system

Abstract

The system according to this embodiment aims to enable sales personnel to acquire effective techniques and conduct sales activities with confidence. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a scenario provision unit, and a feedback unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes sales methods based on the analysis results obtained by the analysis unit. The scenario provision unit provides a scenario for simulated sales. The feedback unit evaluates the skill improvement of the salesperson.

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 the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003] <m

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that many salespersons lack confidence and have limited opportunities to acquire effective techniques.

[0005] The system according to the embodiment aims to enable salespersons to acquire effective techniques and conduct sales activities with confidence.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a scenario provision unit, and a feedback unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes sales methods based on the analysis results obtained by the analysis unit. The scenario provision unit provides a scenario for simulated sales. The feedback unit evaluates the skill improvement of sales personnel. [Effects of the Invention]

[0007] The system according to this embodiment allows sales personnel to acquire effective techniques and conduct sales activities with confidence. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The training system according to an embodiment of the present invention is a system that uses an AI agent to improve the skills of sales personnel. This training system analyzes past sales data in detail to identify sales patterns and trends. Next, it understands customer needs and reflects them in sales strategies. This allows for the proposal of customized sales methods to sales personnel. Next, it evaluates the skill level of individual sales personnel and designs individually optimized training programs. For example, the AI ​​agent provides a simulated sales scenario and conducts the simulated sales together with the sales personnel. During this time, industry-specific discussions are conducted to help acquire more practical skills. Furthermore, the AI ​​agent analyzes sales performance and predicts results. In the training phase, the AI ​​provides scripts and tactics in real time during the simulated sales to support the improvement of sales personnel's skills. In the evaluation phase, continuous skill improvement is possible through an AI-driven feedback loop. This mechanism increases the confidence of sales personnel and improves their sales skills. As a result, performance improves in a short period of time, and training efficiency is significantly improved. In addition, by providing a daily and practical learning environment by the AI ​​agent, it is expected that the performance of the entire sales team and customer satisfaction will improve. For example, an AI agent analyzes past sales data to identify sales patterns for specific products. Next, it understands customer needs and incorporates them into sales strategies. This allows for the proposal of customized sales methods to sales personnel. Furthermore, it provides simulated sales scenarios and conducts these simulations with sales personnel. During these simulations, industry-specific discussions are conducted, enabling the acquisition of more practical skills. In this way, using an AI agent can be expected to improve sales personnel's skills and boost their performance. Thus, the training system can enhance the skills of sales personnel.

[0029] The training system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a scenario provision unit, and a feedback unit. The data collection unit collects data. The data collection unit can, for example, collect past sales data and identify sales patterns and trends. The data collection unit can, for example, collect numerical data and text data such as sales data and customer data. The data collection unit can, for example, collect image data and extract data using image analysis technology. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, analyze the data using statistical analysis or machine learning algorithms. The analysis unit can, for example, understand customer needs and reflect them in sales strategies. The analysis unit can, for example, identify customer needs through surveys or analysis of purchase history. The proposal unit proposes sales methods based on the analysis results obtained by the analysis unit. The proposal unit can, for example, propose customized sales methods to sales personnel. The proposal unit can, for example, propose methods based on customer segments. The proposal unit can, for example, propose methods such as telephone sales, door-to-door sales, and online sales. The scenario provision unit provides scenarios for simulated sales. The scenario provision unit can, for example, conduct simulated sales activities through role-playing or simulations. The scenario provision unit can, for example, conduct industry-specific discussions to help users acquire more practical skills. The feedback unit evaluates the skill improvement of sales personnel. The feedback unit can, for example, analyze sales performance and predict results. The feedback unit can, for example, have AI provide scripts and tactics in real time during simulated sales activities to support the skill improvement of sales personnel. The feedback unit can, for example, enable continuous skill improvement through an AI-driven feedback loop. In this way, the training system according to this embodiment can improve the skills of sales personnel.

[0030] The data collection unit collects data. For example, it can collect historical sales data to identify sales patterns and trends. Specifically, the data collection unit collects numerical data such as sales data, customer data, and transaction history from the company's database and CRM system. This data forms the basis for analyzing increases and decreases in sales and customer purchasing behavior. The data collection unit can also collect text data such as customer feedback and survey results. This allows for an understanding of customer satisfaction and needs. Furthermore, the data collection unit can collect image data and extract data using image analysis technology. For example, it can analyze customer behavior patterns from store surveillance camera footage to identify which products customers tend to stop in front of. This allows for evaluation of product placement and the effectiveness of promotions. The data collection unit can centrally manage and update this diverse data in real time. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. For example, sales data during a specific campaign period can be collected frequently to immediately evaluate the campaign's effectiveness. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The Analysis Department analyzes data collected by the Data Collection Department. For example, the Analysis Department can analyze data using statistical analysis and machine learning algorithms. Specifically, it can use statistical analysis to understand sales data trends and seasonal fluctuations and predict future sales. It can also use machine learning algorithms to analyze customer purchasing patterns and perform customer segmentation. This makes it possible to develop optimal sales strategies for each customer. Furthermore, the Analysis Department can identify customer needs through surveys and analysis of purchase history. For example, it can analyze survey results using text mining techniques to extract the characteristics of products and services that customers desire. Analysis of purchase history can identify frequently purchased products and high-frequency customers, allowing for targeted marketing strategies to be proposed. Based on these data analysis results, the Analysis Department can discover areas for improvement in sales strategies and new business opportunities. Additionally, the Analysis Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analysis. For example, based on historical sales data, it can predict sales fluctuations during specific seasons or events and develop future countermeasures. Furthermore, by using anomaly detection algorithms, it is possible to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department proposes sales methods based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can propose customized sales methods to sales personnel. Specifically, it can propose methods based on customer segments. For example, customers who frequently purchase expensive products can be encouraged to make repeat purchases by receiving personalized emails and special discounts. New customers can be encouraged to purchase by receiving first-time purchase benefits and free samples. The Proposal Department can propose methods such as telemarketing, in-person sales, and online sales. For example, in telemarketing, effective communication can be achieved by discussing topics that interest the customer based on their past purchase history and survey results. In-person sales allow for building trust through direct conversation by visiting the customer's office or home. Online sales allow for efficient negotiations with customers in remote locations using web conferencing systems. The Proposal Department can combine these sales methods to develop the optimal sales strategy. Furthermore, the Proposal Department can provide individually customized training plans according to the skills and experience of sales personnel. For example, new sales personnel can be provided with training to acquire basic sales skills and product knowledge, while experienced sales personnel can be provided with training to improve their more advanced negotiation and leadership skills. In this way, the proposal department can support the skill development of sales personnel and contribute to improving sales performance.

[0033] The Scenario Provision Department provides scenarios for simulated sales. For example, simulated sales can be conducted through role-playing and simulations. Specifically, salespeople can acquire practical skills by role-playing based on actual sales situations. For instance, they can practice explaining products and responding to customer questions by taking on the roles of customer and salesperson. In simulations, a virtual sales scenario is set, and salespeople can gain experience close to actual sales activities by acting according to that scenario. The Scenario Provision Department also conducts industry-specific discussions to help acquire more practical skills. For example, they can create sales scenarios specific to a particular industry, allowing salespeople to learn sales methods tailored to the characteristics of that industry and customer needs. This enables salespeople to acquire the knowledge and skills necessary for sales activities in a specific industry. Furthermore, the Scenario Provision Department can automatically generate simulated sales scenarios using AI. For example, based on past sales data and customer feedback, the AI ​​can generate the optimal sales scenario and provide it to salespeople. This allows salespeople to always conduct simulated sales based on the latest information, thereby improving their skills. The scenario provision department aims to enhance the practical skills of sales personnel through these simulated sales activities, supporting them in achieving high results in actual sales operations.

