system

Abstract

The system according to this embodiment aims to monitor corporate news and social media trends, identify key customer stakeholders, and propose customized sales strategies. [Solution] The system according to the embodiment comprises a monitoring unit, an identification unit, a proposal unit, and a dashboard unit. The monitoring unit monitors corporate news and social media trends. The identification unit identifies key customer personnel based on the information collected by the monitoring unit. The proposal unit proposes customized sales strategies based on the activities and interests of the key personnel identified by the identification unit. The dashboard unit provides a customizable dashboard for each sales representative.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently monitor corporate news and SNS trends, identify customer key persons, and propose customized business strategies.

[0005] The system according to the embodiment aims to monitor corporate news and SNS trends, identify customer key persons, and propose customized business strategies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an identification unit, a proposal unit, and a dashboard unit. The monitoring unit monitors corporate news and social media trends. The identification unit identifies key customer personnel based on the information collected by the monitoring unit. The proposal unit proposes customized sales strategies based on the activities and interests of the key personnel identified by the identification unit. The dashboard unit provides a customizable dashboard for each sales representative. [Effects of the Invention]

[0007] The system according to this embodiment can monitor corporate news and social media trends, identify key customer stakeholders, and propose customized sales strategies. [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, etc. The communication I / F manages communication between multiple 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 AI ​​sales support agent system according to an embodiment of the present invention is a system that monitors corporate news and social media activity to identify key customer stakeholders. This system proposes customized sales strategies based on the activities and interests of key stakeholders, and supports sales by ensuring that upselling and cross-selling opportunities and target audiences are not missed. Furthermore, a customizable dashboard visualizes key stakeholders information and the progress of relationship building, improving the transparency and reproducibility of the process. For example, the AI ​​sales support agent system uses AI to monitor corporate news and social media activity in real time. This allows for the automatic identification of key customer stakeholders. Next, based on the activities and interests of the identified key stakeholders, the AI ​​proposes customized sales strategies. Furthermore, it supports sales by ensuring that upselling and cross-selling opportunities and target audiences are not missed. The AI ​​monitors the activities of key stakeholders and notifies sales representatives at the optimal time. In addition, a customizable dashboard is provided for each sales representative. This dashboard allows for the visualization of key stakeholders information and the progress of relationship building. This increases the transparency of sales activities and improves reproducibility. This system is particularly beneficial for junior to mid-career corporate sales representatives. This system eliminates the time-consuming process of identifying key decision-makers and delays in new proposals caused by being overwhelmed with daily tasks. By having AI identify key decision-makers in real time and propose customized sales strategies, it is expected to improve the efficiency of sales activities and enhance performance. Furthermore, a function will be added to visualize the key decision-making process, allowing sales representatives to track how they found these key decision-makers. This increases the transparency of sales activities, making it easier to identify areas for improvement. It also increases reproducibility, enabling consistently high performance. In this way, the AI ​​sales support agent system can achieve increased efficiency and improved performance in sales activities.

[0029] The AI ​​sales support agent system according to this embodiment comprises a monitoring unit, an identification unit, a proposal unit, and a dashboard unit. The monitoring unit monitors corporate news and social media trends. For example, the monitoring unit can monitor specific industry news or trends on specific social media platforms. The monitoring unit uses AI to collect and analyze data in real time. The identification unit identifies key customer personnel based on the information collected by the monitoring unit. For example, the identification unit can identify key customer personnel based on criteria such as job title, influence, and decision-making authority. The identification unit uses AI to automatically identify key customer personnel. The proposal unit proposes customized sales strategies based on the activities and interests of the key personnel identified by the identification unit. For example, the proposal unit can propose proposals based on individual needs or strategies based on past transaction history. The proposal unit uses AI to propose customized sales strategies. The dashboard unit provides a customizable dashboard for each sales representative. For example, the dashboard unit can be customized by changing the type of information displayed and the layout. The dashboard unit uses AI to visualize key person information and the progress of relationship building. As a result, the AI ​​agent sales support system according to this embodiment can improve the efficiency of sales activities and enhance performance.

[0030] The monitoring department monitors corporate news and social media trends. For example, it can monitor specific industry news or trends on specific social media platforms. Specifically, it collects the latest news articles from news sites and industry-specific information sites, and gathers data such as post content, comments, likes, and shares from social media platforms. This data is collected and analyzed in real time using AI. The AI ​​uses natural language processing technology to analyze the content of news articles and social media posts, extracting important keywords and trends. For example, it can automatically classify positive and negative news about a specific company and assess its impact. It can also analyze the frequency of specific hashtags and keywords on social media, allowing for real-time tracking of trend changes. This enables the monitoring department to quickly grasp corporate trends and market trends, providing information necessary for sales activities. Furthermore, the monitoring department can centrally manage the collected data and share information in collaboration with other departments. For example, collected data can be stored on a cloud server and made accessible to specific departments or the proposal department. Furthermore, the monitoring unit can adjust the data collection frequency and analysis accuracy, enabling flexible responses to specific situations and conditions. This allows the monitoring unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The Identification Department identifies key customer stakeholders based on information collected by the Monitoring Department. The Identification Department can identify key customer stakeholders based on criteria such as job title, influence, and decision-making authority. Specifically, the Identification Department uses AI to analyze a company's organizational chart and job title information to identify individuals holding important positions. Furthermore, it can analyze social media activity, follower counts, and engagement rates to identify influential individuals. The AI ​​integrates this data to automatically generate a list of key customer stakeholders. For example, the Identification Department can identify key stakeholders based on their job titles, such as CEO, CFO, or marketing manager, and further evaluate their social media influence to list the most influential individuals. The Identification Department can also analyze past transaction history and customer behavior history to identify decision-makers. This allows the Identification Department to quickly and accurately identify key customer stakeholders crucial to sales activities and utilize this information in developing sales strategies. Additionally, the Identification Department can centrally manage information on identified key stakeholders and share it with other departments. For example, the list of identified key personnel is stored on a cloud server and made accessible to the proposal and dashboard departments. Furthermore, the identification department can regularly update the key personnel information to provide the most up-to-date information. This allows the identification department to consistently identify key personnel accurately based on the latest information, supporting improved sales efficiency and performance.

[0032] The proposal department proposes customized sales strategies based on the activities and interests of key individuals identified by specific departments. For example, the proposal department can propose strategies based on individual needs or past transaction history. Specifically, the proposal department uses AI to analyze key individuals' past statements and actions to identify their interests. For instance, it analyzes what products and services key individuals have shown interest in in the past, and what events they have attended, and then creates customized proposals based on this analysis. Furthermore, the proposal department analyzes past transaction history to understand what kinds of transactions key individuals have conducted and what their needs were. This allows the proposal department to create proposals that are best suited to the key individuals' needs and provide them to sales representatives. For example, the proposal department can make new proposals related to products and services that key individuals have shown interest in in the past, thereby attracting their attention. Additionally, the proposal department can use AI to evaluate the effectiveness of proposals and continuously improve optimal proposal strategies. For example, it can analyze the reactions and results of proposals to identify highly effective proposals and use them to develop new proposal strategies. This allows the proposal department to consistently provide effective sales strategies based on the latest information, maximizing the results of sales activities. Furthermore, the proposal department can centrally manage proposal content and share information in collaboration with other departments. For example, proposal content can be stored on a cloud server and made accessible to the dashboard department. In addition, the proposal department can regularly update and improve proposal content, ensuring that the latest information is always provided. This enables the proposal department to support the streamlining of sales activities and improve performance.

