system

Abstract

The system according to the embodiment aims to quickly and accurately understand the content of inquiries from customers and provide appropriate responses. [Solution] A system according to an embodiment includes an analysis unit, an estimation unit, a visualization unit, and a response unit. The analysis unit analyzes the content of an inquiry from a customer. The estimation unit estimates the configuration of cloud resources based on the information analyzed by the analysis unit. The visualization unit visualizes the configuration of cloud resources estimated by the estimation unit. The response unit responds quickly based on the information visualized by the visualization unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to quickly and accurately understand customer inquiries and respond appropriately.

[0005] The system according to the embodiment aims to quickly and accurately understand the content of inquiries from customers and provide appropriate responses. [Means for solving the problem]

[0006] A system according to an embodiment includes an analysis unit, an estimation unit, a visualization unit, and a response unit. The analysis unit analyzes the content of an inquiry from a customer. The estimation unit estimates the configuration of cloud resources based on the information analyzed by the analysis unit. The visualization unit visualizes the configuration of cloud resources estimated by the estimation unit. The response unit responds quickly based on the information visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately understand the content of inquiries from customers and respond appropriately. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A support system according to an embodiment of the present invention analyzes customer inquiries, infers cloud resource configurations, and visualizes them. This support system uses a generation AI to efficiently align customer needs and requests. Specifically, the system comprises the following steps: First, the generation AI analyzes the customer's inquiry. Next, the generation AI infers the cloud resource configuration of the customer's environment from the inquiry and visualizes it. Based on this visualized information, support personnel can respond quickly. This reduces response time and improves customer satisfaction. First, the generation AI analyzes the customer's inquiry. The inquiry often includes information about the cloud services and resources the customer uses. For example, if a customer inquires about a slow server response, the generation AI analyzes the inquiry and infers which cloud resources are affected. Next, the generation AI infers the cloud resource configuration of the customer's environment from the inquiry. The generation AI learns past inquiry data and cloud resource configuration information, and infers the most likely cloud resource configuration based on the inquiry. For example, in response to a customer inquiry about a slow server response, the generative AI infers which cloud service the server is on and which resources are affected. The inferred cloud resource configuration is visualized. The generative AI displays the inferred cloud resource configuration in visual formats such as diagrams and graphs. This allows support personnel to quickly grasp the overall picture of the customer's environment. For example, if a server response is slow, the support personnel can see at a glance which cloud service the server is on and which resources are affected. Based on this visualized information, the support personnel can respond quickly. For example, if a server response is slow, the support personnel can check the inferred cloud resource configuration and quickly take specific steps to identify the cause of the problem. This reduces response times and is expected to improve customer satisfaction. This enables the support system to respond to customer inquiries quickly and accurately.

[0029] A support system according to an embodiment includes an analysis unit, an estimation unit, a visualization unit, and a response unit. The analysis unit analyzes customer inquiries. Customer inquiries often include, for example, information about cloud services and resources. The analysis unit analyzes the inquiries using, for example, text analysis technology. The analysis unit can also analyze the inquiries using data mining technology. For example, the analysis unit extracts keywords included in the inquiries and performs analysis based on the extracted keywords. The analysis unit can also analyze the context of the inquiries and improve the accuracy of the analysis based on specific keywords or phrases. The estimation unit estimates the cloud resource configuration based on the information analyzed by the analysis unit. The estimation unit estimates the cloud resource configuration using, for example, a machine learning algorithm. The estimation unit can also estimate the cloud resource configuration using statistical techniques. For example, the estimation unit learns past inquiry data and cloud resource configuration information and makes estimations based on the data. The estimation unit can also optimize the estimation algorithm by taking into account the characteristics of the cloud services used by the customers. The visualization unit visualizes the cloud resource configuration estimated by the estimation unit. The visualization unit visually displays the cloud resource configuration using, for example, graphs or charts. The visualization unit can also display the cloud resource configuration using a dashboard. For example, the visualization unit updates the estimated cloud resource configuration in real time and displays the latest information. The visualization unit can also highlight detailed information about specific cloud resources. The response unit responds quickly based on the information visualized by the visualization unit. For example, the response unit identifies the cause of a problem and quickly executes specific procedures. The response unit can also estimate the customer's emotions and adjust the response procedures based on the estimated customer emotions. For example, if a customer is dissatisfied, the response unit can provide quick and detailed response procedures. If a customer is confused, the response unit can explain the response procedures in easy-to-understand terms. This allows the support system according to the embodiment to respond quickly and accurately to customer inquiries.

[0030] The analysis unit can analyze the cloud service or resource information included in the inquiry content. The analysis unit, for example, analyzes the cloud service or resource information included in the inquiry content. For example, the analysis unit can analyze the type of cloud service and the resource usage status. The analysis unit can also extract keywords included in the inquiry content and analyze the cloud service or resource information based on the keywords. For example, the analysis unit can analyze which cloud service is affected by the inquiry content, such as "the server response is slow." The analysis unit can also analyze the resource usage status included in the inquiry content and identify which resource is causing the problem. This enables more accurate analysis by analyzing the cloud service or resource information included in the inquiry content. Some or all of the above-described processing by the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the inquiry content into a generation AI and have the generation AI analyze the cloud service and resource information.

[0031] The estimation unit can learn past inquiry data and cloud resource configuration information and estimate the cloud resource configuration based on the inquiry content. The estimation unit, for example, learns past inquiry data and cloud resource configuration information and estimates the cloud resource configuration based on the information. For example, if a problem with a specific cloud resource occurs frequently, the estimation unit uses past inquiry data to prioritize estimation of the resource configuration. Furthermore, if a problem occurs during a specific time period, the estimation unit can estimate the resource configuration for that time period based on the past inquiry data. Furthermore, if a problem occurs in a specific region, the estimation unit can estimate the resource configuration for that region based on the past inquiry data. Thus, by learning past inquiry data and cloud resource configuration information, more accurate estimation is possible. Some or all of the above-described processing by the estimation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the estimation unit can input past inquiry data and cloud resource configuration information into the generation AI and have the generation AI estimate the cloud resource configuration.

[0032] The visualization unit can display the inferred cloud resource configuration in a visual format such as a diagram or a graph. The visualization unit, for example, displays the inferred cloud resource configuration in a visual format such as a diagram or a graph. For example, the visualization unit can display cloud resource usage using a bar graph. The visualization unit can also display cloud resource performance fluctuations using a line graph. Furthermore, the visualization unit can visually display the cloud resource configuration using a network diagram. This allows support personnel to respond quickly by visually displaying the inferred cloud resource configuration. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can input the inferred cloud resource configuration into the generation AI and cause the generation AI to display the configuration in a visual format.

