system

Abstract

The system according to this embodiment aims to automatically analyze customer data and provide problems and areas for improvement as a visual report. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an extraction unit, and a provision unit. The collection unit collects customer data. The analysis unit analyzes the data collected by the collection unit. The extraction unit extracts problems and areas for improvement based on the data analyzed by the analysis unit. The provision unit provides the problems and areas for improvement extracted by the extraction unit as a visual report.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, analysis of customer data and extraction of problems and improvement points are often performed manually, and there is a problem that efficient decision-making is difficult.

[0005] The system according to the embodiment aims to automatically analyze customer data and provide problems and improvement points as a visual report.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an extraction unit, and a data provision unit. The data collection unit collects customer data. The analysis unit analyzes the data collected by the data collection unit. The extraction unit extracts problems and areas for improvement based on the data analyzed by the analysis unit. The data provision unit provides the problems and areas for improvement extracted by the extraction unit as a visual report. [Effects of the Invention]

[0007] The system according to this embodiment can automatically analyze customer data and provide problems and areas for improvement as a visual report. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) An Insight Agent according to an embodiment of the present invention is a system that analyzes customer data collected by a company's support center, automatically extracts problems and areas for improvement, and provides them as a visual report. The Insight Agent analyzes customer data collected by a company's support center, automatically extracts problems and areas for improvement, and provides them as a visual report. This enables rapid and efficient decision-making and improves customer satisfaction. For example, the Insight Agent takes customer data collected by a company's support center as input. The Insight Agent analyzes customer inquiries using natural language processing technology and identifies problem trends and patterns using machine learning. This allows it to automatically extract problems and areas for improvement from customer data. Furthermore, the Insight Agent updates data in real time and provides information to relevant parties through a notification system. This enables rapid decision-making and is expected to improve customer satisfaction. For example, it is expected that customer satisfaction will improve by 20%, average response time will be reduced by 30% due to faster problem resolution, and resource utilization will improve by 25% due to increased operational efficiency. In addition, the Insight Agent generates intuitive visual reports to support corporate decision-making. This makes it effective for companies that find it difficult to gain direct insights from data, and for environments that require rapid decision-making. In particular, the introduction of Insight Agent is beneficial for service industries seeking to improve customer satisfaction. Insight Agent features data analysis using the latest AI technology and automated insight delivery for decision-making. This allows companies to prevent delays and misinterpretations in analysis due to the vast volume of customer data, enabling rapid responses. The introduction of Insight Agent is especially recommended for medium to large-sized customer service companies in the telecommunications and financial industries. By implementing Insight Agent, real-time analysis and insight delivery become possible, supporting rapid decision-making. This leads to improved customer satisfaction and significantly enhanced operational efficiency. For example, the data-driven decision support market is expected to expand in the customer service market, with the telecommunications and financial industries being anticipated as initial target markets.Insight Agent's vision is to improve the quality of customer service and strengthen the relationship between companies and their customers. This aims to dramatically improve customer satisfaction and significantly enhance operational efficiency. By doing so, Insight Agent can prevent delays and misunderstandings in analysis caused by the vast volume of customer data, enabling rapid responses.

[0029] The Insight Agent according to this embodiment comprises a collection unit, an analysis unit, an extraction unit, and a provision unit. The collection unit collects customer data. Customer data includes, but is not limited to, purchase history, inquiries, and feedback. For example, the collection unit retrieves customer purchase history from a database. The collection unit can also collect customer inquiries in text format. The collection unit can also collect customer feedback in questionnaire format. For example, the collection unit retrieves customer purchase history from a database and provides it to the analysis unit. Customer inquiries are collected in text format and provided to the analysis unit. Customer feedback is collected in questionnaire format and provided to the analysis unit. The analysis unit analyzes the data collected by the collection unit. The analysis is performed by, but is not limited to, statistical analysis, text mining, and data mining. For example, the analysis unit uses statistical analysis to analyze trends in customer data. The analysis unit can also use text mining to analyze customer inquiries. The analysis unit can also use data mining to extract patterns from customer data. For example, the analysis unit uses statistical analysis to analyze trends in customer data and identify problems. It uses text mining to analyze customer inquiries and extract areas for improvement. It uses data mining to extract patterns from customer data and identify problems and areas for improvement. The extraction unit extracts problems and areas for improvement based on the data analyzed by the analysis unit. Problems and areas for improvement include, but are not limited to, customer dissatisfaction and service improvements. For example, the extraction unit extracts customer dissatisfaction. The extraction unit can also extract areas for service improvement. The extraction unit can also extract problems based on customer feedback. For example, the extraction unit extracts customer dissatisfaction and provides it to the service unit. Service improvements are extracted by the extraction unit and provided to the service unit. Problems are extracted based on customer feedback and provided to the service unit. The service unit provides the problems and areas for improvement extracted by the extraction unit as a visual report.Visual reports are provided in the form of, for example, graphs, charts, and dashboards, but are not limited to such examples. For example, the provider may use graphs to visually display problems. The provider may also use charts to visually display areas for improvement. The provider may also use dashboards to visually display problems and areas for improvement. For example, the provider may use graphs to visually display problems and provide them to stakeholders. The provider may use charts to visually display areas for improvement and provide them to stakeholders. The provider may use dashboards to visually display problems and areas for improvement and provide them to stakeholders. This enables the insight agent according to the embodiment to efficiently collect, analyze, extract, and provide customer data.

[0030] The data collection unit collects customer data. This data includes, but is not limited to, purchase history, inquiries, and feedback. For example, the data collection unit retrieves customer purchase history from a database. Specifically, it collects data such as details of products and services that customers have purchased in the past, purchase date and time, purchase frequency, and purchase amount. The data collection unit can also collect customer inquiries in text format. For example, it collects email and chat logs sent by customers to customer support, and telephone inquiries as text data. The data collection unit can also collect customer feedback in the form of surveys. For example, it collects customer satisfaction, opinions, and requests through online surveys and paper-based surveys. This allows the data collection unit to centrally collect diverse data such as customer purchase history, inquiries, and feedback and provide it to the analytics unit. Furthermore, by collecting this data in real time and providing it quickly to the analytics unit, the data collection unit supports the generation of timely insights. For example, by collecting data on newly purchased products and the latest inquiries from customers and providing it to the analytics unit, a rapid response becomes possible. Furthermore, the data collection unit is equipped with functions to check the integrity and consistency of data, and to detect and correct inaccurate or missing data, in order to ensure data quality. This allows the data collection unit to provide accurate and reliable data, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. Analysis is performed using methods such as statistical analysis, text mining, and data mining, but is not limited to these examples. Specifically, statistical analysis is used to analyze trends in customer data. For example, based on customer purchase history data, the popularity and sales trends of specific products or services are analyzed to understand seasonal sales patterns and changes in customer purchasing behavior. The analysis unit can also analyze customer inquiries using text mining. For example, customer inquiries are analyzed using natural language processing techniques to extract frequently occurring keywords and phrases, identifying common problems and requests among customers. Furthermore, the analysis unit can extract patterns from customer data using data mining. For example, customer purchase history and feedback data are analyzed to reveal the characteristics and behavioral patterns of specific customer groups. This allows the analysis unit to analyze collected data from multiple perspectives and gain a deeper understanding of customer behavior and needs. Additionally, the analysis unit can utilize AI technology to perform more advanced analysis. For example, machine learning algorithms are used to analyze customer data and predict future purchasing behavior and inquiry content. Furthermore, by using deep learning technology, it is possible to analyze customer emotions and intentions and provide more accurate insights. This allows the analysis unit to analyze customer data quickly and accurately, improving the overall system performance.

[0032] The extraction unit extracts problems and areas for improvement based on the data analyzed by the analysis unit. These problems and areas for improvement include, but are not limited to, customer dissatisfaction and service improvements. Specifically, the extraction unit extracts customer dissatisfaction from data analyzed by the analysis unit using statistical analysis, text mining, and data mining. For example, it analyzes customer inquiries and feedback data to identify frequently pointed-out problems and dissatisfactions. The extraction unit can also extract areas for service improvement. For example, it analyzes customer purchase history data to understand evaluations and satisfaction levels for specific products or services and identify areas for improvement. Furthermore, the extraction unit can extract problems based on customer feedback. For example, it analyzes survey data to extract customer requests for new features and services. This allows the extraction unit to quickly and accurately extract customer dissatisfaction and service improvements and provide them to the service provider. Additionally, the extraction unit can prioritize the extracted problems and areas for improvement and propose countermeasures according to their importance and urgency. For example, among customer complaints, we prioritize extracting the issues that are pointed out by the most customers, and among service improvements, we prioritize those that significantly impact customer satisfaction, and provide them to the service department. This allows the extraction department to respond quickly and accurately to customer needs and improve the overall performance of the system.

