system

Abstract

The system according to this embodiment aims to effectively utilize data from the customer management system to provide appropriate information and training to management and agents. [Solution] The system according to the embodiment comprises an analysis unit, a visualization unit, a reporting unit, a training unit, and an agenda generation unit. The analysis unit analyzes data from the customer management system. The visualization unit visualizes the data analyzed by the analysis unit. The reporting unit reports the data visualized by the visualization unit to management. The training unit provides personalized training to each agency based on the data analyzed by the analysis unit. The agenda generation unit generates an agenda for sales meetings based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the data of the customer management system has not been sufficiently utilized effectively, and appropriate information provision and training have not been sufficiently provided to management and agents.

[0005] The system according to the embodiment aims to effectively utilize the data of the customer management system and provide appropriate information and training to management and agents.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an analysis unit, a visualization unit, a reporting unit, a training unit, and an agenda generation unit. The analysis unit analyzes data from the customer management system. The visualization unit visualizes the data analyzed by the analysis unit. The reporting unit reports the data visualized by the visualization unit to management. The training unit provides personalized training to each agency based on the data analyzed by the analysis unit. The agenda generation unit generates an agenda for sales meetings based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively utilize data from the customer management system to provide appropriate information and training to management and agents. [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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The reporting system according to an embodiment of the present invention is a system that uses AI to analyze data from a customer management system and visualizes it using a data visualization tool. This system is designed so that management can intuitively understand it and take concrete action. Furthermore, it also provides personalized training for each agency and generates agendas for sales meetings. For example, the reporting system uses AI to analyze data from the customer management system and analyzes sales data and customer behavior for each agency. Next, it visualizes the data using a data visualization tool and generates a report that management can intuitively understand. This allows management to quickly take concrete action. Furthermore, the AI ​​provides personalized training tailored to the needs of each agency. The AI ​​analyzes past sales data and customer behavior to identify the challenges and strengths of each agency. Based on this, it recommends the optimal learning content and training programs to support the improvement of sales skills. The AI ​​also analyzes past meeting content, sales data, and customer information to automatically generate an efficient sales meeting agenda. It identifies important topics and items to be discussed in advance, taking into account the purpose of the meeting, participants, time, etc. This significantly reduces meeting preparation time and promotes effective discussion. This system will eliminate sales variability among agencies and contribute to overall sales improvement. AI identifies each agency's strengths and weaknesses and uses that information to provide more effective training and guidance. In addition, the AI-generated meeting agenda promotes information sharing among agencies and aims to improve overall performance by sharing best practices. As a result, the reporting system can be intuitively understood by management and concrete actions can be taken quickly.

[0029] The reporting system according to this embodiment comprises an analysis unit, a visualization unit, a reporting unit, a training unit, and an agenda generation unit. The analysis unit analyzes data from a customer management system. The analysis unit analyzes customer behavior patterns using, for example, data mining techniques. The analysis unit can also identify trends in sales data using statistical analysis. Furthermore, the analysis unit can predict customer purchases using machine learning algorithms. For example, the analysis unit predicts future purchasing behavior based on a customer's past purchase history. The visualization unit visualizes the data analyzed by the analysis unit. The visualization unit visually displays the data using, for example, graphs and charts. The visualization unit can also display data in real time using a dashboard. Furthermore, the visualization unit can provide interactive visualizations. For example, the visualization unit provides interactive graphs to make it easier for users to manipulate the data. The reporting unit reports the data visualized by the visualization unit to management. The reporting unit generates and provides periodic reports to management. The reporting unit can also report data in real time. Furthermore, the reporting department can provide customized reports tailored to the management's needs. For example, the reporting department can provide customized dashboards to quickly deliver the information management needs. The training department provides personalized training to each agency based on data analyzed by the analytics department. For example, the training department can analyze each agency's sales data and suggest the most suitable learning content. The training department can also analyze customer data and customize training programs based on each agency's strengths and weaknesses. In addition, the training department can provide interactive training programs. For example, the training department can provide online training sessions to improve agencies' sales skills. The agenda generation department generates sales meeting agendas based on data analyzed by the analytics department. For example, the agenda generation department can analyze past meeting content to create efficient agendas.Furthermore, the agenda generation unit can analyze sales data and customer information to identify important topics. It can also customize the agenda according to the meeting's purpose and participants. For example, the agenda generation unit can determine the priority of topics based on the meeting's objectives. This allows the reporting system according to the embodiment to be intuitively understood by management, enabling them to quickly take concrete action.

[0030] The analytics department analyzes data from customer management systems. For example, it uses data mining techniques to analyze customer behavior patterns. Specifically, it uses data mining techniques to integrate diverse data such as customer purchase history, website browsing history, and inquiry history to extract customer behavior patterns. This makes it possible to predict what products customers are interested in and when they are most likely to purchase them. The analytics department can also use statistical analysis to identify trends in sales data. For example, it uses time series analysis to analyze increases and decreases in sales during specific seasons or events and makes future sales forecasts. Furthermore, the analytics department can use machine learning algorithms to predict customer purchases. For example, it uses methods such as regression analysis and clustering to predict future purchasing behavior based on a customer's past purchase history. This makes it possible to develop personalized marketing strategies for each customer. The analytics department utilizes these technologies to accurately understand customer needs and provide valuable insights to optimize the company's sales strategy.

[0031] The visualization unit visualizes the data analyzed by the analysis unit. For example, the visualization unit visually displays the data using graphs and charts. Specifically, it uses basic graph formats such as bar graphs, line graphs, and pie charts to allow for an intuitive understanding of data trends and distributions. The visualization unit can also display data in real time using dashboards. Dashboards are designed to centrally display information from multiple data sources, allowing users to quickly obtain the information they need. Furthermore, the visualization unit can provide interactive visualizations. For example, it provides interactive graphs to make the data easier for users to manipulate. This allows users to click on specific data points to display detailed information or use filtering functions to display data based on specific conditions. Through these functions, the visualization unit visualizes analysis results in an easy-to-understand manner, helping management and other stakeholders make data-driven decisions quickly.

[0032] The reporting department reports the data visualized by the visualization department to management. For example, the reporting department generates and provides reports to management on a regular basis. Specifically, it creates reports summarizing detailed data analysis results at regular intervals, such as monthly and quarterly reports. The reporting department can also report data in real time. For example, it can build a system that immediately issues alerts and notifies management if key metrics change rapidly. Furthermore, the reporting department can provide customized reports according to management's needs. For example, it can provide customized dashboards containing detailed data on specific projects or campaigns, quickly providing management with the information they need. Through these functions, the reporting department provides management with critical information to make data-driven decisions and supports the company's strategic direction.

[0033] The training department provides personalized training to each agency based on data analyzed by the analytics department. For example, the training department analyzes each agency's sales data and proposes optimal learning content. Specifically, it identifies skills and knowledge that need strengthening based on each agency's sales performance and customer feedback, and designs training programs accordingly. The training department can also analyze customer data and customize training programs based on each agency's strengths and weaknesses. For example, it can provide agency training specifically for a product that lacks knowledge in that area, and conversely, provide training to acquire more advanced skills in areas where the agency has strengths. Furthermore, the training department can provide interactive training programs. For example, it can provide online training sessions to improve agencies' sales skills. This allows agencies to acquire practical skills and improve the quality of their customer service. Through these functions, the training department improves agency performance and strengthens the overall competitiveness of the company.