[0034] The Feedback Department evaluates the skill improvement of sales personnel. For example, the Feedback Department can analyze sales performance and predict results. Specifically, it can analyze performance indicators such as sales data, customer acquisition numbers, and closing rates of sales personnel to identify each sales person's strengths and weaknesses. This allows it to provide specific feedback to individual sales personnel on areas for improvement and skills to strengthen. Furthermore, the Feedback Department can support the skill improvement of sales personnel by having AI provide scripts and tactics in real time during simulated sales. For example, the AI ​​can analyze what the sales person says and the customer's reaction, and provide appropriate advice and areas for improvement in real time. This allows sales personnel to receive immediate feedback during simulated sales and improve their skills. The Feedback Department can enable continuous skill improvement through an AI-driven feedback loop. For example, each time a sales person repeats a simulated sales, the AI ​​can update the feedback based on new data and provide more accurate advice. This allows sales personnel to always receive feedback based on the latest information and continuously improve their skills. The feedback department can support the skill development of sales personnel and contribute to improving sales performance through these evaluations and feedback.

[0035] The data collection unit can collect historical sales data and identify sales patterns and trends. For example, the data collection unit can collect historical sales data and analyze sales and customer data. For example, the data collection unit can analyze sales data in time series and identify sales patterns. For example, the data collection unit can analyze customer data and identify customer purchasing trends. This allows for the formulation of sales strategies by collecting historical sales data and identifying sales patterns and trends. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input historical sales data into a generating AI and have the generating AI identify sales patterns and trends.

[0036] The analytics department can understand customer needs and reflect them in sales strategies. For example, the analytics department can identify customer needs through surveys. For example, the analytics department can identify customer needs through the analysis of purchase history. For example, the analytics department can identify customer needs by analyzing customer behavior data. This enables effective sales activities by understanding customer needs and reflecting them in sales strategies. Some or all of the above processes in the analytics department may be performed using AI, for example, or not using AI. For example, the analytics department can input customer behavior data into a generating AI and have the generating AI perform the identification of customer needs.

[0037] The proposal department can propose customized sales methods to sales personnel. For example, the proposal department can propose methods based on customer segments. For example, the proposal department can propose methods such as telemarketing, in-person sales, and online sales. For example, the proposal department can propose customized sales methods based on customer purchase history. This improves the effectiveness of sales activities by proposing customized sales methods to sales personnel. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input customer purchase history into a generating AI and have the generating AI execute a proposal for a customized sales method.

[0038] The scenario provider can provide simulated sales scenarios and conduct simulated sales with sales personnel. The scenario provider can conduct simulated sales through, for example, role-playing or simulation. The scenario provider can conduct, for example, industry-specific discussions to help users acquire more practical skills. The scenario provider can provide simulated sales scenarios based on, for example, the components of a scenario or the criteria for scenario creation. This allows for the acquisition of practical skills by providing simulated sales scenarios and conducting simulated sales with sales personnel. Some or all of the above processes in the scenario provider may be performed using, for example, AI, or not using AI. For example, the scenario provider can input simulated sales scenarios into a generating AI and have the generating AI provide the scenarios.

[0039] The feedback unit can analyze sales performance and predict results. For example, the feedback unit can analyze sales performance based on sales data analysis methods and evaluation criteria. For example, the feedback unit can predict results using predictive models. For example, the feedback unit can analyze sales data and predict sales. This helps improve sales activities by analyzing sales performance and predicting results. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input sales data into a generating AI and have the generating AI perform result predictions.

[0040] The feedback unit can support the improvement of sales personnel's skills by having AI provide scripts and tactics in real time during simulated sales. The feedback unit can provide scripts and tactics based, for example, on the components of the script or the type of tactics. The feedback unit can provide scripts and tactics based on the scenario of the simulated sales. The feedback unit can support the improvement of sales personnel's skills by providing scripts and tactics in real time during simulated sales. This allows the AI ​​to provide scripts and tactics in real time during simulated sales, thereby supporting the improvement of sales personnel's skills. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the scenario of the simulated sales into a generating AI and have the generating AI perform the task of providing scripts and tactics.

[0041] The feedback unit can enable continuous skill improvement through an AI-driven feedback loop. The feedback unit can implement a feedback loop based, for example, on the frequency of feedback and evaluation criteria. The feedback unit can continuously evaluate the skill improvement of sales personnel and provide feedback. The feedback unit can support the skill improvement of sales personnel by implementing a feedback loop using AI. As a result, the skills of sales personnel will continuously improve through continuous skill improvement using an AI-driven feedback loop. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input sales personnel skill improvement data into a generating AI and have the generating AI execute the feedback loop.

[0042] The scenario provider can conduct industry-specific discussions and enable participants to acquire more practical skills. The scenario provider can conduct industry-specific discussions based, for example, on discussion themes and participant selection criteria. The scenario provider can enable sales personnel to acquire practical skills through discussions on specific industries. The scenario provider can enable sales personnel to acquire industry-specific knowledge and skills through industry-specific discussions. This allows for the acquisition of more practical skills by conducting industry-specific discussions. Some or all of the above processes in the scenario provider can be performed using, for example, AI, or not using AI. For example, the scenario provider can input industry-specific discussion themes into a generating AI and have the generating AI execute the discussions.

[0043] The data collection unit can focus on specific products or campaigns when collecting historical sales data. For example, the data collection unit can collect sales data for a specific product and analyze its sales patterns. For example, the data collection unit can collect sales data during a campaign period to evaluate the effectiveness of past campaigns. For example, the data collection unit can collect sales data before and after the introduction of a new product and evaluate its effectiveness. This allows for the development of more effective sales strategies by focusing data collection on specific products or campaigns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sales data for a specific product into a generating AI and have the generating AI perform an analysis of sales patterns.

[0044] The data collection unit can collect individual performance data of sales personnel during data collection and incorporate it into individual training plans. For example, the data collection unit can collect monthly sales data for each sales person and create an individual training plan. For example, the data collection unit can collect the closing rate of sales personnel and design a training plan to improve the closing rate. For example, the data collection unit can collect the customer interaction time of sales personnel and provide a training plan for efficient customer interaction. In this way, by collecting individual performance data of sales personnel, it is possible to provide individually optimized training plans. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the performance data of sales personnel into a generating AI and have the generating AI create a training plan.

[0045] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of sales personnel during data collection. For example, the data collection unit can prioritize the collection of sales data for areas visited by sales personnel. For example, the data collection unit can collect region-specific customer needs based on the sales personnel's activity area. For example, the data collection unit can prioritize the collection of data along the sales personnel's travel routes. This allows for addressing region-specific needs by collecting data while considering the geographical location information of sales personnel. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of sales personnel into a generating AI and have the generating AI perform the collection of highly relevant data.

[0046] The data collection unit can analyze the social media activities of sales personnel and collect relevant data during data collection. For example, the data collection unit can analyze the content of sales personnel's social media activities and collect relevant customer data. For example, the data collection unit can analyze the reactions of sales personnel's social media followers and collect customer needs. For example, the data collection unit can analyze the content of sales personnel's social media posts and collect sales-related data. In this way, relevant data can be collected by analyzing the social media activities of sales personnel. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sales personnel's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0047] The analytics department can analyze customer purchase history and behavioral patterns in detail during analysis and reflect this in sales strategies. For example, the analytics department can analyze a customer's past purchase history to identify purchasing trends. For example, the analytics department can analyze a customer's website visit history to identify products of interest. For example, the analytics department can analyze a customer's past inquiry history to understand their needs. By analyzing customer purchase history and behavioral patterns in detail, effective sales strategies can be formulated. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input customer purchase history data into a generating AI and have the generating AI perform behavioral pattern analysis.

[0048] The analysis department can customize the analysis results by taking into account trends in specific industries and markets during the analysis process. For example, the analysis department can customize the analysis results by taking into account the latest trends in a specific industry. For example, the analysis department can customize the analysis results by taking into account seasonal fluctuations in the market. For example, the analysis department can customize the analysis results by taking into account the activities of competitors in the industry. By customizing the analysis results by taking into account trends in specific industries and markets, more accurate analysis results can be provided. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input industry trend data into a generating AI and have the generating AI perform the customization of the analysis results.