[0033] The dashboard section provides a customizable dashboard for each sales representative. For example, the type of information displayed and the layout can be customized. Specifically, the dashboard uses AI to display information tailored to the sales representative's needs and work. For instance, it allows sales representatives to quickly view customer information, transaction history, and proposal details that they are interested in. The dashboard also visualizes key contact information and the progress of relationship building. For example, based on key contact information provided by specific departments or proposal departments, it can display contact history and relationship building progress in graphs and charts. This allows sales representatives to understand their relationships with key contacts and plan their next actions. Furthermore, the dashboard can provide the latest information based on real-time updated data. For example, it can display the latest news and social media trends provided by the monitoring department, enabling sales representatives to respond quickly. The dashboard can also collect feedback from sales representatives and continuously improve its functionality and layout. For example, it can add information and functions needed by sales representatives to improve usability. This allows the dashboard to provide sales representatives with efficient and effective information, maximizing the results of their sales activities. Furthermore, the dashboard can collaborate with other departments to share information and improve the overall system performance. For example, the information displayed on the dashboard can be stored on a cloud server and made accessible to the monitoring, specific, and proposal departments. This enables the dashboard to support the streamlining of sales activities and improve performance.

[0034] The analysis unit can analyze the activities and interests of key personnel. For example, the analysis unit can analyze past behavioral history and topics of interest. The analysis unit uses AI to analyze the activities and interests of key personnel. By analyzing the activities and interests of key personnel, it is possible to propose more accurate sales strategies. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0035] The notification unit can notify customers of the timing for upselling and cross-selling. The notification unit can determine the timing for upselling and cross-selling based on, for example, purchase history and customer behavior patterns. The notification unit uses AI to notify customers of the timing for upselling and cross-selling. This ensures that customers do not miss the timing for upselling and cross-selling, which is expected to improve the efficiency of sales activities and enhance performance. Some or all of the above-described processes in the notification unit may be performed using AI or not using AI.

[0036] The visualization unit can visualize the progress of relationship building. The visualization unit can visualize the progress of relationship building based on, for example, the number of contacts and the content of communication. The visualization unit uses AI to visualize the progress of relationship building. By visualizing the progress of relationship building, the transparency and reproducibility of sales activities are improved. Some or all of the above-described processes in the visualization unit may be performed using AI or not using AI.

[0037] The monitoring unit can monitor corporate news and social media activity in real time. For example, the monitoring unit can configure settings such as the data update frequency and the monitoring tools used for real-time monitoring. This allows for the identification of key customer stakeholders based on the latest information by monitoring corporate news and social media activity in real time. Some or all of the above-described processes in the monitoring unit may be performed using AI, or they may not.

[0038] The identification unit can automatically identify key customer contacts based on information collected by the monitoring unit. The identification unit can automatically identify key customer contacts using, for example, machine learning algorithms or rule-based systems. This allows for increased efficiency in sales activities by automatically identifying key customer contacts. Some or all of the above-described processes in the identification unit may be performed using AI or not.

[0039] The proposal department can propose customized sales strategies based on the activities and interests of identified key players. For example, the proposal department can propose strategies based on individual needs or past transaction history. By proposing customized sales strategies, it is expected that sales activities will become more efficient and performance will improve. Some or all of the above processes in the proposal department may be performed using AI or not.

[0040] The dashboard section provides a customizable dashboard for each sales representative, allowing for the visualization of key contact information and the progress of relationship building. The dashboard section allows for customization of, for example, the types of information displayed and the layout. This improves the transparency and reproducibility of sales activities by providing a customizable dashboard for each sales representative. Some or all of the processes described above in the dashboard section may be performed using AI, or not.

[0041] The monitoring unit can select monitoring targets based on a company's performance and market trends during monitoring. For example, the monitoring unit prioritizes monitoring news related to a company's performance when the company announces its quarterly earnings. Furthermore, when market trends change rapidly, the monitoring unit can focus its monitoring on the activities of relevant companies. In addition, when a company announces a new product, the monitoring unit can monitor news and social media reactions related to that product. This allows for the collection of more important information by selecting monitoring targets based on a company's performance and market trends. Some or all of the above-described processes in the monitoring unit may be performed using AI, or they may not.

[0042] The monitoring unit can prioritize the collection of information specific to particular industries or regions during monitoring. For example, the monitoring unit can prioritize monitoring news and social media trends related to a particular industry. It can also focus on monitoring the activities of companies in a specific region. Furthermore, the monitoring unit can prioritize the collection of information on industry-specific trends and new technologies. This allows for the collection of more relevant information by prioritizing the collection of information specific to particular industries or regions. Some or all of the above-described processes in the monitoring unit may be performed using AI, or they may not.

[0043] The monitoring unit can collect information by combining internal and public data of a company during monitoring. For example, the monitoring unit can monitor by combining internal company data with publicly available news. It can also monitor by combining internal company data with social media trends. Furthermore, it can monitor by combining internal company data with market trends. By collecting information by combining internal and public data of a company, a more comprehensive set of results can be obtained. Some or all of the above-described processes in the monitoring unit may be performed using AI, or they may not be performed using AI.

[0044] The monitoring department can simultaneously monitor and comparatively analyze the activities of competitors during its monitoring operations. For example, it can simultaneously monitor news and social media activity of competitors and perform comparative analyses with its own company. Furthermore, the monitoring department can monitor the reactions to new product announcements by competitors and incorporate them into its own strategy. In addition, the monitoring department can monitor the impact of earnings announcements by competitors and consider countermeasures for its own company. This allows for more strategic sales activities by simultaneously monitoring and comparatively analyzing the activities of competitors. Some or all of the above processes in the monitoring department may be performed using AI, or they may not.

[0045] The identification unit can improve the accuracy of identification by analyzing the past activity history of key persons at the time of identification. For example, the identification unit can improve the accuracy of identification by analyzing the past transaction history and events attended by key persons. Furthermore, the identification unit can improve the accuracy of identification based on the past statements and actions of key persons. In addition, the identification unit can improve the accuracy of identification by analyzing the past projects and achievements of key persons. Thus, the accuracy of identification is improved by analyzing the past activity history of key persons. Some or all of the above processing in the identification unit may be performed using AI or not.

[0046] The identification unit can perform identification based on the key person's network and related party information at the time of identification. For example, the identification unit considers the key person's social network data and the influence of related parties when performing identification. Furthermore, the identification unit can improve the accuracy of identification based on the key person's related party information. In addition, the identification unit can visualize the key person's network and improve the accuracy of identification. As a result, the accuracy of identification is improved by performing identification based on the key person's network and related party information. Some or all of the above processing in the identification unit may be performed using AI or not.

[0047] The identification unit can perform identification based on the geographical location information of key persons. The identification unit considers, for example, the key person's GPS data and location information services when performing identification. Furthermore, the identification unit can propose the optimal approach method based on the key person's geographical location information. In addition, the identification unit can visualize the key person's geographical location information to improve the accuracy of identification. Thus, the accuracy of identification is improved by performing identification based on the key person's geographical location information. Some or all of the above processing in the identification unit may be performed using AI or not.

[0048] The identification unit can improve the accuracy of identification based on the key person's relevant literature and patent information at the time of identification. For example, the identification unit can improve the accuracy of identification by referring to the key person's literature database and patent database. The identification unit can also improve the accuracy of identification by analyzing the key person's relevant literature. Furthermore, the identification unit can improve the accuracy of identification based on the key person's patent information. As a result, the accuracy of identification is improved based on the key person's relevant literature and patent information. Some or all of the above processing in the identification unit may be performed using AI or not.

[0049] The proposal department can optimize the proposal content based on the key person's past reactions and feedback. For example, the proposal department can optimize the proposal content by analyzing the key person's past proposal history and feedback data. Furthermore, the proposal department can optimize the proposal content based on the key person's past reactions. In addition, the proposal department can customize the proposal content by considering the key person's feedback. This allows for more effective proposals by optimizing the proposal content based on the key person's past reactions and feedback. Some or all of the above processes in the proposal department may be performed using AI, or they may not.

[0050] The proposal department can make proposals based on key players' industry trends and market trends. For example, the proposal department can optimize proposal content by referring to key players' industry reports and market analysis data. The proposal department can also analyze key players' industry trends and reflect them in the proposal content. Furthermore, the proposal department can customize proposal content based on key players' market trends. This allows for more relevant proposals by basing proposals on key players' industry trends and market trends. Some or all of the above processes in the proposal department may be performed using AI, or they may not.