[0034] The agenda generation unit generates sales meeting agendas based on data analyzed by the analysis unit. For example, the agenda generation unit analyzes past meeting content to create efficient agendas. Specifically, it stores topics and results from past meetings in a database and uses this to identify important topics to be addressed in the next meeting. The agenda generation unit can also analyze sales data and customer information to identify important topics. For example, it extracts issues to be discussed and areas for improvement based on recent sales trends and customer feedback. Furthermore, the agenda generation unit can customize agendas according to the purpose of the meeting and the participants. For example, it determines the priority of topics and optimizes time allocation according to the purpose of the meeting. This maximizes meeting efficiency and allows participants to engage in meaningful discussions. Through these functions, the agenda generation unit improves the quality of sales meetings and supports strategic decision-making within companies.

[0035] The training department can analyze each agency's sales and customer data to propose optimal learning content. For example, the training department can analyze each agency's sales data and propose the most suitable learning content. The training department can also analyze customer data and customize training programs based on each agency's strengths and weaknesses. Furthermore, the training department can provide interactive training programs. For example, the training department can provide online training sessions to improve agencies' sales skills. This helps improve sales skills by providing each agency with the most suitable learning content. Some or all of the above processes in the training department may be performed using AI, or not. For example, the training department can input each agency's sales data into a generating AI and have the generating AI propose optimal learning content.

[0036] The agenda generation unit can generate efficient sales meeting agendas by analyzing past meeting content, sales data, and customer information. For example, the agenda generation unit can analyze past meeting content to create an efficient agenda. It can also analyze sales data and customer information to identify important topics. Furthermore, the agenda generation unit can customize the agenda according to the purpose of the meeting and the participants. For example, the agenda generation unit can determine the priority of topics according to the purpose of the meeting. This reduces meeting preparation time and promotes effective discussion by generating efficient sales meeting agendas. Some or all of the above processes in the agenda generation unit may be performed using AI, for example, or not. For example, the agenda generation unit can input past meeting content and sales data into a generation AI and have the generation AI perform the generation of an efficient agenda.

[0037] The analytics unit can analyze data from the customer management system in real time and provide immediate feedback. For example, the analytics unit can analyze customer purchase history in real time and immediately propose sales strategies. It can also analyze customer behavior data in real time and immediately adjust marketing campaigns. Furthermore, the analytics unit can analyze customer feedback in real time and immediately propose product improvements. For example, the analytics unit can analyze customer purchase history in real time and immediately propose sales strategies. This enables rapid response by analyzing in real time and providing immediate feedback. Some or all of the above processes in the analytics unit may be performed using AI, for example, or not. For example, the analytics unit can input data from the customer management system into a generating AI and have the generating AI perform real-time analysis and provide feedback.

[0038] The analysis unit can identify trends by comparing past and present data during analysis. For example, it can identify sales trends by comparing past and present sales data. It can also identify customer purchasing trends by comparing past and present customer behavior data. Furthermore, it can identify trends in effective campaigns by comparing past and present marketing campaign data. For example, it can identify sales trends by comparing past and present sales data. This allows for the identification of trends and future predictions by comparing past and present 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 input past and present data into a generating AI and have the generating AI perform trend identification.

[0039] The analysis unit can identify sales trends for each region by considering geographical data during analysis. For example, the analysis unit can identify sales trends for each region based on geographical data. The analysis unit can also analyze customer behavior for each region based on geographical data. Furthermore, the analysis unit can optimize marketing strategies for each region based on geographical data. For example, the analysis unit can identify sales trends for each region based on geographical data. This allows for the identification of sales trends for each region and the development of strategies for each region by considering geographical data. 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 input geographical data into a generating AI and have the generating AI identify sales trends for each region.

[0040] The analysis unit can integrate social media data during analysis to analyze customer behavior in more detail. For example, the analysis unit can analyze customer purchase intent based on social media data. It can also analyze customer brand awareness based on social media data. Furthermore, the analysis unit can analyze customer feedback based on social media data. For example, the analysis unit can analyze customer purchase intent based on social media data. By integrating social media data, customer behavior can be analyzed in more detail and marketing strategies can be optimized. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input social media data into a generating AI and have the generating AI perform a detailed analysis of customer behavior.

[0041] The visualization unit can adjust the level of detail displayed based on the importance of the data during visualization. For example, the visualization unit can highlight and display important data in detail. It can also display less important data concisely. Furthermore, the visualization unit can dynamically adjust the level of detail displayed according to the importance of the data. For example, the visualization unit can highlight and display important data in detail. By adjusting the level of detail displayed based on the importance of the data, it highlights important data and enhances the effectiveness of the visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0042] The visualization unit can perform complex visualizations by integrating different datasets during the visualization process. For example, the visualization unit can integrate sales data and customer data to perform complex visualizations. It can also integrate marketing data and feedback data to perform complex visualizations. Furthermore, the visualization unit can integrate geographical data and sales data to perform complex visualizations. For example, the visualization unit can integrate sales data and customer data to perform complex visualizations. By integrating different datasets, complex visualizations are performed, clarifying the relationships between the data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input different datasets into a generating AI and have the generating AI perform complex visualizations.

[0043] The visualization unit can determine the display priority based on the data submission date during visualization. For example, the visualization unit may prioritize displaying the most recent data. It can also postpone the display of older data. Furthermore, the visualization unit can dynamically adjust the display priority according to the data submission date. For example, it may prioritize displaying the most recent data. This ensures that the latest information is displayed preferentially by determining the display priority based on the data submission date. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data submission date into a generating AI and have the generating AI determine the display priority.

[0044] The visualization unit can improve the accuracy of visualizations by referring to relevant external data during the visualization process. For example, the visualization unit can improve the accuracy of visualizations by referring to external market data. It can also improve the accuracy of visualizations by referring to external competitor data. Furthermore, the visualization unit can improve the accuracy of visualizations by referring to external economic data. For example, the visualization unit can improve the accuracy of visualizations by referring to external market data. This improves the accuracy of visualizations by referring to relevant external data, enabling more accurate data presentation. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input external data into a generating AI and have the generating AI perform the visualization accuracy improvement.

[0045] The reporting unit can adjust the level of detail in its reports based on the importance of the data. For example, it can highlight and report on important data in detail. Alternatively, it can report on less important data concisely. Furthermore, the reporting unit can dynamically adjust the level of detail in its reports according to the importance of the data. For example, it can highlight and report on important data in detail. By adjusting the level of detail in the reports based on the importance of the data, it can highlight important data and enhance the effectiveness of the reports. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail in the reports.

[0046] The reporting department can select different reporting formats to meet the needs of management. For example, the reporting department can select a reporting format preferred by management and submit the report. Furthermore, the reporting department can dynamically adjust the reporting format according to management's needs. In addition, the reporting department can select the optimal reporting format based on management's feedback. For example, the reporting department can select a reporting format preferred by management and submit the report. This allows for more effective reporting by selecting a reporting format that meets management's needs. Some or all of the above processes in the reporting department may be performed using AI, or not. For example, the reporting department can input management's needs into a generating AI and have the generating AI select the reporting format.