[0033] The response unit can identify the cause of the problem based on the visualized information and quickly execute specific procedures. The response unit, for example, identifies the cause of the problem based on the visualized information. For example, the response unit can check the usage status of cloud resources and identify which resource is causing the problem. The response unit can also check fluctuations in cloud resource performance and identify which resource is affected. Furthermore, the response unit can check the configuration of cloud resources and identify which resource is causing the problem. This allows for quick response based on the visualized information, shortening the time to response and expected to improve customer satisfaction. Some or all of the above-mentioned processing in the response unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the response unit can input the visualized information into a generation AI and have the generation AI identify the cause of the problem.

[0034] The analysis unit can analyze the context of the inquiry content and improve the accuracy of the analysis based on specific keywords or phrases. The analysis unit, for example, analyzes the context of the inquiry content. For example, the analysis unit can analyze the context using natural language processing technology. The analysis unit can also analyze the context using a context analysis algorithm. The analysis unit can also improve the accuracy of the analysis based on specific keywords or phrases. For example, if the keyword "slow" is included, the analysis unit can analyze the issue as performance-related. If the phrase "error" is included, the analysis unit can focus on analyzing error logs. If the keyword "cannot connect" is included, the analysis unit can also analyze the issue as network-related. This improves the accuracy of the analysis based on specific keywords or phrases, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the inquiry content to a generation AI and have the generation AI perform context analysis.

[0035] The analysis unit can refer to the customer's past inquiry history and prioritize analysis of similar inquiries. The analysis unit can, for example, refer to the customer's past inquiry history. For example, the analysis unit can refer to the inquiry history for the past year. The analysis unit can also refer to the inquiry history for a specific service. Furthermore, the analysis unit can prioritize analysis of similar inquiries. For example, if the customer has previously inquired about "slow server response," the analysis unit can prioritize analysis of similar issues. Furthermore, if the customer has previously inquired about "connection errors," the analysis unit can prioritize analysis of similar issues. Furthermore, if the customer has previously inquired about "database problems," the analysis unit can prioritize analysis of similar issues. This allows similar issues to be analyzed quickly by referring to the past inquiry history. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input past inquiry history into the generation AI and have the generation AI analyze similar inquiries.

[0036] When analyzing the inquiry content, the analysis unit can prioritize analyzing highly relevant information by taking into account the customer's geographical location information. The analysis unit, for example, considers the customer's geographical location information when analyzing the inquiry content. For example, if the customer is making the inquiry from a specific region, the analysis unit can prioritize analyzing the status of cloud resources in that region. Furthermore, if the customer is making the inquiry from a specific country, the analysis unit can prioritize analyzing the status of cloud services in that country. Furthermore, if the customer is making the inquiry from a specific city, the analysis unit can prioritize analyzing the network status of that city. In this way, by taking the customer's geographical location information into consideration, more relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the customer's geographical location information into the generation AI and cause the generation AI to analyze highly relevant information.

[0037] The analysis unit can analyze the customer's social media activity and analyze the related inquiry content. The analysis unit, for example, analyzes the customer's social media activity. For example, if the customer posts on social media that "server response is slow," the analysis unit can reflect that content in the analysis. In addition, if the customer posts on social media that "connection error" the analysis unit can also reflect that content in the analysis. Furthermore, if the customer posts on social media that "database problem" the analysis unit can also reflect that content in the analysis. In this way, by analyzing the customer's social media activity, more relevant information can be analyzed. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input the customer's social media activity into the generation AI and have the generation AI analyze the related inquiry content.

[0038] The estimation unit can refer to past inquiry data and infer the cloud resource configuration based on a specific pattern. The estimation unit, for example, references past inquiry data. For example, the estimation unit can reference inquiry data from the past year. The estimation unit can also reference inquiry data related to a specific service. Furthermore, the estimation unit can infer the cloud resource configuration based on a specific pattern. For example, if the estimation unit determines from the past inquiry data that problems with a specific cloud resource occur frequently, it prioritizes inferring the configuration of that resource. Furthermore, if the estimation unit determines from the past inquiry data that problems occur during a specific time period, it can infer the resource configuration for that time period. Furthermore, if the estimation unit determines from the past inquiry data that problems occur in a specific region, it can infer the resource configuration for that region. This enables inference based on a specific pattern by referring to past inquiry data. Some or all of the above-described processing by the estimation unit may be performed using, or without, a generation AI. For example, the estimation unit can input past inquiry data into the generation AI and cause the generation AI to infer the cloud resource configuration based on a specific pattern.

[0039] The estimation unit can adjust the estimation algorithm taking into account the characteristics of the cloud service used by the customer. The estimation unit, for example, considers the characteristics of the cloud service used by the customer. For example, if the cloud service used by the customer is AWS (registered trademark), the estimation unit can optimize the estimation algorithm based on the characteristics of AWS. Furthermore, if the cloud service used by the customer is Azure (registered trademark), the estimation unit can optimize the estimation algorithm based on the characteristics of Azure. Furthermore, if the cloud service used by the customer is Google (registered trademark) Cloud, the estimation unit can optimize the estimation algorithm based on the characteristics of Google Cloud. This enables more optimal estimation by taking into account the characteristics of the cloud service used by the customer. Some or all of the above-described processing by the estimation unit may be performed using, or without, a generation AI. For example, the estimation unit can input the characteristics of the cloud service used by the customer into the generation AI and cause the generation AI to adjust the estimation algorithm.

[0040] The estimation unit can estimate a highly relevant cloud resource configuration by taking into account the customer's geographical location information during estimation. The estimation unit, for example, takes into account the customer's geographical location information during estimation. For example, if a customer makes an inquiry from a specific region, the estimation unit can prioritize inferring the cloud resource configuration for that region. Furthermore, if a customer makes an inquiry from a specific country, the estimation unit can prioritize inferring the cloud service configuration for that country. Furthermore, if a customer makes an inquiry from a specific city, the estimation unit can prioritize inferring the network conditions of that city. In this way, by taking the customer's geographical location information into consideration, a more highly relevant cloud resource configuration can be estimated. Some or all of the above-described processing in the estimation unit may be performed using, for example, a generation AI. For example, the estimation unit can input the customer's geographical location information into the generation AI and cause the generation AI to estimate a highly relevant cloud resource configuration.

[0041] The estimation unit can customize the estimation algorithm based on the customer's industry and business content. The estimation unit customizes the estimation algorithm based on, for example, the customer's industry and business content. For example, if the customer is in the financial industry, the estimation unit can estimate a cloud resource configuration specialized for the financial industry. Furthermore, if the customer is in the manufacturing industry, the estimation unit can estimate a cloud resource configuration specialized for the manufacturing industry. Furthermore, if the customer is in the retail industry, the estimation unit can estimate a cloud resource configuration specialized for the retail industry. This enables more accurate estimation by customizing the estimation algorithm based on the customer's industry and business content. Some or all of the above-mentioned processing in the estimation unit may be performed using, or without, a generation AI. For example, the estimation unit can input the customer's industry and business content into the generation AI and cause the generation AI to customize the estimation algorithm.