[0051] The proposal department can customize the proposal content based on the key person's social media activity. For example, the proposal department can customize the proposal content by analyzing the key person's social media posts and follower reactions. Furthermore, the proposal department can optimize the proposal content based on the key person's social media statements. In addition, the proposal department can customize the proposal content considering the key person's social media activity. This allows for more effective proposals by customizing the proposal content based on the key person's social media activity. Some or all of the above processes in the proposal department may be performed using AI, or not.

[0052] The proposal department can base its proposals on the projects and activities of key personnel. For example, the proposal department can optimize the proposal by analyzing the progress of key personnel's projects and the results of their activities. The proposal department can also analyze key personnel's related projects and reflect them in the proposal. Furthermore, the proposal department can customize the proposal based on key personnel's activities. This allows for more relevant proposals by basing them on the projects and activities of key personnel. Some or all of the above processes in the proposal department may be performed using AI, or they may not.

[0053] The dashboard section can select the optimal display method based on the user's past operation history when displaying the dashboard. For example, the dashboard section analyzes the user's operation logs and frequently used functions to suggest the optimal display method. The dashboard section can also prioritize the display of functions that the user frequently uses. Furthermore, the dashboard section can suggest the optimal layout based on the user's past operation history. In this way, a more user-friendly dashboard can be provided by selecting the optimal display method based on the user's past operation history. Some or all of the above processing in the dashboard section may be performed using AI, or it may be performed without using AI.

[0054] The dashboard section can provide customized information according to the user's work content and role when the dashboard is displayed. For example, the dashboard section prioritizes displaying information relevant to the user's work processes and job title. Furthermore, the dashboard section can provide customized information according to the user's role. In addition, the dashboard section can provide optimal information considering the user's work content and role. By providing customized information according to the user's work content and role, more relevant information can be provided. Some or all of the above processing in the dashboard section may be performed using AI or not.

[0055] The dashboard section can select the optimal display method based on the user's device information when displaying the dashboard. For example, if the user is using a smartphone, the dashboard section can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the dashboard section can provide a display method optimized for a larger screen. Additionally, if the user is using a desktop, the dashboard section can provide a display method that includes detailed information. By selecting the optimal display method based on the user's device information, a more user-friendly dashboard can be provided. Some or all of the above processing in the dashboard section may be performed using AI, or it may be performed without AI.

[0056] The dashboard section can display the user's schedule and task management information in an integrated manner when the dashboard is displayed. For example, the dashboard section can integrate and display the user's calendar information and data from task management tools. The dashboard section can also display appointments based on the user's schedule information. Furthermore, the dashboard section can display progress based on the user's task management information. By integrating and displaying the user's schedule and task management information, more efficient information management becomes possible. Some or all of the above processing in the dashboard section may be performed using AI, or it may be performed without using AI.

[0057] The analysis unit can improve the accuracy of its analysis based on the key person's past activity data. For example, the analysis unit can improve the accuracy of its analysis by analyzing the key person's past transaction history and events attended. Furthermore, the analysis unit can improve the accuracy of its analysis based on the key person's past statements and actions. In addition, the analysis unit can improve the accuracy of its analysis by analyzing the key person's past projects and achievements. This improves the accuracy of the analysis based on the key person's past activity data. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI.

[0058] The analysis unit can perform analysis based on key players' industry trends and market trends. For example, the analysis unit can optimize the analysis by referring to key players' industry reports and market analysis data. Furthermore, the analysis unit can analyze key players' industry trends and reflect them in the analysis. In addition, the analysis unit can customize the analysis based on key players' market trends. This allows for more relevant analysis by performing analysis based on key players' industry trends and market trends. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not.

[0059] The analysis unit can customize the analysis content based on the key person's social media activity during the analysis. For example, the analysis unit can customize the analysis content by analyzing the content of the key person's social media posts and the reactions of their followers. The analysis unit can also optimize the analysis content based on the key person's statements on social media. Furthermore, the analysis unit can customize the analysis content by taking into account the key person's social media activity. This allows for more effective analysis by customizing the analysis content based on the key person's social media activity. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0060] The analysis unit can perform analysis based on the projects and activities related to key personnel. For example, the analysis unit can optimize the analysis by analyzing the progress of key personnel's projects and the results of their activities. The analysis unit can also analyze the projects related to key personnel and reflect this in the analysis. Furthermore, the analysis unit can customize the analysis based on the activities of key personnel. This allows for more relevant analysis by performing analysis based on the projects and activities related to key personnel. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI.

[0061] The notification unit can monitor the activity status of key personnel in real time and send notifications at the optimal time. For example, the notification unit collects activity logs and real-time data of key personnel and sends notifications at the optimal time. The notification unit can also immediately send notifications when important activities of key personnel occur. Furthermore, the notification unit can send notifications at the optimal time based on the activity status of key personnel. This allows for more effective notifications by monitoring the activity status of key personnel in real time and sending notifications at the optimal time. Some or all of the above processing in the notification unit may be performed using AI or not using AI.

[0062] The notification unit can send notifications based on the user's schedule and task management information. For example, the notification unit can send notifications at the optimal time based on the user's schedule information. The notification unit can also customize the notification content considering the user's task management information. Furthermore, the notification unit can integrate the user's schedule and task management information to send optimal notifications. This allows for more effective notifications by sending notifications based on the user's schedule and task management information. Some or all of the above processing in the notification unit may be performed using AI, or it may be performed without using AI.

[0063] The visualization unit can reflect and display key person activity data in real time during visualization. For example, the visualization unit reflects and visualizes key person activity data in real time. Furthermore, the visualization unit can immediately visualize when a key person's important activity occurs. In addition, the visualization unit can perform real-time visualization based on key person activity data. This allows for more effective information provision by reflecting and displaying key person activity data in real time. Some or all of the above processing in the visualization unit may be performed using AI or not.

[0064] The visualization unit can provide customized information according to the user's work content and role during visualization. For example, the visualization unit prioritizes displaying information relevant to the user's work processes and job title. Furthermore, the visualization unit can provide customized information according to the user's role. In addition, the visualization unit can provide optimal information considering the user's work content and role. This allows for the provision of more relevant information by providing customized information according to the user's work content and role. Some or all of the above processing in the visualization unit may be performed using AI or not.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The sales support AI agent system may also include a forecasting unit. This forecasting unit can predict future customer behavior and market trends based on historical data and current trends. For example, it can analyze past purchase history and seasonal market fluctuations to predict the next purchase timing. It can also predict changes in customer interests based on social media trends and news developments. Furthermore, it can predict customer life events and important company events, and adjust sales strategies accordingly. This allows for proactive sales activities and the development of more effective sales strategies. Some or all of the above-described processes in the forecasting unit may be performed using AI or not.

[0067] The sales support AI agent system can also be equipped with a feedback unit. The feedback unit can collect feedback from sales representatives and improve the system's accuracy and proposals. For example, the feedback unit can record how sales representatives reacted to proposals and reflect this in future proposals. The feedback unit can also collect the results of approaches actually taken by sales representatives and analyze success and failure cases. Furthermore, the feedback unit can collect feedback from sales representatives on the system's usability and areas for improvement, thereby improving the system's usability. As a result, by incorporating a feedback unit, the system's accuracy and proposals are continuously improved, and the effectiveness of sales activities is enhanced. Some or all of the above-described processes in the feedback unit may be performed using AI or not.

[0068] The sales support AI agent system can also be equipped with a personalization function based on the customer's purchase history. This personalization function analyzes the customer's past purchase history and makes suggestions tailored to their individual needs. For example, it can suggest relevant products and services based on products and services the customer has purchased in the past. It can also analyze the customer's purchasing patterns and predict products and services they are likely to purchase next. Furthermore, it can suggest specific campaigns and promotions based on the customer's purchase history. By incorporating this personalization function, suggestions can be made that meet customer needs, improving the effectiveness of sales activities. Some or all of the processing described above in the personalization function may be performed using AI or not.