[0033] The service provider will provide the problems and areas for improvement identified by the extraction service provider as a visual report. This visual report may be provided in the form of graphs, charts, dashboards, etc., but is not limited to these examples. Specifically, the service provider can visually display problems using graphs. For example, it can display customer complaints and areas for service improvement using bar graphs or pie charts and provide them to stakeholders. The service provider can also visually display areas for improvement using charts. For example, it can display customer feedback data using line graphs or heatmaps to clearly show areas that need improvement. Furthermore, the service provider can visually display problems and areas for improvement using dashboards. For example, a real-time updated dashboard can provide a quick overview of customer complaints and areas for service improvement. This allows the service provider to quickly and accurately communicate problems and areas for improvement to stakeholders and encourage appropriate action. The service provider also has a function to customize visual reports. For example, it can select the type of data and graphs to display and adjust the report content according to the needs and objectives of stakeholders. The service provider also has a report distribution function, which can automatically generate reports periodically and distribute them to stakeholders via email or cloud storage. This allows the service provider to always provide relevant parties with the latest information and support a swift and appropriate response.

[0034] The Insight Agent further includes an update unit that updates data in real time. The update unit can update data in real time. For example, the update unit reflects database changes in real time. The update unit can also add new data in real time. The update unit can also delete data in real time. For example, the update unit reflects database changes in real time to provide the latest information. It adds new data in real time to maintain the freshness of the information. It deletes data in real time to eliminate unnecessary information. In this way, by updating data in real time, it can provide the latest information. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, in order to reflect database changes in real time, the update unit can use generative AI to detect data changes and reflect the changes.

[0035] The Insight Agent further includes a notification unit that provides information through a notification system. The notification unit can provide information through the notification system. For example, the notification unit can send email notifications. The notification unit can also send push notifications. The notification unit can also send SMS notifications. For example, the notification unit can send email notifications to provide information to relevant parties. It can send push notifications to quickly transmit information. It can send SMS notifications to ensure that important information is delivered. This enables rapid decision-making by providing information through the notification system. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use generative AI to generate notification content for email notifications and send it to relevant parties.

[0036] The Insight Agent further includes a report generation unit that generates intuitive visual reports. The report generation unit can generate intuitive visual reports. For example, the report generation unit can generate infographics. The report generation unit can also generate dashboards. The report generation unit can also generate charts. For example, the report generation unit can generate infographics to visually display data. It can generate dashboards to grasp the overall picture of the data. It can generate charts to visually show data trends. This makes it easier to understand the data by generating intuitive visual reports. Some or all of the above processes in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit can use generating AI to analyze data and generate a visual report in order to generate an infographic.

[0037] The analysis unit can analyze customer inquiries using natural language processing technology. For example, the analysis unit can perform morphological analysis. It can also perform grammatical analysis. Furthermore, it can perform semantic analysis. For example, the analysis unit can perform morphological analysis to break down customer inquiries into individual words. It can perform grammatical analysis to analyze sentence structure. It can perform semantic analysis to understand the meaning of sentences. This allows for accurate analysis of customer inquiries by utilizing natural language processing technology. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze customer inquiries and break them down into individual words in order to perform morphological analysis.

[0038] The extraction unit can identify problem trends and patterns using machine learning. The extraction unit can, for example, use supervised learning. Alternatively, the extraction unit can use unsupervised learning. Furthermore, the extraction unit can use reinforcement learning. For example, the extraction unit uses supervised learning to learn problem trends from past data. It uses unsupervised learning to cluster the data and identify patterns. It uses reinforcement learning to learn the optimal action and identify problem trends and patterns. Thus, by using machine learning, problem trends and patterns can be accurately identified. Some or all of the above processing in the extraction unit may be performed using, for example, AI, or without AI. For example, the extraction unit can use generative AI to learn from past data and identify problem trends in order to perform supervised learning.

[0039] The data collection unit can analyze past customer data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most effective collection method from past data collection history. The data collection unit can also analyze customer responses and improve collection methods. Furthermore, the data collection unit can identify and optimize collection method patterns based on past data collection history. For example, the data collection unit can identify and apply the most effective collection method from past data collection history. It can analyze customer responses and improve collection methods. It can identify and optimize collection method patterns based on past data collection history. This allows the optimal collection method to be selected by analyzing past collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze data in order to analyze past data collection history and select the optimal collection method.

[0040] The data collection unit can filter data based on the customer's current situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting highly relevant data based on the customer's current situation. The data collection unit can also filter the data to be collected based on the customer's areas of interest. Furthermore, the data collection unit can adjust the type of data to be collected according to the customer's situation and areas of interest. For example, the data collection unit prioritizes collecting highly relevant data based on the customer's current situation. It filters the data to be collected based on the customer's areas of interest. It adjusts the type of data to be collected according to the customer's situation and areas of interest. This allows for the collection of highly relevant data by filtering the data based on the customer's situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze and filter data in order to analyze the customer's current situation and areas of interest.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location information during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on the customer's geographical location information. The data collection unit can also collect region-specific data based on the customer's location information. Furthermore, the data collection unit can adjust the scope of data to be collected by considering the customer's geographical location information. For example, the data collection unit prioritizes the collection of highly relevant data based on the customer's geographical location information. It collects region-specific data based on the customer's location information. It adjusts the scope of data to be collected by considering the customer's geographical location information. This allows for the collection of highly relevant data by considering the customer's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze location information using generative AI to analyze the customer's geographical location information and collect highly relevant data.

[0042] The data collection unit can analyze customers' social media activities and collect relevant data during data collection. For example, the data collection unit can analyze customers' social media activities and collect relevant data. The data collection unit can also filter the data to be collected based on customers' interests on social media. Furthermore, the data collection unit can adjust the types of data to be collected based on customers' social media activities. For example, the data collection unit analyzes customers' social media activities and collects relevant data. It filters the data to be collected based on customers' interests on social media. It adjusts the types of data to be collected based on customers' social media activities. This allows relevant data to be collected by analyzing customers' social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze data and collect relevant data in order to analyze customers' social media activities.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. The analysis unit can adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. It can perform a simplified analysis on data with low importance. It can adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the data using generative AI to evaluate the importance of the data and adjust the level of detail of the analysis based on the importance.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can select the optimal analysis algorithm according to the data category. The analysis unit can also apply different analysis algorithms for each category to improve accuracy. Furthermore, the analysis unit can dynamically switch analysis algorithms based on the data category. For example, the analysis unit can select the optimal analysis algorithm according to the data category. It can apply different analysis algorithms for each category to improve accuracy. It can dynamically switch analysis algorithms based on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can classify the data using generative AI to analyze the data category and then apply the optimal analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit may postpone the analysis of older data. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data, postpone the analysis of older data, and dynamically adjust the priority of analysis based on the data collection timing. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use generative AI to analyze the data in order to determine the data collection timing, and then prioritize the analysis of the most recent data.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data, postpone the analysis of less relevant data, and dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze the data in order to analyze the relevance of the data, and prioritize the analysis of highly relevant data.

[0047] The extraction unit can improve the accuracy of extraction by considering the interrelationships between data during the extraction process. For example, the extraction unit can analyze the interrelationships between data and perform highly accurate extraction. The extraction unit can also improve the accuracy of extraction by considering the relationships between data. Furthermore, the extraction unit can optimize the extraction algorithm based on the interrelationships between data. For example, the extraction unit analyzes the interrelationships between data and performs highly accurate extraction. It improves the accuracy of extraction by considering the relationships between data. It optimizes the extraction algorithm based on the interrelationships between data. As a result, the accuracy of extraction is improved by considering the interrelationships between data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can analyze the data using generative AI to analyze the interrelationships between data and perform highly accurate extraction.

