as root cause analysis and causal relationship identification. In root cause analysis, the source of the problem is identified, and measures are taken to eliminate the cause. In causal relationship identification, the relationship between the cause and effect of the problem is clarified, and the mechanism of the problem's occurrence is understood. This allows the identification unit to accurately identify the cause of the problem and take appropriate measures. The identification unit can also identify the cause using AI. For example, the identification unit can input the analysis results into an AI model and have the AI ​​perform the cause identification. The AI ​​uses machine learning algorithms to identify causal relationships from the data and quickly identify the cause of the problem. This allows the identification unit to quickly and accurately identify the cause of the problem and improve the overall system performance. Furthermore, the identification unit can compare past data with current data to grasp the trends and changes in problem occurrence. This allows the identification unit to quickly and accurately identify the cause of the problem and improve the overall system performance.

[0068] The service provider will provide solutions based on the causes identified by the specific department. Specifically, solutions can be provided using methods such as providing instructions or escalating to the support team. Providing instructions involves providing a document that details how to solve the problem, enabling the user to solve the problem themselves. Escalating to the support team means that if the problem is difficult to solve, the issue will be handed over to a specialized support team for a quick resolution. The service provider can also provide solutions using AI. For example, the service provider can input the identified causes into an AI model and have the AI ​​perform the task of providing solutions. The AI ​​will refer to a database of past solutions and propose the optimal solution. This allows the service provider to provide solutions quickly and accurately, and to resolve user problems rapidly. Furthermore, the service provider can collect feedback from users and continuously improve the accuracy and effectiveness of the solutions. This allows the service provider to improve the overall system performance and increase user satisfaction.

[0069] The AI ​​agent system further includes a generation unit that automatically generates FAQs and forms an inquiry improvement cycle. The generation unit can automatically generate FAQs using methods such as natural language generation technology or template-based generation. The generation unit can, for example, input inquiry data into an AI model and have the AI ​​generate the FAQs. This prevents the recurrence of inquiries and improves operational efficiency through the automatic generation of FAQs. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input inquiry data into an AI model and have the AI ​​generate the FAQs.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the data after the user has calmed down. Conversely, if the user is relaxed, the data collection unit can collect the data immediately and respond quickly. Furthermore, if the user is in a hurry, the data collection unit can accelerate the collection timing to collect the data quickly. By adjusting the collection timing according to the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the collection timing.

[0071] The data collection unit can filter past inquiry data based on specific periods or events. For example, it can prioritize collecting inquiry data from specific campaign periods. It can also filter and collect inquiry data after system updates. Furthermore, it can collect inquiry data from specific event periods, such as the year-end and New Year holidays. This allows for the collection of highly relevant data by filtering data based on specific periods or events. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input inquiry data into an AI model and have the AI ​​perform the data filtering.

[0072] The data collection unit can prioritize collecting information related to specific projects or teams when gathering information from Confluence. For example, it can prioritize collecting pages related to a specific project. It can also prioritize collecting documents created by a specific team. Furthermore, it can prioritize collecting information that has been tagged with a specific tag. This allows for efficient collection of necessary information by prioritizing information related to specific projects or teams. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input Confluence information into an AI model and have the AI ​​perform the information collection.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can also collect data with normal priority. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that requires immediate attention. This allows for the priority collection of important data by determining data priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI model and have the AI ​​determine the data priority.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of query data related to that region. Furthermore, if the user is on the move, the data collection unit can also collect highly relevant data based on their current location. Additionally, if the user is overseas, the data collection unit can prioritize the collection of query data related to that country. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location information into an AI model and have the AI ​​perform the data collection.

[0075] The data collection unit can analyze the user's social media activity and collect relevant inquiry data during the collection process. For example, if the user mentions a specific issue on social media, the data collection unit can collect data related to that issue. It can also collect data related to a specific product if the user mentions that product on social media. Furthermore, if the user mentions a specific event on social media, the data collection unit can collect data related to that event. This allows for the efficient collection of relevant inquiry data by analyzing the user's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI model and have the AI ​​perform the data collection.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI model and have the AI ​​adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the query data during the analysis. For example, the analysis unit can perform a detailed analysis on important query data. It can also perform a standard analysis on general query data. Furthermore, it can perform a simplified analysis on low-priority query data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the query data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input query data into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical inquiries. It can also apply a support-specific analysis algorithm to support-related inquiries. Furthermore, it can apply a general-purpose analysis algorithm to general inquiries. This allows for more appropriate analysis by applying different analysis algorithms depending on the inquiry category. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input inquiry data into an AI model and have the AI ​​perform the application of the analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI model and have the AI ​​adjust the length of the analysis.