[0069] The sales support AI agent system can also be equipped with a predictive function based on customer behavior patterns. This predictive function can analyze past customer behavior patterns and predict future behavior. For example, it can analyze when a customer has purchased products or services in the past and predict the timing of their next purchase. It can also predict what products or services a customer will be interested in next, based on their behavior patterns. Furthermore, it can predict the effectiveness of specific campaigns or promotions based on customer behavior patterns. This predictive function allows for anticipating customer behavior and developing more effective sales strategies. Some or all of the processes described above in the predictive function may be performed using AI or not.

[0070] The sales support AI agent system can also be equipped with an improvement function based on customer feedback. This improvement function can collect customer feedback and improve the system's accuracy and proposals. For example, it can record how customers reacted to proposals and incorporate that feedback into future proposals. It can also collect the results of approaches actually taken by customers and analyze success and failure cases. Furthermore, it can collect customer feedback on the system's usability and areas for improvement, thereby enhancing the system's overall usability. As a result, the system's accuracy and proposals are continuously improved, leading to increased effectiveness in sales activities. Some or all of the processes described above in the improvement function may be performed using AI or not.

[0071] The sales support AI agent system can also be equipped with an analytical function based on the customer's social media activity. This analytical function can analyze the content of the customer's social media posts and the reactions of their followers to optimize the content of the proposals. For example, the analytical function can analyze what topics the customer is interested in on social media and make relevant proposals. Furthermore, the analytical function can predict the effectiveness of proposals based on the reactions of the customer's followers. In addition, the analytical function can optimize the effectiveness of specific campaigns and promotions based on the customer's social media activity. Thus, by incorporating the analytical function, effective proposals can be made based on the customer's social media activity. Some or all of the above-described processes in the analytical function may be performed using AI or not.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The monitoring department monitors corporate news and social media trends. For example, it can monitor news in specific industries or trends on specific social media platforms. The monitoring department uses AI to collect and analyze data in real time. Step 2: The Identification Unit identifies key customer stakeholders based on information collected by the Monitoring Unit. For example, key customer stakeholders can be identified based on criteria such as job title, influence, and decision-making authority. The Identification Unit uses AI to automatically identify key customer stakeholders. Step 3: The proposal department proposes a customized sales strategy based on the activities and interests of key individuals identified by the specific department. For example, it can propose strategies based on individual needs or past transaction history. The proposal department uses AI to propose customized sales strategies. Step 4: The dashboard section provides a customizable dashboard for each sales representative. For example, the types of information displayed and the layout can be customized. The dashboard section uses AI to visualize key contact information and the progress of relationship building.

[0074] (Example of form 2) The AI ​​sales support agent system according to an embodiment of the present invention is a system that monitors corporate news and social media activity to identify key customer stakeholders. This system proposes customized sales strategies based on the activities and interests of key stakeholders, and supports sales by ensuring that upselling and cross-selling opportunities and target audiences are not missed. Furthermore, a customizable dashboard visualizes key stakeholders information and the progress of relationship building, improving the transparency and reproducibility of the process. For example, the AI ​​sales support agent system uses AI to monitor corporate news and social media activity in real time. This allows for the automatic identification of key customer stakeholders. Next, based on the activities and interests of the identified key stakeholders, the AI ​​proposes customized sales strategies. Furthermore, it supports sales by ensuring that upselling and cross-selling opportunities and target audiences are not missed. The AI ​​monitors the activities of key stakeholders and notifies sales representatives at the optimal time. In addition, a customizable dashboard is provided for each sales representative. This dashboard allows for the visualization of key stakeholders information and the progress of relationship building. This increases the transparency of sales activities and improves reproducibility. This system is particularly beneficial for junior to mid-career corporate sales representatives. This system eliminates the time-consuming process of identifying key decision-makers and delays in new proposals caused by being overwhelmed with daily tasks. By having AI identify key decision-makers in real time and propose customized sales strategies, it is expected to improve the efficiency of sales activities and enhance performance. Furthermore, a function will be added to visualize the key decision-making process, allowing sales representatives to track how they found these key decision-makers. This increases the transparency of sales activities, making it easier to identify areas for improvement. It also increases reproducibility, enabling consistently high performance. In this way, the AI ​​sales support agent system can achieve increased efficiency and improved performance in sales activities.

[0075] The AI ​​sales support agent system according to this embodiment comprises a monitoring unit, an identification unit, a proposal unit, and a dashboard unit. The monitoring unit monitors corporate news and social media trends. For example, the monitoring unit can monitor specific industry news or trends on specific social media platforms. The monitoring unit uses AI to collect and analyze data in real time. The identification unit identifies key customer personnel based on the information collected by the monitoring unit. For example, the identification unit can identify key customer personnel based on criteria such as job title, influence, and decision-making authority. The identification unit uses AI to automatically identify key customer personnel. The proposal unit proposes customized sales strategies based on the activities and interests of the key personnel identified by the identification unit. For example, the proposal unit can propose proposals based on individual needs or strategies based on past transaction history. The proposal unit uses AI to propose customized sales strategies. The dashboard unit provides a customizable dashboard for each sales representative. For example, the dashboard unit can be customized by changing the type of information displayed and the layout. The dashboard unit uses AI to visualize key person information and the progress of relationship building. As a result, the AI ​​agent sales support system according to this embodiment can improve the efficiency of sales activities and enhance performance.

[0076] The monitoring department monitors corporate news and social media trends. For example, it can monitor specific industry news or trends on specific social media platforms. Specifically, it collects the latest news articles from news sites and industry-specific information sites, and gathers data such as post content, comments, likes, and shares from social media platforms. This data is collected and analyzed in real time using AI. The AI ​​uses natural language processing technology to analyze the content of news articles and social media posts, extracting important keywords and trends. For example, it can automatically classify positive and negative news about a specific company and assess its impact. It can also analyze the frequency of specific hashtags and keywords on social media, allowing for real-time tracking of trend changes. This enables the monitoring department to quickly grasp corporate trends and market trends, providing information necessary for sales activities. Furthermore, the monitoring department can centrally manage the collected data and share information in collaboration with other departments. For example, collected data can be stored on a cloud server and made accessible to specific departments or the proposal department. Furthermore, the monitoring unit can adjust the data collection frequency and analysis accuracy, enabling flexible responses to specific situations and conditions. This allows the monitoring unit to collect data efficiently and effectively, improving the overall system performance.

[0077] The Identification Department identifies key customer stakeholders based on information collected by the Monitoring Department. The Identification Department can identify key customer stakeholders based on criteria such as job title, influence, and decision-making authority. Specifically, the Identification Department uses AI to analyze a company's organizational chart and job title information to identify individuals holding important positions. Furthermore, it can analyze social media activity, follower counts, and engagement rates to identify influential individuals. The AI ​​integrates this data to automatically generate a list of key customer stakeholders. For example, the Identification Department can identify key stakeholders based on their job titles, such as CEO, CFO, or marketing manager, and further evaluate their social media influence to list the most influential individuals. The Identification Department can also analyze past transaction history and customer behavior history to identify decision-makers. This allows the Identification Department to quickly and accurately identify key customer stakeholders crucial to sales activities and utilize this information in developing sales strategies. Additionally, the Identification Department can centrally manage information on identified key stakeholders and share it with other departments. For example, the list of identified key personnel is stored on a cloud server and made accessible to the proposal and dashboard departments. Furthermore, the identification department can regularly update the key personnel information to provide the most up-to-date information. This allows the identification department to consistently identify key personnel accurately based on the latest information, supporting improved sales efficiency and performance.

[0078] The proposal department proposes customized sales strategies based on the activities and interests of key individuals identified by specific departments. For example, the proposal department can propose strategies based on individual needs or past transaction history. Specifically, the proposal department uses AI to analyze key individuals' past statements and actions to identify their interests. For instance, it analyzes what products and services key individuals have shown interest in in the past, and what events they have attended, and then creates customized proposals based on this analysis. Furthermore, the proposal department analyzes past transaction history to understand what kinds of transactions key individuals have conducted and what their needs were. This allows the proposal department to create proposals that are best suited to the key individuals' needs and provide them to sales representatives. For example, the proposal department can make new proposals related to products and services that key individuals have shown interest in in the past, thereby attracting their attention. Additionally, the proposal department can use AI to evaluate the effectiveness of proposals and continuously improve optimal proposal strategies. For example, it can analyze the reactions and results of proposals to identify highly effective proposals and use them to develop new proposal strategies. This allows the proposal department to consistently provide effective sales strategies based on the latest information, maximizing the results of sales activities. Furthermore, the proposal department can centrally manage proposal content and share information in collaboration with other departments. For example, proposal content can be stored on a cloud server and made accessible to the dashboard department. In addition, the proposal department can regularly update and improve proposal content, ensuring that the latest information is always provided. This enables the proposal department to support the streamlining of sales activities and improve performance.

