system

Abstract

The system according to this embodiment aims to match customers with store staff who are suited to their preferences and needs. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a matching unit, and an evaluation unit. The data collection unit collects customer data. The analysis unit analyzes the data collected by the data collection unit and creates a customer profile. The matching unit matches store staff based on the profile created by the analysis unit. The evaluation unit evaluates the store staff matched by the matching unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult to appropriately match a store clerk who meets the preferences and needs of customers.

[0005] The system according to the embodiment aims to match a store clerk who meets the preferences and needs of customers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a matching unit, and an evaluation unit. The data collection unit collects customer data. The analysis unit analyzes the data collected by the data collection unit and creates a customer profile. The matching unit matches store employees based on the profile created by the analysis unit. The evaluation unit evaluates the store employees matched by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can match customers with store staff who are suited to their preferences and needs. [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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent service according to an embodiment of the present invention is a system that matches customers in restaurants and clothing stores with staff who match their preferences and needs. This system collects data such as the customer's past purchase history, ratings, and preferences, and the AI ​​analyzes this data to create a detailed customer profile. Next, it performs real-time matching based on the skills and personality of the staff and their current work status. Furthermore, it aggregates customer feedback and continuously improves the staff evaluation and matching algorithm. This enables the provision of personalized services and exclusive experiences to customers. For example, it collects data such as the customer's past purchase history, ratings, and preferences. In this process, it collects detailed data such as what products the customer has purchased, what ratings they have given, and what preferences they have. For example, by collecting data on the customer's past purchase history and the products they have rated, and having the AI ​​analyze this data, it is possible to understand the customer's preferences and needs. Next, the AI ​​analyzes the collected data and creates a detailed customer profile. The AI ​​analyzes the collected data and determines the customer's preferences and needs. For example, it can identify customers who have a high level of interest in a particular brand or style and suggest an appropriate staff member to that customer. This enables personalized services tailored to the customer's needs. Furthermore, the system performs real-time matching based on the skills and personality of the staff, comparing them with their current work schedule. For example, it identifies staff with specific skills or personality traits and checks if they are currently working. This allows for real-time matching of the most suitable staff member to each customer. In addition, customer feedback is collected to continuously improve staff evaluations and the matching algorithm. For instance, data on customer evaluations of the services provided is collected and analyzed by AI to assess staff skills and personality. This allows for staff skill development and improved matching accuracy. This system enables the provision of personalized services and exclusive experiences to customers.For example, if a customer has a high level of interest in a particular brand or style, special offers and recommendations can be provided to that customer. This can improve customer satisfaction and is expected to increase repeat business. In this way, AI agent services can match customers with sales staff who match their preferences and needs, and provide personalized services.

[0029] The AI ​​agent service according to this embodiment comprises a data collection unit, an analysis unit, a matching unit, and an evaluation unit. The data collection unit collects customer data. Customer data includes, but is not limited to, purchase history, ratings, preferences, and behavioral history. The data collection unit collects, for example, the customer's past purchase history. For example, the data collection unit can collect a history of products that the customer has purchased in the past. The data collection unit can also collect customer ratings. For example, the data collection unit can collect data on products that the customer has rated. The data collection unit can also collect customer preferences. For example, the data collection unit can collect the customer's tastes and interests. The analysis unit analyzes the data collected by the data collection unit and creates a customer profile. The analysis unit analyzes the data using, for example, data mining techniques. For example, the analysis unit can analyze the customer's purchase history and determine the customer's preferences and needs. The analysis unit can also analyze the data using statistical analysis techniques. For example, the analysis unit can analyze customer rating data and determine customer satisfaction. The analysis unit can also analyze the data using machine learning algorithms. For example, the analysis department can analyze customer behavior history and determine their purchasing intent. The matching department matches customers with staff based on profiles created by the analysis department. The matching department can also perform matching based on staff skills. For example, the matching department can identify staff with specific skills and match them with customers. The matching department can also perform matching based on staff personalities. For example, the matching department can identify staff with specific personalities and match them with customers. The matching department can also perform matching based on staff work status. For example, the matching department can identify staff currently on duty and match them with customers. The evaluation department evaluates the staff matched by the matching department. The evaluation department can, for example, collect customer feedback. For example, the evaluation department can collect customer evaluations of the services provided. The evaluation department can also evaluate staff skills.For example, the evaluation unit can assess the skills of store employees based on customer feedback. The evaluation unit can also assess the personality of store employees. This allows the AI ​​agent service according to the embodiment to provide personalized services to customers by collecting and analyzing customer data, matching them with store employees, and performing evaluations.

[0030] The data collection unit collects customer data. This data includes, but is not limited to, purchase history, ratings, preferences, and behavioral history. For example, the data collection unit can collect a customer's past purchase history. For instance, it can collect a history of products a customer has previously purchased. The data collection unit can also collect customer ratings. For example, it can collect data on products a customer has rated. Furthermore, the data collection unit can collect customer preferences. For example, it can collect customer tastes and interests. The data collection unit can collect this data in a variety of ways. Examples include data collection from online shopping sites and applications, the use of customer surveys and feedback forms, and social media analysis. Online shopping sites can record in detail the history of products viewed, added to carts, and purchased by customers. This allows for an understanding of customer purchasing patterns and product categories of interest. It is also possible to collect customer satisfaction and specific requests through surveys and feedback forms. This allows for service improvements tailored to customer needs and expectations. Social media analytics allows businesses to understand customer preferences and interests by analyzing comments, reviews, likes, and shares posted by customers. This data is centrally managed by the data collection unit and stored in a database. The data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by intensifying data collection during a specific campaign period, businesses can gain a detailed understanding of customer reactions and effectiveness. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes data collected by the data collection department to create customer profiles. The analysis department analyzes data using, for example, data mining techniques. For instance, it can analyze customer purchase history to determine customer preferences and needs. The analysis department can also analyze data using statistical analysis techniques. For example, it can analyze customer evaluation data to determine customer satisfaction. Furthermore, the analysis department can analyze data using machine learning algorithms. For example, it can analyze customer behavior history to determine customer purchasing intent. Specifically, it uses data mining techniques to extract patterns of frequently purchased and related products from customer purchase history to identify customer preferences and needs. It uses statistical analysis techniques to analyze customer evaluation data to reveal customer satisfaction and dissatisfaction. It uses machine learning algorithms to analyze customer behavior history to predict customer purchasing intent and future purchasing behavior. For example, if a customer frequently views a particular product, it can be determined that they have a high level of interest in that product and related products can be recommended. In addition, when creating customer profiles, the analysis department integrates multiple data sources to perform comprehensive analysis. For example, detailed customer profiles can be created by combining data such as purchase history, ratings, preferences, and behavioral history. This allows for a more accurate understanding of customer preferences and needs, enabling personalized services. The analytics department can also utilize historical data and statistical information to analyze long-term trends and patterns, which can be used for future predictions and strategic planning. As a result, the analytics department can not only create customer profiles but also conduct long-term risk assessments and trend analyses, improving the reliability and security of the entire system.