[0047] The reporting unit can determine the priority of reports based on the data submission timing. For example, the reporting unit may prioritize reporting the most recent data. Alternatively, it may postpone reporting older data. Furthermore, the reporting unit can dynamically adjust the reporting priority according to the data submission timing. For example, the reporting unit may prioritize reporting the most recent data. This ensures that the latest information is reported first by prioritizing reports based on the data submission timing. Some or all of the above processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input the data submission timing into a generating AI and have the generating AI determine the reporting priority.

[0048] The reporting unit can improve the accuracy of its reports by referring to relevant market data during the reporting process. For example, the reporting unit can improve the accuracy of its reports by referring to external market data. It can also improve the accuracy of its reports by referring to external competitor data. Furthermore, the reporting unit can improve the accuracy of its reports by referring to external economic data. For example, the reporting unit can improve the accuracy of its reports by referring to external market data. This improves the accuracy of reports by referring to relevant market data, enabling more accurate reporting. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input external data into a generating AI and have the generating AI perform the task of improving the accuracy of the reports.

[0049] The training department can provide an optimal training program by referring to each agency's past performance data during training. For example, the training department can provide an optimal training program based on each agency's past sales data. Furthermore, the training department can provide an optimal training program based on each agency's past customer data. In addition, the training department can provide an optimal training program based on each agency's past feedback data. For example, the training department can provide an optimal training program based on each agency's past sales data. This allows the training department to provide an optimal training program by referring to each agency's past performance data. Some or all of the above processes in the training department may be performed using AI, for example, or not. For example, the training department can input each agency's past performance data into a generating AI and have the generating AI execute the provision of an optimal training program.

[0050] The training department can provide customized training tailored to the characteristics of each agency. For example, the training department can provide customized training according to the size of each agency. Furthermore, the training department can provide customized training according to the industry of each agency. In addition, the training department can provide customized training according to the region of each agency. For example, the training department can provide customized training according to the size of each agency. This allows for more effective training by providing customized training tailored to the characteristics of each agency. Some or all of the above processes in the training department may be performed using AI, or not. For example, the training department can input characteristic data for each agency into a generating AI and have the generating AI perform the provision of customized training.

[0051] The training department can identify regional training needs by considering geographical data during training. For example, the training department can identify regional training needs based on geographical data. The training department can also customize regional training programs based on geographical data. Furthermore, the training department can adjust regional training schedules based on geographical data. For example, the training department can identify regional training needs based on geographical data. This allows for the identification of regional training needs and the provision of optimal training by considering geographical data. Some or all of the above processes in the training department may be performed using AI, for example, or not using AI. For example, the training department can input geographical data into a generating AI and have the generating AI perform the identification of regional training needs.

[0052] The training department can integrate social media data during training to analyze agency behavior in more detail. For example, the training department can analyze agency behavior based on social media data. The training department can also identify the strengths and weaknesses of agencies based on social media data. Furthermore, the training department can identify the training needs of agencies based on social media data. For example, the training department can analyze agency behavior based on social media data. By integrating social media data, the training department can analyze agency behavior in more detail and improve the effectiveness of training. Some or all of the above processes in the training department may be performed using AI, for example, or not. For example, the training department can input social media data into a generating AI and have the generating AI perform a detailed analysis of agency behavior.

[0053] The agenda generation unit can select the most suitable topics by referring to past meeting data when generating an agenda. For example, the agenda generation unit selects the most suitable topics based on past meeting data. The agenda generation unit can also determine the priority of discussions based on past meeting data. Furthermore, the agenda generation unit can adjust the level of detail of the topics based on past meeting data. For example, the agenda generation unit selects the most suitable topics based on past meeting data. This allows for the selection of the most suitable topics by referring to past meeting data, thereby enabling effective meetings. Some or all of the above processes in the agenda generation unit may be performed using AI, for example, or without AI. For example, the agenda generation unit can input past meeting data into a generation AI and have the generation AI select the most suitable topics.

[0054] The agenda generation unit can apply different agenda generation algorithms depending on the purpose of the meeting when generating an agenda. For example, if the purpose of the meeting is strategic, the agenda generation unit can apply an algorithm that prioritizes strategic topics. It can also apply an algorithm that prioritizes tactical topics if the purpose of the meeting is tactical. Furthermore, if the purpose of the meeting is information sharing, the agenda generation unit can apply an algorithm that prioritizes information sharing. For example, if the purpose of the meeting is strategic, the agenda generation unit can apply an algorithm that prioritizes strategic topics. This allows for more effective meetings by applying an agenda generation algorithm appropriate to the purpose of the meeting. Some or all of the above-described processes in the agenda generation unit may be performed using AI, for example, or without AI. For example, the agenda generation unit can input the purpose of the meeting into a generation AI and have the generation AI execute the application of the agenda generation algorithm.

[0055] The agenda generation unit can select the most appropriate topics when generating an agenda, taking into account the attribute information of the meeting participants. For example, if the meeting participants are management, the agenda generation unit will prioritize strategic topics. It can also prioritize tactical topics if the participants are the sales team, and technical topics if the participants are the technical team. In this way, by considering the attribute information of the meeting participants, the agenda generation unit can select the most appropriate topics and achieve an effective meeting. Some or all of the above processing in the agenda generation unit may be performed using AI, for example, or without AI. For example, the agenda generation unit can input the attribute information of the meeting participants into a generation AI and have the generation AI select the most appropriate topics.

[0056] The agenda generation unit can improve the accuracy of the agenda by referring to relevant external data during agenda generation. For example, the agenda generation unit can improve the accuracy of the agenda by referring to external market data. It can also improve the accuracy of the agenda by referring to external competitor data. Furthermore, it can improve the accuracy of the agenda by referring to external economic data. For example, the agenda generation unit can improve the accuracy of the agenda by referring to external market data. This allows for improved agenda accuracy and more effective meetings by referring to relevant external data. Some or all of the above processing in the agenda generation unit may be performed using AI, for example, or without AI. For example, the agenda generation unit can input external data into a generation AI and have the generation AI perform agenda accuracy improvement.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The analysis unit can analyze data from the customer management system in real time and provide immediate feedback. For example, it can analyze customer purchase history in real time and immediately propose sales strategies. It can also analyze customer behavior data in real time and immediately adjust marketing campaigns. Furthermore, it can analyze customer feedback in real time and immediately propose product improvements. This enables rapid response by analyzing in real time and providing immediate feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data from the customer management system into a generating AI and have the generating AI perform real-time analysis and provide feedback.

[0059] The analysis unit can identify trends by comparing past and present data during analysis. For example, it can identify sales trends by comparing past and present sales data. It can also identify customer purchasing trends by comparing past and present customer behavior data. Furthermore, it can identify trends in effective campaigns by comparing past and present marketing campaign data with current data. This allows for the identification of trends and future predictions by comparing past and present 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 input past and present data into a generating AI and have the generating AI perform trend identification.

[0060] The analysis unit can identify sales trends for each region by considering geographical data during analysis. For example, it can identify sales trends for each region based on geographical data. It can also analyze customer behavior for each region based on geographical data. Furthermore, it can optimize marketing strategies for each region based on geographical data. In this way, by considering geographical data, it is possible to identify sales trends for each region and develop strategies for each region. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input geographical data into a generating AI and have the generating AI perform the identification of sales trends for each region.