[0048] The extraction unit can perform extraction while considering the attribute information of the data submitter. For example, the extraction unit can improve the accuracy of extraction based on the attribute information of the data submitter. The extraction unit can also optimize the extraction algorithm by considering the attribute information of the submitter. Furthermore, the extraction unit can determine the extraction priority based on the attribute information of the submitter. For example, the extraction unit improves the accuracy of extraction based on the attribute information of the data submitter. It optimizes the extraction algorithm by considering the attribute information of the submitter. It determines the extraction priority by considering the attribute information of the submitter. As a result, the accuracy of extraction is improved by considering the attribute information of the data submitter. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can analyze the data using generative AI to analyze the attribute information of the data submitter and perform highly accurate extraction.

[0049] The extraction unit can perform extraction while considering the geographical distribution of the data. For example, the extraction unit can improve the accuracy of extraction based on the geographical distribution of the data. The extraction unit can also optimize the extraction algorithm while considering the geographical distribution. Furthermore, the extraction unit can determine the extraction priority based on the geographical distribution. For example, the extraction unit improves the accuracy of extraction based on the geographical distribution of the data. It optimizes the extraction algorithm while considering the geographical distribution. It determines the extraction priority based on the geographical distribution. As a result, the accuracy of extraction is improved by considering the geographical distribution of the data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can analyze the data using generative AI to analyze the geographical distribution of the data and perform highly accurate extraction.

[0050] The extraction unit can improve the accuracy of the extraction by referring to relevant literature during the extraction process. For example, the extraction unit can improve the accuracy of the extraction by referring to relevant literature. The extraction unit can also optimize the extraction algorithm based on the relevant literature. The extraction unit can also determine the extraction priority based on the relevant literature. For example, the extraction unit can improve the accuracy of the extraction by referring to relevant literature. The extraction algorithm is optimized based on the relevant literature. The extraction priority is determined based on the relevant literature. As a result, the accuracy of the extraction is improved by referring to relevant literature. Some or all of the above processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can use generative AI to analyze literature in order to refer to relevant literature for the data and perform highly accurate extraction.

[0051] The service provider can select the optimal display method by referring to the user's past operation history when providing reports. For example, the service provider selects the optimal display method based on the user's past operation history. The service provider can also analyze the operation history and improve the display method. Furthermore, the service provider can identify and optimize display method patterns based on past operation history. For example, the service provider selects the optimal display method based on the user's past operation history. It analyzes the operation history and improves the display method. It identifies and optimizes display method patterns based on past operation history. This allows the service provider to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use generative AI to analyze data in order to analyze the user's past operation history and select the optimal display method.

[0052] The service provider can apply different report formats depending on the user's job title and work content when providing reports. For example, the service provider can select an appropriate report format based on the user's job title. The service provider can also customize the report content based on the work content. Furthermore, the service provider can adjust the level of detail in the report depending on the job title and work content. For example, the service provider can select an appropriate report format based on the user's job title, customize the report content based on the work content, and adjust the level of detail in the report depending on the job title and work content. This allows for the provision of appropriate information by applying a report format appropriate to the user's job title and work content. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can use generative AI to analyze data in order to analyze the user's job title and work content, and then apply an appropriate report format.

[0053] The service provider can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. The service provider can also provide a display method optimized for larger screens if the user is using a tablet. Furthermore, the service provider can provide a concise and highly visible display method if the user is using a smartwatch. This allows the service provider to select the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use generative AI to analyze the user's device information and select the optimal display method.

[0054] The service provider can adjust the timing of report delivery, taking into account the user's work schedule. For example, the service provider can provide reports at the optimal time based on the user's work schedule. The service provider can also adjust the frequency of report delivery, taking into account the work schedule. Furthermore, the service provider can dynamically adjust the timing of report delivery to match the user's schedule. For example, the service provider can provide reports at the optimal time based on the user's work schedule. The frequency of report delivery can be adjusted, taking into account the work schedule. The timing of report delivery can be dynamically adjusted to match the user's schedule. This allows for the provision of reports at the optimal time by considering the user's work schedule. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use generative AI to analyze schedule information in order to analyze the user's work schedule and provide reports at the optimal time.

[0055] The update unit can apply the optimal update algorithm by referring to past update history during an update. For example, the update unit can select the optimal update algorithm based on past update history. The update unit can also analyze the update history and improve the update algorithm. Furthermore, the update unit can identify and optimize update algorithm patterns based on past update history. For example, the update unit selects the optimal update algorithm based on past update history. It analyzes the update history and improves the update algorithm. It identifies and optimizes update algorithm patterns based on past update history. This allows the optimal update algorithm to be applied by referring to past update history. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can use generative AI to analyze data in order to analyze past update history and apply the optimal update algorithm.

[0056] The update unit can weight the updated data based on the data collection timing during the update process. For example, the update unit can prioritize updating the latest data. It can also postpone updating older data. Furthermore, the update unit can dynamically adjust the weighting of the updated data based on the data collection timing. For example, the update unit can prioritize updating the latest data, postpone updating older data, and dynamically adjust the weighting of the updated data based on the data collection timing. This allows the update unit to prioritize updating the latest data by weighting the updated data based on the data collection timing. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can analyze the data using generative AI to analyze the data collection timing and prioritize updating the latest data.

[0057] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit selects the optimal notification method based on the user's past notification history. The notification unit can also analyze the notification history and improve the notification method. Furthermore, the notification unit can identify and optimize notification method patterns based on past notification history. For example, the notification unit selects the optimal notification method based on the user's past notification history. It analyzes the notification history and improves the notification method. It identifies and optimizes notification method patterns based on past notification history. This allows the notification unit to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use generative AI to analyze data in order to analyze the user's past notification history and select the optimal notification method.

[0058] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit can provide a notification method that matches the screen size. It can also provide a notification method optimized for larger screens if the user is using a tablet. Furthermore, it can provide a concise and highly visible notification method if the user is using a smartwatch. This allows the notification unit to select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use generative AI to analyze the user's device information and select the optimal notification method.

[0059] The report generation unit can generate the optimal report by referring to the user's past report viewing history when generating a report. For example, the report generation unit generates the optimal report based on the user's past report viewing history. The report generation unit can also analyze the viewing history and improve the content of the report. Furthermore, the report generation unit can identify and optimize report patterns based on past report viewing history. For example, the report generation unit generates the optimal report based on the user's past report viewing history. It analyzes the viewing history and improves the content of the report. It identifies and optimizes report patterns based on past report viewing history. This allows the report generation unit to generate the optimal report by referring to the user's past report viewing history. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can analyze data using generating AI to analyze the user's past report viewing history and generate the optimal report.

[0060] The report generation unit can generate an optimal report by considering the user's geographical location information during report generation. For example, the report generation unit generates an optimal report based on the user's geographical location information. The report generation unit can also customize the content of the report by considering the geographical location information. Furthermore, the report generation unit can determine the priority of reports based on the geographical location information. For example, the report generation unit generates an optimal report based on the user's geographical location information. It customizes the content of the report by considering the geographical location information. It determines the priority of reports by considering the geographical location information. In this way, an optimal report can be generated by considering the user's geographical location information. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can analyze data using generating AI to analyze the user's geographical location information and generate an optimal report.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can analyze customer purchase history and identify customer purchasing patterns. For example, the data collection unit can identify a customer's tendency to frequently purchase certain products and prioritize the collection of data related to those products. It can also identify a customer's tendency to purchase certain products during specific seasons and collect data related to those seasons. Furthermore, it can identify a customer's tendency to purchase during specific campaign periods and collect data related to those periods. This allows for the efficient collection of highly relevant data by identifying customer purchasing patterns. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For instance, the data collection unit can use generative AI to analyze data and identify purchasing patterns in order to analyze customer purchase history.

[0063] The analysis unit can analyze customer feedback and evaluate customer satisfaction. For example, the analysis unit can analyze customer feedback using text mining techniques and quantify customer satisfaction. The analysis unit can also classify customer feedback using clustering techniques to identify highly and less satisfied customers. Furthermore, the analysis unit can analyze customer feedback using time-series analysis techniques to understand fluctuations in satisfaction. This allows for an accurate evaluation of customer satisfaction by analyzing customer feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze data and evaluate satisfaction in order to analyze customer feedback.