[0032] The matching department matches store staff based on profiles created by the analysis department. The matching department can, for example, match staff based on their skills. For instance, it can identify staff with specific skills and match them with customers. It can also match staff based on their personalities. For example, it can identify staff with specific personalities and match them with customers. Furthermore, the matching department can match staff based on their work status. For instance, it can identify currently employed staff and match them with customers. Specifically, it analyzes staff skill sets in detail and selects the staff best suited to the customer's needs. For example, it can identify staff with expertise in specific products or skills in specific services and match them with customers. Considering staff personalities can also improve compatibility with customers. For example, if a customer prefers a relaxed atmosphere, it can match them with a staff member who has a calm and friendly personality. Furthermore, by monitoring the work status of store employees in real time and quickly identifying employees currently on duty, prompt service to customers becomes possible. The matching department comprehensively considers these factors and can improve customer satisfaction by matching the most suitable employee to the customer. The matching department can utilize AI to improve the accuracy of matching employees with customers. For example, by using machine learning algorithms to analyze past matching data and learn patterns of successful matches, the accuracy of future matches can be improved. As a result, the matching department can quickly and accurately match the most suitable employee to the customer and provide personalized service.

[0033] The evaluation department evaluates the staff members matched by the matching department. The evaluation department, for example, aggregates customer feedback. For example, the evaluation department can collect customer evaluations of the services provided. The evaluation department can also evaluate the skills of the staff members. For example, the evaluation department can evaluate staff members' skills based on customer feedback. Furthermore, the evaluation department can also evaluate the personalities of the staff members. For example, the evaluation department can evaluate staff members' personalities based on customer feedback. Specifically, the evaluation department collects customer evaluations of the services provided and evaluates the staff members' performance. For example, it aggregates customer evaluations of the services provided and evaluates the skills and personalities of the staff members. The evaluation department can also evaluate staff members' skills based on customer feedback. For example, it evaluates staff members' skills based on customer evaluations of the services provided. Furthermore, the evaluation department can evaluate staff members' personalities based on customer feedback. For example, it evaluates staff members' personalities based on customer evaluations of the services provided. The evaluation department can use these evaluation results to provide feedback to improve employee performance. For example, the evaluation department can provide feedback on employees' skills and personalities, and offer training and support to improve employee performance. This allows the evaluation department to improve employee performance and increase customer satisfaction. Furthermore, the evaluation department can use the evaluation results to make decisions regarding employee compensation and promotions. For example, the evaluation department can make decisions regarding employee compensation and promotions based on evaluation results regarding employees' skills and personalities. This allows the evaluation department to improve employee motivation and increase customer satisfaction.

[0034] The data collection unit can collect data such as a customer's past purchase history, ratings, and preferences. For example, the data collection unit can collect a customer's past purchase history. For example, the data collection unit can collect a history of products that a customer has purchased in the past. The data collection unit can also collect customer ratings. For example, the data collection unit can collect data on products that a customer has rated. The data collection unit can also collect customer preferences. For example, the data collection unit can collect a customer's tastes and interests. By collecting data such as a customer's past purchase history, ratings, and preferences, a detailed customer profile can be created. 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 a customer's past purchase history into AI, and the AI ​​can analyze the data to identify the customer's preferences.

[0035] The analysis department can analyze collected data to determine customer preferences and needs. For example, the analysis department can analyze data using data mining techniques. For instance, it can analyze customer purchase history to determine customer preferences and needs. The analysis department can also analyze data using statistical analysis techniques. For example, it can analyze customer evaluation data to determine customer satisfaction. Furthermore, the analysis department can analyze data using machine learning algorithms. For example, it can analyze customer behavior history to determine customer purchasing intent. This allows the analysis department to analyze collected data and determine customer preferences and needs, thereby providing appropriate services to customers. Some or all of the above-described processes in the analysis department may be performed using AI, or without AI. For example, the analysis department can input collected data into an AI, which can then analyze the data to determine customer preferences and needs.

[0036] The matching unit can perform real-time matching based on the skills and personality of store employees, comparing them with their current work status. For example, the matching unit can perform matching based on the employee's skills. For example, the matching unit can identify an employee with specific skills and match that employee with a customer. The matching unit can also perform matching based on the employee's personality. For example, the matching unit can identify an employee with specific personality traits and match that employee with a customer. The matching unit can also perform matching based on the employee's work status. For example, the matching unit can identify an employee currently on duty and match that employee with a customer. This allows the system to provide customers with the most suitable employee by performing real-time matching based on the employee's skills and personality. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input data on the employee's skills and personality into an AI, which can analyze the data to identify the most suitable employee.

[0037] The evaluation unit can collect customer feedback and evaluate the skills and personalities of store employees. For example, the evaluation unit can collect customer feedback. For example, the evaluation unit can collect customer evaluations of the services provided. The evaluation unit can also evaluate the skills of store employees. For example, the evaluation unit can evaluate the skills of store employees based on customer feedback. The evaluation unit can also evaluate the personalities of store employees. For example, the evaluation unit can evaluate the personalities of store employees based on customer feedback. By collecting customer feedback and evaluating the skills and personalities of store employees, it is possible to improve the skills of store employees and the accuracy of customer matching. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input customer feedback data into AI, and the AI ​​can analyze the data to evaluate the skills and personalities of store employees.

[0038] The data collection unit can analyze a customer's past purchase history and select the optimal data collection method. For example, the data collection unit can collect data through online questionnaires based on a customer's past online purchase history. Alternatively, if a customer has a high purchase history at physical stores, the data collection unit can collect data through in-store interviews. Furthermore, if a customer shows a strong interest in a particular brand, the data collection unit can prioritize collecting data related to that brand. This allows for efficient data collection by analyzing the customer's past purchase history and selecting the optimal data collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the customer's past purchase history data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0039] The data collection unit can filter data based on the customer's current lifestyle and areas of interest during data collection. For example, if a customer starts a new hobby, the data collection unit will prioritize collecting data related to that hobby. It can also collect data related to a new living environment if a customer moves. Furthermore, if a customer participates in a specific event, the data collection unit can collect data related to that event. This allows for the collection of highly relevant data by filtering based on the customer's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer lifestyle data into a generating AI, which can then analyze and filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location during data collection. For example, if a customer lives in a specific region, the data collection unit can collect data on products related to that region. Furthermore, if a customer is traveling, the data collection unit can collect data on products related to their travel destination. Additionally, if a customer frequently visits a particular store, the data collection unit can collect data on products related to that store. This allows for the provision of region-specific services by prioritizing the collection of highly relevant data while considering the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the customer's geographical location information into a generating AI, which can then analyze the data to identify highly relevant information.

[0041] The data collection unit can analyze customers' social media activity and collect relevant data during data collection. For example, if a customer mentions a particular brand on social media, the data collection unit can collect data related to that brand. It can also collect data related to an event if a customer participates in that event on social media. Furthermore, if a customer posts a review of a particular product on social media, the data collection unit can collect data on that product. This allows for the provision of services tailored to customer interests by analyzing customers' social media activity and collecting relevant data. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer social media activity data into a generating AI, which can then analyze the data to identify relevant information.