[0080] The analysis department can prioritize analyses based on when inquiries were submitted. For example, it might prioritize analyzing recently submitted inquiry data. It could also prioritize analyzing inquiry data submitted during a specific period. Furthermore, it could analyze past inquiry data to determine priorities for preventing recurrence. This allows for the prioritization of data requiring immediate attention by prioritizing analyses based on when inquiries were submitted. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department could input inquiry data into an AI model and have the AI ​​determine the analysis priorities.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the queries during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant query data. It can also postpone the analysis of less relevant query data. Furthermore, the analysis unit can group relevant query data for analysis. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the queries. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input query data into an AI model and have the AI ​​perform the adjustment of the analysis order.

[0082] The identification unit can estimate the user's emotions and adjust the criteria for identifying the cause based on the estimated emotions. For example, if the user is feeling anxious, the identification unit can perform detailed cause identification. It can also perform normal cause identification if the user is relaxed. Furthermore, if the user is in a hurry, the identification unit can perform rapid cause identification. This allows for more appropriate cause identification by adjusting the criteria for identifying the cause 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 identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the criteria for identifying the cause.

[0083] The identification unit can improve the accuracy of cause identification by considering the interrelationships of queries during the identification process. For example, the identification unit can improve the accuracy of cause identification based on related query data. The identification unit can also identify causes by grouping related query data. Furthermore, the identification unit can improve the accuracy of cause identification by analyzing the interrelationships of query data. In this way, the accuracy of cause identification is improved by considering the interrelationships of queries. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input query data into an AI model and have the AI ​​perform the process of improving the accuracy of cause identification.

[0084] The identification unit can identify the cause by considering the attribute information of the person submitting the inquiry. For example, the identification unit can identify the cause based on the submitter's job title and department. The identification unit can also identify the cause by considering the submitter's past inquiry history. Furthermore, the identification unit can identify the cause based on the submitter's skill level. This allows for more appropriate cause identification by considering the attribute information of the person submitting the inquiry. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input the submitter's attribute information into an AI model and have the AI ​​perform the cause identification.

[0085] The identification unit can estimate the user's emotions and adjust the order in which cause identification results are displayed based on the estimated user emotions. For example, if the user is feeling anxious, the identification unit will display the most important causes first. Alternatively, if the user is relaxed, the identification unit can display the causes in the normal order. Furthermore, if the user is in a hurry, the identification unit can display causes requiring immediate attention first. This allows for more appropriate result display by adjusting the order in which cause identification results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into an AI model and have the AI ​​adjust the order in which results are displayed.

[0086] The identification unit can identify the cause by considering the geographical distribution of queries at the time of identification. For example, the identification unit can identify the cause based on query data that frequently occurs in a particular region. The identification unit can also identify the cause by grouping geographically related query data. Furthermore, the identification unit can analyze the geographical distribution to improve the accuracy of cause identification. This allows for more appropriate cause identification by considering the geographical distribution of queries. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input query data into an AI model and have the AI ​​perform the cause identification.

[0087] The identification unit can improve the accuracy of cause identification by referring to relevant literature for the inquiry during the identification process. For example, the identification unit can identify the cause by referring to relevant technical literature. The identification unit can also identify the cause based on literature related to past inquiries. Furthermore, the identification unit can analyze relevant literature to improve the accuracy of cause identification. In this way, the accuracy of cause identification is improved by referring to relevant literature for the inquiry. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input relevant literature into an AI model and have the AI ​​perform the cause identification.

[0088] The service provider can estimate the user's emotions and adjust how it delivers solutions based on those emotions. For example, if the user is feeling anxious, the service provider can provide a detailed solution. If the user is relaxed, the service provider can also provide a standard solution. Furthermore, if the user is in a hurry, the service provider can provide a quick solution. By adjusting how the solution is delivered according to the user's emotions, a more appropriate solution can be provided. 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI model and have the AI ​​adjust how the solution is delivered.