[0064] The extraction unit can analyze customer inquiries and classify them by type. For example, the extraction unit can analyze customer inquiries using natural language processing techniques to identify the type of inquiry. It can also classify customer inquiries using clustering techniques and group similar inquiries together. Furthermore, it can analyze customer inquiries using topic modeling techniques and extract key topics. This allows for accurate classification of inquiry types by analyzing customer inquiries. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For instance, the extraction unit can use generative AI to analyze data and classify inquiries in order to analyze customer inquiries.

[0065] The service provider can customize the content of reports based on customer attribute information. For example, the service provider can adjust the content of reports based on customer attribute information such as age, gender, and region. The service provider can also customize the content of reports based on customer purchase history and inquiry history. Furthermore, the service provider can improve the content of reports based on customer feedback. This allows the service provider to provide customers with the most relevant information by customizing the content of reports based on customer attribute information. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can use generative AI to analyze data and customize the content of reports in order to analyze customer attribute information.

[0066] The notification unit can adjust the timing of notifications based on the customer's behavior history. For example, the notification unit can send notifications at the optimal time based on the customer's website browsing history or app usage history. The notification unit can also send follow-up notifications after a purchase based on the customer's purchase history. Furthermore, the notification unit can send response notifications to inquiries based on the customer's inquiry history. In this way, by adjusting the timing of notifications based on the customer's behavior history, notifications can be sent at the appropriate time. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can analyze data using generative AI to analyze the customer's behavior history and adjust the timing of notifications.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects customer data. This data includes purchase history, inquiries, and feedback. The data collection unit retrieves purchase history from the database, collects inquiries in text format, and collects feedback in questionnaire format. The collected data is then provided to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as statistical analysis, text mining, and data mining. The analysis unit uses statistical analysis to analyze trends in customer data, text mining to analyze the content of inquiries, and data mining to extract patterns. Step 3: The extraction unit extracts problems and areas for improvement based on the data analyzed by the analysis unit. These problems and areas for improvement include customer dissatisfaction and areas for service improvement. The extraction unit extracts customer dissatisfaction and areas for service improvement and provides them to the service unit. Step 4: The provisioning department provides a visual report of the problems and areas for improvement identified by the extraction department. The visual report is provided in the form of graphs, charts, dashboards, etc. The provisioning department visually displays the problems and areas for improvement using graphs, charts, and dashboards and provides them to the relevant parties.

[0069] (Example of form 2) An Insight Agent according to an embodiment of the present invention is a system that analyzes customer data collected by a company's support center, automatically extracts problems and areas for improvement, and provides them as a visual report. The Insight Agent analyzes customer data collected by a company's support center, automatically extracts problems and areas for improvement, and provides them as a visual report. This enables rapid and efficient decision-making and improves customer satisfaction. For example, the Insight Agent takes customer data collected by a company's support center as input. The Insight Agent analyzes customer inquiries using natural language processing technology and identifies problem trends and patterns using machine learning. This allows it to automatically extract problems and areas for improvement from customer data. Furthermore, the Insight Agent updates data in real time and provides information to relevant parties through a notification system. This enables rapid decision-making and is expected to improve customer satisfaction. For example, it is expected that customer satisfaction will improve by 20%, average response time will be reduced by 30% due to faster problem resolution, and resource utilization will improve by 25% due to increased operational efficiency. In addition, the Insight Agent generates intuitive visual reports to support corporate decision-making. This makes it effective for companies that find it difficult to gain direct insights from data, and for environments that require rapid decision-making. In particular, the introduction of Insight Agent is beneficial for service industries seeking to improve customer satisfaction. Insight Agent features data analysis using the latest AI technology and automated insight delivery for decision-making. This allows companies to prevent delays and misinterpretations in analysis due to the vast volume of customer data, enabling rapid responses. The introduction of Insight Agent is especially recommended for medium to large-sized customer service companies in the telecommunications and financial industries. By implementing Insight Agent, real-time analysis and insight delivery become possible, supporting rapid decision-making. This leads to improved customer satisfaction and significantly enhanced operational efficiency. For example, the data-driven decision support market is expected to expand in the customer service market, with the telecommunications and financial industries being anticipated as initial target markets.Insight Agent's vision is to improve the quality of customer service and strengthen the relationship between companies and their customers. This aims to dramatically improve customer satisfaction and significantly enhance operational efficiency. By doing so, Insight Agent can prevent delays and misunderstandings in analysis caused by the vast volume of customer data, enabling rapid responses.

[0070] The Insight Agent according to this embodiment comprises a collection unit, an analysis unit, an extraction unit, and a provision unit. The collection unit collects customer data. Customer data includes, but is not limited to, purchase history, inquiries, and feedback. For example, the collection unit retrieves customer purchase history from a database. The collection unit can also collect customer inquiries in text format. The collection unit can also collect customer feedback in questionnaire format. For example, the collection unit retrieves customer purchase history from a database and provides it to the analysis unit. Customer inquiries are collected in text format and provided to the analysis unit. Customer feedback is collected in questionnaire format and provided to the analysis unit. The analysis unit analyzes the data collected by the collection unit. The analysis is performed by, but is not limited to, statistical analysis, text mining, and data mining. For example, the analysis unit uses statistical analysis to analyze trends in customer data. The analysis unit can also use text mining to analyze customer inquiries. The analysis unit can also use data mining to extract patterns from customer data. For example, the analysis unit uses statistical analysis to analyze trends in customer data and identify problems. It uses text mining to analyze customer inquiries and extract areas for improvement. It uses data mining to extract patterns from customer data and identify problems and areas for improvement. The extraction unit extracts problems and areas for improvement based on the data analyzed by the analysis unit. Problems and areas for improvement include, but are not limited to, customer dissatisfaction and service improvements. For example, the extraction unit extracts customer dissatisfaction. The extraction unit can also extract areas for service improvement. The extraction unit can also extract problems based on customer feedback. For example, the extraction unit extracts customer dissatisfaction and provides it to the service unit. Service improvements are extracted by the extraction unit and provided to the service unit. Problems are extracted based on customer feedback and provided to the service unit. The service unit provides the problems and areas for improvement extracted by the extraction unit as a visual report.Visual reports are provided in the form of, for example, graphs, charts, and dashboards, but are not limited to such examples. For example, the provider may use graphs to visually display problems. The provider may also use charts to visually display areas for improvement. The provider may also use dashboards to visually display problems and areas for improvement. For example, the provider may use graphs to visually display problems and provide them to stakeholders. The provider may use charts to visually display areas for improvement and provide them to stakeholders. The provider may use dashboards to visually display problems and areas for improvement and provide them to stakeholders. This enables the insight agent according to the embodiment to efficiently collect, analyze, extract, and provide customer data.

[0071] The data collection unit collects customer data. This data includes, but is not limited to, purchase history, inquiries, and feedback. For example, the data collection unit retrieves customer purchase history from a database. Specifically, it collects data such as details of products and services that customers have purchased in the past, purchase date and time, purchase frequency, and purchase amount. The data collection unit can also collect customer inquiries in text format. For example, it collects email and chat logs sent by customers to customer support, and telephone inquiries as text data. The data collection unit can also collect customer feedback in the form of surveys. For example, it collects customer satisfaction, opinions, and requests through online surveys and paper-based surveys. This allows the data collection unit to centrally collect diverse data such as customer purchase history, inquiries, and feedback and provide it to the analytics unit. Furthermore, by collecting this data in real time and providing it quickly to the analytics unit, the data collection unit supports the generation of timely insights. For example, by collecting data on newly purchased products and the latest inquiries from customers and providing it to the analytics unit, a rapid response becomes possible. Furthermore, the data collection unit is equipped with functions to check the integrity and consistency of data, and to detect and correct inaccurate or missing data, in order to ensure data quality. This allows the data collection unit to provide accurate and reliable data, improving the overall performance of the system.