[0042] The analytics department can adjust the level of detail in customer profiles based on their importance during data analysis. For example, if a customer is a VIP, the analytics department can provide a detailed profile and suggest special services. Alternatively, if a customer is a new customer, the analytics department can provide a basic profile and suggest initial services. Furthermore, if a customer is a repeat customer, the analytics department can provide a profile based on their past purchase history to encourage repeat purchases. This allows the analytics department to provide appropriate services to customers by adjusting the level of detail in their profiles based on their importance. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For instance, the analytics department can input customer importance data into a generating AI, which can then analyze the data and adjust the level of detail in the profiles.

[0043] The analysis department can apply different analysis algorithms depending on the customer's category during data analysis. For example, if a customer is interested in fashion, the analysis department can apply a fashion-related algorithm to create a profile. Similarly, if a customer is interested in gourmet food, the analysis department can apply a gourmet-related algorithm to create a profile. Furthermore, if a customer is interested in technology, the analysis department can apply a technology-related algorithm to create a profile. This allows the analysis department to provide customers with appropriate profiles by applying different analysis algorithms depending on their category. 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 customer category data into a generating AI, which can then analyze the data and apply an appropriate analysis algorithm.

[0044] The analytics department can prioritize profiles based on customer submission timing during data analysis. For example, if a customer has recently submitted data, the analytics department will prioritize analyzing that data and update the profile. Alternatively, if a customer has previously submitted data, the analytics department can prioritize analyzing new data while referencing that data. Furthermore, if a customer has participated in a specific event, the analytics department can prioritize analyzing data related to that event. This allows for the provision of the most up-to-date information by prioritizing profiles based on customer submission timing. Some or all of the above processes in the analytics department may be performed using AI, or not. For example, the analytics department could input customer submission timing data into a generating AI, which would then analyze the data to determine profile priorities.

[0045] The analytics department can adjust the order of customer profiles based on their relevance during data analysis. For example, if a customer shows a high level of interest in a particular brand, the analytics department will prioritize analyzing data related to that brand. Similarly, if a customer shows a high level of interest in a particular category, the analytics department can prioritize analyzing data related to that category. Furthermore, if a customer frequently reviews a particular product, the analytics department can prioritize analyzing data related to that product. This allows for the prioritization of information important to customers by adjusting the order of profiles based on their relevance. Some or all of the above processing in the analytics department may be performed using AI, for example, or not. For instance, the analytics department can input customer relevance data into a generating AI, which can then analyze the data and adjust the order of profiles.

[0046] The matching unit can improve the accuracy of matching by considering the relationships between employees during the matching process. For example, the matching unit can consider the compatibility between employees and match employees with good teamwork. It can also match employees who are more likely to cooperate based on their past cooperation record. Furthermore, the matching unit can consider the skill sets of employees and match employees who can complement each other. In this way, by considering the relationships between employees, it is possible to match employees with good teamwork. Some or all of the above processes in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input employee relationship data into a generating AI, which can then analyze the data to improve the accuracy of the matching.

[0047] The matching unit can perform matching while considering the attribute information of the store staff. For example, the matching unit can consider the store staff's expertise and match them with a store staff member that suits the customer's needs. The matching unit can also consider the store staff's language skills and match them with a store staff member who can communicate in the customer's language. Furthermore, the matching unit can consider the store staff member's personality and match them with a store staff member that suits the customer's personality. In this way, by considering the attribute information of the store staff, it is possible to match them with a store staff member that suits the customer's needs. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input store staff attribute information data into a generating AI, and the generating AI can analyze the data to identify the most suitable store staff member.

[0048] The matching unit can perform matching while considering the geographical distribution of store employees. For example, if an employee is nearby, the matching unit will prioritize matching with that employee. Furthermore, if an employee is far away, the matching unit can also consider travel time when matching. Additionally, if an employee is familiar with a particular area, the matching unit can prioritize matching with customers associated with that area. This allows the system to match customers with the most suitable employee by considering the geographical distribution of employees. Some or all of the above processing in the matching unit may be performed using AI, or without AI. For example, the matching unit can input geographical distribution data of employees into a generating AI, which can then analyze the data to identify the most suitable employee.

[0049] The matching unit can improve the accuracy of matching by referring to relevant literature on store employees during the matching process. For example, the matching unit can match store employees to customers based on their past evaluations. The matching unit can also refer to literature on store employees' skills to match appropriate store employees. Furthermore, the matching unit can refer to literature on store employees' personalities to match store employees to customers with personalities that suit them. In this way, by referring to relevant literature on store employees, it is possible to match store employees to customers that meet their needs. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input data on relevant literature on store employees into a generating AI, which can then analyze the data to improve the accuracy of matching.

[0050] The evaluation unit can analyze past customer feedback during the evaluation process to select the optimal evaluation method. For example, if the customer has provided detailed feedback in the past, the evaluation unit can provide a detailed evaluation method. Alternatively, if the customer has provided concise feedback in the past, the evaluation unit can provide a concise evaluation method. Furthermore, if the customer has preferred visual feedback in the past, the evaluation unit can provide a visually appealing evaluation method. In this way, by analyzing past customer feedback, the evaluation unit can provide the most suitable evaluation method for the customer. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past customer feedback data into a generating AI, which can then analyze the data to select the optimal evaluation method.

[0051] The evaluation unit can customize the evaluation method based on the customer's current lifestyle during the evaluation process. For example, if the customer is busy, the evaluation unit can provide a concise evaluation method. If the customer is relaxed, the evaluation unit can also provide a detailed evaluation method. Furthermore, if the customer is participating in a specific event, the evaluation unit can provide an evaluation method related to that event. This allows the evaluation unit to provide an appropriate evaluation method for the customer by customizing the evaluation method based on the customer's current lifestyle. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input customer lifestyle data into a generating AI, which can then analyze the data to customize the evaluation method.

[0052] The evaluation unit can select the optimal evaluation method during the evaluation process, taking into account the customer's geographical location information. For example, if the customer lives in a specific region, the evaluation unit can provide an evaluation method relevant to that region. Furthermore, if the customer is traveling, the evaluation unit can provide an evaluation method relevant to their travel destination. Additionally, if the customer frequently visits a particular store, the evaluation unit can provide an evaluation method relevant to that store. This allows for the provision of region-specific evaluation methods by considering the customer's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For instance, the evaluation unit can input the customer's geographical location data into a generating AI, which can then analyze the data and select the optimal evaluation method.

[0053] The evaluation unit can analyze a customer's social media activity during the evaluation process and propose evaluation methods. For example, if a customer mentions a specific brand on social media, the evaluation unit can provide an evaluation method related to that brand. It can also provide an evaluation method related to an event if a customer participates in a specific event on social media. Furthermore, if a customer posts a review of a specific product on social media, the evaluation unit can provide an evaluation method for that product. This allows the evaluation unit to provide an appropriate evaluation method for the customer by analyzing their social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input customer social media activity data into a generating AI, which can then analyze the data and propose evaluation methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze customers' past purchase history and select the most suitable data collection method. For example, it can collect data through online surveys based on a customer's past online purchase history. If a customer has a high purchase history at physical stores, data can also be collected through in-store interviews. Furthermore, if a customer shows a strong interest in a particular brand, data related to that brand can be prioritized. This allows for efficient data collection by analyzing customers' past purchase history and selecting the optimal data collection method.