[0079] The dashboard section provides a customizable dashboard for each sales representative. For example, the type of information displayed and the layout can be customized. Specifically, the dashboard uses AI to display information tailored to the sales representative's needs and work. For instance, it allows sales representatives to quickly view customer information, transaction history, and proposal details that they are interested in. The dashboard also visualizes key contact information and the progress of relationship building. For example, based on key contact information provided by specific departments or proposal departments, it can display contact history and relationship building progress in graphs and charts. This allows sales representatives to understand their relationships with key contacts and plan their next actions. Furthermore, the dashboard can provide the latest information based on real-time updated data. For example, it can display the latest news and social media trends provided by the monitoring department, enabling sales representatives to respond quickly. The dashboard can also collect feedback from sales representatives and continuously improve its functionality and layout. For example, it can add information and functions needed by sales representatives to improve usability. This allows the dashboard to provide sales representatives with efficient and effective information, maximizing the results of their sales activities. Furthermore, the dashboard can collaborate with other departments to share information and improve the overall system performance. For example, the information displayed on the dashboard can be stored on a cloud server and made accessible to the monitoring, specific, and proposal departments. This enables the dashboard to support the streamlining of sales activities and improve performance.

[0080] The analysis unit can analyze the activities and interests of key personnel. For example, the analysis unit can analyze past behavioral history and topics of interest. The analysis unit uses AI to analyze the activities and interests of key personnel. By analyzing the activities and interests of key personnel, it is possible to propose more accurate sales strategies. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0081] The notification unit can notify customers of the timing for upselling and cross-selling. The notification unit can determine the timing for upselling and cross-selling based on, for example, purchase history and customer behavior patterns. The notification unit uses AI to notify customers of the timing for upselling and cross-selling. This ensures that customers do not miss the timing for upselling and cross-selling, which is expected to improve the efficiency of sales activities and enhance performance. Some or all of the above-described processes in the notification unit may be performed using AI or not using AI.

[0082] The visualization unit can visualize the progress of relationship building. The visualization unit can visualize the progress of relationship building based on, for example, the number of contacts and the content of communication. The visualization unit uses AI to visualize the progress of relationship building. By visualizing the progress of relationship building, the transparency and reproducibility of sales activities are improved. Some or all of the above-described processes in the visualization unit may be performed using AI or not using AI.

[0083] The monitoring unit can monitor corporate news and social media activity in real time. For example, the monitoring unit can configure settings such as the data update frequency and the monitoring tools used for real-time monitoring. This allows for the identification of key customer stakeholders based on the latest information by monitoring corporate news and social media activity in real time. Some or all of the above-described processes in the monitoring unit may be performed using AI, or they may not.

[0084] The identification unit can automatically identify key customer contacts based on information collected by the monitoring unit. The identification unit can automatically identify key customer contacts using, for example, machine learning algorithms or rule-based systems. This allows for increased efficiency in sales activities by automatically identifying key customer contacts. Some or all of the above-described processes in the identification unit may be performed using AI or not.

[0085] The proposal department can propose customized sales strategies based on the activities and interests of identified key players. For example, the proposal department can propose strategies based on individual needs or past transaction history. By proposing customized sales strategies, it is expected that sales activities will become more efficient and performance will improve. Some or all of the above processes in the proposal department may be performed using AI or not.

[0086] The dashboard section provides a customizable dashboard for each sales representative, allowing for the visualization of key contact information and the progress of relationship building. The dashboard section allows for customization of, for example, the types of information displayed and the layout. This improves the transparency and reproducibility of sales activities by providing a customizable dashboard for each sales representative. Some or all of the processes described above in the dashboard section may be performed using AI, or not.

[0087] The monitoring unit can estimate the user's emotions and adjust the priority of the information it monitors based on the estimated emotions. For example, if the user is stressed, the monitoring unit will prioritize monitoring important news and social media trends, while eliminating unnecessary information. If the user is relaxed, the monitoring unit can collect a wide range of information and perform detailed analysis. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring information that requires immediate attention. This allows for the collection of more relevant information by adjusting the priority of the information monitored based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not.

[0088] The monitoring unit can select monitoring targets based on a company's performance and market trends during monitoring. For example, the monitoring unit prioritizes monitoring news related to a company's performance when the company announces its quarterly earnings. Furthermore, when market trends change rapidly, the monitoring unit can focus its monitoring on the activities of relevant companies. In addition, when a company announces a new product, the monitoring unit can monitor news and social media reactions related to that product. This allows for the collection of more important information by selecting monitoring targets based on a company's performance and market trends. Some or all of the above-described processes in the monitoring unit may be performed using AI, or they may not.

[0089] The monitoring unit can prioritize the collection of information specific to particular industries or regions during monitoring. For example, the monitoring unit can prioritize monitoring news and social media trends related to a particular industry. It can also focus on monitoring the activities of companies in a specific region. Furthermore, the monitoring unit can prioritize the collection of information on industry-specific trends and new technologies. This allows for the collection of more relevant information by prioritizing the collection of information specific to particular industries or regions. Some or all of the above-described processes in the monitoring unit may be performed using AI, or they may not.

[0090] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise display method. In this way, by adjusting the display method of the monitoring results based on the user's emotions, more appropriate information 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 monitoring unit may be performed using AI or not using AI.

[0091] The monitoring unit can collect information by combining internal and public data of a company during monitoring. For example, the monitoring unit can monitor by combining internal company data with publicly available news. It can also monitor by combining internal company data with social media trends. Furthermore, it can monitor by combining internal company data with market trends. By collecting information by combining internal and public data of a company, a more comprehensive set of results can be obtained. Some or all of the above-described processes in the monitoring unit may be performed using AI, or they may not be performed using AI.

[0092] The monitoring department can simultaneously monitor and comparatively analyze the activities of competitors during its monitoring operations. For example, it can simultaneously monitor news and social media activity of competitors and perform comparative analyses with its own company. Furthermore, the monitoring department can monitor the reactions to new product announcements by competitors and incorporate them into its own strategy. In addition, the monitoring department can monitor the impact of earnings announcements by competitors and consider countermeasures for its own company. This allows for more strategic sales activities by simultaneously monitoring and comparatively analyzing the activities of competitors. Some or all of the above processes in the monitoring department may be performed using AI, or they may not.

[0093] The identification unit can estimate the user's emotions and determine the priority of key individuals to identify based on the estimated emotions. For example, if the user is stressed, the identification unit will prioritize identifying high-priority key individuals. If the user is relaxed, the identification unit can identify a wide range of key individuals and perform a detailed analysis. Furthermore, if the user is in a hurry, the identification unit can prioritize identifying key individuals who require immediate attention. This allows for the prioritization of more important key individuals by determining the priority of key individuals based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not.

[0094] The identification unit can improve the accuracy of identification by analyzing the past activity history of key persons at the time of identification. For example, the identification unit can improve the accuracy of identification by analyzing the past transaction history and events attended by key persons. Furthermore, the identification unit can improve the accuracy of identification based on the past statements and actions of key persons. In addition, the identification unit can improve the accuracy of identification by analyzing the past projects and achievements of key persons. Thus, the accuracy of identification is improved by analyzing the past activity history of key persons. Some or all of the above processing in the identification unit may be performed using AI or not.

[0095] The identification unit can perform identification based on the key person's network and related party information at the time of identification. For example, the identification unit considers the key person's social network data and the influence of related parties when performing identification. Furthermore, the identification unit can improve the accuracy of identification based on the key person's related party information. In addition, the identification unit can visualize the key person's network and improve the accuracy of identification. As a result, the accuracy of identification is improved by performing identification based on the key person's network and related party information. Some or all of the above processing in the identification unit may be performed using AI or not.