[0061] The analysis unit can integrate social media data during analysis to analyze customer behavior in more detail. For example, it can analyze customer purchasing intent based on social media data. It can also analyze customer brand awareness based on social media data. Furthermore, it can analyze customer feedback based on social media data. By integrating social media data, it is possible to analyze customer behavior in more detail and optimize marketing strategies. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input social media data into a generating AI and have the generating AI perform a detailed analysis of customer behavior.

[0062] The visualization unit can adjust the level of detail displayed based on the importance of the data during visualization. For example, it can highlight and display important data in detail. It can also display less important data concisely. Furthermore, it can dynamically adjust the level of detail displayed according to the importance of the data. By adjusting the level of detail displayed based on the importance of the data, it can highlight important data and enhance the effectiveness of the visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The analysis unit analyzes the data from the customer management system. The analysis unit uses data mining techniques to analyze customer behavior patterns and statistical analysis to identify trends in sales data. It can also use machine learning algorithms to predict customer purchases. For example, it can predict future purchasing behavior based on a customer's past purchase history. Step 2: The visualization unit visualizes the data analyzed by the analysis unit. The visualization unit can visually display the data using graphs and charts, and can also display the data in real time using dashboards. Furthermore, it provides interactive visualizations and interactive graphs to make it easier for users to manipulate the data. Step 3: The reporting department reports the data visualized by the visualization department to management. The reporting department generates and provides reports to management on a regular basis. It can also report data in real time and provide customized reports according to management's needs. For example, it can provide customized dashboards to quickly provide management with the information they need. Step 4: The training department provides personalized training to each agency based on the data analyzed by the analytics department. The training department analyzes each agency's sales data and proposes the most suitable learning content. It can also analyze customer data and customize training programs based on each agency's strengths and weaknesses. Furthermore, it provides interactive training programs and improves agencies' sales skills through online training sessions. Step 5: The agenda generation unit generates the sales meeting agenda based on the data analyzed by the analysis unit. The agenda generation unit analyzes past meeting content to create an efficient agenda. It can also analyze sales data and customer information to identify important topics. Furthermore, it customizes the agenda according to the meeting's purpose and participants, and determines the priority of the topics.

[0065] (Example of form 2) The reporting system according to an embodiment of the present invention is a system that uses AI to analyze data from a customer management system and visualizes it using a data visualization tool. This system is designed so that management can intuitively understand it and take concrete action. Furthermore, it also provides personalized training for each agency and generates agendas for sales meetings. For example, the reporting system uses AI to analyze data from the customer management system and analyzes sales data and customer behavior for each agency. Next, it visualizes the data using a data visualization tool and generates a report that management can intuitively understand. This allows management to quickly take concrete action. Furthermore, the AI ​​provides personalized training tailored to the needs of each agency. The AI ​​analyzes past sales data and customer behavior to identify the challenges and strengths of each agency. Based on this, it recommends the optimal learning content and training programs to support the improvement of sales skills. The AI ​​also analyzes past meeting content, sales data, and customer information to automatically generate an efficient sales meeting agenda. It identifies important topics and items to be discussed in advance, taking into account the purpose of the meeting, participants, time, etc. This significantly reduces meeting preparation time and promotes effective discussion. This system will eliminate sales variability among agencies and contribute to overall sales improvement. AI identifies each agency's strengths and weaknesses and uses that information to provide more effective training and guidance. In addition, the AI-generated meeting agenda promotes information sharing among agencies and aims to improve overall performance by sharing best practices. As a result, the reporting system can be intuitively understood by management and concrete actions can be taken quickly.

[0066] The reporting system according to this embodiment comprises an analysis unit, a visualization unit, a reporting unit, a training unit, and an agenda generation unit. The analysis unit analyzes data from a customer management system. The analysis unit analyzes customer behavior patterns using, for example, data mining techniques. The analysis unit can also identify trends in sales data using statistical analysis. Furthermore, the analysis unit can predict customer purchases using machine learning algorithms. For example, the analysis unit predicts future purchasing behavior based on a customer's past purchase history. The visualization unit visualizes the data analyzed by the analysis unit. The visualization unit visually displays the data using, for example, graphs and charts. The visualization unit can also display data in real time using a dashboard. Furthermore, the visualization unit can provide interactive visualizations. For example, the visualization unit provides interactive graphs to make it easier for users to manipulate the data. The reporting unit reports the data visualized by the visualization unit to management. The reporting unit generates and provides periodic reports to management. The reporting unit can also report data in real time. Furthermore, the reporting department can provide customized reports tailored to the management's needs. For example, the reporting department can provide customized dashboards to quickly deliver the information management needs. The training department provides personalized training to each agency based on data analyzed by the analytics department. For example, the training department can analyze each agency's sales data and suggest the most suitable learning content. The training department can also analyze customer data and customize training programs based on each agency's strengths and weaknesses. In addition, the training department can provide interactive training programs. For example, the training department can provide online training sessions to improve agencies' sales skills. The agenda generation department generates sales meeting agendas based on data analyzed by the analytics department. For example, the agenda generation department can analyze past meeting content to create efficient agendas.Furthermore, the agenda generation unit can analyze sales data and customer information to identify important topics. It can also customize the agenda according to the meeting's purpose and participants. For example, the agenda generation unit can determine the priority of topics based on the meeting's objectives. This allows the reporting system according to the embodiment to be intuitively understood by management, enabling them to quickly take concrete action.

[0067] The analytics department analyzes data from customer management systems. For example, it uses data mining techniques to analyze customer behavior patterns. Specifically, it uses data mining techniques to integrate diverse data such as customer purchase history, website browsing history, and inquiry history to extract customer behavior patterns. This makes it possible to predict what products customers are interested in and when they are most likely to purchase them. The analytics department can also use statistical analysis to identify trends in sales data. For example, it uses time series analysis to analyze increases and decreases in sales during specific seasons or events and makes future sales forecasts. Furthermore, the analytics department can use machine learning algorithms to predict customer purchases. For example, it uses methods such as regression analysis and clustering to predict future purchasing behavior based on a customer's past purchase history. This makes it possible to develop personalized marketing strategies for each customer. The analytics department utilizes these technologies to accurately understand customer needs and provide valuable insights to optimize the company's sales strategy.

[0068] The visualization unit visualizes the data analyzed by the analysis unit. For example, the visualization unit visually displays the data using graphs and charts. Specifically, it uses basic graph formats such as bar graphs, line graphs, and pie charts to allow for an intuitive understanding of data trends and distributions. The visualization unit can also display data in real time using dashboards. Dashboards are designed to centrally display information from multiple data sources, allowing users to quickly obtain the information they need. Furthermore, the visualization unit can provide interactive visualizations. For example, it provides interactive graphs to make the data easier for users to manipulate. This allows users to click on specific data points to display detailed information or use filtering functions to display data based on specific conditions. Through these functions, the visualization unit visualizes analysis results in an easy-to-understand manner, helping management and other stakeholders make data-driven decisions quickly.

[0069] The reporting department reports the data visualized by the visualization department to management. For example, the reporting department generates and provides reports to management on a regular basis. Specifically, it creates reports summarizing detailed data analysis results at regular intervals, such as monthly and quarterly reports. The reporting department can also report data in real time. For example, it can build a system that immediately issues alerts and notifies management if key metrics change rapidly. Furthermore, the reporting department can provide customized reports according to management's needs. For example, it can provide customized dashboards containing detailed data on specific projects or campaigns, quickly providing management with the information they need. Through these functions, the reporting department provides management with critical information to make data-driven decisions and supports the company's strategic direction.