[0072] The analysis unit analyzes the data collected by the data collection unit. Analysis is performed using methods such as statistical analysis, text mining, and data mining, but is not limited to these examples. Specifically, statistical analysis is used to analyze trends in customer data. For example, based on customer purchase history data, the popularity and sales trends of specific products or services are analyzed to understand seasonal sales patterns and changes in customer purchasing behavior. The analysis unit can also analyze customer inquiries using text mining. For example, customer inquiries are analyzed using natural language processing techniques to extract frequently occurring keywords and phrases, identifying common problems and requests among customers. Furthermore, the analysis unit can extract patterns from customer data using data mining. For example, customer purchase history and feedback data are analyzed to reveal the characteristics and behavioral patterns of specific customer groups. This allows the analysis unit to analyze collected data from multiple perspectives and gain a deeper understanding of customer behavior and needs. Additionally, the analysis unit can utilize AI technology to perform more advanced analysis. For example, machine learning algorithms are used to analyze customer data and predict future purchasing behavior and inquiry content. Furthermore, by using deep learning technology, it is possible to analyze customer emotions and intentions and provide more accurate insights. This allows the analysis unit to analyze customer data quickly and accurately, improving the overall system performance.

[0073] The extraction unit extracts problems and areas for improvement based on the data analyzed by the analysis unit. These problems and areas for improvement include, but are not limited to, customer dissatisfaction and service improvements. Specifically, the extraction unit extracts customer dissatisfaction from data analyzed by the analysis unit using statistical analysis, text mining, and data mining. For example, it analyzes customer inquiries and feedback data to identify frequently pointed-out problems and dissatisfactions. The extraction unit can also extract areas for service improvement. For example, it analyzes customer purchase history data to understand evaluations and satisfaction levels for specific products or services and identify areas for improvement. Furthermore, the extraction unit can extract problems based on customer feedback. For example, it analyzes survey data to extract customer requests for new features and services. This allows the extraction unit to quickly and accurately extract customer dissatisfaction and service improvements and provide them to the service provider. Additionally, the extraction unit can prioritize the extracted problems and areas for improvement and propose countermeasures according to their importance and urgency. For example, among customer complaints, we prioritize extracting the issues that are pointed out by the most customers, and among service improvements, we prioritize those that significantly impact customer satisfaction, and provide them to the service department. This allows the extraction department to respond quickly and accurately to customer needs and improve the overall performance of the system.

[0074] The service provider will provide the problems and areas for improvement identified by the extraction service provider as a visual report. This visual report may be provided in the form of graphs, charts, dashboards, etc., but is not limited to these examples. Specifically, the service provider can visually display problems using graphs. For example, it can display customer complaints and areas for service improvement using bar graphs or pie charts and provide them to stakeholders. The service provider can also visually display areas for improvement using charts. For example, it can display customer feedback data using line graphs or heatmaps to clearly show areas that need improvement. Furthermore, the service provider can visually display problems and areas for improvement using dashboards. For example, a real-time updated dashboard can provide a quick overview of customer complaints and areas for service improvement. This allows the service provider to quickly and accurately communicate problems and areas for improvement to stakeholders and encourage appropriate action. The service provider also has a function to customize visual reports. For example, it can select the type of data and graphs to display and adjust the report content according to the needs and objectives of stakeholders. The service provider also has a report distribution function, which can automatically generate reports periodically and distribute them to stakeholders via email or cloud storage. This allows the service provider to always provide relevant parties with the latest information and support a swift and appropriate response.

[0075] The Insight Agent further includes an update unit that updates data in real time. The update unit can update data in real time. For example, the update unit reflects database changes in real time. The update unit can also add new data in real time. The update unit can also delete data in real time. For example, the update unit reflects database changes in real time to provide the latest information. It adds new data in real time to maintain the freshness of the information. It deletes data in real time to eliminate unnecessary information. In this way, by updating data in real time, it can provide the latest information. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, in order to reflect database changes in real time, the update unit can use generative AI to detect data changes and reflect the changes.

[0076] The Insight Agent further includes a notification unit that provides information through a notification system. The notification unit can provide information through the notification system. For example, the notification unit can send email notifications. The notification unit can also send push notifications. The notification unit can also send SMS notifications. For example, the notification unit can send email notifications to provide information to relevant parties. It can send push notifications to quickly transmit information. It can send SMS notifications to ensure that important information is delivered. This enables rapid decision-making by providing information through the notification system. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use generative AI to generate notification content for email notifications and send it to relevant parties.

[0077] The Insight Agent further includes a report generation unit that generates intuitive visual reports. The report generation unit can generate intuitive visual reports. For example, the report generation unit can generate infographics. The report generation unit can also generate dashboards. The report generation unit can also generate charts. For example, the report generation unit can generate infographics to visually display data. It can generate dashboards to grasp the overall picture of the data. It can generate charts to visually show data trends. This makes it easier to understand the data by generating intuitive visual reports. Some or all of the above processes in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit can use generating AI to analyze data and generate a visual report in order to generate an infographic.

[0078] The analysis unit can analyze customer inquiries using natural language processing technology. For example, the analysis unit can perform morphological analysis. It can also perform grammatical analysis. Furthermore, it can perform semantic analysis. For example, the analysis unit can perform morphological analysis to break down customer inquiries into individual words. It can perform grammatical analysis to analyze sentence structure. It can perform semantic analysis to understand the meaning of sentences. This allows for accurate analysis of customer inquiries by utilizing natural language processing technology. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze customer inquiries and break them down into individual words in order to perform morphological analysis.

[0079] The extraction unit can identify problem trends and patterns using machine learning. The extraction unit can, for example, use supervised learning. Alternatively, the extraction unit can use unsupervised learning. Furthermore, the extraction unit can use reinforcement learning. For example, the extraction unit uses supervised learning to learn problem trends from past data. It uses unsupervised learning to cluster the data and identify patterns. It uses reinforcement learning to learn the optimal action and identify problem trends and patterns. Thus, by using machine learning, problem trends and patterns can be accurately identified. Some or all of the above processing in the extraction unit may be performed using, for example, AI, or without AI. For example, the extraction unit can use generative AI to learn from past data and identify problem trends in order to perform supervised learning.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to gather more detailed information. Furthermore, if the user is in a hurry, the data collection unit can shorten the timing of data collection to quickly obtain information. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0081] The data collection unit can analyze past customer data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most effective collection method from past data collection history. The data collection unit can also analyze customer responses and improve collection methods. Furthermore, the data collection unit can identify and optimize collection method patterns based on past data collection history. For example, the data collection unit can identify and apply the most effective collection method from past data collection history. It can analyze customer responses and improve collection methods. It can identify and optimize collection method patterns based on past data collection history. This allows the optimal collection method to be selected by analyzing past collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze data in order to analyze past data collection history and select the optimal collection method.

[0082] The data collection unit can filter data based on the customer's current situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting highly relevant data based on the customer's current situation. The data collection unit can also filter the data to be collected based on the customer's areas of interest. Furthermore, the data collection unit can adjust the type of data to be collected according to the customer's situation and areas of interest. For example, the data collection unit prioritizes collecting highly relevant data based on the customer's current situation. It filters the data to be collected based on the customer's areas of interest. It adjusts the type of data to be collected according to the customer's situation and areas of interest. This allows for the collection of highly relevant data by filtering the data based on the customer's situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze and filter data in order to analyze the customer's current situation and areas of interest.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can also prioritize collecting detailed data. If the user is in a hurry, the data collection unit can also prioritize collecting data that can be collected quickly. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, it will prioritize collecting detailed data. If the user is in a hurry, it will prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by determining data priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location information during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on the customer's geographical location information. The data collection unit can also collect region-specific data based on the customer's location information. Furthermore, the data collection unit can adjust the scope of data to be collected by considering the customer's geographical location information. For example, the data collection unit prioritizes the collection of highly relevant data based on the customer's geographical location information. It collects region-specific data based on the customer's location information. It adjusts the scope of data to be collected by considering the customer's geographical location information. This allows for the collection of highly relevant data by considering the customer's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze location information using generative AI to analyze the customer's geographical location information and collect highly relevant data.

[0085] The data collection unit can analyze customers' social media activities and collect relevant data during data collection. For example, the data collection unit can analyze customers' social media activities and collect relevant data. The data collection unit can also filter the data to be collected based on customers' interests on social media. Furthermore, the data collection unit can adjust the types of data to be collected based on customers' social media activities. For example, the data collection unit analyzes customers' social media activities and collects relevant data. It filters the data to be collected based on customers' interests on social media. It adjusts the types of data to be collected based on customers' social media activities. This allows relevant data to be collected by analyzing customers' social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze data and collect relevant data in order to analyze customers' social media activities.