[0049] The analysis unit can customize the analysis results by taking into account the customer's geographical location information during the analysis. For example, the analysis unit can provide analysis results that reflect region-specific needs based on the customer's location. For example, the analysis unit can customize the analysis results by taking into account the customer's geographical travel patterns. For example, the analysis unit can propose the optimal sales strategy based on the customer's location. In this way, by customizing the analysis results by taking into account the customer's geographical location information, region-specific needs can be addressed. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the customer's geographical location information into a generating AI and have the generating AI perform the customization of the analysis results.

[0050] The analysis unit can supplement its analysis results by referring to the customer's social media activity during the analysis process. For example, the analysis unit can analyze the customer's social media activity to understand their purchasing intent. For example, the analysis unit can analyze the reactions of the customer's social media followers to identify their needs. For example, the analysis unit can analyze the content of the customer's social media posts to identify products of interest. By referring to the customer's social media activity, it is possible to provide more accurate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer social media activity data into a generating AI and have the generating AI perform the task of supplementing the analysis results.

[0051] The proposal department can propose customized sales methods by referring to the salesperson's past success stories when making a proposal. For example, the proposal department can analyze the salesperson's past success stories and propose the optimal sales method. For example, the proposal department can propose effective sales methods based on the salesperson's past closing rate. For example, the proposal department can refer to the salesperson's past customer interaction history and propose customized sales methods. In this way, effective sales methods can be proposed by referring to the salesperson's past success stories. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input salesperson success story data into a generating AI and have the generating AI execute a proposal for a customized sales method.

[0052] The proposal unit can apply different proposal algorithms to specific customer segments when making proposals. For example, the proposal unit can apply different proposal algorithms depending on the customer's age group. For example, the proposal unit can apply different proposal algorithms based on the customer's purchase history. For example, the proposal unit can apply different proposal algorithms depending on the customer's regional characteristics. This makes it possible to make more effective proposals by applying different proposal algorithms to specific customer segments. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input customer segment data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0053] The proposal unit can make optimal proposals by considering the geographical location information of sales personnel. For example, the proposal unit can make region-specific proposals based on the sales personnel's activity area. For example, the proposal unit can make proposals along the sales personnel's travel routes. For example, the proposal unit can make optimal proposals by considering the characteristics of the regions visited by sales personnel. In this way, by making proposals while considering the geographical location information of sales personnel, region-specific needs can be addressed. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the geographical location information of sales personnel into a generating AI and have the generating AI execute the optimal proposal.

[0054] The proposal department can analyze the social media activities of sales personnel and make relevant proposals when making proposals. For example, the proposal department can analyze the content of sales personnel's social media activities and make relevant proposals. For example, the proposal department can analyze the reactions of sales personnel's social media followers and make proposals that meet their needs. For example, the proposal department can analyze the content of sales personnel's social media posts and make proposals based on products of interest. In this way, relevant proposals can be made by analyzing the social media activities of sales personnel. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input sales personnel's social media activity data into a generating AI and have the generating AI execute relevant proposals.

[0055] The scenario provider can provide simulated sales scenarios that reflect trends in specific industries or markets. For example, the scenario provider can provide simulated sales scenarios that reflect the latest industry trends. For example, the scenario provider can provide simulated sales scenarios that take into account seasonal market fluctuations. For example, the scenario provider can provide simulated sales scenarios that reflect the activities of competitors in the industry. This allows users to acquire more practical skills by providing simulated sales scenarios that reflect trends in specific industries or markets. Some or all of the above-described processes in the scenario provider may be performed using AI, for example, or not using AI. For example, the scenario provider can input industry trend data into a generating AI and have the generating AI perform the task of providing simulated sales scenarios.

[0056] The scenario provider can provide customized scenarios by referencing the salesperson's past performance data when providing scenarios. For example, the scenario provider can provide a customized simulated sales scenario based on the salesperson's past closing rate. For example, the scenario provider can provide a customized scenario by referencing the salesperson's past customer interaction history. For example, the scenario provider can provide an optimal simulated sales scenario based on the salesperson's past sales data. This allows for the provision of customized scenarios by referencing the salesperson's past performance data. Some or all of the above processing in the scenario provider may be performed using AI, for example, or without AI. For example, the scenario provider can input the salesperson's performance data into a generating AI and have the generating AI execute the provision of a customized scenario.

[0057] The scenario provider can provide the optimal scenario by considering the geographical location information of the salesperson when providing a scenario. For example, the scenario provider can provide region-specific scenarios based on the salesperson's activity area. For example, the scenario provider can provide scenarios that follow the salesperson's travel route. For example, the scenario provider can provide the optimal scenario by considering the characteristics of the region the salesperson visits. In this way, by providing scenarios that consider the geographical location information of the salesperson, region-specific needs can be addressed. Some or all of the above processing in the scenario provider can be performed using AI, for example, or without using AI. For example, the scenario provider can input the geographical location information of the salesperson into a generating AI and have the generating AI execute the provision of the optimal scenario.

[0058] The scenario provisioning unit can analyze the social media activities of sales personnel and provide relevant scenarios when providing scenarios. For example, the scenario provisioning unit can analyze the content of sales personnel's social media activities and provide relevant scenarios. For example, the scenario provisioning unit can analyze the reactions of sales personnel's social media followers and provide scenarios that meet their needs. For example, the scenario provisioning unit can analyze the content of sales personnel's social media posts and provide scenarios based on products of interest. In this way, relevant scenarios can be provided by analyzing the social media activities of sales personnel. Some or all of the above processing in the scenario provisioning unit may be performed using AI, for example, or without AI. For example, the scenario provisioning unit can input sales personnel's social media activity data into a generating AI and have the generating AI perform the provision of relevant scenarios.

[0059] The feedback unit can provide customized feedback by referring to the salesperson's past performance data when providing feedback. For example, the feedback unit can provide customized feedback based on the salesperson's past closing rate. For example, the feedback unit can provide customized feedback by referring to the salesperson's past customer interaction history. For example, the feedback unit can provide optimal feedback based on the salesperson's past sales data. In this way, customized feedback can be provided by referring to the salesperson's past performance data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the salesperson's performance data into a generating AI and have the generating AI perform the task of providing customized feedback.

[0060] The feedback unit can provide feedback that reflects trends in specific industries or markets. For example, it can provide feedback that reflects the latest industry trends. For example, it can provide feedback that takes into account seasonal market fluctuations. For example, it can provide feedback that reflects the actions of competitors in the industry. This allows for more practical advice by providing feedback that reflects trends in specific industries or markets. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input industry trend data into a generating AI and have the generating AI perform the task of providing feedback.

[0061] The feedback unit can provide optimal feedback by considering the geographical location information of the salesperson. For example, the feedback unit can provide region-specific feedback based on the salesperson's activity area. For example, the feedback unit can provide feedback along the salesperson's travel route. For example, the feedback unit can provide optimal feedback by considering the characteristics of the region the salesperson visits. This allows for addressing region-specific needs by providing feedback while considering the geographical location information of the salesperson. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the salesperson's geographical location information into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0062] The feedback unit can analyze the social media activities of sales personnel and provide relevant feedback when providing feedback. For example, the feedback unit can analyze the content of sales personnel's social media activities and provide relevant feedback. For example, the feedback unit can analyze the reactions of sales personnel's social media followers and provide feedback tailored to their needs. For example, the feedback unit can analyze the content of sales personnel's social media posts and provide feedback based on products of interest. In this way, relevant feedback can be provided by analyzing the social media activities of sales personnel. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the social media activity data of sales personnel into a generating AI and have the generating AI perform the provision of relevant feedback.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The training system can further incorporate gamification elements. For example, salespeople could be awarded badges or points for achieving high scores in simulated sales activities. This would motivate salespeople and encourage active participation in training. A ranking system could also be implemented to allow salespeople to compete with each other, offering rewards to top performers. Furthermore, training progress could be visualized, and real-time feedback could be provided to help them achieve their goals. This would allow salespeople to more easily feel their own growth and encourage continuous skill improvement.