[0070] The training department provides personalized training to each agency based on data analyzed by the analytics department. For example, the training department analyzes each agency's sales data and proposes optimal learning content. Specifically, it identifies skills and knowledge that need strengthening based on each agency's sales performance and customer feedback, and designs training programs accordingly. The training department can also analyze customer data and customize training programs based on each agency's strengths and weaknesses. For example, it can provide agency training specifically for a product that lacks knowledge in that area, and conversely, provide training to acquire more advanced skills in areas where the agency has strengths. Furthermore, the training department can provide interactive training programs. For example, it can provide online training sessions to improve agencies' sales skills. This allows agencies to acquire practical skills and improve the quality of their customer service. Through these functions, the training department improves agency performance and strengthens the overall competitiveness of the company.

[0071] The agenda generation unit generates sales meeting agendas based on data analyzed by the analysis unit. For example, the agenda generation unit analyzes past meeting content to create efficient agendas. Specifically, it stores topics and results from past meetings in a database and uses this to identify important topics to be addressed in the next meeting. The agenda generation unit can also analyze sales data and customer information to identify important topics. For example, it extracts issues to be discussed and areas for improvement based on recent sales trends and customer feedback. Furthermore, the agenda generation unit can customize agendas according to the purpose of the meeting and the participants. For example, it determines the priority of topics and optimizes time allocation according to the purpose of the meeting. This maximizes meeting efficiency and allows participants to engage in meaningful discussions. Through these functions, the agenda generation unit improves the quality of sales meetings and supports strategic decision-making within companies.

[0072] The training department can analyze each agency's sales and customer data to propose optimal learning content. For example, the training department can analyze each agency's sales data and propose the most suitable learning content. The training department can also analyze customer data and customize training programs based on each agency's strengths and weaknesses. Furthermore, the training department can provide interactive training programs. For example, the training department can provide online training sessions to improve agencies' sales skills. This helps improve sales skills by providing each agency with the most suitable learning content. Some or all of the above processes in the training department may be performed using AI, or not. For example, the training department can input each agency's sales data into a generating AI and have the generating AI propose optimal learning content.

[0073] The agenda generation unit can generate efficient sales meeting agendas by analyzing past meeting content, sales data, and customer information. For example, the agenda generation unit can analyze past meeting content to create an efficient agenda. It can also analyze sales data and customer information to identify important topics. Furthermore, the agenda generation unit can customize the agenda according to the purpose of the meeting and the participants. For example, the agenda generation unit can determine the priority of topics according to the purpose of the meeting. This reduces meeting preparation time and promotes effective discussion by generating efficient sales meeting agendas. Some or all of the above processes in the agenda generation unit may be performed using AI, for example, or not. For example, the agenda generation unit can input past meeting content and sales data into a generation AI and have the generation AI perform the generation of an efficient agenda.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize the analysis of important data and provide results quickly. If the user is relaxed, the analysis unit can perform detailed data analysis and provide comprehensive results. Furthermore, if the user is in a hurry, the analysis unit can analyze only the most important data and provide results quickly. For example, the analysis unit estimates the user's emotions and, if the user is stressed, prioritizes the analysis of important data. This allows for more appropriate analysis results by adjusting the analysis priority 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis priority.

[0075] The analytics unit can analyze data from the customer management system in real time and provide immediate feedback. For example, the analytics unit can analyze customer purchase history in real time and immediately propose sales strategies. It can also analyze customer behavior data in real time and immediately adjust marketing campaigns. Furthermore, the analytics unit can analyze customer feedback in real time and immediately propose product improvements. For example, the analytics unit can analyze customer purchase history in real time and immediately propose sales strategies. This enables rapid response by analyzing in real time and providing immediate feedback. Some or all of the above processes in the analytics unit may be performed using AI, for example, or not. For example, the analytics unit can input data from the customer management system into a generating AI and have the generating AI perform real-time analysis and provide feedback.

[0076] The analysis unit can identify trends by comparing past and present data during analysis. For example, it can identify sales trends by comparing past and present sales data. It can also identify customer purchasing trends by comparing past and present customer behavior data. Furthermore, it can identify trends in effective campaigns by comparing past and present marketing campaign data. For example, it can identify sales trends by comparing past and present sales data. This allows for the identification of trends and future predictions by comparing past and present 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 input past and present data into a generating AI and have the generating AI perform trend identification.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit estimates the user's emotions and, if the user is nervous, provides a simple and highly visible display method. This allows for a more easily understandable display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, 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, or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0078] The analysis unit can identify sales trends for each region by considering geographical data during analysis. For example, the analysis unit can identify sales trends for each region based on geographical data. The analysis unit can also analyze customer behavior for each region based on geographical data. Furthermore, the analysis unit can optimize marketing strategies for each region based on geographical data. For example, the analysis unit can identify sales trends for each region based on geographical data. This allows for the identification of sales trends for each region and the development of strategies for each region by considering geographical data. 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 input geographical data into a generating AI and have the generating AI identify sales trends for each region.

[0079] The analysis unit can integrate social media data during analysis to analyze customer behavior in more detail. For example, the analysis unit can analyze customer purchase intent based on social media data. It can also analyze customer brand awareness based on social media data. Furthermore, the analysis unit can analyze customer feedback based on social media data. For example, the analysis unit can analyze customer purchase intent based on social media data. By integrating social media data, customer behavior can be analyzed in more detail and marketing strategies can be optimized. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input social media data into a generating AI and have the generating AI perform a detailed analysis of customer behavior.

[0080] The visualization unit can estimate the user's emotions and adjust the visualization style based on the estimated emotions. For example, if the user is nervous, the visualization unit can provide a visualization with calm colors. It can also provide a visualization with bright colors if the user is relaxed. Furthermore, if the user is in a hurry, the visualization unit can provide a simple and highly visible visualization. For example, the visualization unit estimates the user's emotions and, if they are nervous, provides a visualization with calm colors. This allows for more effective data visualization by adjusting the visualization style according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the visualization style.

[0081] The visualization unit can adjust the level of detail displayed based on the importance of the data during visualization. For example, the visualization unit can highlight and display important data in detail. It can also display less important data concisely. Furthermore, the visualization unit can dynamically adjust the level of detail displayed according to the importance of the data. For example, the visualization unit can highlight and display important data in detail. By adjusting the level of detail displayed based on the importance of the data, it highlights important data and enhances the effectiveness of the visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0082] The visualization unit can perform complex visualizations by integrating different datasets during the visualization process. For example, the visualization unit can integrate sales data and customer data to perform complex visualizations. It can also integrate marketing data and feedback data to perform complex visualizations. Furthermore, the visualization unit can integrate geographical data and sales data to perform complex visualizations. For example, the visualization unit can integrate sales data and customer data to perform complex visualizations. By integrating different datasets, complex visualizations are performed, clarifying the relationships between the data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input different datasets into a generating AI and have the generating AI perform complex visualizations.

[0083] The visualization unit can estimate the user's emotions and adjust the order of visualizations based on the estimated emotions. For example, if the user is nervous, the visualization unit can display important data first. Conversely, if the user is relaxed, the visualization unit can display detailed data later. Furthermore, if the user is in a hurry, the visualization unit can display key points first. For example, the visualization unit estimates the user's emotions and, if they are nervous, displays important data first. This allows for more effective data presentation by adjusting the order of visualizations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, or not. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the order of visualizations.