[0096] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is nervous, the identification unit can provide a simple and highly visible display method. If the user is relaxed, the identification unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the identification unit can provide a concise display method. In this way, by adjusting the display method of the identification results based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not using AI.

[0097] The identification unit can perform identification based on the geographical location information of key persons. The identification unit considers, for example, the key person's GPS data and location information services when performing identification. Furthermore, the identification unit can propose the optimal approach method based on the key person's geographical location information. In addition, the identification unit can visualize the key person's geographical location information to improve the accuracy of identification. Thus, the accuracy of identification is improved by performing identification based on the key person's geographical location information. Some or all of the above processing in the identification unit may be performed using AI or not.

[0098] The identification unit can improve the accuracy of identification based on the key person's relevant literature and patent information at the time of identification. For example, the identification unit can improve the accuracy of identification by referring to the key person's literature database and patent database. The identification unit can also improve the accuracy of identification by analyzing the key person's relevant literature. Furthermore, the identification unit can improve the accuracy of identification based on the key person's patent information. As a result, the accuracy of identification is improved based on the key person's relevant literature and patent information. Some or all of the above processing in the identification unit may be performed using AI or not.

[0099] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present simple and easily understandable suggestions. If the user is relaxed, it can present suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can present suggestions that get straight to the point. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. 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 processing described above in the suggestion unit may be performed using AI or not.

[0100] The proposal department can optimize the proposal content based on the key person's past reactions and feedback. For example, the proposal department can optimize the proposal content by analyzing the key person's past proposal history and feedback data. Furthermore, the proposal department can optimize the proposal content based on the key person's past reactions. In addition, the proposal department can customize the proposal content by considering the key person's feedback. This allows for more effective proposals by optimizing the proposal content based on the key person's past reactions and feedback. Some or all of the above processes in the proposal department may be performed using AI, or they may not.

[0101] The proposal department can make proposals based on key players' industry trends and market trends. For example, the proposal department can optimize proposal content by referring to key players' industry reports and market analysis data. The proposal department can also analyze key players' industry trends and reflect them in the proposal content. Furthermore, the proposal department can customize proposal content based on key players' market trends. This allows for more relevant proposals by basing proposals on key players' industry trends and market trends. Some or all of the above processes in the proposal department may be performed using AI, or they may not.

[0102] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize high-priority suggestions. If the user is relaxed, the suggestion unit can offer a wide range of suggestions and provide detailed analysis. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggestions that require immediate attention. This allows for prioritizing more important suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not.

[0103] The proposal department can customize the proposal content based on the key person's social media activity. For example, the proposal department can customize the proposal content by analyzing the key person's social media posts and follower reactions. Furthermore, the proposal department can optimize the proposal content based on the key person's social media statements. In addition, the proposal department can customize the proposal content considering the key person's social media activity. This allows for more effective proposals by customizing the proposal content based on the key person's social media activity. Some or all of the above processes in the proposal department may be performed using AI, or not.

[0104] The proposal department can base its proposals on the projects and activities of key personnel. For example, the proposal department can optimize the proposal by analyzing the progress of key personnel's projects and the results of their activities. The proposal department can also analyze key personnel's related projects and reflect them in the proposal. Furthermore, the proposal department can customize the proposal based on key personnel's activities. This allows for more relevant proposals by basing them on the projects and activities of key personnel. Some or all of the above processes in the proposal department may be performed using AI, or they may not.

[0105] The dashboard can estimate the user's emotions and adjust the dashboard display based on the estimated emotions. For example, if the user is stressed, the dashboard can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting the dashboard display based on the user's emotions, more appropriate information can be provided. 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 dashboard may be performed using AI or not.

[0106] The dashboard section can select the optimal display method based on the user's past operation history when displaying the dashboard. For example, the dashboard section analyzes the user's operation logs and frequently used functions to suggest the optimal display method. The dashboard section can also prioritize the display of functions that the user frequently uses. Furthermore, the dashboard section can suggest the optimal layout based on the user's past operation history. In this way, a more user-friendly dashboard can be provided by selecting the optimal display method based on the user's past operation history. Some or all of the above processing in the dashboard section may be performed using AI, or it may be performed without using AI.

[0107] The dashboard section can provide customized information according to the user's work content and role when the dashboard is displayed. For example, the dashboard section prioritizes displaying information relevant to the user's work processes and job title. Furthermore, the dashboard section can provide customized information according to the user's role. In addition, the dashboard section can provide optimal information considering the user's work content and role. By providing customized information according to the user's work content and role, more relevant information can be provided. Some or all of the above processing in the dashboard section may be performed using AI or not.

[0108] The dashboard can estimate the user's emotions and adjust the dashboard's operation procedures based on the estimated emotions. For example, if the user is tense, the dashboard can provide simple and intuitive operation procedures. If the user is relaxed, it can provide detailed operation procedures. Furthermore, if the user is in a hurry, it can provide procedures that allow for quick operation. In this way, by adjusting the dashboard's operation procedures based on the user's emotions, a more user-friendly operation procedure 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 dashboard may be performed using AI or not.

[0109] The dashboard section can select the optimal display method based on the user's device information when displaying the dashboard. For example, if the user is using a smartphone, the dashboard section can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the dashboard section can provide a display method optimized for a larger screen. Additionally, if the user is using a desktop, the dashboard section can provide a display method that includes detailed information. By selecting the optimal display method based on the user's device information, a more user-friendly dashboard can be provided. Some or all of the above processing in the dashboard section may be performed using AI, or it may be performed without AI.

[0110] The dashboard section can display the user's schedule and task management information in an integrated manner when the dashboard is displayed. For example, the dashboard section can integrate and display the user's calendar information and data from task management tools. The dashboard section can also display appointments based on the user's schedule information. Furthermore, the dashboard section can display progress based on the user's task management information. By integrating and displaying the user's schedule and task management information, more efficient information management becomes possible. Some or all of the above processing in the dashboard section may be performed using AI, or it may be performed without using AI.

[0111] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI.

[0112] The analysis unit can improve the accuracy of its analysis based on the key person's past activity data. For example, the analysis unit can improve the accuracy of its analysis by analyzing the key person's past transaction history and events attended. Furthermore, the analysis unit can improve the accuracy of its analysis based on the key person's past statements and actions. In addition, the analysis unit can improve the accuracy of its analysis by analyzing the key person's past projects and achievements. This improves the accuracy of the analysis based on the key person's past activity data. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI.

[0113] The analysis unit can perform analysis based on key players' industry trends and market trends. For example, the analysis unit can optimize the analysis by referring to key players' industry reports and market analysis data. Furthermore, the analysis unit can analyze key players' industry trends and reflect them in the analysis. In addition, the analysis unit can customize the analysis based on key players' market trends. This allows for more relevant analysis by performing analysis based on key players' industry trends and market trends. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not.

[0114] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying high-priority analysis results. If the user is relaxed, the analysis unit can provide a wide range of analysis results and perform detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that require immediate attention. In this way, by prioritizing analysis results based on the user's emotions, more important analysis results can be provided preferentially. 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 analysis unit may be performed using AI or not.

[0115] The analysis unit can customize the analysis content based on the key person's social media activity during the analysis. For example, the analysis unit can customize the analysis content by analyzing the content of the key person's social media posts and the reactions of their followers. The analysis unit can also optimize the analysis content based on the key person's statements on social media. Furthermore, the analysis unit can customize the analysis content by taking into account the key person's social media activity. This allows for more effective analysis by customizing the analysis content based on the key person's social media activity. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0116] The analysis unit can perform analysis based on the projects and activities related to key personnel. For example, the analysis unit can optimize the analysis by analyzing the progress of key personnel's projects and the results of their activities. The analysis unit can also analyze the projects related to key personnel and reflect this in the analysis. Furthermore, the analysis unit can customize the analysis based on the activities of key personnel. This allows for more relevant analysis by performing analysis based on the projects and activities related to key personnel. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI.