[0042] The visualization unit can update the inferred cloud resource configuration in real time and display the latest information. The visualization unit, for example, updates the inferred cloud resource configuration in real time. For example, the visualization unit can update the visualization results in real time when the cloud resource configuration is changed. The visualization unit can also update the visualization results in real time when a new query is generated. Furthermore, the visualization unit can also update the visualization results in real time when the performance of the cloud resources fluctuates. This makes it possible to always provide the latest information by updating in real time. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can input the inferred cloud resource configuration to the generation AI and cause the generation AI to perform real-time updates.

[0043] The visualization unit can highlight detailed information of a specific cloud resource during visualization. The visualization unit, for example, highlights detailed information of a specific cloud resource during visualization. For example, the visualization unit can highlight a cloud resource experiencing a problem, enabling it to be quickly identified. The visualization unit can also highlight important cloud resources, enabling them to be addressed as a priority. Furthermore, the visualization unit can highlight a cloud resource experiencing a decline in performance, enabling measures to be taken quickly. In this way, highlighting detailed information of a specific cloud resource allows problems to be quickly identified. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input detailed information of a specific cloud resource into the generation AI and cause the generation AI to perform the highlighting.

[0044] The visualization unit can select the optimal display format by taking into account the customer's device information when visualizing. The visualization unit, for example, takes into account the customer's device information when visualizing. For example, if the customer is using a smartphone, the visualization unit can provide a display format that matches the screen size. Furthermore, if the customer is using a tablet, the visualization unit can provide a display format optimized for a large screen. Furthermore, if the customer is using a desktop, the visualization unit can provide a format that displays detailed information. In this way, the optimal display format can be provided by taking into account the customer's device information. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the customer's device information into the generation AI and cause the generation AI to select the optimal display format.

[0045] The visualization unit can provide an interface for incorporating the visualized information into a customer's workflow. For example, the visualization unit can provide an interface for incorporating the visualized information into a customer's workflow. For example, the visualization unit can provide an API for incorporating the visualized information into a customer's workflow. The visualization unit can also provide a dashboard for incorporating the visualized information into a customer's workflow. Furthermore, the visualization unit can provide a customizable widget for incorporating the visualized information into a customer's workflow. This enables more efficient business operations by incorporating the visualized information into a workflow. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can input the visualized information into a generation AI and cause the generation AI to provide an interface for incorporating the visualized information into a workflow.

[0046] The response unit can automatically generate a specific problem-solving procedure based on the visualized information. The response unit automatically generates a specific problem-solving procedure based on, for example, the visualized information. For example, the response unit can automatically generate a solution procedure for when a server response is slow from the visualized information. The response unit can also automatically generate a solution procedure for when a connection error occurs from the visualized information. Furthermore, the response unit can automatically generate a solution procedure for when a database problem occurs from the visualized information. This enables rapid problem resolution using a procedure automatically generated based on the visualized information. Some or all of the above-mentioned processing in the response unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the response unit can input the visualized information into a generation AI and cause the generation AI to automatically generate a problem-solving procedure.

[0047] The response unit can refer to the customer's past response history and select the optimal response procedure. The response unit, for example, refers to the customer's past response history. For example, the response unit can refer to the response history for the past year. The response unit can also refer to the response history for a specific service. Furthermore, the response unit can select the optimal response procedure. For example, if the customer has a history of inquiring about a "slow server response," the response unit can refer to the solution procedure for that issue. Also, if the customer has a history of inquiring about a "connection error," the response unit can refer to the solution procedure for that issue. Furthermore, if the customer has a history of inquiring about a "database problem," the response unit can refer to the solution procedure for that issue. In this way, by referring to the past response history, the optimal response procedure can be selected. Some or all of the above-described processing in the response unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the response unit can input past response history into a generation AI and have the generation AI select the optimal response procedure.

[0048] The response unit can provide optimal response procedures by taking into account the customer's geographical location information when responding. The response unit, for example, considers the customer's geographical location information when responding. For example, if a customer makes an inquiry from a specific region, the response unit can provide response procedures by taking into account the cloud resource status of that region. Furthermore, if a customer makes an inquiry from a specific country, the response unit can provide response procedures by taking into account the cloud service status of that country. Furthermore, if a customer makes an inquiry from a specific city, the response unit can provide response procedures by taking into account the network status of that city. In this way, optimal response procedures can be provided by taking into account the customer's geographical location information. Some or all of the above-described processing in the response unit may be performed using, or without, a generation AI. For example, the response unit can input the customer's geographical location information into the generation AI and cause the generation AI to provide optimal response procedures.

[0049] The response unit can provide an interface for incorporating the response procedure into a customer's workflow. For example, the response unit can provide an interface for incorporating the response procedure into a customer's workflow. For example, the response unit can provide an API for incorporating the response procedure into a customer's workflow. The response unit can also provide a dashboard for incorporating the response procedure into a customer's workflow. Furthermore, the response unit can provide a customizable widget for incorporating the response procedure into a customer's workflow. This enables more efficient business operations by incorporating the response procedure into a workflow. Some or all of the above-described processing in the response unit can be performed using, or without, a generation AI. For example, the response unit can input the response procedure into the generation AI and cause the generation AI to provide an interface for incorporating the response procedure into a workflow.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When analyzing the content of a customer's inquiry, the analysis unit can refer to the customer's past purchase history and usage history. For example, if a customer has frequently used a specific cloud service in the past, it can infer that there is a high possibility that a problem related to that service has occurred. Also, if a customer has used a specific resource a lot in the past, it can infer that there is a high possibility that a problem related to that resource has occurred. Furthermore, if a customer has made an inquiry during a specific time period in the past, it can infer that there is a high possibility that a problem related to that time period has occurred. In this way, by referring to the customer's past usage history, more accurate analysis is possible.

[0052] When analyzing the content of an inquiry, the analysis unit can improve the accuracy of the analysis by taking into account the customer's industry and business details. For example, if the customer is in the financial industry, it can prioritize analysis of information on cloud services and resources specialized for the financial industry. Also, if the customer is in the manufacturing industry, it can prioritize analysis of information on cloud services and resources specialized for the manufacturing industry. Furthermore, if the customer is in the retail industry, it can prioritize analysis of information on cloud services and resources specialized for the retail industry. This allows for more accurate analysis by taking into account the customer's industry and business details.

[0053] The estimation unit can refer to real-time cloud resource performance data when estimating cloud resource configurations based on customer inquiries. For example, it can obtain current cloud resource usage and performance data in real time and make estimations based on that data. In addition, using real-time performance data makes it possible to make estimations based on the current situation, not just past data. Furthermore, using real-time performance data enables quick responses, which is expected to improve customer satisfaction.