[0065] The data collection unit can analyze the tone of voice and speaking patterns of salespeople to evaluate the quality of their communication with customers. For example, it can analyze how a salesperson's tone of voice affects customer reactions and suggest optimal speaking styles. By analyzing speaking patterns, it can also identify effective communication techniques for capturing customer interest. Furthermore, by transcribing customer conversations into text and analyzing the frequency of keyword and phrase usage, it is possible to gain a deeper understanding of customer needs and interests. This allows salespeople to improve their communication skills with customers.

[0066] The proposal department can analyze past failures of sales personnel and make suggestions to avoid similar mistakes. For example, they can identify the reasons why deals were not closed in the past and propose methods to resolve those causes. They can also share lessons learned from failures to improve the skills of all sales personnel. Furthermore, by creating scenarios based on failures and putting them into practice in simulated sales, they can provide sales personnel with opportunities to learn through experience. This allows sales personnel to challenge themselves without fear of failure and grow.

[0067] The feedback department can evaluate salespeople's nonverbal communication skills and suggest areas for improvement. For example, it can analyze a salesperson's body language and the frequency of eye contact to suggest effective nonverbal communication techniques. It can also analyze a salesperson's facial expressions and gestures to provide advice on improving their impression on customers. Furthermore, it can provide real-time feedback on nonverbal communication during simulated sales activities, allowing salespeople to implement improvements on the spot. This can ultimately improve the overall communication skills of salespeople.

[0068] The data collection unit can gather health data from sales personnel and incorporate it into training plans. For example, it can monitor sales personnel's heart rate and stress levels to provide training plans tailored to their health condition. It can also collect sleep data from sales personnel to check if they are getting enough rest. Furthermore, it can record sales personnel's diet and exercise habits to provide advice to support a healthy lifestyle. This allows for the provision of training plans that take into account the health condition of sales personnel, thereby improving overall performance.

[0069] The scenario provision department can provide scenarios that take into account the cultural background of sales personnel. For example, when sales personnel interact with customers from different cultural backgrounds, the department can help them understand cultural differences and propose appropriate communication methods. It can also provide scenarios based on cultural backgrounds, enabling sales personnel to acquire cross-cultural communication skills. Furthermore, it can create simulated sales scenarios that take cultural differences into account, allowing sales personnel to acquire practical skills. As a result, sales personnel can conduct effective sales activities with customers from diverse cultural backgrounds.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection unit collects data. For example, it can collect historical sales data to identify sales patterns and trends. The data collection unit can collect numerical data such as sales data and customer data, as well as text data. It can also collect image data and extract data using image analysis technology. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they can use statistical analysis and machine learning algorithms to analyze the data, understand customer needs, and reflect them in sales strategies. They can also identify customer needs through surveys and analysis of purchase history. Step 3: The proposal department proposes sales methods based on the analysis results obtained by the analysis department. For example, they can propose customized sales methods to sales personnel. They can propose methods based on customer segments, as well as methods such as telephone sales, in-person sales, and online sales. Step 4: The scenario provider will provide scenarios for simulated sales activities. For example, simulated sales activities can be conducted through role-playing or simulations. Industry-specific discussions can be held to help participants acquire more practical skills. Step 5: The feedback department evaluates the skill improvement of sales personnel. For example, it can analyze sales performance and predict results. In simulated sales, AI can provide scripts and tactics in real time to support the skill improvement of sales personnel. Continuous skill improvement can be achieved through an AI-driven feedback loop.

[0072] (Example of form 2) The training system according to an embodiment of the present invention is a system that uses an AI agent to improve the skills of sales personnel. This training system analyzes past sales data in detail to identify sales patterns and trends. Next, it understands customer needs and reflects them in sales strategies. This allows for the proposal of customized sales methods to sales personnel. Next, it evaluates the skill level of individual sales personnel and designs individually optimized training programs. For example, the AI ​​agent provides a simulated sales scenario and conducts the simulated sales together with the sales personnel. During this time, industry-specific discussions are conducted to help acquire more practical skills. Furthermore, the AI ​​agent analyzes sales performance and predicts results. In the training phase, the AI ​​provides scripts and tactics in real time during the simulated sales to support the improvement of sales personnel's skills. In the evaluation phase, continuous skill improvement is possible through an AI-driven feedback loop. This mechanism increases the confidence of sales personnel and improves their sales skills. As a result, performance improves in a short period of time, and training efficiency is significantly improved. In addition, by providing a daily and practical learning environment by the AI ​​agent, it is expected that the performance of the entire sales team and customer satisfaction will improve. For example, an AI agent analyzes past sales data to identify sales patterns for specific products. Next, it understands customer needs and incorporates them into sales strategies. This allows for the proposal of customized sales methods to sales personnel. Furthermore, it provides simulated sales scenarios and conducts these simulations with sales personnel. During these simulations, industry-specific discussions are conducted, enabling the acquisition of more practical skills. In this way, using an AI agent can be expected to improve sales personnel's skills and boost their performance. Thus, the training system can enhance the skills of sales personnel.

[0073] The training system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a scenario provision unit, and a feedback unit. The data collection unit collects data. The data collection unit can, for example, collect past sales data and identify sales patterns and trends. The data collection unit can, for example, collect numerical data and text data such as sales data and customer data. The data collection unit can, for example, collect image data and extract data using image analysis technology. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, analyze the data using statistical analysis or machine learning algorithms. The analysis unit can, for example, understand customer needs and reflect them in sales strategies. The analysis unit can, for example, identify customer needs through surveys or analysis of purchase history. The proposal unit proposes sales methods based on the analysis results obtained by the analysis unit. The proposal unit can, for example, propose customized sales methods to sales personnel. The proposal unit can, for example, propose methods based on customer segments. The proposal unit can, for example, propose methods such as telephone sales, door-to-door sales, and online sales. The scenario provision unit provides scenarios for simulated sales. The scenario provision unit can, for example, conduct simulated sales activities through role-playing or simulations. The scenario provision unit can, for example, conduct industry-specific discussions to help users acquire more practical skills. The feedback unit evaluates the skill improvement of sales personnel. The feedback unit can, for example, analyze sales performance and predict results. The feedback unit can, for example, have AI provide scripts and tactics in real time during simulated sales activities to support the skill improvement of sales personnel. The feedback unit can, for example, enable continuous skill improvement through an AI-driven feedback loop. In this way, the training system according to this embodiment can improve the skills of sales personnel.

[0074] The data collection unit collects data. For example, it can collect historical sales data to identify sales patterns and trends. Specifically, the data collection unit collects numerical data such as sales data, customer data, and transaction history from the company's database and CRM system. This data forms the basis for analyzing increases and decreases in sales and customer purchasing behavior. The data collection unit can also collect text data such as customer feedback and survey results. This allows for an understanding of customer satisfaction and needs. Furthermore, the data collection unit can collect image data and extract data using image analysis technology. For example, it can analyze customer behavior patterns from store surveillance camera footage to identify which products customers tend to stop in front of. This allows for evaluation of product placement and the effectiveness of promotions. The data collection unit can centrally manage and update this diverse data in real time. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. For example, sales data during a specific campaign period can be collected frequently to immediately evaluate the campaign's effectiveness. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0075] The Analysis Department analyzes data collected by the Data Collection Department. For example, the Analysis Department can analyze data using statistical analysis and machine learning algorithms. Specifically, it can use statistical analysis to understand sales data trends and seasonal fluctuations and predict future sales. It can also use machine learning algorithms to analyze customer purchasing patterns and perform customer segmentation. This makes it possible to develop optimal sales strategies for each customer. Furthermore, the Analysis Department can identify customer needs through surveys and analysis of purchase history. For example, it can analyze survey results using text mining techniques to extract the characteristics of products and services that customers desire. Analysis of purchase history can identify frequently purchased products and high-frequency customers, allowing for targeted marketing strategies to be proposed. Based on these data analysis results, the Analysis Department can discover areas for improvement in sales strategies and new business opportunities. Additionally, the Analysis Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analysis. For example, based on historical sales data, it can predict sales fluctuations during specific seasons or events and develop future countermeasures. Furthermore, by using anomaly detection algorithms, it is possible to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0076] The Proposal Department proposes sales methods based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can propose customized sales methods to sales personnel. Specifically, it can propose methods based on customer segments. For example, customers who frequently purchase expensive products can be encouraged to make repeat purchases by receiving personalized emails and special discounts. New customers can be encouraged to purchase by receiving first-time purchase benefits and free samples. The Proposal Department can propose methods such as telemarketing, in-person sales, and online sales. For example, in telemarketing, effective communication can be achieved by discussing topics that interest the customer based on their past purchase history and survey results. In-person sales allow for building trust through direct conversation by visiting the customer's office or home. Online sales allow for efficient negotiations with customers in remote locations using web conferencing systems. The Proposal Department can combine these sales methods to develop the optimal sales strategy. Furthermore, the Proposal Department can provide individually customized training plans according to the skills and experience of sales personnel. For example, new sales personnel can be provided with training to acquire basic sales skills and product knowledge, while experienced sales personnel can be provided with training to improve their more advanced negotiation and leadership skills. In this way, the proposal department can support the skill development of sales personnel and contribute to improving sales performance.