[0117] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize only important notifications. If the user is relaxed, the notification unit can provide a wide range of notifications and detailed information. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications that require immediate attention. By adjusting the timing of notifications based on the user's emotions, notifications can be delivered at a more appropriate time. 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 notification unit may be performed using AI or not.

[0118] The notification unit can monitor the activity status of key personnel in real time and send notifications at the optimal time. For example, the notification unit collects activity logs and real-time data of key personnel and sends notifications at the optimal time. The notification unit can also immediately send notifications when important activities of key personnel occur. Furthermore, the notification unit can send notifications at the optimal time based on the activity status of key personnel. This allows for more effective notifications by monitoring the activity status of key personnel in real time and sending notifications at the optimal time. Some or all of the above processing in the notification unit may be performed using AI or not using AI.

[0119] The notification unit can estimate the user's emotions and customize the notification content based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible notification. If the user is relaxed, the notification unit can provide a notification containing detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a notification that gets straight to the point. By customizing the notification content based on the user's emotions, more appropriate information can be provided. 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 notification unit may be performed using AI or not.

[0120] The notification unit can send notifications based on the user's schedule and task management information. For example, the notification unit can send notifications at the optimal time based on the user's schedule information. The notification unit can also customize the notification content considering the user's task management information. Furthermore, the notification unit can integrate the user's schedule and task management information to send optimal notifications. This allows for more effective notifications by sending notifications based on the user's schedule and task management information. Some or all of the above processing in the notification unit may be performed using AI, or it may be performed without using AI.

[0121] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is tense, the visualization unit can provide a simple and highly visible display method. If the user is relaxed, the visualization unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the visualization unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the visualization based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI or not using AI.

[0122] The visualization unit can reflect and display key person activity data in real time during visualization. For example, the visualization unit reflects and visualizes key person activity data in real time. Furthermore, the visualization unit can immediately visualize when a key person's important activity occurs. In addition, the visualization unit can perform real-time visualization based on key person activity data. This allows for more effective information provision by reflecting and displaying key person activity data in real time. Some or all of the above processing in the visualization unit may be performed using AI or not.

[0123] The visualization unit can estimate the user's emotions and determine visualization priorities based on the estimated emotions. For example, if the user is stressed, the visualization unit will prioritize visualizing information of high importance. If the user is relaxed, the visualization unit can provide a wide range of information and perform detailed analysis. Furthermore, if the user is in a hurry, the visualization unit can prioritize visualizing information that requires immediate attention. In this way, by determining visualization priorities based on the user's emotions, more important information can be provided preferentially. 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.

[0124] The visualization unit can provide customized information according to the user's work content and role during visualization. For example, the visualization unit prioritizes displaying information relevant to the user's work processes and job title. Furthermore, the visualization unit can provide customized information according to the user's role. In addition, the visualization unit can provide optimal information considering the user's work content and role. This allows for the provision of more relevant information by providing customized information according to the user's work content and role. Some or all of the above processing in the visualization unit may be performed using AI or not.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The sales support AI agent system may also include a forecasting unit. This forecasting unit can predict future customer behavior and market trends based on historical data and current trends. For example, it can analyze past purchase history and seasonal market fluctuations to predict the next purchase timing. It can also predict changes in customer interests based on social media trends and news developments. Furthermore, it can predict customer life events and important company events, and adjust sales strategies accordingly. This allows for proactive sales activities and the development of more effective sales strategies. Some or all of the above-described processes in the forecasting unit may be performed using AI or not.

[0127] The sales support AI agent system can also be equipped with a feedback unit. The feedback unit can collect feedback from sales representatives and improve the system's accuracy and proposals. For example, the feedback unit can record how sales representatives reacted to proposals and reflect this in future proposals. The feedback unit can also collect the results of approaches actually taken by sales representatives and analyze success and failure cases. Furthermore, the feedback unit can collect feedback from sales representatives on the system's usability and areas for improvement, thereby improving the system's usability. As a result, by incorporating a feedback unit, the system's accuracy and proposals are continuously improved, and the effectiveness of sales activities is enhanced. Some or all of the above-described processes in the feedback unit may be performed using AI or not.

[0128] The sales support AI agent system can further estimate customer emotions using emotion estimation functionality and adjust proposals based on the estimated emotions. For example, if the customer is stressed, the proposal department can make simple and easy-to-understand proposals. If the customer is relaxed, the proposal department can make proposals that include detailed information. Furthermore, if the customer is in a hurry, the proposal department can make proposals that get straight to the point. By adjusting proposals based on customer emotions, more appropriate proposals can be made. Emotion estimation is achieved using emotion estimation functionality, 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 proposal department may be performed using AI or not.

[0129] The sales support AI agent system can further estimate customer emotions using emotion estimation functionality and adjust notification timing based on the estimated emotions. For example, if a customer is stressed, the notification unit will prioritize only important notifications. If a customer is relaxed, the notification unit can provide broader notifications and more detailed information. Furthermore, if a customer is in a hurry, the notification unit can prioritize notifications requiring immediate attention. This allows for more timely notifications by adjusting the timing based on customer emotions. Emotion estimation is achieved using emotion estimation functionality, 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 notification unit may be performed using AI or not.

[0130] The sales support AI agent system can further estimate customer emotions using emotion estimation functionality and adjust the dashboard display based on the estimated customer emotions. For example, the dashboard may provide a simple and highly visible display when the customer is tense. It may also provide a display with detailed information when the customer is relaxed. Furthermore, it may provide a concise display when the customer is in a hurry. By adjusting the dashboard display based on customer emotions, more relevant information can be provided. Emotion estimation is achieved using emotion estimation functionality, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dashboard may be performed using AI or not.

[0131] The sales support AI agent system can further estimate customer emotions using an emotion estimation function and adjust the display method of the analysis results based on the estimated customer emotions. For example, if the customer is tense, the analysis unit can provide a simple and highly visible display method. If the customer is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the customer is in a hurry, the analysis unit can provide a concise display method. In this way, by adjusting the display method of the analysis results based on customer emotions, more appropriate information 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 analysis unit may be performed using AI or not using AI.

[0132] The sales support AI agent system can also be equipped with a personalization function based on the customer's purchase history. This personalization function analyzes the customer's past purchase history and makes suggestions tailored to their individual needs. For example, it can suggest relevant products and services based on products and services the customer has purchased in the past. It can also analyze the customer's purchasing patterns and predict products and services they are likely to purchase next. Furthermore, it can suggest specific campaigns and promotions based on the customer's purchase history. By incorporating this personalization function, suggestions can be made that meet customer needs, improving the effectiveness of sales activities. Some or all of the processing described above in the personalization function may be performed using AI or not.

[0133] The sales support AI agent system can also be equipped with a predictive function based on customer behavior patterns. This predictive function can analyze past customer behavior patterns and predict future behavior. For example, it can analyze when a customer has purchased products or services in the past and predict the timing of their next purchase. It can also predict what products or services a customer will be interested in next, based on their behavior patterns. Furthermore, it can predict the effectiveness of specific campaigns or promotions based on customer behavior patterns. This predictive function allows for anticipating customer behavior and developing more effective sales strategies. Some or all of the processes described above in the predictive function may be performed using AI or not.

[0134] The sales support AI agent system can also be equipped with an improvement function based on customer feedback. This improvement function can collect customer feedback and improve the system's accuracy and proposals. For example, it can record how customers reacted to proposals and incorporate that feedback into future proposals. It can also collect the results of approaches actually taken by customers and analyze success and failure cases. Furthermore, it can collect customer feedback on the system's usability and areas for improvement, thereby enhancing the system's overall usability. As a result, the system's accuracy and proposals are continuously improved, leading to increased effectiveness in sales activities. Some or all of the processes described above in the improvement function may be performed using AI or not.