[0084] The visualization unit can determine the display priority based on the data submission date during visualization. For example, the visualization unit may prioritize displaying the most recent data. It can also postpone the display of older data. Furthermore, the visualization unit can dynamically adjust the display priority according to the data submission date. For example, it may prioritize displaying the most recent data. This ensures that the latest information is displayed preferentially by determining the display priority based on the data submission date. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data submission date into a generating AI and have the generating AI determine the display priority.

[0085] The visualization unit can improve the accuracy of visualizations by referring to relevant external data during the visualization process. For example, the visualization unit can improve the accuracy of visualizations by referring to external market data. It can also improve the accuracy of visualizations by referring to external competitor data. Furthermore, the visualization unit can improve the accuracy of visualizations by referring to external economic data. For example, the visualization unit can improve the accuracy of visualizations by referring to external market data. This improves the accuracy of visualizations by referring to relevant external data, enabling more accurate data presentation. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input external data into a generating AI and have the generating AI perform the visualization accuracy improvement.

[0086] The reporting unit can estimate the user's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the user is nervous, the reporting unit can provide a simple and easy-to-read report. If the user is relaxed, the reporting unit can also provide a report with more detailed information. Furthermore, if the user is in a hurry, the reporting unit can provide a concise report. For example, the reporting unit can estimate the user's emotions and, if they are nervous, provide a simple and easy-to-read report. This allows for a more easily understandable report by adjusting the presentation of the report according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the report.

[0087] The reporting unit can adjust the level of detail in its reports based on the importance of the data. For example, it can highlight and report on important data in detail. Alternatively, it can report on less important data concisely. Furthermore, the reporting unit can dynamically adjust the level of detail in its reports according to the importance of the data. For example, it can highlight and report on important data in detail. By adjusting the level of detail in the reports based on the importance of the data, it can highlight important data and enhance the effectiveness of the reports. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail in the reports.

[0088] The reporting department can select different reporting formats to meet the needs of management. For example, the reporting department can select a reporting format preferred by management and submit the report. Furthermore, the reporting department can dynamically adjust the reporting format according to management's needs. In addition, the reporting department can select the optimal reporting format based on management's feedback. For example, the reporting department can select a reporting format preferred by management and submit the report. This allows for more effective reporting by selecting a reporting format that meets management's needs. Some or all of the above processes in the reporting department may be performed using AI, or not. For example, the reporting department can input management's needs into a generating AI and have the generating AI select the reporting format.

[0089] The reporting unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is nervous, the reporting unit can provide a short, concise report. If the user is relaxed, the reporting unit can provide a longer report with more detailed information. Furthermore, if the user is in a hurry, the reporting unit can provide a short report that can be read quickly. For example, the reporting unit can estimate the user's emotions and, if they are nervous, provide a short, concise report. This allows for the provision of more appropriate reports by adjusting the length of the report 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 reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the length of the report.

[0090] The reporting unit can determine the priority of reports based on the data submission timing. For example, the reporting unit may prioritize reporting the most recent data. Alternatively, it may postpone reporting older data. Furthermore, the reporting unit can dynamically adjust the reporting priority according to the data submission timing. For example, the reporting unit may prioritize reporting the most recent data. This ensures that the latest information is reported first by prioritizing reports based on the data submission timing. Some or all of the above processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input the data submission timing into a generating AI and have the generating AI determine the reporting priority.

[0091] The reporting unit can improve the accuracy of its reports by referring to relevant market data during the reporting process. For example, the reporting unit can improve the accuracy of its reports by referring to external market data. It can also improve the accuracy of its reports by referring to external competitor data. Furthermore, the reporting unit can improve the accuracy of its reports by referring to external economic data. For example, the reporting unit can improve the accuracy of its reports by referring to external market data. This improves the accuracy of reports by referring to relevant market data, enabling more accurate reporting. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input external data into a generating AI and have the generating AI perform the task of improving the accuracy of the reports.

[0092] The training unit can estimate the user's emotions and adjust the training content based on the estimated emotions. For example, if the user is nervous, the training unit can provide relaxing training content. It can also provide detailed training content if the user is relaxed. Furthermore, if the user is in a hurry, the training unit can provide effective training content in a short amount of time. For example, the training unit estimates the user's emotions and, if they are nervous, provides relaxing training content. By adjusting the training content according to the user's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, or not. For example, the training unit can input user emotion data into a generative AI and have the generative AI adjust the training content.

[0093] The training department can provide an optimal training program by referring to each agency's past performance data during training. For example, the training department can provide an optimal training program based on each agency's past sales data. Furthermore, the training department can provide an optimal training program based on each agency's past customer data. In addition, the training department can provide an optimal training program based on each agency's past feedback data. For example, the training department can provide an optimal training program based on each agency's past sales data. This allows the training department to provide an optimal training program by referring to each agency's past performance data. Some or all of the above processes in the training department may be performed using AI, for example, or not. For example, the training department can input each agency's past performance data into a generating AI and have the generating AI execute the provision of an optimal training program.

[0094] The training department can provide customized training tailored to the characteristics of each agency. For example, the training department can provide customized training according to the size of each agency. Furthermore, the training department can provide customized training according to the industry of each agency. In addition, the training department can provide customized training according to the region of each agency. For example, the training department can provide customized training according to the size of each agency. This allows for more effective training by providing customized training tailored to the characteristics of each agency. Some or all of the above processes in the training department may be performed using AI, or not. For example, the training department can input characteristic data for each agency into a generating AI and have the generating AI perform the provision of customized training.

[0095] The training unit can estimate the user's emotions and determine training priorities based on those emotions. For example, if the user is nervous, the training unit will prioritize relaxing training. It can also prioritize detailed training if the user is relaxed. Furthermore, if the user is in a hurry, the training unit can prioritize short, effective training. For example, the training unit estimates the user's emotions and, if they are nervous, prioritizes relaxing training. This allows for more effective training by prioritizing training according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, or not. For example, the training unit can input user emotion data into a generative AI and have the generative AI determine the training priorities.

[0096] The training department can identify regional training needs by considering geographical data during training. For example, the training department can identify regional training needs based on geographical data. The training department can also customize regional training programs based on geographical data. Furthermore, the training department can adjust regional training schedules based on geographical data. For example, the training department can identify regional training needs based on geographical data. This allows for the identification of regional training needs and the provision of optimal training by considering geographical data. Some or all of the above processes in the training department may be performed using AI, for example, or not using AI. For example, the training department can input geographical data into a generating AI and have the generating AI perform the identification of regional training needs.

[0097] The training department can integrate social media data during training to analyze agency behavior in more detail. For example, the training department can analyze agency behavior based on social media data. The training department can also identify the strengths and weaknesses of agencies based on social media data. Furthermore, the training department can identify the training needs of agencies based on social media data. For example, the training department can analyze agency behavior based on social media data. By integrating social media data, the training department can analyze agency behavior in more detail and improve the effectiveness of training. Some or all of the above processes in the training department may be performed using AI, for example, or not. For example, the training department can input social media data into a generating AI and have the generating AI perform a detailed analysis of agency behavior.