[0056] The data collection unit can filter data based on the customer's current lifestyle and areas of interest during the data collection process. For example, if a customer starts a new hobby, the system can prioritize collecting data related to that hobby. Similarly, if a customer moves, it can collect data related to their new living environment. Furthermore, if a customer participates in a specific event, it can collect data related to that event. This allows for the collection of highly relevant data by filtering based on the customer's current lifestyle and areas of interest.

[0057] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location during data collection. For example, if a customer lives in a specific region, it can collect data on products related to that region. If a customer is traveling, it can also collect data on products related to their travel destination. Furthermore, if a customer frequently visits a particular store, it can collect data on products related to that store. This allows for the provision of region-specific services by prioritizing the collection of highly relevant data based on the customer's geographical location.

[0058] The data collection unit can analyze customers' social media activity and collect relevant data during the data collection process. For example, if a customer mentions a specific brand on social media, it can collect data related to that brand. It can also collect data related to an event if the customer participates in it on social media. Furthermore, if a customer posts a review of a specific product on social media, it can collect data on that product. This allows for the provision of services tailored to customer interests by analyzing customer social media activity and collecting relevant data.

[0059] The analytics department can adjust the level of detail in customer profiles based on their importance during data analysis. For example, if a customer is a VIP, a detailed profile can be provided, and special services can be suggested. If a customer is a new customer, a basic profile can be provided, and initial services can be suggested. Furthermore, if a customer is a repeat customer, a profile based on their past purchase history can be provided to encourage repeat purchases. In this way, by adjusting the level of detail in profiles based on customer importance, appropriate services can be provided to customers.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects customer data. This data includes purchase history, ratings, preferences, and behavioral history. For example, the data collection unit can collect data on products a customer has purchased in the past, products they have rated, and their preferences and interests. Step 2: The analysis department analyzes the data collected by the data collection department and creates customer profiles. The analysis department can analyze the data using data mining techniques, statistical analysis techniques, and machine learning algorithms to determine customer preferences, needs, satisfaction levels, and purchase intent. Step 3: The matching department matches store employees based on the profiles created by the analysis department. The matching department matches employees based on their skills, personality, and work status, and can match customers with employees who have specific skills or personalities, or who are currently working at the store. Step 4: The evaluation department evaluates the staff members matched by the matching department. The evaluation department can collect customer feedback and evaluate the staff members' skills and personalities. This allows for staff member evaluations based on customer feedback.

[0062] (Example of form 2) An AI agent service according to an embodiment of the present invention is a system that matches customers in restaurants and clothing stores with staff who match their preferences and needs. This system collects data such as the customer's past purchase history, ratings, and preferences, and the AI ​​analyzes this data to create a detailed customer profile. Next, it performs real-time matching based on the skills and personality of the staff and their current work status. Furthermore, it aggregates customer feedback and continuously improves the staff evaluation and matching algorithm. This enables the provision of personalized services and exclusive experiences to customers. For example, it collects data such as the customer's past purchase history, ratings, and preferences. In this process, it collects detailed data such as what products the customer has purchased, what ratings they have given, and what preferences they have. For example, by collecting data on the customer's past purchase history and the products they have rated, and having the AI ​​analyze this data, it is possible to understand the customer's preferences and needs. Next, the AI ​​analyzes the collected data and creates a detailed customer profile. The AI ​​analyzes the collected data and determines the customer's preferences and needs. For example, it can identify customers who have a high level of interest in a particular brand or style and suggest an appropriate staff member to that customer. This enables personalized services tailored to the customer's needs. Furthermore, the system performs real-time matching based on the skills and personality of the staff, comparing them with their current work schedule. For example, it identifies staff with specific skills or personality traits and checks if they are currently working. This allows for real-time matching of the most suitable staff member to each customer. In addition, customer feedback is collected to continuously improve staff evaluations and the matching algorithm. For instance, data on customer evaluations of the services provided is collected and analyzed by AI to assess staff skills and personality. This allows for staff skill development and improved matching accuracy. This system enables the provision of personalized services and exclusive experiences to customers.For example, if a customer has a high level of interest in a particular brand or style, special offers and recommendations can be provided to that customer. This can improve customer satisfaction and is expected to increase repeat business. In this way, AI agent services can match customers with sales staff who match their preferences and needs, and provide personalized services.

[0063] The AI ​​agent service according to this embodiment comprises a data collection unit, an analysis unit, a matching unit, and an evaluation unit. The data collection unit collects customer data. Customer data includes, but is not limited to, purchase history, ratings, preferences, and behavioral history. The data collection unit collects, for example, the customer's past purchase history. For example, the data collection unit can collect a history of products that the customer has purchased in the past. The data collection unit can also collect customer ratings. For example, the data collection unit can collect data on products that the customer has rated. The data collection unit can also collect customer preferences. For example, the data collection unit can collect the customer's tastes and interests. The analysis unit analyzes the data collected by the data collection unit and creates a customer profile. The analysis unit analyzes the data using, for example, data mining techniques. For example, the analysis unit can analyze the customer's purchase history and determine the customer's preferences and needs. The analysis unit can also analyze the data using statistical analysis techniques. For example, the analysis unit can analyze customer rating data and determine customer satisfaction. The analysis unit can also analyze the data using machine learning algorithms. For example, the analysis department can analyze customer behavior history and determine their purchasing intent. The matching department matches customers with staff based on profiles created by the analysis department. The matching department can also perform matching based on staff skills. For example, the matching department can identify staff with specific skills and match them with customers. The matching department can also perform matching based on staff personalities. For example, the matching department can identify staff with specific personalities and match them with customers. The matching department can also perform matching based on staff work status. For example, the matching department can identify staff currently on duty and match them with customers. The evaluation department evaluates the staff matched by the matching department. The evaluation department can, for example, collect customer feedback. For example, the evaluation department can collect customer evaluations of the services provided. The evaluation department can also evaluate staff skills.For example, the evaluation unit can assess the skills of store employees based on customer feedback. The evaluation unit can also assess the personality of store employees. This allows the AI ​​agent service according to the embodiment to provide personalized services to customers by collecting and analyzing customer data, matching them with store employees, and performing evaluations.