[0054] When visualizing the inferred cloud resource configuration, the visualization unit can provide a customizable dashboard tailored to the customer's business flow. For example, if the customer manages cloud resources according to a specific business flow, a dashboard tailored to that business flow can be provided. Also, if the customer monitors cloud resources according to a specific business flow, a customizable widget tailored to that business flow can be provided. Furthermore, if the customer optimizes cloud resources according to a specific business flow, a customizable report tailored to that business flow can be provided. This enables visualization tailored to the customer's business flow, enabling more efficient cloud resource management.

[0055] When identifying the cause of a problem based on the visualized information, the response department can refer to the customer's past inquiry history and response history. For example, if the customer has a history of inquiring about a similar problem in the past, the cause of the problem can be identified by referring to that history. Also, by referring to how the customer has responded to similar problems in the past, a quicker response is possible. Furthermore, by referring to what solutions the customer has adopted in the past for similar problems, the optimal solution can be provided. This allows for faster and more accurate response by referring to the past inquiry history and response history.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The analysis unit analyzes the customer inquiry. Customer inquiries often include information about cloud services and resources. The analysis unit uses text analysis and data mining technologies to analyze the inquiry. For example, it extracts keywords from the inquiry and performs analysis based on them. It also analyzes the context of the inquiry and improves the accuracy of the analysis based on specific keywords and phrases. Step 2: The prediction unit predicts the cloud resource configuration based on the information analyzed by the analysis unit. The prediction unit uses machine learning algorithms and statistical methods to predict the cloud resource configuration. For example, it learns past inquiry data and cloud resource configuration information and makes predictions based on that. It can also optimize the prediction algorithm by taking into account the characteristics of the cloud services used by customers. Step 3: The visualization unit visualizes the cloud resource configuration inferred by the estimation unit. The visualization unit visually displays the cloud resource configuration using graphs, charts, and dashboards. For example, the visualization unit updates the inferred cloud resource configuration in real time to display the latest information. It can also highlight detailed information about specific cloud resources. Step 4: The response department responds quickly based on the information visualized by the visualization department. The response department identifies the cause of the problem and promptly implements specific procedures. The response department also estimates the customer's emotions and adjusts the response procedures based on the estimated customer emotions. For example, if the customer is dissatisfied, the response department provides quick and detailed response procedures, and if the customer is confused, the response procedures are explained in easy-to-understand terms.

[0058] (Example 2) A support system according to an embodiment of the present invention analyzes customer inquiries, infers cloud resource configurations, and visualizes them. This support system uses a generation AI to efficiently align customer needs and requests. Specifically, the system comprises the following steps: First, the generation AI analyzes the customer's inquiry. Next, the generation AI infers the cloud resource configuration of the customer's environment from the inquiry and visualizes it. Based on this visualized information, support personnel can respond quickly. This reduces response time and improves customer satisfaction. First, the generation AI analyzes the customer's inquiry. The inquiry often includes information about the cloud services and resources the customer uses. For example, if a customer inquires about a slow server response, the generation AI analyzes the inquiry and infers which cloud resources are affected. Next, the generation AI infers the cloud resource configuration of the customer's environment from the inquiry. The generation AI learns past inquiry data and cloud resource configuration information, and infers the most likely cloud resource configuration based on the inquiry. For example, in response to a customer inquiry about a slow server response, the generative AI infers which cloud service the server is on and which resources are affected. The inferred cloud resource configuration is visualized. The generative AI displays the inferred cloud resource configuration in visual formats such as diagrams and graphs. This allows support personnel to quickly grasp the overall picture of the customer's environment. For example, if a server response is slow, the support personnel can see at a glance which cloud service the server is on and which resources are affected. Based on this visualized information, the support personnel can respond quickly. For example, if a server response is slow, the support personnel can check the inferred cloud resource configuration and quickly take specific steps to identify the cause of the problem. This reduces response times and is expected to improve customer satisfaction. This enables the support system to respond to customer inquiries quickly and accurately.

[0059] A support system according to an embodiment includes an analysis unit, an estimation unit, a visualization unit, and a response unit. The analysis unit analyzes customer inquiries. Customer inquiries often include, for example, information about cloud services and resources. The analysis unit analyzes the inquiries using, for example, text analysis technology. The analysis unit can also analyze the inquiries using data mining technology. For example, the analysis unit extracts keywords included in the inquiries and performs analysis based on the extracted keywords. The analysis unit can also analyze the context of the inquiries and improve the accuracy of the analysis based on specific keywords or phrases. The estimation unit estimates the cloud resource configuration based on the information analyzed by the analysis unit. The estimation unit estimates the cloud resource configuration using, for example, a machine learning algorithm. The estimation unit can also estimate the cloud resource configuration using statistical techniques. For example, the estimation unit learns past inquiry data and cloud resource configuration information and makes estimations based on the data. The estimation unit can also optimize the estimation algorithm by taking into account the characteristics of the cloud services used by the customers. The visualization unit visualizes the cloud resource configuration estimated by the estimation unit. The visualization unit visually displays the cloud resource configuration using, for example, graphs or charts. The visualization unit can also display the cloud resource configuration using a dashboard. For example, the visualization unit updates the estimated cloud resource configuration in real time and displays the latest information. The visualization unit can also highlight detailed information about specific cloud resources. The response unit responds quickly based on the information visualized by the visualization unit. For example, the response unit identifies the cause of a problem and quickly executes specific procedures. The response unit can also estimate the customer's emotions and adjust the response procedures based on the estimated customer emotions. For example, if a customer is dissatisfied, the response unit can provide quick and detailed response procedures. If a customer is confused, the response unit can explain the response procedures in easy-to-understand terms. This allows the support system according to the embodiment to respond quickly and accurately to customer inquiries.

[0060] The analysis unit can analyze the cloud service or resource information included in the inquiry content. The analysis unit, for example, analyzes the cloud service or resource information included in the inquiry content. For example, the analysis unit can analyze the type of cloud service and the resource usage status. The analysis unit can also extract keywords included in the inquiry content and analyze the cloud service or resource information based on the keywords. For example, the analysis unit can analyze which cloud service is affected by the inquiry content, such as "the server response is slow." The analysis unit can also analyze the resource usage status included in the inquiry content and identify which resource is causing the problem. This enables more accurate analysis by analyzing the cloud service or resource information included in the inquiry content. Some or all of the above-described processing by the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the inquiry content into a generation AI and have the generation AI analyze the cloud service and resource information.