[0077] The Scenario Provision Department provides scenarios for simulated sales. For example, simulated sales can be conducted through role-playing and simulations. Specifically, salespeople can acquire practical skills by role-playing based on actual sales situations. For instance, they can practice explaining products and responding to customer questions by taking on the roles of customer and salesperson. In simulations, a virtual sales scenario is set, and salespeople can gain experience close to actual sales activities by acting according to that scenario. The Scenario Provision Department also conducts industry-specific discussions to help acquire more practical skills. For example, they can create sales scenarios specific to a particular industry, allowing salespeople to learn sales methods tailored to the characteristics of that industry and customer needs. This enables salespeople to acquire the knowledge and skills necessary for sales activities in a specific industry. Furthermore, the Scenario Provision Department can automatically generate simulated sales scenarios using AI. For example, based on past sales data and customer feedback, the AI ​​can generate the optimal sales scenario and provide it to salespeople. This allows salespeople to always conduct simulated sales based on the latest information, thereby improving their skills. The scenario provision department aims to enhance the practical skills of sales personnel through these simulated sales activities, supporting them in achieving high results in actual sales operations.

[0078] The Feedback Department evaluates the skill improvement of sales personnel. For example, the Feedback Department can analyze sales performance and predict results. Specifically, it can analyze performance indicators such as sales data, customer acquisition numbers, and closing rates of sales personnel to identify each sales person's strengths and weaknesses. This allows it to provide specific feedback to individual sales personnel on areas for improvement and skills to strengthen. Furthermore, the Feedback Department can support the skill improvement of sales personnel by having AI provide scripts and tactics in real time during simulated sales. For example, the AI ​​can analyze what the sales person says and the customer's reaction, and provide appropriate advice and areas for improvement in real time. This allows sales personnel to receive immediate feedback during simulated sales and improve their skills. The Feedback Department can enable continuous skill improvement through an AI-driven feedback loop. For example, each time a sales person repeats a simulated sales, the AI ​​can update the feedback based on new data and provide more accurate advice. This allows sales personnel to always receive feedback based on the latest information and continuously improve their skills. The feedback department can support the skill development of sales personnel and contribute to improving sales performance through these evaluations and feedback.

[0079] The data collection unit can collect historical sales data and identify sales patterns and trends. For example, the data collection unit can collect historical sales data and analyze sales and customer data. For example, the data collection unit can analyze sales data in time series and identify sales patterns. For example, the data collection unit can analyze customer data and identify customer purchasing trends. This allows for the formulation of sales strategies by collecting historical sales data and identifying sales patterns and trends. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input historical sales data into a generating AI and have the generating AI identify sales patterns and trends.

[0080] The analytics department can understand customer needs and reflect them in sales strategies. For example, the analytics department can identify customer needs through surveys. For example, the analytics department can identify customer needs through the analysis of purchase history. For example, the analytics department can identify customer needs by analyzing customer behavior data. This enables effective sales activities by understanding customer needs and reflecting them in sales strategies. Some or all of the above processes in the analytics department may be performed using AI, for example, or not using AI. For example, the analytics department can input customer behavior data into a generating AI and have the generating AI perform the identification of customer needs.

[0081] The proposal department can propose customized sales methods to sales personnel. For example, the proposal department can propose methods based on customer segments. For example, the proposal department can propose methods such as telemarketing, in-person sales, and online sales. For example, the proposal department can propose customized sales methods based on customer purchase history. This improves the effectiveness of sales activities by proposing customized sales methods to sales personnel. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input customer purchase history into a generating AI and have the generating AI execute a proposal for a customized sales method.

[0082] The scenario provider can provide simulated sales scenarios and conduct simulated sales with sales personnel. The scenario provider can conduct simulated sales through, for example, role-playing or simulation. The scenario provider can conduct, for example, industry-specific discussions to help users acquire more practical skills. The scenario provider can provide simulated sales scenarios based on, for example, the components of a scenario or the criteria for scenario creation. This allows for the acquisition of practical skills by providing simulated sales scenarios and conducting simulated sales with sales personnel. Some or all of the above processes in the scenario provider may be performed using, for example, AI, or not using AI. For example, the scenario provider can input simulated sales scenarios into a generating AI and have the generating AI provide the scenarios.

[0083] The feedback unit can analyze sales performance and predict results. For example, the feedback unit can analyze sales performance based on sales data analysis methods and evaluation criteria. For example, the feedback unit can predict results using predictive models. For example, the feedback unit can analyze sales data and predict sales. This helps improve sales activities by analyzing sales performance and predicting results. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input sales data into a generating AI and have the generating AI perform result predictions.

[0084] The feedback unit can support the improvement of sales personnel's skills by having AI provide scripts and tactics in real time during simulated sales. The feedback unit can provide scripts and tactics based, for example, on the components of the script or the type of tactics. The feedback unit can provide scripts and tactics based on the scenario of the simulated sales. The feedback unit can support the improvement of sales personnel's skills by providing scripts and tactics in real time during simulated sales. This allows the AI ​​to provide scripts and tactics in real time during simulated sales, thereby supporting the improvement of sales personnel's skills. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the scenario of the simulated sales into a generating AI and have the generating AI perform the task of providing scripts and tactics.

[0085] The feedback unit can enable continuous skill improvement through an AI-driven feedback loop. The feedback unit can implement a feedback loop based, for example, on the frequency of feedback and evaluation criteria. The feedback unit can continuously evaluate the skill improvement of sales personnel and provide feedback. The feedback unit can support the skill improvement of sales personnel by implementing a feedback loop using AI. As a result, the skills of sales personnel will continuously improve through continuous skill improvement using an AI-driven feedback loop. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input sales personnel skill improvement data into a generating AI and have the generating AI execute the feedback loop.

[0086] The scenario provider can conduct industry-specific discussions and enable participants to acquire more practical skills. The scenario provider can conduct industry-specific discussions based, for example, on discussion themes and participant selection criteria. The scenario provider can enable sales personnel to acquire practical skills through discussions on specific industries. The scenario provider can enable sales personnel to acquire industry-specific knowledge and skills through industry-specific discussions. This allows for the acquisition of more practical skills by conducting industry-specific discussions. Some or all of the above processes in the scenario provider can be performed using, for example, AI, or not using AI. For example, the scenario provider can input industry-specific discussion themes into a generating AI and have the generating AI execute the discussions.

[0087] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The data collection unit can estimate the user's emotions using, for example, emotion recognition technology. The data collection unit can estimate emotions by, for example, analyzing the user's facial expressions and voice. The data collection unit can adjust the frequency and timing of data collection based on the user's emotions. This reduces the burden on the user by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0088] The data collection unit can focus on specific products or campaigns when collecting historical sales data. For example, the data collection unit can collect sales data for a specific product and analyze its sales patterns. For example, the data collection unit can collect sales data during a campaign period to evaluate the effectiveness of past campaigns. For example, the data collection unit can collect sales data before and after the introduction of a new product and evaluate its effectiveness. This allows for the development of more effective sales strategies by focusing data collection on specific products or campaigns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sales data for a specific product into a generating AI and have the generating AI perform an analysis of sales patterns.