[0098] The agenda generation unit can estimate the user's emotions and adjust the agenda content based on those emotions. For example, if the user is nervous, the agenda generation unit can provide a simple and concise agenda. If the user is relaxed, the agenda generation unit can also provide a detailed agenda. Furthermore, if the user is in a hurry, the agenda generation unit can provide an agenda that allows for quick discussion. For example, the agenda generation unit estimates the user's emotions and, if they are nervous, provides a simple and concise agenda. By adjusting the agenda content according to the user's emotions, more effective meetings become possible. 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 processing in the agenda generation unit may be performed using AI, for example, or without AI. For example, the agenda generation unit can input user emotion data into the generation AI and have the generation AI adjust the content of the agenda.

[0099] The agenda generation unit can select the most suitable topics by referring to past meeting data when generating an agenda. For example, the agenda generation unit selects the most suitable topics based on past meeting data. The agenda generation unit can also determine the priority of discussions based on past meeting data. Furthermore, the agenda generation unit can adjust the level of detail of the topics based on past meeting data. For example, the agenda generation unit selects the most suitable topics based on past meeting data. This allows for the selection of the most suitable topics by referring to past meeting data, thereby enabling effective meetings. Some or all of the above processes in the agenda generation unit may be performed using AI, for example, or without AI. For example, the agenda generation unit can input past meeting data into a generation AI and have the generation AI select the most suitable topics.

[0100] The agenda generation unit can apply different agenda generation algorithms depending on the purpose of the meeting when generating an agenda. For example, if the purpose of the meeting is strategic, the agenda generation unit can apply an algorithm that prioritizes strategic topics. It can also apply an algorithm that prioritizes tactical topics if the purpose of the meeting is tactical. Furthermore, if the purpose of the meeting is information sharing, the agenda generation unit can apply an algorithm that prioritizes information sharing. For example, if the purpose of the meeting is strategic, the agenda generation unit can apply an algorithm that prioritizes strategic topics. This allows for more effective meetings by applying an agenda generation algorithm appropriate to the purpose of the meeting. Some or all of the above-described processes in the agenda generation unit may be performed using AI, for example, or without AI. For example, the agenda generation unit can input the purpose of the meeting into a generation AI and have the generation AI execute the application of the agenda generation algorithm.

[0101] The agenda generation unit can estimate the user's emotions and determine agenda priorities based on those emotions. For example, if the user is nervous, the agenda generation unit will discuss important topics first. Conversely, if the user is relaxed, the agenda generation unit can discuss more detailed topics later. Furthermore, if the user is in a hurry, the agenda generation unit can discuss key points first. For example, the agenda generation unit estimates the user's emotions and, if they are nervous, discusses important topics first. This allows for more effective meetings by prioritizing the agenda according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the agenda generation unit may be performed using AI, for example, or without AI. For example, the agenda generation unit can input user emotion data into a generation AI and have the generation AI determine the priorities of the agenda.

[0102] The agenda generation unit can select the most appropriate topics when generating an agenda, taking into account the attribute information of the meeting participants. For example, if the meeting participants are management, the agenda generation unit will prioritize strategic topics. It can also prioritize tactical topics if the participants are the sales team, and technical topics if the participants are the technical team. In this way, by considering the attribute information of the meeting participants, the agenda generation unit can select the most appropriate topics and achieve an effective meeting. Some or all of the above processing in the agenda generation unit may be performed using AI, for example, or without AI. For example, the agenda generation unit can input the attribute information of the meeting participants into a generation AI and have the generation AI select the most appropriate topics.

[0103] The agenda generation unit can improve the accuracy of the agenda by referring to relevant external data during agenda generation. For example, the agenda generation unit can improve the accuracy of the agenda by referring to external market data. It can also improve the accuracy of the agenda by referring to external competitor data. Furthermore, it can improve the accuracy of the agenda by referring to external economic data. For example, the agenda generation unit can improve the accuracy of the agenda by referring to external market data. This allows for improved agenda accuracy and more effective meetings by referring to relevant external data. Some or all of the above processing in the agenda generation unit may be performed using AI, for example, or without AI. For example, the agenda generation unit can input external data into a generation AI and have the generation AI perform agenda accuracy improvement.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, it can prioritize the analysis of important data and provide results quickly. If the user is relaxed, it can perform detailed data analysis and provide comprehensive results. Furthermore, if the user is in a hurry, it can analyze only the most important data and provide results quickly. In this way, by adjusting the analysis priority according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis priority.

[0106] The visualization unit can estimate the user's emotions and adjust the visualization style based on the estimated emotions. For example, if the user is tense, it can provide a visualization with calm colors. If the user is relaxed, it can provide a visualization with bright colors. Furthermore, if the user is in a hurry, it can provide a simple and highly visible visualization. This allows for more effective data visualization by adjusting the visualization style 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 visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the visualization style.

[0107] The reporting unit can estimate the user's emotions and adjust the way the report is presented based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read report. If the user is relaxed, it can provide a report with more detailed information. Furthermore, if the user is in a hurry, it can provide a report that gets straight to the point. By adjusting the way the report is presented according to the user's emotions, a more easily understandable report can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the way the report is presented.

[0108] The training unit can estimate the user's emotions and adjust the training content based on those emotions. For example, if the user is nervous, it can provide relaxing training content. If the user is relaxed, it can provide more detailed training content. Furthermore, if the user is in a hurry, it can provide effective training content in a short amount of time. In this way, more effective training can be provided by adjusting the training content 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 training unit may be performed using AI, for example, or not using AI. For example, the training unit can input user emotion data into a generative AI and have the generative AI adjust the training content.

[0109] The agenda generation unit can estimate the user's emotions and adjust the agenda content based on the estimated emotions. For example, if the user is nervous, it can provide a simple, to-the-point agenda. If the user is relaxed, it can provide a detailed agenda. Furthermore, if the user is in a hurry, it can provide an agenda that allows for quick discussion. By adjusting the agenda content according to the user's emotions, more effective meetings can be achieved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the agenda generation unit may be performed using AI or not. For example, the agenda generation unit can input user emotion data into the generative AI and have the generative AI adjust the agenda content.

[0110] The analysis unit can analyze data from the customer management system in real time and provide immediate feedback. For example, it can analyze customer purchase history in real time and immediately propose sales strategies. It can also analyze customer behavior data in real time and immediately adjust marketing campaigns. Furthermore, it can analyze customer feedback in real time and immediately propose product improvements. This enables rapid response by analyzing in real time and providing immediate feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data from the customer management system into a generating AI and have the generating AI perform real-time analysis and provide feedback.

[0111] The analysis unit can identify trends by comparing past and present data during analysis. For example, it can identify sales trends by comparing past and present sales data. It can also identify customer purchasing trends by comparing past and present customer behavior data. Furthermore, it can identify trends in effective campaigns by comparing past and present marketing campaign data with current data. This allows for the identification of trends and future predictions by comparing past and present 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 input past and present data into a generating AI and have the generating AI perform trend identification.

[0112] The analysis unit can identify sales trends for each region by considering geographical data during analysis. For example, it can identify sales trends for each region based on geographical data. It can also analyze customer behavior for each region based on geographical data. Furthermore, it can optimize marketing strategies for each region based on geographical data. In this way, by considering geographical data, it is possible to identify sales trends for each region and develop strategies for each region. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input geographical data into a generating AI and have the generating AI perform the identification of sales trends for each region.