[0061] The estimation unit can learn past inquiry data and cloud resource configuration information and estimate the cloud resource configuration based on the inquiry content. The estimation unit, for example, learns past inquiry data and cloud resource configuration information and estimates the cloud resource configuration based on the information. For example, if a problem with a specific cloud resource occurs frequently, the estimation unit uses past inquiry data to prioritize estimation of the resource configuration. Furthermore, if a problem occurs during a specific time period, the estimation unit can estimate the resource configuration for that time period based on the past inquiry data. Furthermore, if a problem occurs in a specific region, the estimation unit can estimate the resource configuration for that region based on the past inquiry data. Thus, by learning past inquiry data and cloud resource configuration information, more accurate estimation is possible. Some or all of the above-described processing by the estimation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the estimation unit can input past inquiry data and cloud resource configuration information into the generation AI and have the generation AI estimate the cloud resource configuration.

[0062] The visualization unit can display the inferred cloud resource configuration in a visual format such as a diagram or a graph. The visualization unit, for example, displays the inferred cloud resource configuration in a visual format such as a diagram or a graph. For example, the visualization unit can display cloud resource usage using a bar graph. The visualization unit can also display cloud resource performance fluctuations using a line graph. Furthermore, the visualization unit can visually display the cloud resource configuration using a network diagram. This allows support personnel to respond quickly by visually displaying the inferred cloud resource configuration. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can input the inferred cloud resource configuration into the generation AI and cause the generation AI to display the configuration in a visual format.

[0063] The response unit can identify the cause of the problem based on the visualized information and quickly execute specific procedures. The response unit, for example, identifies the cause of the problem based on the visualized information. For example, the response unit can check the usage status of cloud resources and identify which resource is causing the problem. The response unit can also check fluctuations in cloud resource performance and identify which resource is affected. Furthermore, the response unit can check the configuration of cloud resources and identify which resource is causing the problem. This allows for quick response based on the visualized information, shortening the time to response and expected to improve customer satisfaction. Some or all of the above-mentioned processing in the response unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the response unit can input the visualized information into a generation AI and have the generation AI identify the cause of the problem.

[0064] The analysis unit can estimate a customer's emotion and adjust the analysis priority based on the estimated customer's emotion. The analysis unit, for example, estimates the customer's emotion. For example, the analysis unit can estimate the customer's emotion using text analysis technology. The analysis unit can also estimate the customer's emotion using voice analysis technology. Furthermore, the analysis unit can estimate the customer's emotion using facial expression analysis technology. This enables more appropriate responses by estimating the customer's emotion and adjusting the analysis priority based on the estimated customer's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input customer emotion data into the generation AI and have the generation AI perform emotion estimation. This enables more appropriate responses by adjusting the analysis priority based on the customer's emotion.

[0065] The analysis unit can analyze the context of the inquiry content and improve the accuracy of the analysis based on specific keywords or phrases. The analysis unit, for example, analyzes the context of the inquiry content. For example, the analysis unit can analyze the context using natural language processing technology. The analysis unit can also analyze the context using a context analysis algorithm. The analysis unit can also improve the accuracy of the analysis based on specific keywords or phrases. For example, if the keyword "slow" is included, the analysis unit can analyze the issue as performance-related. If the phrase "error" is included, the analysis unit can focus on analyzing error logs. If the keyword "cannot connect" is included, the analysis unit can also analyze the issue as network-related. This improves the accuracy of the analysis based on specific keywords or phrases, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the inquiry content to a generation AI and have the generation AI perform context analysis.

[0066] The analysis unit can refer to the customer's past inquiry history and prioritize analysis of similar inquiries. The analysis unit can, for example, refer to the customer's past inquiry history. For example, the analysis unit can refer to the inquiry history for the past year. The analysis unit can also refer to the inquiry history for a specific service. Furthermore, the analysis unit can prioritize analysis of similar inquiries. For example, if the customer has previously inquired about "slow server response," the analysis unit can prioritize analysis of similar issues. Furthermore, if the customer has previously inquired about "connection errors," the analysis unit can prioritize analysis of similar issues. Furthermore, if the customer has previously inquired about "database problems," the analysis unit can prioritize analysis of similar issues. This allows similar issues to be analyzed quickly by referring to the past inquiry history. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input past inquiry history into the generation AI and have the generation AI analyze similar inquiries.

[0067] The analysis unit can estimate the customer's emotion and adjust the display method of the analysis results based on the estimated customer's emotion. The analysis unit, for example, estimates the customer's emotion. For example, the analysis unit can estimate the customer's emotion using text analysis technology. The analysis unit can also estimate the customer's emotion using voice analysis technology. Furthermore, the analysis unit can estimate the customer's emotion using facial expression analysis technology. This makes it possible to provide more appropriate information by estimating the customer's emotion and adjusting the display method of the analysis results based on the estimated customer's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input customer emotion data into the generation AI and have the generation AI perform emotion estimation. This makes it possible to provide more appropriate information by adjusting the display method of the analysis results based on the customer's emotion.

[0068] When analyzing the inquiry content, the analysis unit can prioritize analyzing highly relevant information by taking into account the customer's geographical location information. The analysis unit, for example, considers the customer's geographical location information when analyzing the inquiry content. For example, if the customer is making the inquiry from a specific region, the analysis unit can prioritize analyzing the status of cloud resources in that region. Furthermore, if the customer is making the inquiry from a specific country, the analysis unit can prioritize analyzing the status of cloud services in that country. Furthermore, if the customer is making the inquiry from a specific city, the analysis unit can prioritize analyzing the network status of that city. In this way, by taking the customer's geographical location information into consideration, more relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the customer's geographical location information into the generation AI and cause the generation AI to analyze highly relevant information.

[0069] The analysis unit can analyze the customer's social media activity and analyze the related inquiry content. The analysis unit, for example, analyzes the customer's social media activity. For example, if the customer posts on social media that "server response is slow," the analysis unit can reflect that content in the analysis. In addition, if the customer posts on social media that "connection error" the analysis unit can also reflect that content in the analysis. Furthermore, if the customer posts on social media that "database problem" the analysis unit can also reflect that content in the analysis. In this way, by analyzing the customer's social media activity, more relevant information can be analyzed. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input the customer's social media activity into the generation AI and have the generation AI analyze the related inquiry content.

[0070] The estimation unit can estimate a customer's emotion and adjust the accuracy of the estimation based on the estimated customer's emotion. The estimation unit, for example, estimates the customer's emotion. For example, the estimation unit can estimate the customer's emotion using text analysis technology. The estimation unit can also estimate the customer's emotion using voice analysis technology. Furthermore, the estimation unit can estimate the customer's emotion using facial expression analysis technology. This enables more accurate estimation by estimating the customer's emotion and adjusting the accuracy of the estimation based on the estimated customer's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the estimation unit can input customer emotion data into the generation AI and cause the generation AI to adjust the accuracy of the estimation. This enables more accurate estimation by adjusting the accuracy of the estimation based on the customer's emotion.