[0089] The data collection unit can collect individual performance data of sales personnel during data collection and incorporate it into individual training plans. For example, the data collection unit can collect monthly sales data for each sales person and create an individual training plan. For example, the data collection unit can collect the closing rate of sales personnel and design a training plan to improve the closing rate. For example, the data collection unit can collect the customer interaction time of sales personnel and provide a training plan for efficient customer interaction. In this way, by collecting individual performance data of sales personnel, it is possible to provide individually optimized training plans. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the performance data of sales personnel into a generating AI and have the generating AI create a training plan.

[0090] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. The data collection unit can estimate the user's emotions using, for example, emotion recognition technology. The data collection unit can estimate emotions by, for example, analyzing the user's facial expressions and voice. The data collection unit can determine the priority of data to collect based on the user's emotions. This allows for the priority collection of important data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of sales personnel during data collection. For example, the data collection unit can prioritize the collection of sales data for areas visited by sales personnel. For example, the data collection unit can collect region-specific customer needs based on the sales personnel's activity area. For example, the data collection unit can prioritize the collection of data along the sales personnel's travel routes. This allows for addressing region-specific needs by collecting data while considering the geographical location information of sales personnel. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of sales personnel into a generating AI and have the generating AI perform the collection of highly relevant data.

[0092] The data collection unit can analyze the social media activities of sales personnel and collect relevant data during data collection. For example, the data collection unit can analyze the content of sales personnel's social media activities and collect relevant customer data. For example, the data collection unit can analyze the reactions of sales personnel's social media followers and collect customer needs. For example, the data collection unit can analyze the content of sales personnel's social media posts and collect sales-related data. In this way, relevant data can be collected by analyzing the social media activities of sales personnel. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sales personnel's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0093] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, emotion recognition technology. The analysis unit can estimate emotions by analyzing the user's facial expressions and voice, for example. The analysis unit can adjust the presentation of the analysis results based on the user's emotions, for example. By adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The analytics department can analyze customer purchase history and behavioral patterns in detail during analysis and reflect this in sales strategies. For example, the analytics department can analyze a customer's past purchase history to identify purchasing trends. For example, the analytics department can analyze a customer's website visit history to identify products of interest. For example, the analytics department can analyze a customer's past inquiry history to understand their needs. By analyzing customer purchase history and behavioral patterns in detail, effective sales strategies can be formulated. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input customer purchase history data into a generating AI and have the generating AI perform behavioral pattern analysis.

[0095] The analysis department can customize the analysis results by taking into account trends in specific industries and markets during the analysis process. For example, the analysis department can customize the analysis results by taking into account the latest trends in a specific industry. For example, the analysis department can customize the analysis results by taking into account seasonal fluctuations in the market. For example, the analysis department can customize the analysis results by taking into account the activities of competitors in the industry. By customizing the analysis results by taking into account trends in specific industries and markets, more accurate analysis results can be provided. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input industry trend data into a generating AI and have the generating AI perform the customization of the analysis results.

[0096] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, emotion recognition technology. The analysis unit can estimate emotions by, for example, analyzing the user's facial expressions and voice. The analysis unit can determine the priority of analysis results based on the user's emotions. This allows important analysis results to be provided preferentially by determining the priority of analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The analysis unit can customize the analysis results by taking into account the customer's geographical location information during the analysis. For example, the analysis unit can provide analysis results that reflect region-specific needs based on the customer's location. For example, the analysis unit can customize the analysis results by taking into account the customer's geographical travel patterns. For example, the analysis unit can propose the optimal sales strategy based on the customer's location. In this way, by customizing the analysis results by taking into account the customer's geographical location information, region-specific needs can be addressed. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the customer's geographical location information into a generating AI and have the generating AI perform the customization of the analysis results.

[0098] The analysis unit can supplement its analysis results by referring to the customer's social media activity during the analysis process. For example, the analysis unit can analyze the customer's social media activity to understand their purchasing intent. For example, the analysis unit can analyze the reactions of the customer's social media followers to identify their needs. For example, the analysis unit can analyze the content of the customer's social media posts to identify products of interest. By referring to the customer's social media activity, it is possible to provide more accurate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer social media activity data into a generating AI and have the generating AI perform the task of supplementing the analysis results.

[0099] The proposal unit can estimate the user's emotions and adjust the way the proposal is presented based on the estimated user emotions. The proposal unit can estimate the user's emotions using, for example, emotion recognition technology. The proposal unit can estimate emotions by, for example, analyzing the user's facial expressions and voice. The proposal unit can adjust the way the proposal is presented based on the user's emotions. By adjusting the way the proposal is presented based on the user's emotions, it is possible to provide proposals that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using 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 proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The proposal department can propose customized sales methods by referring to the salesperson's past success stories when making a proposal. For example, the proposal department can analyze the salesperson's past success stories and propose the optimal sales method. For example, the proposal department can propose effective sales methods based on the salesperson's past closing rate. For example, the proposal department can refer to the salesperson's past customer interaction history and propose customized sales methods. In this way, effective sales methods can be proposed by referring to the salesperson's past success stories. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input salesperson success story data into a generating AI and have the generating AI execute a proposal for a customized sales method.

[0101] The proposal unit can apply different proposal algorithms to specific customer segments when making proposals. For example, the proposal unit can apply different proposal algorithms depending on the customer's age group. For example, the proposal unit can apply different proposal algorithms based on the customer's purchase history. For example, the proposal unit can apply different proposal algorithms depending on the customer's regional characteristics. This makes it possible to make more effective proposals by applying different proposal algorithms to specific customer segments. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input customer segment data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0102] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. The suggestion unit can estimate the user's emotions using, for example, emotion recognition technology. The suggestion unit can estimate emotions by, for example, analyzing the user's facial expressions or voice. The suggestion unit can determine the priority of suggestions based on the user's emotions. This allows important suggestions to be provided preferentially by determining the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The proposal unit can make optimal proposals by considering the geographical location information of sales personnel. For example, the proposal unit can make region-specific proposals based on the sales personnel's activity area. For example, the proposal unit can make proposals along the sales personnel's travel routes. For example, the proposal unit can make optimal proposals by considering the characteristics of the regions visited by sales personnel. In this way, by making proposals while considering the geographical location information of sales personnel, region-specific needs can be addressed. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the geographical location information of sales personnel into a generating AI and have the generating AI execute the optimal proposal.

[0104] The proposal department can analyze the social media activities of sales personnel and make relevant proposals when making proposals. For example, the proposal department can analyze the content of sales personnel's social media activities and make relevant proposals. For example, the proposal department can analyze the reactions of sales personnel's social media followers and make proposals that meet their needs. For example, the proposal department can analyze the content of sales personnel's social media posts and make proposals based on products of interest. In this way, relevant proposals can be made by analyzing the social media activities of sales personnel. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input sales personnel's social media activity data into a generating AI and have the generating AI execute relevant proposals.

[0105] The scenario provider can estimate the user's emotions and adjust the way the scenario is presented based on the estimated user emotions. For example, the scenario provider can estimate the user's emotions using emotion recognition technology. For example, the scenario provider can estimate emotions by analyzing the user's facial expressions and voice. For example, the scenario provider can adjust the way the scenario is presented based on the user's emotions. By adjusting the way the scenario is presented based on the user's emotions, it is possible to provide a scenario that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the scenario provider may be performed using AI, for example, or without using AI. For example, the scenario provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The scenario provider can provide simulated sales scenarios that reflect trends in specific industries or markets. For example, the scenario provider can provide simulated sales scenarios that reflect the latest industry trends. For example, the scenario provider can provide simulated sales scenarios that take into account seasonal market fluctuations. For example, the scenario provider can provide simulated sales scenarios that reflect the activities of competitors in the industry. This allows users to acquire more practical skills by providing simulated sales scenarios that reflect trends in specific industries or markets. Some or all of the above-described processes in the scenario provider may be performed using AI, for example, or not using AI. For example, the scenario provider can input industry trend data into a generating AI and have the generating AI perform the task of providing simulated sales scenarios.