[0135] The sales support AI agent system can also be equipped with an analytical function based on the customer's social media activity. This analytical function can analyze the content of the customer's social media posts and the reactions of their followers to optimize the content of the proposals. For example, the analytical function can analyze what topics the customer is interested in on social media and make relevant proposals. Furthermore, the analytical function can predict the effectiveness of proposals based on the reactions of the customer's followers. In addition, the analytical function can optimize the effectiveness of specific campaigns and promotions based on the customer's social media activity. Thus, by incorporating the analytical function, effective proposals can be made based on the customer's social media activity. Some or all of the above-described processes in the analytical function may be performed using AI or not.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The monitoring department monitors corporate news and social media trends. For example, it can monitor news in specific industries or trends on specific social media platforms. The monitoring department uses AI to collect and analyze data in real time. Step 2: The Identification Unit identifies key customer stakeholders based on information collected by the Monitoring Unit. For example, key customer stakeholders can be identified based on criteria such as job title, influence, and decision-making authority. The Identification Unit uses AI to automatically identify key customer stakeholders. Step 3: The proposal department proposes a customized sales strategy based on the activities and interests of key individuals identified by the specific department. For example, it can propose strategies based on individual needs or past transaction history. The proposal department uses AI to propose customized sales strategies. Step 4: The dashboard section provides a customizable dashboard for each sales representative. For example, the types of information displayed and the layout can be customized. The dashboard section uses AI to visualize key contact information and the progress of relationship building.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the monitoring unit, identification unit, proposal unit, dashboard unit, analysis unit, notification unit, and visualization unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors corporate news and social media trends using the camera 42 and microphone 38B of the smart device 14 and collects data with the control unit 46A. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies key customer personnel based on the collected information. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes a customized sales strategy based on the activities and interests of the identified key personnel. The dashboard unit is implemented, for example, by the control unit 46A of the smart device 14 and provides a customizable dashboard for each sales representative. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the activities and interests of key personnel. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and notifies the timing of upsells and cross-sells. The visualization unit is implemented, for example, by the control unit 46A of the smart device 14, and visualizes the progress of relationship building. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the monitoring unit, identification unit, proposal unit, dashboard unit, analysis unit, notification unit, and visualization unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors corporate news and social media trends using the camera 42 and microphone 238 of the smart glasses 214 and collects data by the control unit 46A. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies key customer personnel based on the collected information. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes a customized sales strategy based on the activities and interests of the identified key personnel. The dashboard unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides a customizable dashboard for each sales representative. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the activities and interests of key personnel. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and notifies the timing of upsells and cross-sells. The visualization unit is implemented, for example, by the control unit 46A of the smart glasses 214, and visualizes the progress of relationship building. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the monitoring unit, identification unit, proposal unit, dashboard unit, analysis unit, notification unit, and visualization unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors corporate news and social media trends using the camera 42 and microphone 238 of the headset terminal 314 and collects data with the control unit 46A. The identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and identifies key customer personnel based on the collected information. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes a customized sales strategy based on the activities and interests of the identified key personnel. The dashboard unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides a customizable dashboard for each sales representative. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the activities and interests of key personnel. The notification unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and notifies the timing of upsells and cross-sells. The visualization unit is implemented, for example, by the control unit 46A of the headset terminal 314, and visualizes the progress of relationship building. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the monitoring unit, identification unit, proposal unit, dashboard unit, analysis unit, notification unit, and visualization unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors corporate news and social media trends using the camera 42 and microphone 238 of the robot 414 and collects data with the control unit 46A. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies key customer personnel based on the collected information. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes a customized sales strategy based on the activities and interests of the identified key personnel. The dashboard unit is implemented, for example, by the control unit 46A of the robot 414 and provides a customizable dashboard for each sales representative. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the activities and interests of key personnel. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and notifies the timing of upsells and cross-sells. The visualization unit is implemented, for example, by the control unit 46A of the robot 414, and visualizes the progress of relationship building. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) A monitoring department that monitors corporate news and social media trends, An identification unit that identifies key customer personnel based on information collected by the aforementioned monitoring unit, A proposal department that proposes customized sales strategies based on the activities and interests of key persons identified by the aforementioned specific department, It includes a dashboard section that provides a customizable dashboard for each sales representative. A system characterized by the following features. (Note 2) It includes an analysis unit that analyzes the activities and interests of key individuals. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a notification unit that notifies the timing of upselling and crossselling. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a visualization section that visualizes the progress of relationship building. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, Monitoring corporate news and social media trends in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The specified part is, Based on the information collected by the aforementioned monitoring unit, the system automatically identifies key customer contacts. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We propose customized sales strategies based on the activities and interests of identified key players. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned dashboard section is We provide a customizable dashboard for each sales representative, visualizing key contact information and the progress of relationship building. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, It estimates user sentiment and adjusts the priority of information to monitor based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, During monitoring, targets are selected based on the company's performance and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, During monitoring, prioritize the collection of information specific to particular industries or regions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned monitoring unit, During monitoring, information is collected by combining the company's internal data and publicly available data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned monitoring unit, During monitoring, the activities of competitors are also monitored and compared. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, Estimate user sentiment and prioritize key individuals to identify based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, At the time of identification, we analyze the past activity history of key individuals to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, Identifying key individuals is done based on their networks and contact information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, It estimates the user's emotions and adjusts how specific results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, When identifying key individuals, the identification process is based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, At the time of identification, the accuracy of the identification is improved based on relevant literature and patent information of key personnel. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, optimize the proposal based on the key person's past reactions and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, base it on the industry trends and market developments of key players. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, customize the proposal based on the social media activity of key decision-makers. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, base it on the projects and activities related to the key person. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dashboard section is It estimates the user's emotions and adjusts how the dashboard is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dashboard section is When displaying the dashboard, the system selects the optimal display method based on the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dashboard section is When the dashboard is displayed, it provides information customized according to the user's work content and role. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dashboard section is It estimates the user's emotions and adjusts the dashboard's operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned dashboard section is When displaying the dashboard, the system selects the optimal display method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned dashboard section is When the dashboard is displayed, the user's schedule and task management information are integrated and shown. The system described in Appendix 1, characterized by the features described herein. (Note 33) 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 34) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by using past activity data of key individuals. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit, During the analysis, we will conduct the analysis based on key players' industry trends and market developments. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned analysis unit, During the analysis, the analysis content will be customized based on the social media activity of key individuals. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned analysis unit, During the analysis, the analysis will be based on the projects and activities related to the key individuals. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned notification unit, When sending a notification, the system monitors the activity status of key personnel in real time and sends notifications at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned notification unit, It estimates the user's emotions and customizes notification content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned notification unit, When sending notifications, the system will use the user's schedule and task management information as a basis for the notification. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned visualization unit, When visualizing, key person activity data is reflected and displayed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned visualization unit, It estimates the user's emotions and determines the visualization priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned visualization unit, When visualization is performed, the information is customized according to the user's work content and role. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring department that monitors corporate news and social media trends, An identification unit that identifies key customer personnel based on information collected by the aforementioned monitoring unit, A proposal department that proposes customized sales strategies based on the activities and interests of key persons identified by the aforementioned specific department, It includes a dashboard section that provides a customizable dashboard for each sales representative. A system characterized by the following features.

2. It includes an analysis unit that analyzes the activities and interests of key individuals. The system according to feature 1.

3. It includes a notification unit that notifies the timing of upselling and crossselling. The system according to feature 1.

4. It includes a visualization section that visualizes the progress of relationship building. The system according to feature 1.

5. The aforementioned monitoring unit, Monitoring corporate news and social media trends in real time. The system according to feature 1.

6. The specified part is, Based on the information collected by the aforementioned monitoring unit, the system automatically identifies key customer contacts. The system according to feature 1.

7. The aforementioned proposal section is, We propose customized sales strategies based on the activities and interests of identified key players. The system according to feature 1.

8. The aforementioned dashboard section is We provide a customizable dashboard for each sales representative, visualizing key contact information and the progress of relationship building. The system according to feature 1.

9. The aforementioned monitoring unit, It estimates user sentiment and adjusts the priority of information to monitor based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned monitoring unit, During monitoring, targets are selected based on the company's performance and market trends. The system according to feature 1.

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