[0071] The estimation unit can refer to past inquiry data and infer the cloud resource configuration based on a specific pattern. The estimation unit, for example, references past inquiry data. For example, the estimation unit can reference inquiry data from the past year. The estimation unit can also reference inquiry data related to a specific service. Furthermore, the estimation unit can infer the cloud resource configuration based on a specific pattern. For example, if the estimation unit determines from the past inquiry data that problems with a specific cloud resource occur frequently, it prioritizes inferring the configuration of that resource. Furthermore, if the estimation unit determines from the past inquiry data that problems occur during a specific time period, it can infer the resource configuration for that time period. Furthermore, if the estimation unit determines from the past inquiry data that problems occur in a specific region, it can infer the resource configuration for that region. This enables inference based on a specific pattern by referring to past inquiry data. Some or all of the above-described processing by the estimation unit may be performed using, or without, a generation AI. For example, the estimation unit can input past inquiry data into the generation AI and cause the generation AI to infer the cloud resource configuration based on a specific pattern.

[0072] The estimation unit can adjust the estimation algorithm taking into account the characteristics of the cloud service used by the customer. The estimation unit, for example, considers the characteristics of the cloud service used by the customer. For example, if the cloud service used by the customer is AWS, the estimation unit can optimize the estimation algorithm based on the characteristics of AWS. Furthermore, if the cloud service used by the customer is Azure, the estimation unit can optimize the estimation algorithm based on the characteristics of Azure. Furthermore, if the cloud service used by the customer is Google Cloud, the estimation unit can optimize the estimation algorithm based on the characteristics of Google Cloud. This enables more optimal estimation by taking into account the characteristics of the cloud service used by the customer. Some or all of the above-described processing in the estimation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the estimation unit can input the characteristics of the cloud service used by the customer into the generation AI and cause the generation AI to adjust the estimation algorithm.

[0073] The estimation unit can estimate the customer's emotion and adjust the display method of the estimation result based on the estimated customer's emotion. The estimation unit, for example, estimates the customer's emotion. For example, the estimation unit can estimate the customer's emotion using text analysis technology. The estimation unit can also estimate the customer's emotion using voice analysis technology. Furthermore, the estimation unit can estimate the customer's emotion using facial expression analysis technology. This makes it possible to provide more appropriate information by estimating the customer's emotion and adjusting the display method of the estimation result based on the estimated customer's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the estimation unit can input customer emotion data into the generation AI and cause the generation AI to adjust the display method of the estimation result. This makes it possible to provide more appropriate information by adjusting the display method of the estimation result based on the customer's emotion.

[0074] The estimation unit can estimate a highly relevant cloud resource configuration by taking into account the customer's geographical location information during estimation. The estimation unit, for example, takes into account the customer's geographical location information during estimation. For example, if a customer makes an inquiry from a specific region, the estimation unit can prioritize inferring the cloud resource configuration for that region. Furthermore, if a customer makes an inquiry from a specific country, the estimation unit can prioritize inferring the cloud service configuration for that country. Furthermore, if a customer makes an inquiry from a specific city, the estimation unit can prioritize inferring the network conditions of that city. In this way, by taking the customer's geographical location information into consideration, a more highly relevant cloud resource configuration can be estimated. Some or all of the above-described processing in the estimation unit may be performed using, for example, a generation AI. For example, the estimation unit can input the customer's geographical location information into the generation AI and cause the generation AI to estimate a highly relevant cloud resource configuration.

[0075] The estimation unit can customize the estimation algorithm based on the customer's industry and business content. The estimation unit customizes the estimation algorithm based on, for example, the customer's industry and business content. For example, if the customer is in the financial industry, the estimation unit can estimate a cloud resource configuration specialized for the financial industry. Furthermore, if the customer is in the manufacturing industry, the estimation unit can estimate a cloud resource configuration specialized for the manufacturing industry. Furthermore, if the customer is in the retail industry, the estimation unit can estimate a cloud resource configuration specialized for the retail industry. This enables more accurate estimation by customizing the estimation algorithm based on the customer's industry and business content. Some or all of the above-mentioned processing in the estimation unit may be performed using, or without, a generation AI. For example, the estimation unit can input the customer's industry and business content into the generation AI and cause the generation AI to customize the estimation algorithm.

[0076] The visualization unit can estimate a customer's emotion and adjust the visualization format based on the estimated customer's emotion. The visualization unit, for example, estimates the customer's emotion. For example, the visualization unit can estimate the customer's emotion using text analysis technology. The visualization unit can also estimate the customer's emotion using voice analysis technology. Furthermore, the visualization unit can estimate the customer's emotion using facial expression analysis technology. This makes it possible to provide more appropriate information by estimating the customer's emotion and adjusting the visualization format based on the estimated customer's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the visualization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the visualization unit can input customer emotion data into the generation AI and have the generation AI adjust the visualization format. This makes it possible to provide more appropriate information by adjusting the visualization format based on the customer's emotion.

[0077] The visualization unit can update the inferred cloud resource configuration in real time and display the latest information. The visualization unit, for example, updates the inferred cloud resource configuration in real time. For example, the visualization unit can update the visualization results in real time when the cloud resource configuration is changed. The visualization unit can also update the visualization results in real time when a new query is generated. Furthermore, the visualization unit can also update the visualization results in real time when the performance of the cloud resources fluctuates. This makes it possible to always provide the latest information by updating in real time. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can input the inferred cloud resource configuration to the generation AI and cause the generation AI to perform real-time updates.

[0078] The visualization unit can highlight detailed information of a specific cloud resource during visualization. The visualization unit, for example, highlights detailed information of a specific cloud resource during visualization. For example, the visualization unit can highlight a cloud resource experiencing a problem, enabling it to be quickly identified. The visualization unit can also highlight important cloud resources, enabling them to be addressed as a priority. Furthermore, the visualization unit can highlight a cloud resource experiencing a decline in performance, enabling measures to be taken quickly. In this way, highlighting detailed information of a specific cloud resource allows problems to be quickly identified. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input detailed information of a specific cloud resource into the generation AI and cause the generation AI to perform the highlighting.

[0079] The visualization unit can estimate a customer's emotion and determine the visualization priority based on the estimated customer's emotion. The visualization unit, for example, estimates the customer's emotion. For example, the visualization unit can estimate the customer's emotion using text analysis technology. The visualization unit can also estimate the customer's emotion using voice analysis technology. Furthermore, the visualization unit can estimate the customer's emotion using facial expression analysis technology. This enables more appropriate information to be provided by estimating the customer's emotion and determining the visualization priority based on the estimated customer's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the visualization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input customer emotion data into the generation AI and have the generation AI determine the visualization priority. This enables more appropriate information to be provided by determining the visualization priority based on the customer's emotion.