[0107] The scenario provider can provide customized scenarios by referencing the salesperson's past performance data when providing scenarios. For example, the scenario provider can provide a customized simulated sales scenario based on the salesperson's past closing rate. For example, the scenario provider can provide a customized scenario by referencing the salesperson's past customer interaction history. For example, the scenario provider can provide an optimal simulated sales scenario based on the salesperson's past sales data. This allows for the provision of customized scenarios by referencing the salesperson's past performance data. Some or all of the above processing in the scenario provider may be performed using AI, for example, or without AI. For example, the scenario provider can input the salesperson's performance data into a generating AI and have the generating AI execute the provision of a customized scenario.

[0108] The scenario provider can estimate the user's emotions and determine the priority of scenarios based on the estimated user emotions. The scenario provider can estimate the user's emotions using, for example, emotion recognition technology. The scenario provider can estimate emotions by, for example, analyzing the user's facial expressions and voice. The scenario provider can determine the priority of scenarios based on the user's emotions. This allows important scenarios to be provided preferentially by determining the priority of scenarios based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the scenario provider may be performed using, for example, AI, or not using AI. For example, the scenario provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0109] The scenario provider can provide the optimal scenario by considering the geographical location information of the salesperson when providing a scenario. For example, the scenario provider can provide region-specific scenarios based on the salesperson's activity area. For example, the scenario provider can provide scenarios that follow the salesperson's travel route. For example, the scenario provider can provide the optimal scenario by considering the characteristics of the region the salesperson visits. In this way, by providing scenarios that consider the geographical location information of the salesperson, region-specific needs can be addressed. Some or all of the above processing in the scenario provider can be performed using AI, for example, or without using AI. For example, the scenario provider can input the geographical location information of the salesperson into a generating AI and have the generating AI execute the provision of the optimal scenario.

[0110] The scenario provisioning unit can analyze the social media activities of sales personnel and provide relevant scenarios when providing scenarios. For example, the scenario provisioning unit can analyze the content of sales personnel's social media activities and provide relevant scenarios. For example, the scenario provisioning unit can analyze the reactions of sales personnel's social media followers and provide scenarios that meet their needs. For example, the scenario provisioning unit can analyze the content of sales personnel's social media posts and provide scenarios based on products of interest. In this way, relevant scenarios can be provided by analyzing the social media activities of sales personnel. Some or all of the above processing in the scenario provisioning unit may be performed using AI, for example, or without AI. For example, the scenario provisioning unit can input sales personnel's social media activity data into a generating AI and have the generating AI perform the provision of relevant scenarios.

[0111] The feedback unit can estimate the user's emotions and adjust the way feedback is expressed based on the estimated user emotions. The feedback unit can estimate the user's emotions using, for example, emotion recognition technology. The feedback unit can estimate emotions by, for example, analyzing the user's facial expressions and voice. The feedback unit can adjust the way feedback is expressed based on the user's emotions. By adjusting the way feedback is expressed based on the user's emotions, feedback that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0112] The feedback unit can provide customized feedback by referring to the salesperson's past performance data when providing feedback. For example, the feedback unit can provide customized feedback based on the salesperson's past closing rate. For example, the feedback unit can provide customized feedback by referring to the salesperson's past customer interaction history. For example, the feedback unit can provide optimal feedback based on the salesperson's past sales data. In this way, customized feedback can be provided by referring to the salesperson's past performance data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the salesperson's performance data into a generating AI and have the generating AI perform the task of providing customized feedback.

[0113] The feedback unit can provide feedback that reflects trends in specific industries or markets. For example, it can provide feedback that reflects the latest industry trends. For example, it can provide feedback that takes into account seasonal market fluctuations. For example, it can provide feedback that reflects the actions of competitors in the industry. This allows for more practical advice by providing feedback that reflects trends in specific industries or markets. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input industry trend data into a generating AI and have the generating AI perform the task of providing feedback.

[0114] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. The feedback unit can estimate the user's emotions using, for example, emotion recognition technology. The feedback unit can estimate emotions by, for example, analyzing the user's facial expressions and voice. The feedback unit can determine the priority of feedback based on the user's emotions. This allows important feedback to be provided preferentially by determining the priority of feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0115] The feedback unit can provide optimal feedback by considering the geographical location information of the salesperson. For example, the feedback unit can provide region-specific feedback based on the salesperson's activity area. For example, the feedback unit can provide feedback along the salesperson's travel route. For example, the feedback unit can provide optimal feedback by considering the characteristics of the region the salesperson visits. This allows for addressing region-specific needs by providing feedback while considering the geographical location information of the salesperson. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the salesperson's geographical location information into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0116] The feedback unit can analyze the social media activities of sales personnel and provide relevant feedback when providing feedback. For example, the feedback unit can analyze the content of sales personnel's social media activities and provide relevant feedback. For example, the feedback unit can analyze the reactions of sales personnel's social media followers and provide feedback tailored to their needs. For example, the feedback unit can analyze the content of sales personnel's social media posts and provide feedback based on products of interest. In this way, relevant feedback can be provided by analyzing the social media activities of sales personnel. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the social media activity data of sales personnel into a generating AI and have the generating AI perform the provision of relevant feedback.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The training system can further incorporate gamification elements. For example, salespeople could be awarded badges or points for achieving high scores in simulated sales activities. This would motivate salespeople and encourage active participation in training. A ranking system could also be implemented to allow salespeople to compete with each other, offering rewards to top performers. Furthermore, training progress could be visualized, and real-time feedback could be provided to help them achieve their goals. This would allow salespeople to more easily feel their own growth and encourage continuous skill improvement.

[0119] The data collection unit can analyze the tone of voice and speaking patterns of salespeople to evaluate the quality of their communication with customers. For example, it can analyze how a salesperson's tone of voice affects customer reactions and suggest optimal speaking styles. By analyzing speaking patterns, it can also identify effective communication techniques for capturing customer interest. Furthermore, by transcribing customer conversations into text and analyzing the frequency of keyword and phrase usage, it is possible to gain a deeper understanding of customer needs and interests. This allows salespeople to improve their communication skills with customers.

[0120] The analytics department can estimate customer emotions and adjust sales strategies based on those estimates. For example, if a customer shows positive emotions, proactive proposals can be made. On the other hand, if a customer shows negative emotions, a cautious approach can be taken. It is also possible to monitor changes in customer emotions in real time and follow up at the appropriate time. Furthermore, by accumulating customer emotion data and analyzing long-term emotional trends, strategies can be formulated to deepen relationships with customers. This can improve customer satisfaction.

[0121] The proposal department can analyze past failures of sales personnel and make suggestions to avoid similar mistakes. For example, they can identify the reasons why deals were not closed in the past and propose methods to resolve those causes. They can also share lessons learned from failures to improve the skills of all sales personnel. Furthermore, by creating scenarios based on failures and putting them into practice in simulated sales, they can provide sales personnel with opportunities to learn through experience. This allows sales personnel to challenge themselves without fear of failure and grow.

[0122] The scenario provider can estimate the user's emotions and adjust the difficulty level of the scenario based on those emotions. For example, if the user is feeling stressed, the difficulty level can be lowered to allow for a more relaxed training experience. On the other hand, if the user is confident, the difficulty level can be increased to provide a more challenging scenario. It is also possible to adjust the scenario content in real time according to changes in the user's emotions to provide an optimal training environment. Furthermore, by accumulating user emotion data and analyzing long-term emotional trends, it is possible to provide individually optimized training plans. This maximizes the effectiveness of the user's training.