[0064] The data collection unit collects customer data. This data includes, but is not limited to, purchase history, ratings, preferences, and behavioral history. For example, the data collection unit can collect a customer's past purchase history. For instance, it can collect a history of products a customer has previously purchased. The data collection unit can also collect customer ratings. For example, it can collect data on products a customer has rated. Furthermore, the data collection unit can collect customer preferences. For example, it can collect customer tastes and interests. The data collection unit can collect this data in a variety of ways. Examples include data collection from online shopping sites and applications, the use of customer surveys and feedback forms, and social media analysis. Online shopping sites can record in detail the history of products viewed, added to carts, and purchased by customers. This allows for an understanding of customer purchasing patterns and product categories of interest. It is also possible to collect customer satisfaction and specific requests through surveys and feedback forms. This allows for service improvements tailored to customer needs and expectations. Social media analytics allows businesses to understand customer preferences and interests by analyzing comments, reviews, likes, and shares posted by customers. This data is centrally managed by the data collection unit and stored in a database. The data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by intensifying data collection during a specific campaign period, businesses can gain a detailed understanding of customer reactions and effectiveness. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0065] The analysis department analyzes data collected by the data collection department to create customer profiles. The analysis department analyzes data using, for example, data mining techniques. For instance, it can analyze customer purchase history to determine customer preferences and needs. The analysis department can also analyze data using statistical analysis techniques. For example, it can analyze customer evaluation data to determine customer satisfaction. Furthermore, the analysis department can analyze data using machine learning algorithms. For example, it can analyze customer behavior history to determine customer purchasing intent. Specifically, it uses data mining techniques to extract patterns of frequently purchased and related products from customer purchase history to identify customer preferences and needs. It uses statistical analysis techniques to analyze customer evaluation data to reveal customer satisfaction and dissatisfaction. It uses machine learning algorithms to analyze customer behavior history to predict customer purchasing intent and future purchasing behavior. For example, if a customer frequently views a particular product, it can be determined that they have a high level of interest in that product and related products can be recommended. In addition, when creating customer profiles, the analysis department integrates multiple data sources to perform comprehensive analysis. For example, detailed customer profiles can be created by combining data such as purchase history, ratings, preferences, and behavioral history. This allows for a more accurate understanding of customer preferences and needs, enabling personalized services. The analytics department can also utilize historical data and statistical information to analyze long-term trends and patterns, which can be used for future predictions and strategic planning. As a result, the analytics department can not only create customer profiles but also conduct long-term risk assessments and trend analyses, improving the reliability and security of the entire system.

[0066] The matching department matches store staff based on profiles created by the analysis department. The matching department can, for example, match staff based on their skills. For instance, it can identify staff with specific skills and match them with customers. It can also match staff based on their personalities. For example, it can identify staff with specific personalities and match them with customers. Furthermore, the matching department can match staff based on their work status. For instance, it can identify currently employed staff and match them with customers. Specifically, it analyzes staff skill sets in detail and selects the staff best suited to the customer's needs. For example, it can identify staff with expertise in specific products or skills in specific services and match them with customers. Considering staff personalities can also improve compatibility with customers. For example, if a customer prefers a relaxed atmosphere, it can match them with a staff member who has a calm and friendly personality. Furthermore, by monitoring the work status of store employees in real time and quickly identifying employees currently on duty, prompt service to customers becomes possible. The matching department comprehensively considers these factors and can improve customer satisfaction by matching the most suitable employee to the customer. The matching department can utilize AI to improve the accuracy of matching employees with customers. For example, by using machine learning algorithms to analyze past matching data and learn patterns of successful matches, the accuracy of future matches can be improved. As a result, the matching department can quickly and accurately match the most suitable employee to the customer and provide personalized service.

[0067] The evaluation department evaluates the staff members matched by the matching department. The evaluation department, for example, aggregates customer feedback. For example, the evaluation department can collect customer evaluations of the services provided. The evaluation department can also evaluate the skills of the staff members. For example, the evaluation department can evaluate staff members' skills based on customer feedback. Furthermore, the evaluation department can also evaluate the personalities of the staff members. For example, the evaluation department can evaluate staff members' personalities based on customer feedback. Specifically, the evaluation department collects customer evaluations of the services provided and evaluates the staff members' performance. For example, it aggregates customer evaluations of the services provided and evaluates the skills and personalities of the staff members. The evaluation department can also evaluate staff members' skills based on customer feedback. For example, it evaluates staff members' skills based on customer evaluations of the services provided. Furthermore, the evaluation department can evaluate staff members' personalities based on customer feedback. For example, it evaluates staff members' personalities based on customer evaluations of the services provided. The evaluation department can use these evaluation results to provide feedback to improve employee performance. For example, the evaluation department can provide feedback on employees' skills and personalities, and offer training and support to improve employee performance. This allows the evaluation department to improve employee performance and increase customer satisfaction. Furthermore, the evaluation department can use the evaluation results to make decisions regarding employee compensation and promotions. For example, the evaluation department can make decisions regarding employee compensation and promotions based on evaluation results regarding employees' skills and personalities. This allows the evaluation department to improve employee motivation and increase customer satisfaction.

[0068] The data collection unit can collect data such as a customer's past purchase history, ratings, and preferences. For example, the data collection unit can collect a customer's past purchase history. For example, the data collection unit can collect a history of products that a customer has purchased in the past. The data collection unit can also collect customer ratings. For example, the data collection unit can collect data on products that a customer has rated. The data collection unit can also collect customer preferences. For example, the data collection unit can collect a customer's tastes and interests. By collecting data such as a customer's past purchase history, ratings, and preferences, a detailed customer profile can be created. 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 a customer's past purchase history into AI, and the AI ​​can analyze the data to identify the customer's preferences.

[0069] The analysis department can analyze collected data to determine customer preferences and needs. For example, the analysis department can analyze data using data mining techniques. For instance, it can analyze customer purchase history to determine customer preferences and needs. The analysis department can also analyze data using statistical analysis techniques. For example, it can analyze customer evaluation data to determine customer satisfaction. Furthermore, the analysis department can analyze data using machine learning algorithms. For example, it can analyze customer behavior history to determine customer purchasing intent. This allows the analysis department to analyze collected data and determine customer preferences and needs, thereby providing appropriate services to customers. Some or all of the above-described processes in the analysis department may be performed using AI, or without AI. For example, the analysis department can input collected data into an AI, which can then analyze the data to determine customer preferences and needs.

[0070] The matching unit can perform real-time matching based on the skills and personality of store employees, comparing them with their current work status. For example, the matching unit can perform matching based on the employee's skills. For example, the matching unit can identify an employee with specific skills and match that employee with a customer. The matching unit can also perform matching based on the employee's personality. For example, the matching unit can identify an employee with specific personality traits and match that employee with a customer. The matching unit can also perform matching based on the employee's work status. For example, the matching unit can identify an employee currently on duty and match that employee with a customer. This allows the system to provide customers with the most suitable employee by performing real-time matching based on the employee's skills and personality. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input data on the employee's skills and personality into an AI, which can analyze the data to identify the most suitable employee.

[0071] The evaluation unit can collect customer feedback and evaluate the skills and personalities of store employees. For example, the evaluation unit can collect customer feedback. For example, the evaluation unit can collect customer evaluations of the services provided. The evaluation unit can also evaluate the skills of store employees. For example, the evaluation unit can evaluate the skills of store employees based on customer feedback. The evaluation unit can also evaluate the personalities of store employees. For example, the evaluation unit can evaluate the personalities of store employees based on customer feedback. By collecting customer feedback and evaluating the skills and personalities of store employees, it is possible to improve the skills of store employees and the accuracy of customer matching. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input customer feedback data into AI, and the AI ​​can analyze the data to evaluate the skills and personalities of store employees.

[0072] The data collection unit can estimate the customer's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the customer is relaxed, the data collection unit can select a time to collect data to avoid stressing the customer. The data collection unit can also postpone data collection if the customer is busy, collecting it when the customer has calmed down. Furthermore, if the customer is excited, the data collection unit can collect data quickly to obtain information before the customer's interest wanes. This allows for data collection in a stress-free manner by adjusting the timing of data collection based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer facial expression data into a generative AI, which can analyze the data to estimate the customer's emotions.