[0080] The visualization unit can select the optimal display format by taking into account the customer's device information when visualizing. The visualization unit, for example, takes into account the customer's device information when visualizing. For example, if the customer is using a smartphone, the visualization unit can provide a display format that matches the screen size. Furthermore, if the customer is using a tablet, the visualization unit can provide a display format optimized for a large screen. Furthermore, if the customer is using a desktop, the visualization unit can provide a format that displays detailed information. In this way, the optimal display format can be provided by taking into account the customer's device information. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the customer's device information into the generation AI and cause the generation AI to select the optimal display format.

[0081] The visualization unit can provide an interface for incorporating the visualized information into a customer's workflow. For example, the visualization unit can provide an interface for incorporating the visualized information into a customer's workflow. For example, the visualization unit can provide an API for incorporating the visualized information into a customer's workflow. The visualization unit can also provide a dashboard for incorporating the visualized information into a customer's workflow. Furthermore, the visualization unit can provide a customizable widget for incorporating the visualized information into a customer's workflow. This enables more efficient business operations by incorporating the visualized information into a workflow. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can input the visualized information into a generation AI and cause the generation AI to provide an interface for incorporating the visualized information into a workflow.

[0082] The response unit can estimate the customer's emotion and adjust the response procedure based on the estimated customer's emotion. The response unit, for example, estimates the customer's emotion. For example, the response unit can estimate the customer's emotion using text analysis technology. The response unit can also estimate the customer's emotion using voice analysis technology. Furthermore, the response unit can estimate the customer's emotion using facial expression analysis technology. This enables more appropriate response by estimating the customer's emotion and adjusting the response procedure based on the estimated customer's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the response unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the response unit can input customer emotion data into the generation AI and have the generation AI adjust the response procedure. This enables more appropriate response by adjusting the response procedure based on the customer's emotion.

[0083] The response unit can automatically generate a specific problem-solving procedure based on the visualized information. The response unit automatically generates a specific problem-solving procedure based on, for example, the visualized information. For example, the response unit can automatically generate a solution procedure for when a server response is slow from the visualized information. The response unit can also automatically generate a solution procedure for when a connection error occurs from the visualized information. Furthermore, the response unit can automatically generate a solution procedure for when a database problem occurs from the visualized information. This enables rapid problem resolution using a procedure automatically generated based on the visualized information. Some or all of the above-mentioned processing in the response unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the response unit can input the visualized information into a generation AI and cause the generation AI to automatically generate a problem-solving procedure.

[0084] The response unit can refer to the customer's past response history and select the optimal response procedure. The response unit, for example, refers to the customer's past response history. For example, the response unit can refer to the response history for the past year. The response unit can also refer to the response history for a specific service. Furthermore, the response unit can select the optimal response procedure. For example, if the customer has a history of inquiring about a "slow server response," the response unit can refer to the solution procedure for that issue. Also, if the customer has a history of inquiring about a "connection error," the response unit can refer to the solution procedure for that issue. Furthermore, if the customer has a history of inquiring about a "database problem," the response unit can refer to the solution procedure for that issue. In this way, by referring to the past response history, the optimal response procedure can be selected. Some or all of the above-described processing in the response unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the response unit can input past response history into a generation AI and have the generation AI select the optimal response procedure.

[0085] The response unit can estimate a customer's emotion and determine a priority order of responses based on the estimated customer's emotion. The response unit, for example, estimates the customer's emotion. For example, the response unit can estimate the customer's emotion using text analysis technology. The response unit can also estimate the customer's emotion using voice analysis technology. Furthermore, the response unit can estimate the customer's emotion using facial expression analysis technology. This enables more appropriate responses by estimating the customer's emotion and determining priority orders of responses based on the estimated customer's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the response unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the response unit can input customer emotion data into the generation AI and have the generation AI determine the priority order of responses. This enables more appropriate responses by determining priority orders of responses based on the customer's emotion.

[0086] The response unit can provide optimal response procedures by taking into account the customer's geographical location information when responding. The response unit, for example, considers the customer's geographical location information when responding. For example, if a customer makes an inquiry from a specific region, the response unit can provide response procedures by taking into account the cloud resource status of that region. Furthermore, if a customer makes an inquiry from a specific country, the response unit can provide response procedures by taking into account the cloud service status of that country. Furthermore, if a customer makes an inquiry from a specific city, the response unit can provide response procedures by taking into account the network status of that city. In this way, optimal response procedures can be provided by taking into account the customer's geographical location information. Some or all of the above-described processing in the response unit may be performed using, or without, a generation AI. For example, the response unit can input the customer's geographical location information into the generation AI and cause the generation AI to provide optimal response procedures.

[0087] The response unit can provide an interface for incorporating the response procedure into a customer's workflow. For example, the response unit can provide an interface for incorporating the response procedure into a customer's workflow. For example, the response unit can provide an API for incorporating the response procedure into a customer's workflow. The response unit can also provide a dashboard for incorporating the response procedure into a customer's workflow. Furthermore, the response unit can provide a customizable widget for incorporating the response procedure into a customer's workflow. This enables more efficient business operations by incorporating the response procedure into a workflow. Some or all of the above-described processing in the response unit can be performed using, or without, a generation AI. For example, the response unit can input the response procedure into the generation AI and cause the generation AI to provide an interface for incorporating the response procedure into a workflow. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, estimation unit, visualization unit, and response unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 and analyzes the content of an inquiry from a customer. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the configuration of cloud resources based on the analyzed information. The visualization unit is realized, for example, by the output device 40 of the smart device 14 and visualizes the estimated configuration of cloud resources. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and quickly responds based on the visualized information. === Hard Collateral 1-2 === Each of the multiple elements including the above-described analysis unit, estimation unit, visualization unit, and response unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 and analyzes the content of an inquiry from a customer. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the configuration of cloud resources based on the analyzed information. The visualization unit is realized, for example, by the display of the smart glasses 214 and visualizes the estimated configuration of cloud resources. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and quickly responds based on the visualized information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, estimation unit, visualization unit, and response unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 and analyzes the content of an inquiry from a customer. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the configuration of cloud resources based on the analyzed information. The visualization unit is realized, for example, by the display 343 of the headset type terminal 314 and visualizes the estimated cloud resource configuration. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and quickly responds based on the visualized information. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, estimation unit, visualization unit, and response unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 and analyzes the content of an inquiry from a customer. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the configuration of cloud resources based on the analyzed information. The visualization unit is realized, for example, by the display of the robot 414 and visualizes the estimated configuration of cloud resources. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and quickly responds based on the visualized information.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] When analyzing the content of a customer's inquiry, the analysis unit can refer to the customer's past purchase history and usage history. For example, if a customer has frequently used a specific cloud service in the past, it can infer that there is a high possibility that a problem related to that service has occurred. Also, if a customer has used a specific resource a lot in the past, it can infer that there is a high possibility that a problem related to that resource has occurred. Furthermore, if a customer has made an inquiry during a specific time period in the past, it can infer that there is a high possibility that a problem related to that time period has occurred. In this way, by referring to the customer's past usage history, more accurate analysis is possible.