[0123] The feedback department can evaluate salespeople's nonverbal communication skills and suggest areas for improvement. For example, it can analyze a salesperson's body language and the frequency of eye contact to suggest effective nonverbal communication techniques. It can also analyze a salesperson's facial expressions and gestures to provide advice on improving their impression on customers. Furthermore, it can provide real-time feedback on nonverbal communication during simulated sales activities, allowing salespeople to implement improvements on the spot. This can ultimately improve the overall communication skills of salespeople.

[0124] The feedback unit can estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, if a user is showing positive emotions, feedback can be provided immediately to boost their motivation. On the other hand, if a user is showing negative emotions, feedback can be provided after a short delay to allow them to receive it calmly. It is also possible to monitor changes in the user's emotions in real time and provide feedback at the optimal time. Furthermore, by accumulating user emotion data and analyzing long-term emotional trends, it is possible to provide individually optimized feedback plans. This maximizes the effectiveness of feedback for the user.

[0125] The data collection unit can gather health data from sales personnel and incorporate it into training plans. For example, it can monitor sales personnel's heart rate and stress levels to provide training plans tailored to their health condition. It can also collect sleep data from sales personnel to check if they are getting enough rest. Furthermore, it can record sales personnel's diet and exercise habits to provide advice to support a healthy lifestyle. This allows for the provision of training plans that take into account the health condition of sales personnel, thereby improving overall performance.

[0126] The proposal function can estimate the user's emotions and adjust the content of the proposal based on those emotions. For example, if the user is showing positive emotions, it can make proactive proposals. On the other hand, if the user is showing negative emotions, it can take a more cautious approach. It can also monitor changes in the user's emotions in real time and make proposals at the appropriate time. Furthermore, by accumulating user emotion data and analyzing long-term emotional trends, it can provide individually optimized proposals. This can improve user satisfaction.

[0127] The scenario provision department can provide scenarios that take into account the cultural background of sales personnel. For example, when sales personnel interact with customers from different cultural backgrounds, the department can help them understand cultural differences and propose appropriate communication methods. It can also provide scenarios based on cultural backgrounds, enabling sales personnel to acquire cross-cultural communication skills. Furthermore, it can create simulated sales scenarios that take cultural differences into account, allowing sales personnel to acquire practical skills. As a result, sales personnel can conduct effective sales activities with customers from diverse cultural backgrounds.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The data collection unit collects data. For example, it can collect historical sales data to identify sales patterns and trends. The data collection unit can collect numerical data such as sales data and customer data, as well as text data. It can also collect image data and extract data using image analysis technology. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they can use statistical analysis and machine learning algorithms to analyze the data, understand customer needs, and reflect them in sales strategies. They can also identify customer needs through surveys and analysis of purchase history. Step 3: The proposal department proposes sales methods based on the analysis results obtained by the analysis department. For example, they can propose customized sales methods to sales personnel. They can propose methods based on customer segments, as well as methods such as telephone sales, in-person sales, and online sales. Step 4: The scenario provider will provide scenarios for simulated sales activities. For example, simulated sales activities can be conducted through role-playing or simulations. Industry-specific discussions can be held to help participants acquire more practical skills. Step 5: The feedback department evaluates the skill improvement of sales personnel. For example, it can analyze sales performance and predict results. In simulated sales, AI can provide scripts and tactics in real time to support the skill improvement of sales personnel. Continuous skill improvement can be achieved through an AI-driven feedback loop.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, scenario provision unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects sales data and customer data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the data using statistical analysis and machine learning algorithms. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes customized sales methods. The scenario provision unit is implemented in the specific processing unit 46A of the smart device 14, for example, and provides a simulated sales scenario. The feedback unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and performs sales performance analysis and provides scripts. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, scenario provision unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects sales data and customer data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the data using statistical analysis and machine learning algorithms. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes customized sales methods. The scenario provision unit is implemented in the specific processing unit 46A of the smart glasses 214, for example, and provides a simulated sales scenario. The feedback unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and performs sales performance analysis and provides scripts. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In 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.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 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.

[0165] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, scenario provision unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects sales data and customer data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the data using statistical analysis and machine learning algorithms. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes customized sales methods. The scenario provision unit is implemented in the specific processing unit 46A of the headset terminal 314, for example, and provides a simulated sales scenario. The feedback unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and performs sales performance analysis and provides scripts. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, scenario provision unit, and feedback unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects sales data and customer data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the data using statistical analysis and machine learning algorithms. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes customized sales methods. The scenario provision unit is implemented in the control unit 46A of the robot 414, for example, and provides a simulated sales scenario. The feedback unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and performs sales performance analysis and provides scripts. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis department, the proposal department proposes sales methods. The Scenario Provision Department provides scenarios for simulated sales, It includes a feedback department that evaluates the skill improvement of sales personnel. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect past sales data to identify sales patterns and trends. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Understand customer needs and reflect them in sales strategies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose customized sales methods to sales personnel. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned scenario provision unit, We provide a simulated sales scenario and conduct a simulated sales session together with sales personnel. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Analyze sales performance and predict results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is In simulated sales sessions, AI provides scripts and tactics in real time, supporting the skill development of sales personnel. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned feedback unit is Continuous skill improvement through AI-powered feedback loops. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned scenario provision unit, We conduct industry-specific discussions to help participants acquire more practical skills. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting historical sales data, focus on collecting data for specific products or campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, individual performance data for sales personnel is collected and used to create personalized training plans. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of sales personnel. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, analyze the social media activities of sales personnel and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, we conduct a detailed analysis of customer purchase history and behavioral patterns, and reflect this in our sales strategy. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, customize the results by taking into account trends in specific industries and markets. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is 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 20) The aforementioned analysis unit is During analysis, the analysis results are customized by taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During analysis, refer to customers' social media activity to complement the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned proposal section is, When making a proposal, we will suggest a customized sales approach by referencing the salesperson's past success stories. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm to specific customer segments. The system described in Appendix 1, characterized by the features described herein. (Note 25) 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 26) The aforementioned proposal section is, When making a proposal, we take into account the geographical location of the salesperson to provide the most suitable proposal. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, we analyze the social media activity of sales personnel and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned scenario provision unit, It estimates the user's emotions and adjusts how the scenario is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned scenario provision unit, When providing scenarios, we offer simulated sales scenarios that reflect trends in specific industries and markets. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned scenario provision unit, When providing scenarios, we offer customized scenarios based on the past performance data of sales personnel. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned scenario provision unit, It estimates user emotions and prioritizes scenarios based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned scenario provision unit, When providing scenarios, we will provide the optimal scenario considering the geographical location information of the sales personnel. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned scenario provision unit, When providing scenarios, we analyze the social media activities of sales personnel and provide relevant scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, we refer to the salesperson's past performance data to provide customized feedback. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, offer feedback that reflects trends in specific industries or markets. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned feedback unit is When providing feedback, we take into account the geographical location of the salesperson to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned feedback unit is During feedback sessions, we analyze the social media activity of sales personnel and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0202] 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 data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis department, the proposal department proposes sales methods. The Scenario Provision Department provides scenarios for simulated sales, It includes a feedback department that evaluates the skill improvement of sales personnel. A system characterized by the following features.

2. The aforementioned collection unit is Collect past sales data to identify sales patterns and trends. The system according to feature 1.

3. The aforementioned analysis unit is Understand customer needs and reflect them in sales strategies. The system according to feature 1.

4. The aforementioned proposal section is, We propose customized sales methods to sales personnel. The system according to feature 1.

5. The aforementioned scenario provision unit, We provide a simulated sales scenario and conduct a simulated sales session together with sales personnel. The system according to feature 1.

6. The aforementioned feedback unit is Analyze sales performance and predict results. The system according to feature 1.

7. The aforementioned feedback unit is In simulated sales sessions, AI provides scripts and tactics in real time, supporting the skill development of sales personnel. The system according to feature 1.

8. The aforementioned feedback unit is Continuous skill improvement through AI-driven feedback loops. The system according to feature 1.

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