[0089] The service provider can adjust the level of detail in the solution based on the importance of the inquiry at the time of delivery. For example, the service provider will provide a detailed solution for important inquiries. It can also provide a standard solution for general inquiries. Furthermore, it can provide a simplified solution for low-priority inquiries. This allows for the provision of detailed solutions for important inquiries by adjusting the level of detail based on the importance of the inquiry. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input inquiry data into an AI model and have the AI ​​perform the adjustment of the level of detail in the solution.

[0090] The service provider can apply different solution provision algorithms depending on the category of the inquiry at the time of provision. For example, the service provider can apply a technical solution provision algorithm to technical inquiries. It can also apply a support-specific solution provision algorithm to support-related inquiries. Furthermore, it can apply a general-purpose solution provision algorithm to general inquiries. This allows for the provision of more appropriate solutions by applying different solution provision algorithms depending on the category of the inquiry. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input inquiry data into an AI model and have the AI ​​perform the application of the solution provision algorithm.

[0091] The service provider can estimate the user's emotions and adjust the length of the solution based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide a short, concise solution. If the user is relaxed, the service provider can also provide a detailed solution. Furthermore, if the user is excited, the service provider can provide a solution with visually stimulating effects. By adjusting the length of the solution according to the user's emotions, a more appropriate solution can be provided. 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI model and have the AI ​​adjust the length of the solution.

[0092] The service provider can prioritize solutions based on when the inquiry was submitted. For example, it can provide quick solutions to recently submitted inquiries. It can also prioritize solutions to inquiries submitted within a specific period. Furthermore, it can provide solutions to prevent recurrence for past inquiries. By prioritizing solutions based on when the inquiry was submitted, it is possible to provide priority solutions to inquiries that require a quick response. Some or all of the above processes in the service provider may or may not be performed using AI. For example, the service provider can input inquiry data into an AI model and have the AI ​​determine the priority of solutions.

[0093] The service provider can adjust the order of solutions based on the relevance of the inquiries at the time of delivery. For example, the service provider can prioritize providing solutions to highly relevant inquiries. It can also postpone providing solutions to less relevant inquiries. Furthermore, the service provider can group relevant inquiries together and provide solutions accordingly. This allows for prioritizing solutions to highly relevant inquiries by adjusting the order of solutions based on the relevance of the inquiries. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input inquiry data into an AI model and have the AI ​​perform the adjustment of the order of solutions.

[0094] The generation unit can estimate the user's emotions and adjust the FAQ generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a detailed FAQ. If the user is in a hurry, the generation unit can also generate a concise FAQ. Furthermore, if the user is excited, the generation unit can generate an FAQ with visually stimulating effects. This allows for the generation of more appropriate FAQs by adjusting the FAQ generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI model and have the AI ​​adjust the FAQ generation method.

[0095] The generation unit can generate optimal FAQs by referring to past inquiry data during FAQ generation. For example, the generation unit can generate FAQs for frequently asked questions based on past inquiry data. The generation unit can also analyze past inquiry data and generate FAQs that include the most effective solutions. Furthermore, the generation unit can refer to past inquiry data to generate FAQs tailored to user needs. In this way, optimal FAQs can be generated by referring to past inquiry data. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input past inquiry data into an AI model and have the AI ​​perform the FAQ generation.

[0096] The generation unit can apply different FAQ generation methods to each category of inquiry when generating FAQs. For example, the generation unit can apply a technical FAQ generation method to technical inquiries. It can also apply a support-specific FAQ generation method to support-related inquiries. Furthermore, it can apply a general-purpose FAQ generation method to general inquiries. By applying different FAQ generation methods to each category of inquiry, more appropriate FAQs can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input inquiry data into an AI model and have the AI ​​perform the application of FAQ generation methods.

[0097] The generation unit can estimate the user's emotions and determine the priority of FAQs based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will prioritize displaying important FAQs. It can also display FAQs with normal priority if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can prioritize displaying FAQs that require immediate attention. This allows for the provision of more appropriate FAQs by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI model and have the AI ​​determine the priority of FAQs.