[0086] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is stressed, the analysis unit uses a simple presentation. If the user is relaxed, the analysis unit can also use a detailed presentation. If the user is in a hurry, the analysis unit can also use a concise presentation. For example, if the user is stressed, the analysis unit uses a simple presentation. If the user is relaxed, it uses a detailed presentation. If the user is in a hurry, it uses a concise presentation. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. The analysis unit can adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. It can perform a simplified analysis on data with low importance. It can adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the data using generative AI to evaluate the importance of the data and adjust the level of detail of the analysis based on the importance.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can select the optimal analysis algorithm according to the data category. The analysis unit can also apply different analysis algorithms for each category to improve accuracy. Furthermore, the analysis unit can dynamically switch analysis algorithms based on the data category. For example, the analysis unit can select the optimal analysis algorithm according to the data category. It can apply different analysis algorithms for each category to improve accuracy. It can dynamically switch analysis algorithms based on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can classify the data using generative AI to analyze the data category and then apply the optimal analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a short analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is in a hurry, the analysis unit can perform a rapid analysis. For example, if the user is stressed, the analysis unit provides a short analysis. If the user is relaxed, it provides a detailed analysis. If the user is in a hurry, it performs a rapid analysis. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use a generative AI to analyze the user's facial expressions and voice to estimate their emotions.

[0090] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit may postpone the analysis of older data. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data, postpone the analysis of older data, and dynamically adjust the priority of analysis based on the data collection timing. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use generative AI to analyze the data in order to determine the data collection timing, and then prioritize the analysis of the most recent data.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data, postpone the analysis of less relevant data, and dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze the data in order to analyze the relevance of the data, and prioritize the analysis of highly relevant data.

[0092] The extraction unit can estimate the user's emotions and determine the priority of problems and areas for improvement to extract based on the estimated emotions. For example, if the user is stressed, the extraction unit will prioritize extracting high-priority problems. If the user is relaxed, the extraction unit can also extract detailed problems. If the user is in a hurry, the extraction unit can also prioritize extracting problems that can be extracted quickly. For example, if the user is stressed, the extraction unit will prioritize extracting high-priority problems. If the user is relaxed, it will extract detailed problems. If the user is in a hurry, it will prioritize extracting problems that can be extracted quickly. This allows for the priority of important problems to be extracted by determining the priority of problems and areas for improvement according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0093] The extraction unit can improve the accuracy of extraction by considering the interrelationships between data during the extraction process. For example, the extraction unit can analyze the interrelationships between data and perform highly accurate extraction. The extraction unit can also improve the accuracy of extraction by considering the relationships between data. Furthermore, the extraction unit can optimize the extraction algorithm based on the interrelationships between data. For example, the extraction unit analyzes the interrelationships between data and performs highly accurate extraction. It improves the accuracy of extraction by considering the relationships between data. It optimizes the extraction algorithm based on the interrelationships between data. As a result, the accuracy of extraction is improved by considering the interrelationships between data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can analyze the data using generative AI to analyze the interrelationships between data and perform highly accurate extraction.

[0094] The extraction unit can perform extraction while considering the attribute information of the data submitter. For example, the extraction unit can improve the accuracy of extraction based on the attribute information of the data submitter. The extraction unit can also optimize the extraction algorithm by considering the attribute information of the submitter. Furthermore, the extraction unit can determine the extraction priority based on the attribute information of the submitter. For example, the extraction unit improves the accuracy of extraction based on the attribute information of the data submitter. It optimizes the extraction algorithm by considering the attribute information of the submitter. It determines the extraction priority by considering the attribute information of the submitter. As a result, the accuracy of extraction is improved by considering the attribute information of the data submitter. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can analyze the data using generative AI to analyze the attribute information of the data submitter and perform highly accurate extraction.

[0095] The extraction unit can estimate the user's emotions and adjust the display method of the extracted problems and areas for improvement based on the estimated emotions. For example, if the user is stressed, the extraction unit can use a simple display method. If the user is relaxed, the extraction unit can also use a detailed display method. If the user is in a hurry, the extraction unit can also use a concise display method. For example, if the user is stressed, the extraction unit uses a simple display method. If the user is relaxed, it uses a detailed display method. If the user is in a hurry, it uses a concise display method. By adjusting the display method according to the user's emotions, it becomes possible to display information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is 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 processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0096] The extraction unit can perform extraction while considering the geographical distribution of the data. For example, the extraction unit can improve the accuracy of extraction based on the geographical distribution of the data. The extraction unit can also optimize the extraction algorithm while considering the geographical distribution. Furthermore, the extraction unit can determine the extraction priority based on the geographical distribution. For example, the extraction unit improves the accuracy of extraction based on the geographical distribution of the data. It optimizes the extraction algorithm while considering the geographical distribution. It determines the extraction priority based on the geographical distribution. As a result, the accuracy of extraction is improved by considering the geographical distribution of the data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can analyze the data using generative AI to analyze the geographical distribution of the data and perform highly accurate extraction.

[0097] The extraction unit can improve the accuracy of the extraction by referring to relevant literature during the extraction process. For example, the extraction unit can improve the accuracy of the extraction by referring to relevant literature. The extraction unit can also optimize the extraction algorithm based on the relevant literature. The extraction unit can also determine the extraction priority based on the relevant literature. For example, the extraction unit can improve the accuracy of the extraction by referring to relevant literature. The extraction algorithm is optimized based on the relevant literature. The extraction priority is determined based on the relevant literature. As a result, the accuracy of the extraction is improved by referring to relevant literature. Some or all of the above processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can use generative AI to analyze literature in order to refer to relevant literature for the data and perform highly accurate extraction.

[0098] The service provider can estimate the user's emotions and adjust the report display method based on the estimated emotions. For example, if the user is stressed, the service provider can use a simple display method. If the user is relaxed, the service provider can also use a detailed display method. If the user is in a hurry, the service provider can also use a concise display method. For example, if the user is stressed, the service provider can use a simple display method. If the user is relaxed, it can use a detailed display method. If the user is in a hurry, it can use a concise display method. By adjusting the report display method according to the user's emotions, it becomes possible to display information in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use generative AI to analyze the user's facial expressions and voice to estimate the user's emotions.

[0099] The service provider can select the optimal display method by referring to the user's past operation history when providing reports. For example, the service provider selects the optimal display method based on the user's past operation history. The service provider can also analyze the operation history and improve the display method. Furthermore, the service provider can identify and optimize display method patterns based on past operation history. For example, the service provider selects the optimal display method based on the user's past operation history. It analyzes the operation history and improves the display method. It identifies and optimizes display method patterns based on past operation history. This allows the service provider to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use generative AI to analyze data in order to analyze the user's past operation history and select the optimal display method.

[0100] The service provider can apply different report formats depending on the user's job title and work content when providing reports. For example, the service provider can select an appropriate report format based on the user's job title. The service provider can also customize the report content based on the work content. Furthermore, the service provider can adjust the level of detail in the report depending on the job title and work content. For example, the service provider can select an appropriate report format based on the user's job title, customize the report content based on the work content, and adjust the level of detail in the report depending on the job title and work content. This allows for the provision of appropriate information by applying a report format appropriate to the user's job title and work content. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can use generative AI to analyze data in order to analyze the user's job title and work content, and then apply an appropriate report format.

[0101] The service provider can estimate the user's emotions and prioritize reports based on those emotions. For example, if the user is stressed, the service provider will prioritize providing high-priority reports. If the user is relaxed, the service provider can also prioritize providing detailed reports. Furthermore, if the user is in a hurry, the service provider can prioritize providing reports that can be delivered quickly. This allows for the prioritization of important reports based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0102] The service provider can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. The service provider can also provide a display method optimized for larger screens if the user is using a tablet. Furthermore, the service provider can provide a concise and highly visible display method if the user is using a smartwatch. This allows the service provider to select the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use generative AI to analyze the user's device information and select the optimal display method.