[0090] When analyzing the content of an inquiry, the analysis unit can improve the accuracy of the analysis by taking into account the customer's industry and business details. For example, if the customer is in the financial industry, it can prioritize analysis of information on cloud services and resources specialized for the financial industry. Also, if the customer is in the manufacturing industry, it can prioritize analysis of information on cloud services and resources specialized for the manufacturing industry. Furthermore, if the customer is in the retail industry, it can prioritize analysis of information on cloud services and resources specialized for the retail industry. This allows for more accurate analysis by taking into account the customer's industry and business details.

[0091] The estimation unit can refer to real-time cloud resource performance data when estimating cloud resource configurations based on customer inquiries. For example, it can obtain current cloud resource usage and performance data in real time and make estimations based on that data. In addition, using real-time performance data makes it possible to make estimations based on the current situation, not just past data. Furthermore, using real-time performance data enables quick responses, which is expected to improve customer satisfaction.

[0092] When visualizing the inferred cloud resource configuration, the visualization unit can provide a customizable dashboard tailored to the customer's business flow. For example, if the customer manages cloud resources according to a specific business flow, a dashboard tailored to that business flow can be provided. Also, if the customer monitors cloud resources according to a specific business flow, a customizable widget tailored to that business flow can be provided. Furthermore, if the customer optimizes cloud resources according to a specific business flow, a customizable report tailored to that business flow can be provided. This enables visualization tailored to the customer's business flow, enabling more efficient cloud resource management.

[0093] When identifying the cause of a problem based on the visualized information, the response department can refer to the customer's past inquiry history and response history. For example, if the customer has a history of inquiring about a similar problem in the past, the cause of the problem can be identified by referring to that history. Also, by referring to how the customer has responded to similar problems in the past, a quicker response is possible. Furthermore, by referring to what solutions the customer has adopted in the past for similar problems, the optimal solution can be provided. This allows for faster and more accurate response by referring to the past inquiry history and response history.

[0094] The analysis unit can estimate the customer's emotions and adjust the analysis priority based on the estimated customer emotions. For example, if a customer is dissatisfied, the content of the inquiry can be analyzed with priority. Also, if a customer is confused, the content of the inquiry can be analyzed in detail. Furthermore, if a customer is satisfied, the content of the inquiry can be analyzed with normal priority. In this way, adjusting the analysis priority based on the customer's emotions enables more appropriate responses.

[0095] The estimation unit can estimate the customer's emotions and adjust the accuracy of estimation based on the estimated customer emotions. For example, if the customer is dissatisfied, a more detailed estimation can be made for the inquiry. If the customer is confused, a simpler estimation can be made for the inquiry. Furthermore, if the customer is satisfied, an estimation can be made with normal accuracy for the inquiry. In this way, by adjusting the accuracy of estimation based on the customer's emotions, it becomes possible to provide more appropriate information.

[0096] The visualization unit can estimate the customer's emotions and adjust the visualization format based on the estimated customer emotions. For example, if the customer is dissatisfied, the visualization can be performed in a format that provides more detailed information. If the customer is confused, the visualization can be performed in a more concise and easy-to-understand format. Furthermore, if the customer is satisfied, the visualization can be performed in a normal format. In this way, by adjusting the visualization format based on the customer's emotions, it is possible to provide more appropriate information.

[0097] The response unit can estimate the customer's emotions and adjust the response procedures based on the estimated customer emotions. For example, if the customer is dissatisfied, it can provide quick and detailed response procedures. If the customer is confused, it can explain the response procedures in easy-to-understand terms. Furthermore, if the customer is satisfied, it can provide a standard response procedure. This allows for more appropriate response by adjusting the response procedures based on the customer's emotions.

[0098] The response unit can estimate the customer's emotions and determine the priority of responses based on the estimated customer emotions. For example, if a customer is dissatisfied, the inquiry can be given priority. Also, if a customer is confused, the inquiry can be responded to quickly. Furthermore, if a customer is satisfied, the inquiry can be responded to with normal priority. Thus, by determining the priority of responses based on the customer's emotions, more appropriate responses can be made.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The analysis unit analyzes the customer inquiry. Customer inquiries often include information about cloud services and resources. The analysis unit uses text analysis and data mining technologies to analyze the inquiry. For example, it extracts keywords from the inquiry and performs analysis based on them. It also analyzes the context of the inquiry and improves the accuracy of the analysis based on specific keywords and phrases. Step 2: The prediction unit predicts the cloud resource configuration based on the information analyzed by the analysis unit. The prediction unit uses machine learning algorithms and statistical methods to predict the cloud resource configuration. For example, it learns past inquiry data and cloud resource configuration information and makes predictions based on that. It can also optimize the prediction algorithm by taking into account the characteristics of the cloud services used by customers. Step 3: The visualization unit visualizes the cloud resource configuration inferred by the estimation unit. The visualization unit visually displays the cloud resource configuration using graphs, charts, and dashboards. For example, the visualization unit updates the inferred cloud resource configuration in real time to display the latest information. It can also highlight detailed information about specific cloud resources. Step 4: The response department responds quickly based on the information visualized by the visualization department. The response department identifies the cause of the problem and promptly implements specific procedures. The response department also estimates the customer's emotions and adjusts the response procedures based on the estimated customer emotions. For example, if the customer is dissatisfied, the response department provides quick and detailed response procedures, and if the customer is confused, the response procedures are explained in easy-to-understand terms.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the content of inquiries from customers; an estimation unit that estimates a configuration of cloud resources based on the information analyzed by the analysis unit; a visualization unit that visualizes the cloud resource configuration estimated by the estimation unit; a response unit that responds quickly based on the information visualized by the visualization unit; Equipped with A system characterized by:

2. The analysis unit Analyze the cloud service or resource information included in the inquiry The system of claim 1 .

3. The estimation unit It learns from past inquiry data and cloud resource configuration information, and infers cloud resource configuration based on the inquiry content. The system of claim 1 .

4. The visualization unit View the inferred cloud resource configuration in a visual format of a diagram or graph The system of claim 1 .

5. The corresponding part is Identify the cause of the problem based on visualized information and quickly take specific steps The system of claim 1 .

6. The analysis unit Estimate customer sentiment and adjust analysis priorities based on estimated customer sentiment The system of claim 1 .

7. The analysis unit Analyze the context of inquiries and refine analysis based on specific keywords and phrases The system of claim 1 .

8. The analysis unit Refer to the customer's past inquiry history and prioritize analysis of similar inquiries The system of claim 1 .

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