[0098] The generation unit can adjust the content of FAQs based on when the inquiry was submitted. For example, the generation unit can generate the latest FAQ for recently submitted inquiries. It can also generate FAQs relevant to a specific period for inquiries submitted during that period. Furthermore, it can generate FAQs for past inquiries to help prevent recurrence. By adjusting the content of FAQs based on when the inquiry was submitted, it is possible to provide more appropriate FAQs. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input inquiry data into an AI model and have the AI ​​perform the adjustment of the FAQ content.

[0099] The generation unit can generate FAQs by referring to relevant market data for inquiries. For example, the generation unit can generate FAQs that correspond to the latest trends based on relevant market data. The generation unit can also analyze market data and generate FAQs that meet user needs. Furthermore, the generation unit can refer to market data and generate FAQs that are based on those of competitors. This allows for the generation of more appropriate FAQs by referring to relevant market data for inquiries. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input relevant market data into an AI model and have the AI ​​perform the FAQ generation.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can estimate the user's emotions and adjust the method of collecting inquiry data based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the burden of data collection by using gentle language with the user. If the user is relaxed, the data collection unit can ask detailed questions to collect more information. Furthermore, if the user is in a hurry, the data collection unit can ask concise questions to quickly collect the necessary data. This allows for more appropriate data collection by adjusting the collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI model and have the AI ​​adjust the collection method.

[0102] The data collection unit can filter past inquiry data based on specific periods or events. For example, it can prioritize the collection of inquiry data from a specific campaign period. The data collection unit can also filter and collect inquiry data after a system update. Furthermore, it can collect inquiry data during specific event periods, such as the year-end and New Year holidays. This allows for the collection of highly relevant data by filtering it based on specific periods or events. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input inquiry data into an AI model and have the AI ​​perform the data filtering.

[0103] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, important data can be collected first. If the user is relaxed, data can be collected with normal priority. Furthermore, if the user is in a hurry, data requiring immediate attention can be collected first. This allows for the priority collection of important data by determining data priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI model and have the AI ​​determine the data priority.

[0104] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, it can prioritize the collection of query data related to that region. Furthermore, if the user is on the move, the data collection unit can also collect highly relevant data based on their current location. Additionally, if the user is overseas, the data collection unit can prioritize the collection of query data related to that country. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location information into an AI model and have the AI ​​perform the data collection.

[0105] The data collection unit can analyze the user's social media activity and collect relevant inquiry data during the collection process. For example, if a user mentions a specific issue on social media, it can collect data related to that issue. The data collection unit can also collect data related to a specific product if the user mentions it on social media. Furthermore, if the user mentions a specific event on social media, it can collect data related to that event. This allows for the efficient collection of relevant inquiry data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI model and have the AI ​​perform the data collection.

[0106] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise analysis result that gets straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI model and have the AI ​​adjust the presentation of the analysis.

[0107] The analysis unit can adjust the level of detail of the analysis based on the importance of the query data. For example, it can perform a detailed analysis on important query data, a standard analysis on general query data, and a simplified analysis on low-priority query data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the query data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input query data into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0108] The identification unit can estimate the user's emotions and adjust the criteria for identifying the cause based on the estimated emotions. For example, if the user is feeling anxious, it can perform detailed cause identification. If the user is relaxed, it can perform normal cause identification. Furthermore, if the user is in a hurry, it can perform rapid cause identification. By adjusting the criteria for identifying the cause according to the user's emotions, more appropriate cause identification can be achieved. 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 identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the criteria for identifying the cause.

[0109] The identification unit can improve the accuracy of cause identification by considering the interrelationships of queries during the identification process. For example, it can improve the accuracy of cause identification based on related query data. It can also identify causes by grouping related query data. Furthermore, it can improve the accuracy of cause identification by analyzing the interrelationships of query data. In this way, the accuracy of cause identification is improved by considering the interrelationships of queries. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input query data into an AI model and have the AI ​​perform the task of improving the accuracy of cause identification.