[0113] The analysis unit can integrate social media data during analysis to analyze customer behavior in more detail. For example, it can analyze customer purchasing intent based on social media data. It can also analyze customer brand awareness based on social media data. Furthermore, it can analyze customer feedback based on social media data. By integrating social media data, it is possible to analyze customer behavior in more detail and optimize marketing strategies. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input social media data into a generating AI and have the generating AI perform a detailed analysis of customer behavior.

[0114] The visualization unit can adjust the level of detail displayed based on the importance of the data during visualization. For example, it can highlight and display important data in detail. It can also display less important data concisely. Furthermore, it can dynamically adjust the level of detail displayed according to the importance of the data. By adjusting the level of detail displayed based on the importance of the data, it can highlight important data and enhance the effectiveness of the visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The analysis unit analyzes the data from the customer management system. The analysis unit uses data mining techniques to analyze customer behavior patterns and statistical analysis to identify trends in sales data. It can also use machine learning algorithms to predict customer purchases. For example, it can predict future purchasing behavior based on a customer's past purchase history. Step 2: The visualization unit visualizes the data analyzed by the analysis unit. The visualization unit can visually display the data using graphs and charts, and can also display the data in real time using dashboards. Furthermore, it provides interactive visualizations and interactive graphs to make it easier for users to manipulate the data. Step 3: The reporting department reports the data visualized by the visualization department to management. The reporting department generates and provides reports to management on a regular basis. It can also report data in real time and provide customized reports according to management's needs. For example, it can provide customized dashboards to quickly provide management with the information they need. Step 4: The training department provides personalized training to each agency based on the data analyzed by the analytics department. The training department analyzes each agency's sales data and proposes the most suitable learning content. It can also analyze customer data and customize training programs based on each agency's strengths and weaknesses. Furthermore, it provides interactive training programs and improves agencies' sales skills through online training sessions. Step 5: The agenda generation unit generates the sales meeting agenda based on the data analyzed by the analysis unit. The agenda generation unit analyzes past meeting content to create an efficient agenda. It can also analyze sales data and customer information to identify important topics. Furthermore, it customizes the agenda according to the meeting's purpose and participants, and determines the priority of the topics.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the analysis unit, visualization unit, reporting unit, training unit, and agenda generation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes data from the customer management system. The visualization unit is implemented by, for example, the control unit 46A of the smart device 14 and visualizes the analyzed data. The reporting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reports the visualized data to management. The training unit is implemented by, for example, the control unit 46A of the smart device 14 and provides personalized training to each agent. The agenda generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an agenda for a sales meeting. 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.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the analysis unit, visualization unit, reporting unit, training unit, and agenda generation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes data from the customer management system. The visualization unit is implemented, for example, by the control unit 46A of the smart glasses 214 and visualizes the analyzed data. The reporting unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and reports the visualized data to management. The training unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides personalized training to each agent. The agenda generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an agenda for a sales meeting. 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.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the analysis unit, visualization unit, reporting unit, training unit, and agenda generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes data from the customer management system. The visualization unit is implemented by, for example, the control unit 46A of the headset terminal 314 and visualizes the analyzed data. The reporting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reports the visualized data to management. The training unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides personalized training to each agent. The agenda generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an agenda for a sales meeting. 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.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the analysis unit, visualization unit, reporting unit, training unit, and agenda generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes data from the customer management system. The visualization unit is implemented by, for example, the control unit 46A of the robot 414 and visualizes the analyzed data. The reporting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reports the visualized data to management. The training unit is implemented by, for example, the control unit 46A of the robot 414 and provides personalized training to each agent. The agenda generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an agenda for a sales meeting. 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) The analysis department analyzes data from the customer management system, A visualization unit that visualizes the data analyzed by the aforementioned analysis unit, A reporting unit reports the data visualized by the visualization unit to management, A training unit provides personalized training to each agency based on the data analyzed by the aforementioned analysis unit, The system includes an agenda generation unit that generates an agenda for a sales meeting based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned training department We analyze sales and customer data from each agency to propose the most suitable learning content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The agenda generation unit, By analyzing past meeting content, sales data, and customer information, we generate efficient sales meeting agendas. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze customer management system data in real time and provide immediate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, During analysis, historical and current data are compared to identify trends. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, geographical data is taken into consideration to identify sales trends for each region. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, social media data is integrated to provide a more detailed analysis of customer behavior. The system described in Appendix 1, characterized by the features described herein. (Note 10) The visualization unit, It estimates the user's emotions and adjusts the visualization style based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The visualization unit, When visualizing data, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 12) The visualization unit, When creating visualizations, integrate different datasets to perform complex visualizations. The system described in Appendix 1, characterized by the features described herein. (Note 13) The visualization unit, It estimates the user's emotions and adjusts the order of visualizations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The visualization unit, When visualizing data, prioritize the display based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The visualization unit, When creating visualizations, referencing relevant external data improves the accuracy of the visualizations. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reporting department, We estimate the user's emotions and adjust the way the report is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reporting department, When reporting, adjust the level of detail in the report based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reporting department, When reporting, select different reporting formats to provide reports tailored to the management's needs. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reporting department, The system estimates the user's sentiment and adjusts the length of the report based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reporting department, When reporting, prioritize reports based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting department, When reporting, we refer to relevant market data to improve the accuracy of the report. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned training department It estimates the user's emotions and adjusts the training content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned training department During training, we provide the optimal training program by referring to each agency's past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned training department During training, we provide customized training tailored to the specific characteristics of each agency. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned training department It estimates the user's emotions and determines training priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned training department During training, geographical data is taken into consideration to identify regional training needs. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned training department During training, social media data is integrated to analyze agency behavior in more detail. The system described in Appendix 1, characterized by the features described herein. (Note 28) The agenda generation unit, We estimate the user's emotions and adjust the agenda content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The agenda generation unit, When generating the agenda, the system selects the most suitable topics by referring to past meeting data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The agenda generation unit, When generating the agenda, different agenda generation algorithms are applied depending on the purpose of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 31) The agenda generation unit, It estimates user sentiment and prioritizes the agenda based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The agenda generation unit, When generating the agenda, the most suitable topics are selected by considering the attribute information of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 33) The agenda generation unit, When generating an agenda, the accuracy of the agenda is improved by referencing relevant external data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes data from the customer management system, A visualization unit that visualizes the data analyzed by the aforementioned analysis unit, A reporting unit reports the data visualized by the visualization unit to management, A training unit provides personalized training to each agency based on the data analyzed by the aforementioned analysis unit, The system includes an agenda generation unit that generates an agenda for a sales meeting based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned training department We analyze sales and customer data from each agency to propose the most suitable learning content. The system according to feature 1.

3. The agenda generation unit, By analyzing past meeting content, sales data, and customer information, we generate efficient sales meeting agendas. The system according to feature 1.

4. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system according to feature 1.

5. The aforementioned analysis unit, Analyze customer management system data in real time and provide immediate feedback. The system according to feature 1.

6. The aforementioned analysis unit, During analysis, historical and current data are compared to identify trends. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, geographical data is taken into consideration to identify sales trends for each region. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, social media data is integrated to provide a more detailed analysis of customer behavior. The system according to feature 1.

10. The visualization unit, It estimates the user's emotions and adjusts the visualization style based on the estimated emotions. The system according to feature 1.

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