[0073] The data collection unit can analyze a customer's past purchase history and select the optimal data collection method. For example, the data collection unit can collect data through online questionnaires based on a customer's past online purchase history. Alternatively, if a customer has a high purchase history at physical stores, the data collection unit can collect data through in-store interviews. Furthermore, if a customer shows a strong interest in a particular brand, the data collection unit can prioritize collecting data related to that brand. This allows for efficient data collection by analyzing the customer's past purchase history and selecting the optimal data collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the customer's past purchase history data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0074] The data collection unit can filter data based on the customer's current lifestyle and areas of interest during data collection. For example, if a customer starts a new hobby, the data collection unit will prioritize collecting data related to that hobby. It can also collect data related to a new living environment if a customer moves. Furthermore, if a customer participates in a specific event, the data collection unit can collect data related to that event. This allows for the collection of highly relevant data by filtering based on the customer's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer lifestyle data into a generating AI, which can then analyze and filter the data.

[0075] The data collection unit can estimate the customer's emotions and prioritize the data to collect based on the estimated emotions. For example, if the customer is excited, the data collection unit may prioritize collecting data on products of interest. If the customer is relaxed, the data collection unit may also prioritize collecting data on overall purchasing trends. Furthermore, if the customer is stressed, the data collection unit may prioritize collecting data on products that help reduce stress. This allows for the priority collection of data tailored to the customer's interests by prioritizing the data to be collected based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer facial expression data into a generative AI, which can analyze the data to estimate the customer's emotions and determine the priority of the data to collect.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location during data collection. For example, if a customer lives in a specific region, the data collection unit can collect data on products related to that region. Furthermore, if a customer is traveling, the data collection unit can collect data on products related to their travel destination. Additionally, if a customer frequently visits a particular store, the data collection unit can collect data on products related to that store. This allows for the provision of region-specific services by prioritizing the collection of highly relevant data while considering the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the customer's geographical location information into a generating AI, which can then analyze the data to identify highly relevant information.

[0077] The data collection unit can analyze customers' social media activity and collect relevant data during data collection. For example, if a customer mentions a particular brand on social media, the data collection unit can collect data related to that brand. It can also collect data related to an event if a customer participates in that event on social media. Furthermore, if a customer posts a review of a particular product on social media, the data collection unit can collect data on that product. This allows for the provision of services tailored to customer interests by analyzing customers' social media activity and collecting relevant data. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer social media activity data into a generating AI, which can then analyze the data to identify relevant information.

[0078] The analysis unit can estimate the customer's emotions and adjust the way the profile is presented based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can provide a detailed profile to capture the customer's interest. If the customer is in a hurry, the analysis unit can also provide a concise profile to quickly convey information. Furthermore, if the customer is excited, the analysis unit can provide a visually appealing profile to maintain the customer's interest. This allows for the provision of a profile that is easy for the customer to understand by adjusting the way the profile is presented based on the customer'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 AI or not using AI. For example, the analysis unit can input customer facial expression data into a generative AI, which can analyze the data to estimate the customer's emotions and adjust the way the profile is presented.

[0079] The analytics department can adjust the level of detail in customer profiles based on their importance during data analysis. For example, if a customer is a VIP, the analytics department can provide a detailed profile and suggest special services. Alternatively, if a customer is a new customer, the analytics department can provide a basic profile and suggest initial services. Furthermore, if a customer is a repeat customer, the analytics department can provide a profile based on their past purchase history to encourage repeat purchases. This allows the analytics department to provide appropriate services to customers by adjusting the level of detail in their profiles based on their importance. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For instance, the analytics department can input customer importance data into a generating AI, which can then analyze the data and adjust the level of detail in the profiles.

[0080] The analysis department can apply different analysis algorithms depending on the customer's category during data analysis. For example, if a customer is interested in fashion, the analysis department can apply a fashion-related algorithm to create a profile. Similarly, if a customer is interested in gourmet food, the analysis department can apply a gourmet-related algorithm to create a profile. Furthermore, if a customer is interested in technology, the analysis department can apply a technology-related algorithm to create a profile. This allows the analysis department to provide customers with appropriate profiles by applying different analysis algorithms depending on their category. 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 customer category data into a generating AI, which can then analyze the data and apply an appropriate analysis algorithm.

[0081] The analysis unit can estimate the customer's emotions and adjust the length of the profile based on the estimated emotions. For example, if the customer is in a hurry, the analysis unit can provide a short, concise profile. If the customer is relaxed, the analysis unit can also provide a longer profile with more detailed descriptions. Furthermore, if the customer is excited, the analysis unit can provide a profile with visually stimulating effects. By adjusting the length of the profile based on the customer's emotions, the appropriate amount of information can be provided to the customer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input customer facial expression data into a generative AI, which can analyze the data to estimate the customer's emotions and adjust the length of the profile.

[0082] The analytics department can prioritize profiles based on customer submission timing during data analysis. For example, if a customer has recently submitted data, the analytics department will prioritize analyzing that data and update the profile. Alternatively, if a customer has previously submitted data, the analytics department can prioritize analyzing new data while referencing that data. Furthermore, if a customer has participated in a specific event, the analytics department can prioritize analyzing data related to that event. This allows for the provision of the most up-to-date information by prioritizing profiles based on customer submission timing. Some or all of the above processes in the analytics department may be performed using AI, or not. For example, the analytics department could input customer submission timing data into a generating AI, which would then analyze the data to determine profile priorities.

[0083] The analytics department can adjust the order of customer profiles based on their relevance during data analysis. For example, if a customer shows a high level of interest in a particular brand, the analytics department will prioritize analyzing data related to that brand. Similarly, if a customer shows a high level of interest in a particular category, the analytics department can prioritize analyzing data related to that category. Furthermore, if a customer frequently reviews a particular product, the analytics department can prioritize analyzing data related to that product. This allows for the prioritization of information important to customers by adjusting the order of profiles based on their relevance. Some or all of the above processing in the analytics department may be performed using AI, for example, or not. For instance, the analytics department can input customer relevance data into a generating AI, which can then analyze the data and adjust the order of profiles.

[0084] The matching unit can estimate the customer's emotions and adjust the matching criteria based on the estimated emotions. For example, if the customer is relaxed, the matching unit will prioritize matching them with a friendly staff member. If the customer is in a hurry, the matching unit can also prioritize matching them with a staff member who can respond quickly. Furthermore, if the customer is excited, the matching unit can prioritize matching them with an energetic staff member. This allows the system to match the customer with the most suitable staff member by adjusting the matching criteria based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input customer facial expression data into a generative AI, which can analyze the data to estimate the customer's emotions and adjust the matching criteria.

[0085] The matching unit can improve the accuracy of matching by considering the relationships between employees during the matching process. For example, the matching unit can consider the compatibility between employees and match employees with good teamwork. It can also match employees who are more likely to cooperate based on their past cooperation record. Furthermore, the matching unit can consider the skill sets of employees and match employees who can complement each other. In this way, by considering the relationships between employees, it is possible to match employees with good teamwork. Some or all of the above processes in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input employee relationship data into a generating AI, which can then analyze the data to improve the accuracy of the matching.