[0110] The service provider can estimate the user's emotions and adjust the method of providing solutions based on the estimated emotions. For example, if the user is feeling anxious, it can provide a detailed solution. If the user is relaxed, it can provide a standard solution. Furthermore, if the user is in a hurry, it can provide a quick solution. In this way, by adjusting the method of providing solutions according to the user's emotions, more appropriate solutions can be provided. 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI model and have the AI ​​adjust the method of providing solutions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects past inquiry data and Confluence information. For example, it collects inquiry data such as customer feedback and support requests, and information such as Confluence project documents and meeting notes. Processing in the collection unit may or may not be performed using AI. Step 2: The analysis department analyzes the data collected by the collection department. For example, they may use methods such as data mining or statistical analysis to analyze the data. The analysis department can also use AI to analyze the data and identify the cause of the inquiry. Step 3: The identification unit identifies the cause based on the analysis results obtained by the analysis unit. For example, it identifies the cause using methods such as root cause analysis or identification of causal relationships. The identification unit can also identify the cause using AI. Step 4: The service provider provides a solution based on the cause identified by the specific service provider. This solution may be provided by methods such as providing instructions or escalating the issue to the support team. The service provider can also use AI to provide solutions.

[0113] 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.

[0114] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, provision unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects past inquiry data and Confluence information. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the cause based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides a solution based on the identified cause. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically generates FAQs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, provision unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects past query data and Confluence information. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies the cause based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides a solution based on the identified cause. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automatically generates FAQs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, provision unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects past inquiry data and Confluence information. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the cause based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides a solution based on the identified cause. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically generates FAQs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, provision unit, and generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects past query data and Confluence information. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and identifies the cause based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides a solution based on the identified cause. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically generates FAQs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection unit that collects past inquiry data and Confluence information, An analysis unit analyzes the data collected by the aforementioned collection unit, An identification unit that identifies the cause based on the analysis results obtained from the aforementioned analysis unit, The system includes a providing unit that provides a solution based on the cause identified by the aforementioned identifying unit. A system characterized by the following features. (Note 2) It also includes a generation unit that automatically generates FAQs and forms an inquiry improvement cycle. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of inquiry data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting past query data, filter the data based on specific periods or events. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When gathering information from Confluence, prioritize collecting information related to specific projects or teams. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant inquiry data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the inquiry data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, We estimate the user's emotions and adjust the criteria for identifying causes based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, When identifying a specific cause, consider the interrelationships between inquiries to improve the accuracy of root cause identification. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, When identifying the cause, consider the attribute information of the person who submitted the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, It estimates the user's emotions and adjusts the order in which cause-identification results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, When identifying the cause, consider the geographical distribution of the inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, When identifying a cause, referencing relevant literature related to the inquiry improves the accuracy of root cause identification. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts how solutions are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing a solution, we adjust the level of detail based on the importance of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing a solution, different solution delivery algorithms are applied depending on the category of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the solution based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing a solution, we will prioritize the solution based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing solutions, we will adjust the order of solutions based on the relevance of the inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is We estimate the user's emotions and adjust the FAQ generation method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The generating unit is When generating FAQs, the system references past inquiry data to generate the most suitable FAQs. The system described in Appendix 2, characterized by the features described herein. (Note 29) The generating unit is When generating FAQs, different FAQ generation methods are applied depending on the category of the inquiry. The system described in Appendix 2, characterized by the features described herein. (Note 30) The generating unit is It estimates user sentiment and prioritizes FAQs based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 31) The generating unit is When generating FAQs, the content of the FAQs is adjusted based on when the inquiry was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 32) The generating unit is When generating FAQs, the system references relevant market data related to the inquiry to generate the FAQs. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects past inquiry data and Confluence information, An analysis unit analyzes the data collected by the aforementioned collection unit, An identification unit that identifies the cause based on the analysis results obtained from the aforementioned analysis unit, The system includes a providing unit that provides a solution based on the cause identified by the aforementioned identifying unit. A system characterized by the following features.

2. It also includes a generation unit that automatically generates FAQs and forms an inquiry improvement cycle. The system according to feature 1.

3. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of inquiry data collection based on the estimated user emotions. The system according to feature 1.

4. The aforementioned collection unit is When collecting past query data, filter the data based on specific periods or events. The system according to feature 1.

5. The aforementioned collection unit is When gathering information from Confluence, prioritize collecting information related to specific projects or teams. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant inquiry data. The system according to feature 1.

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