[0103] The service provider can adjust the timing of report delivery, taking into account the user's work schedule. For example, the service provider can provide reports at the optimal time based on the user's work schedule. The service provider can also adjust the frequency of report delivery, taking into account the work schedule. Furthermore, the service provider can dynamically adjust the timing of report delivery to match the user's schedule. For example, the service provider can provide reports at the optimal time based on the user's work schedule. The frequency of report delivery can be adjusted, taking into account the work schedule. The timing of report delivery can be dynamically adjusted to match the user's schedule. This allows for the provision of reports at the optimal time by considering the user's work schedule. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use generative AI to analyze schedule information in order to analyze the user's work schedule and provide reports at the optimal time.

[0104] The update unit can estimate the user's emotions and adjust the frequency of data updates based on the estimated emotions. For example, if the user is stressed, the update unit can reduce the frequency of data updates to alleviate the user's burden. If the user is relaxed, the update unit can increase the frequency of data updates to provide more detailed information. If the user is in a hurry, the update unit can shorten the timing of data updates to provide information quickly. For example, if the user is stressed, the update unit can reduce the frequency of data updates to alleviate the user's burden. If the user is relaxed, it can increase the frequency of data updates to provide more detailed information. If the user is in a hurry, it can shorten the timing of data updates to provide information quickly. In this way, the user's burden can be reduced by adjusting the frequency of data updates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0105] The update unit can apply the optimal update algorithm by referring to past update history during an update. For example, the update unit can select the optimal update algorithm based on past update history. The update unit can also analyze the update history and improve the update algorithm. Furthermore, the update unit can identify and optimize update algorithm patterns based on past update history. For example, the update unit selects the optimal update algorithm based on past update history. It analyzes the update history and improves the update algorithm. It identifies and optimizes update algorithm patterns based on past update history. This allows the optimal update algorithm to be applied by referring to past update history. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can use generative AI to analyze data in order to analyze past update history and apply the optimal update algorithm.

[0106] The update unit can estimate the user's emotions and determine the priority of update data based on the estimated emotions. For example, if the user is stressed, the update unit will prioritize updating high-priority data. If the user is relaxed, the update unit can also prioritize updating detailed data. If the user is in a hurry, the update unit can also prioritize updating data that can be updated quickly. For example, if the user is stressed, the update unit will prioritize updating high-priority data. If the user is relaxed, it will prioritize updating detailed data. If the user is in a hurry, it will prioritize updating data that can be updated quickly. This allows for prioritizing important data by determining the priority of update data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0107] The update unit can weight the updated data based on the data collection timing during the update process. For example, the update unit can prioritize updating the latest data. It can also postpone updating older data. Furthermore, the update unit can dynamically adjust the weighting of the updated data based on the data collection timing. For example, the update unit can prioritize updating the latest data, postpone updating older data, and dynamically adjust the weighting of the updated data based on the data collection timing. This allows the update unit to prioritize updating the latest data by weighting the updated data based on the data collection timing. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can analyze the data using generative AI to analyze the data collection timing and prioritize updating the latest data.

[0108] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple notification. If the user is relaxed, the notification unit can provide a detailed notification. If the user is in a hurry, the notification unit can provide a concise notification. For example, if the user is stressed, the notification unit can provide a simple notification. If the user is relaxed, it can provide a detailed notification. If the user is in a hurry, it can provide a concise notification. By adjusting the content of the notification according to the user's emotions, it becomes possible to provide notifications that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0109] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit selects the optimal notification method based on the user's past notification history. The notification unit can also analyze the notification history and improve the notification method. Furthermore, the notification unit can identify and optimize notification method patterns based on past notification history. For example, the notification unit selects the optimal notification method based on the user's past notification history. It analyzes the notification history and improves the notification method. It identifies and optimizes notification method patterns based on past notification history. This allows the notification unit to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use generative AI to analyze data in order to analyze the user's past notification history and select the optimal notification method.

[0110] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize high-priority notifications. It can also prioritize detailed notifications if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize notifications that can be delivered quickly. For example, if the user is stressed, the notification unit will prioritize high-priority notifications. If the user is relaxed, it will prioritize detailed notifications. If the user is in a hurry, it will prioritize notifications that can be delivered quickly. This allows important notifications to be prioritized by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0111] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit can provide a notification method that matches the screen size. It can also provide a notification method optimized for larger screens if the user is using a tablet. Furthermore, it can provide a concise and highly visible notification method if the user is using a smartwatch. This allows the notification unit to select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use generative AI to analyze the user's device information and select the optimal notification method.

[0112] The report generation unit can estimate the user's emotions and adjust the report generation method based on the estimated emotions. For example, if the user is stressed, the report generation unit can generate a simple report. If the user is relaxed, the report generation unit can also generate a detailed report. If the user is in a hurry, the report generation unit can also generate a concise report. For example, if the user is stressed, the report generation unit generates a simple report. If the user is relaxed, it generates a detailed report. If the user is in a hurry, it generates a concise report. By adjusting the report generation method according to the user's emotions, a report that is easy for the user to understand is generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit can use a generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0113] The report generation unit can generate the optimal report by referring to the user's past report viewing history when generating a report. For example, the report generation unit generates the optimal report based on the user's past report viewing history. The report generation unit can also analyze the viewing history and improve the content of the report. Furthermore, the report generation unit can identify and optimize report patterns based on past report viewing history. For example, the report generation unit generates the optimal report based on the user's past report viewing history. It analyzes the viewing history and improves the content of the report. It identifies and optimizes report patterns based on past report viewing history. This allows the report generation unit to generate the optimal report by referring to the user's past report viewing history. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can analyze data using generating AI to analyze the user's past report viewing history and generate the optimal report.

[0114] The report generation unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, if the user is stressed, the report generation unit will prioritize generating high-priority reports. It can also prioritize generating detailed reports if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize generating reports that can be generated quickly. For example, if the user is stressed, the report generation unit will prioritize generating high-priority reports. If the user is relaxed, it will prioritize generating detailed reports. If the user is in a hurry, it will prioritize generating reports that can be generated quickly. This allows for the prioritization of important reports based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the report generation unit may be performed using AI, or not. For example, the report generation unit can use generating AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0115] The report generation unit can generate an optimal report by considering the user's geographical location information during report generation. For example, the report generation unit generates an optimal report based on the user's geographical location information. The report generation unit can also customize the content of the report by considering the geographical location information. Furthermore, the report generation unit can determine the priority of reports based on the geographical location information. For example, the report generation unit generates an optimal report based on the user's geographical location information. It customizes the content of the report by considering the geographical location information. It determines the priority of reports by considering the geographical location information. In this way, an optimal report can be generated by considering the user's geographical location information. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can analyze data using generating AI to analyze the user's geographical location information and generate an optimal report.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, it will prioritize the analysis of high-priority data. If the user is relaxed, it will prioritize the analysis of detailed data. If the user is in a hurry, it will prioritize the analysis of data that can be quickly analyzed. In this way, by determining the priority of analysis according to the user's emotions, important data can be analyzed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can use generative AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0118] The extraction unit can estimate the user's emotions and adjust the level of detail in the extracted data based on the estimated emotions. For example, if the user is stressed, it extracts concise data. If the user is relaxed, it extracts detailed data. If the user is in a hurry, it prioritizes extracting data that can be extracted quickly. This allows the system to provide the user with the most relevant data by adjusting the level of detail according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using AI or not. For example, the extraction unit can use generative AI to analyze the user's facial expressions and voice to estimate their emotions.

[0119] The service provider can estimate the user's emotions and adjust the report content based on the estimated emotions. For example, if the user is stressed, a simple report is provided. If the user is relaxed, a detailed report is provided. If the user is in a hurry, a concise report is provided. By adjusting the report content according to the user's emotions, a report that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use generative AI to analyze the user's facial expressions and voice to estimate the user's emotions.

[0120] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the frequency of notifications can be reduced to lessen the user's burden. If the user is relaxed, the frequency of notifications can be increased to provide more detailed information. If the user is in a hurry, notifications can be sent quickly. In this way, the user's burden can be reduced by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use generative AI to analyze the user's facial expressions and voice to estimate the user's emotions.