[0086] The matching unit can perform matching while considering the attribute information of the store staff. For example, the matching unit can consider the store staff's expertise and match them with a store staff member that suits the customer's needs. The matching unit can also consider the store staff's language skills and match them with a store staff member who can communicate in the customer's language. Furthermore, the matching unit can consider the store staff member's personality and match them with a store staff member that suits the customer's personality. In this way, by considering the attribute information of the store staff, it is possible to match them with a store staff member that suits the customer's needs. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input store staff attribute information data into a generating AI, and the generating AI can analyze the data to identify the most suitable store staff member.

[0087] The matching unit can estimate the customer's emotions and adjust the order in which the matching results are displayed based on the estimated emotions. For example, if the customer is relaxed, the matching unit can display the matching results in an order that includes detailed information. If the customer is in a hurry, the matching unit can also display the matching results in an order that gets straight to the point. Furthermore, if the customer is excited, the matching unit can display the matching results in a visually appealing order. This allows for the provision of easily understandable information to the customer by adjusting the order in which the matching results are displayed based on their 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 matching unit may be performed using AI or not using AI. For example, the matching unit can input customer facial expression data into a generative AI, which can analyze the data to estimate the customer's emotions and adjust the order in which the matching results are displayed.

[0088] The matching unit can perform matching while considering the geographical distribution of store employees. For example, if an employee is nearby, the matching unit will prioritize matching with that employee. Furthermore, if an employee is far away, the matching unit can also consider travel time when matching. Additionally, if an employee is familiar with a particular area, the matching unit can prioritize matching with customers associated with that area. This allows the system to match customers with the most suitable employee by considering the geographical distribution of employees. Some or all of the above processing in the matching unit may be performed using AI, or without AI. For example, the matching unit can input geographical distribution data of employees into a generating AI, which can then analyze the data to identify the most suitable employee.

[0089] The matching unit can improve the accuracy of matching by referring to relevant literature on store employees during the matching process. For example, the matching unit can match store employees to customers based on their past evaluations. The matching unit can also refer to literature on store employees' skills to match appropriate store employees. Furthermore, the matching unit can refer to literature on store employees' personalities to match store employees to customers with personalities that suit them. In this way, by referring to relevant literature on store employees, it is possible to match store employees to customers that meet their needs. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input data on relevant literature on store employees into a generating AI, which can then analyze the data to improve the accuracy of matching.

[0090] The evaluation unit can estimate the customer's emotions and adjust the evaluation method based on the estimated emotions. For example, if the customer is relaxed, the evaluation unit may provide an evaluation method that requests detailed feedback. If the customer is in a hurry, the evaluation unit may provide a concise evaluation method. Furthermore, if the customer is excited, the evaluation unit may provide a visually appealing evaluation method. In this way, by adjusting the evaluation method based on the customer's emotions, an appropriate evaluation method can be provided for the customer. 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 evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input customer facial expression data into a generative AI, which can analyze the data to estimate the customer's emotions and adjust the evaluation method.

[0091] The evaluation unit can analyze past customer feedback during the evaluation process to select the optimal evaluation method. For example, if the customer has provided detailed feedback in the past, the evaluation unit can provide a detailed evaluation method. Alternatively, if the customer has provided concise feedback in the past, the evaluation unit can provide a concise evaluation method. Furthermore, if the customer has preferred visual feedback in the past, the evaluation unit can provide a visually appealing evaluation method. In this way, by analyzing past customer feedback, the evaluation unit can provide the most suitable evaluation method for the customer. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past customer feedback data into a generating AI, which can then analyze the data to select the optimal evaluation method.

[0092] The evaluation unit can customize the evaluation method based on the customer's current lifestyle during the evaluation process. For example, if the customer is busy, the evaluation unit can provide a concise evaluation method. If the customer is relaxed, the evaluation unit can also provide a detailed evaluation method. Furthermore, if the customer is participating in a specific event, the evaluation unit can provide an evaluation method related to that event. This allows the evaluation unit to provide an appropriate evaluation method for the customer by customizing the evaluation method based on the customer's current lifestyle. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input customer lifestyle data into a generating AI, which can then analyze the data to customize the evaluation method.

[0093] The evaluation unit can estimate the customer's emotions and determine evaluation priorities based on the estimated emotions. For example, if the customer is relaxed, the evaluation unit may prioritize collecting detailed feedback. It may also prioritize collecting concise feedback if the customer is in a hurry. Furthermore, if the customer is excited, the evaluation unit may prioritize collecting visually appealing feedback. This allows for the prioritization of feedback important to the customer by determining evaluation priorities based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input customer facial expression data into a generative AI, which can analyze the data to estimate the customer's emotions and determine evaluation priorities.

[0094] The evaluation unit can select the optimal evaluation method during the evaluation process, taking into account the customer's geographical location information. For example, if the customer lives in a specific region, the evaluation unit can provide an evaluation method relevant to that region. Furthermore, if the customer is traveling, the evaluation unit can provide an evaluation method relevant to their travel destination. Additionally, if the customer frequently visits a particular store, the evaluation unit can provide an evaluation method relevant to that store. This allows for the provision of region-specific evaluation methods by considering the customer's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For instance, the evaluation unit can input the customer's geographical location data into a generating AI, which can then analyze the data and select the optimal evaluation method.

[0095] The evaluation unit can analyze a customer's social media activity during the evaluation process and propose evaluation methods. For example, if a customer mentions a specific brand on social media, the evaluation unit can provide an evaluation method related to that brand. It can also provide an evaluation method related to an event if a customer participates in a specific event on social media. Furthermore, if a customer posts a review of a specific product on social media, the evaluation unit can provide an evaluation method for that product. This allows the evaluation unit to provide an appropriate evaluation method for the customer by analyzing their social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input customer social media activity data into a generating AI, which can then analyze the data and propose evaluation methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can estimate the customer's emotions and adjust the timing of data collection based on those emotions. For example, if the customer is relaxed, it can select a time to collect data to avoid causing stress. If the customer is busy, data collection can be postponed until the customer has calmed down. Furthermore, if the customer is excited, data collection can be carried out quickly to obtain information before the customer's interest wanes. In this way, by adjusting the timing of data collection based on the customer's emotions, data can be collected in a way that does not cause stress to the customer.

[0098] The analytics department can estimate customer emotions and adjust the way the profile is presented based on those emotions. For example, if a customer is relaxed, a detailed profile can be provided to capture their interest. If a customer is in a hurry, a concise profile can be provided to convey information quickly. Furthermore, if a customer is excited, a visually appealing profile can be provided to maintain their interest. In this way, by adjusting the way the profile is presented based on customer emotions, a profile that is easy for customers to understand can be provided.

[0099] The matching unit can estimate the customer's emotions and adjust the matching criteria based on those emotions. For example, if a customer is relaxed, it will prioritize matching them with a friendly staff member. If a customer is in a hurry, it can prioritize matching them with a staff member who can respond quickly. Furthermore, if a customer is excited, it can prioritize matching them with an energetic staff member. In this way, by adjusting the matching criteria based on the customer's emotions, the system can match the customer with the most suitable staff member.