[0121] The update unit can estimate the user's emotions and adjust the content of data updates based on the estimated emotions. For example, if the user is stressed, it will prioritize updating high-priority data. If the user is relaxed, it will update detailed data. If the user is in a hurry, it will prioritize updating data that can be updated quickly. In this way, by adjusting the content of data updates according to the user's emotions, important data can be updated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can use generative AI to analyze the user's facial expressions and voice to estimate the user's emotions.

[0122] The data collection unit can analyze customer purchase history and identify customer purchasing patterns. For example, the data collection unit can identify a customer's tendency to frequently purchase certain products and prioritize the collection of data related to those products. It can also identify a customer's tendency to purchase certain products during specific seasons and collect data related to those seasons. Furthermore, it can identify a customer's tendency to purchase during specific campaign periods and collect data related to those periods. This allows for the efficient collection of highly relevant data by identifying customer purchasing patterns. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For instance, the data collection unit can use generative AI to analyze data and identify purchasing patterns in order to analyze customer purchase history.

[0123] The analysis unit can analyze customer feedback and evaluate customer satisfaction. For example, the analysis unit can analyze customer feedback using text mining techniques and quantify customer satisfaction. The analysis unit can also classify customer feedback using clustering techniques to identify highly and less satisfied customers. Furthermore, the analysis unit can analyze customer feedback using time-series analysis techniques to understand fluctuations in satisfaction. This allows for an accurate evaluation of customer satisfaction by analyzing customer feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze data and evaluate satisfaction in order to analyze customer feedback.

[0124] The extraction unit can analyze customer inquiries and classify them by type. For example, the extraction unit can analyze customer inquiries using natural language processing techniques to identify the type of inquiry. It can also classify customer inquiries using clustering techniques and group similar inquiries together. Furthermore, it can analyze customer inquiries using topic modeling techniques and extract key topics. This allows for accurate classification of inquiry types by analyzing customer inquiries. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For instance, the extraction unit can use generative AI to analyze data and classify inquiries in order to analyze customer inquiries.

[0125] The service provider can customize the content of reports based on customer attribute information. For example, the service provider can adjust the content of reports based on customer attribute information such as age, gender, and region. The service provider can also customize the content of reports based on customer purchase history and inquiry history. Furthermore, the service provider can improve the content of reports based on customer feedback. This allows the service provider to provide customers with the most relevant information by customizing the content of reports based on customer attribute information. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can use generative AI to analyze data and customize the content of reports in order to analyze customer attribute information.

[0126] The notification unit can adjust the timing of notifications based on the customer's behavior history. For example, the notification unit can send notifications at the optimal time based on the customer's website browsing history or app usage history. The notification unit can also send follow-up notifications after a purchase based on the customer's purchase history. Furthermore, the notification unit can send response notifications to inquiries based on the customer's inquiry history. In this way, by adjusting the timing of notifications based on the customer's behavior history, notifications can be sent at the appropriate time. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can analyze data using generative AI to analyze the customer's behavior history and adjust the timing of notifications.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects customer data. This data includes purchase history, inquiries, and feedback. The data collection unit retrieves purchase history from the database, collects inquiries in text format, and collects feedback in questionnaire format. The collected data is then provided to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as statistical analysis, text mining, and data mining. The analysis unit uses statistical analysis to analyze trends in customer data, text mining to analyze the content of inquiries, and data mining to extract patterns. Step 3: The extraction unit extracts problems and areas for improvement based on the data analyzed by the analysis unit. These problems and areas for improvement include customer dissatisfaction and areas for service improvement. The extraction unit extracts customer dissatisfaction and areas for service improvement and provides them to the service unit. Step 4: The provisioning department provides a visual report of the problems and areas for improvement identified by the extraction department. The visual report is provided in the form of graphs, charts, dashboards, etc. The provisioning department visually displays the problems and areas for improvement using graphs, charts, and dashboards and provides them to the relevant parties.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, provision unit, update unit, notification unit, and report generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects customer data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The extraction unit is implemented by the specific processing unit 290 of the data processing device 12 and extracts problems and areas for improvement from the analyzed data. The provision unit is implemented by the control unit 46A of the smart device 14 and provides a visual report. The update unit is implemented by the specific processing unit 290 of the data processing device 12 and updates the data in real time. The notification unit is implemented by the control unit 46A of the smart device 14 and provides information through a notification system. The report generation unit is implemented by the control unit 46A of the smart device 14 and generates an intuitive visual report. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, provision unit, update unit, notification unit, and report generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects customer data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts problems and areas for improvement from the analyzed data. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides a visual report. The update unit is implemented by the identification processing unit 290 of the data processing unit 12 and updates the data in real time. The notification unit is implemented by the control unit 46A of the smart glasses 214 and provides information through a notification system. The report generation unit is implemented by the control unit 46A of the smart glasses 214 and generates an intuitive visual report. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, provision unit, update unit, notification unit, and report generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects customer data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The extraction unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts problems and areas for improvement from the analyzed data. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides a visual report. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the data in real time. The notification unit is implemented by the control unit 46A of the headset terminal 314 and provides information through a notification system. The report generation unit is implemented by the control unit 46A of the headset terminal 314 and generates an intuitive visual report. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, provision unit, update unit, notification unit, and report generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects customer data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The extraction unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts problems and areas for improvement from the analyzed data. The provision unit is implemented by the control unit 46A of the robot 414 and provides a visual report. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the data in real time. The notification unit is implemented by the control unit 46A of the robot 414 and provides information through a notification system. The report generation unit is implemented by the control unit 46A of the robot 414 and generates an intuitive visual report. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) The data collection department collects customer data, An analysis unit analyzes the data collected by the aforementioned collection unit, An extraction unit extracts problems and areas for improvement based on the data analyzed by the aforementioned analysis unit, The providing unit provides the problems and areas for improvement identified by the extraction unit as a visual report, Equipped with A system characterized by the following features. (Note 2) It also includes an update unit that updates data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) It further includes a notification unit that provides information through a notification system. The system described in Appendix 1, characterized by the features described herein. (Note 4) It further includes a report generation unit that generates intuitive visual reports. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We use natural language processing technology to analyze customer inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 6) The extraction unit is Identifying problem trends and patterns using machine learning The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past customer data collection history to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the customer's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze customers' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The extraction unit is We estimate the user's emotions and determine the priority of problems and areas for improvement based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The extraction unit is During extraction, consider the interrelationships between data to improve extraction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The extraction unit is During the extraction process, the attribute information of the data submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The extraction unit is We estimate the user's emotions and adjust how problems and areas for improvement are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The extraction unit is When extracting data, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The extraction unit is During extraction, we refer to relevant literature to improve the accuracy of the extraction. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing reports, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing reports, different report formats will be applied depending on the user's job title and responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing reports, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing reports, we adjust the timing of report delivery to take into account the user's work schedule. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned update unit is It estimates the user's sentiment and adjusts the frequency of data updates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned update unit is During updates, the system refers to past update history to apply the optimal update algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned update unit is It estimates user sentiment and prioritizes update data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned update unit is During updates, the updated data is weighted based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 39) The report generation unit, We estimate user sentiment and adjust how reports are generated based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The report generation unit, When generating a report, the system references the user's past report viewing history to generate the most suitable report. The system described in Appendix 1, characterized by the features described herein. (Note 41) The report generation unit, It estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 42) The report generation unit, When generating reports, the system takes into account the user's geographical location to create the most optimal report. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects customer data, An analysis unit analyzes the data collected by the aforementioned collection unit, An extraction unit extracts problems and areas for improvement based on the data analyzed by the aforementioned analysis unit, The providing unit provides the problems and areas for improvement identified by the extraction unit as a visual report, Equipped with A system characterized by the following features.

2. It also includes an update unit that updates data in real time. The system according to feature 1.

3. It further includes a notification unit that provides information through a notification system. The system according to feature 1.

4. It further includes a report generation unit that generates intuitive visual reports. The system according to feature 1.

5. The aforementioned analysis unit, We use natural language processing technology to analyze customer inquiries. The system according to feature 1.

6. The extraction unit is Identifying problem trends and patterns using machine learning The system according to feature 1.

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

8. The aforementioned collection unit is Analyze past customer data collection history to select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the customer's current situation and areas of interest. The system according to feature 1.

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

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