[0100] The evaluation unit can estimate the customer's emotions and adjust the evaluation method based on those emotions. For example, if the customer is relaxed, it can provide an evaluation method that requests detailed feedback. If the customer is in a hurry, it can provide a concise evaluation method. Furthermore, if the customer is excited, it can provide a visually appealing evaluation method. In this way, by adjusting the evaluation method based on the customer's emotions, it is possible to provide an evaluation method that is appropriate for the customer.

[0101] The evaluation unit can estimate the customer's emotions and prioritize evaluations based on those emotions. For example, if the customer is relaxed, detailed feedback can be prioritized. If the customer is in a hurry, concise feedback can be prioritized. Furthermore, if the customer is excited, visually appealing feedback can be prioritized. By prioritizing evaluations based on the customer's emotions, the system can prioritize collecting feedback that is important to the customer.

[0102] The data collection unit can analyze customers' past purchase history and select the most suitable data collection method. For example, it can collect data through online surveys based on a customer's past online purchase history. If a customer has a high purchase history at physical stores, data can also be collected through in-store interviews. Furthermore, if a customer shows a strong interest in a particular brand, data related to that brand can be prioritized. This allows for efficient data collection by analyzing customers' past purchase history and selecting the optimal data collection method.

[0103] The data collection unit can filter data based on the customer's current lifestyle and areas of interest during the data collection process. For example, if a customer starts a new hobby, the system can prioritize collecting data related to that hobby. Similarly, if a customer moves, it can collect data related to their new living environment. Furthermore, if a customer participates in a specific event, it can collect data related to that event. This allows for the collection of highly relevant data by filtering based on the customer's current lifestyle and areas of interest.

[0104] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location during data collection. For example, if a customer lives in a specific region, it can collect data on products related to that region. If a customer is traveling, it can also collect data on products related to their travel destination. Furthermore, if a customer frequently visits a particular store, it can collect data on products related to that store. This allows for the provision of region-specific services by prioritizing the collection of highly relevant data based on the customer's geographical location.

[0105] The data collection unit can analyze customers' social media activity and collect relevant data during the data collection process. For example, if a customer mentions a specific brand on social media, it can collect data related to that brand. It can also collect data related to an event if the customer participates in it on social media. Furthermore, if a customer posts a review of a specific product on social media, it can collect data on that product. This allows for the provision of services tailored to customer interests by analyzing customer social media activity and collecting relevant data.

[0106] The analytics department can adjust the level of detail in customer profiles based on their importance during data analysis. For example, if a customer is a VIP, a detailed profile can be provided, and special services can be suggested. If a customer is a new customer, a basic profile can be provided, and initial services can be suggested. Furthermore, if a customer is a repeat customer, a profile based on their past purchase history can be provided to encourage repeat purchases. In this way, by adjusting the level of detail in profiles based on customer importance, appropriate services can be provided to customers.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects customer data. This data includes purchase history, ratings, preferences, and behavioral history. For example, the data collection unit can collect data on products a customer has purchased in the past, products they have rated, and their preferences and interests. Step 2: The analysis department analyzes the data collected by the data collection department and creates customer profiles. The analysis department can analyze the data using data mining techniques, statistical analysis techniques, and machine learning algorithms to determine customer preferences, needs, satisfaction levels, and purchase intent. Step 3: The matching department matches store employees based on the profiles created by the analysis department. The matching department matches employees based on their skills, personality, and work status, and can match customers with employees who have specific skills or personalities, or who are currently working at the store. Step 4: The evaluation department evaluates the staff members matched by the matching department. The evaluation department can collect customer feedback and evaluate the staff members' skills and personalities. This allows for staff member evaluations based on customer feedback.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and evaluation 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 customer data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which analyzes the collected data to create a customer profile. The matching unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which matches store employees based on the created profile. The evaluation unit is implemented in the data processing unit 12, for example, which aggregates customer feedback to evaluate store employees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects customer data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to create a customer profile. The matching unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which matches the store clerk based on the created profile. The evaluation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which aggregates customer feedback to evaluate the store clerk. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and evaluation 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 customer data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to create a customer profile. The matching unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which matches store employees based on the created profile. The evaluation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which aggregates customer feedback to evaluate store employees. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, and evaluation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects customer data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to create a customer profile. The matching unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which matches store employees based on the created profile. The evaluation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which aggregates customer feedback to evaluate store employees. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects customer data, An analysis unit analyzes the data collected by the aforementioned collection unit and creates a customer profile, Based on the profile created by the aforementioned analysis unit, a matching unit matches store employees, The system includes an evaluation unit that evaluates the store clerk matched by the matching unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as customers' past purchase history, ratings, and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to determine customer preferences and needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Based on the skills and personality of the staff, matching is performed in real time by comparing them with their current work status. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit described above, We collect customer feedback and use it to evaluate the skills and personalities of our staff. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate customer 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 7) The aforementioned collection unit is Analyze the customer's past purchase history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the customer's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate customer emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze customers' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate customer emotions and adjust how the profile is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is When analyzing data, adjust the level of detail in the profile based on the importance of the customer. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing data, different analytical algorithms are applied depending on the customer category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Estimate customer emotions and adjust the profile length based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing data, prioritize profiles based on when the customer submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During data analysis, reorder profiles based on customer relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is We estimate customer emotions and adjust matching criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is When matching customers, we improve the accuracy of the matching process by considering the relationships between staff members. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is During the matching process, the store staff's attribute information is taken into consideration when matching customers. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is It estimates customer sentiment and adjusts the order in which matching results are displayed based on the estimated customer sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is During the matching process, the geographical distribution of store employees is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is During the matching process, we improve the accuracy of the matching by referring to relevant literature provided by the store staff. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit described above, We estimate customer emotions and adjust the evaluation method based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit described above, During the evaluation process, we analyze past customer feedback to select the most suitable evaluation method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit described above, During the evaluation process, the evaluation method will be customized based on the customer's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit described above, Estimate customer emotions and determine evaluation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit described above, During the evaluation process, the optimal evaluation method will be selected, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit described above, During the evaluation process, we will analyze the customer's social media activity and propose evaluation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 customer data, An analysis unit analyzes the data collected by the aforementioned collection unit and creates a customer profile, Based on the profile created by the aforementioned analysis unit, a matching unit matches store employees, The system includes an evaluation unit that evaluates the store clerk matched by the matching unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as customers' past purchase history, ratings, and preferences. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to determine customer preferences and needs. The system according to feature 1.

4. The matching unit is Based on the skills and personality of the staff, matching is performed in real time by comparing them with their current work status. The system according to feature 1.

5. The evaluation unit described above, We collect customer feedback and use it to evaluate the skills and personalities of our staff. The system according to feature 1.

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

7. The aforementioned collection unit is Analyze the customer's past purchase history and select the optimal data collection method. The system according to feature 1.

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

9. The aforementioned collection unit is We estimate customer emotions and prioritize the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the customer's geographical location. The system according to feature 1.

Images