system

Abstract

The system according to this embodiment aims to enable all staff to provide consistently high-quality customer service. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an advice unit, a generation unit, and a display unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The advice unit provides advice based on the analysis results obtained by the analysis unit. The generation unit generates a customer service script based on the advice provided by the advice unit. The display unit displays customer information based on the customer service script generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the quality of customer service varies and it is difficult for all staff to exhibit the same level of performance.

[0005] The system according to the embodiment aims to enable all staff to provide consistent high-quality customer service. [[ID=4K]]

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an advice unit, a generation unit, and a display unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The advice unit provides advice based on the analysis results obtained by the analysis unit. The generation unit generates a customer service script based on the advice provided by the advice unit. The display unit displays customer information based on the customer service script generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable all staff to provide consistently high-quality customer service. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The customer service support system according to an embodiment of the present invention is a system that improves the quality of customer service by utilizing an AI agent. This customer service support system uses AI to acquire input from a camera and microphone and provide advice in real time to the staff member's earphone. It also displays customer-oriented information on a tablet. This allows staff members to receive optimal suggestions and sales pitches during customer service. Furthermore, it utilizes AR data glasses to display customer facial recognition and purchase history, and to support staff in real time. This allows staff members to instantly grasp customer information and respond appropriately. In addition, the AI ​​automatically generates an optimal customer service script, enabling all staff members to provide consistent quality customer service. This eliminates variations in the quality of customer service, allowing all staff members to perform at the same level. This system enables all staff members to provide consistently high-quality customer service, improving customer satisfaction and sales. It also reduces the burden on staff and improves the working environment. For example, the customer service support system allows staff members to receive advice in real time during customer service. For example, the customer service support system displays customer facial recognition and purchase history, enabling staff members to instantly grasp customer information. For example, the customer service support system uses AI to automatically generate an optimal customer service script, enabling all staff members to provide consistent quality customer service. This customer service support system enables all staff to provide consistently high-quality service, leading to increased customer satisfaction and sales. Furthermore, it reduces staff workload and improves the working environment.

[0029] The customer service support system according to the embodiment comprises a collection unit, an analysis unit, an advice unit, a generation unit, and a display unit. The collection unit collects information. The collection unit acquires input from, for example, a camera and a microphone. The collection unit can, for example, collect customer facial recognition and purchase history. The collection unit can also, for example, collect customer behavior data. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using, for example, data mining techniques. The analysis unit can also, for example, analyze the information using statistical analysis techniques. The analysis unit can also, for example, analyze the information using machine learning algorithms. The advice unit provides advice based on the analysis results obtained by the analysis unit. The advice unit can, for example, suggest customer service methods. The advice unit can also, for example, recommend products. The advice unit can also, for example, point out areas for improvement in customer service. The generation unit generates a customer service script based on the advice provided by the advice unit. The generation unit generates a customer service script that includes, for example, the flow of a conversation. The generation unit can also, for example, generate a customer service script that includes recommended phrases. The generation unit can, for example, generate a customer service script that includes customer service procedures. The display unit displays customer information based on the customer service script generated by the generation unit. The display unit can, for example, present customer information on a tablet. The display unit can also, for example, use AR data glasses to display customer facial recognition and purchase history. The display unit can also, for example, display the customer's name and purchase history. As a result, the customer service support system according to the embodiment can efficiently collect, analyze, provide advice on, generate customer service scripts, and display customer information.

[0030] The data collection unit collects information. For example, it obtains input from cameras and microphones. Specifically, the camera recognizes the customer's face and captures their expressions and movements in real time. This allows for inferring the customer's emotions and interests. The microphone collects the customer's voice and converts the conversation into text data using speech recognition technology. This allows for an accurate understanding of the customer's requests and questions. Furthermore, the data collection unit can retrieve the customer's purchase history from a database and analyze past purchase patterns and preferences. For example, it collects information on products the customer has previously purchased and products they have frequently viewed to understand their preferences. It can also collect customer behavior data. For example, it can record movement routes within the store, time spent there, and actions at specific product shelves using sensors and beacons. This allows for a more detailed understanding of the customer's interests and concerns. The data collection unit collects this diverse data in real time and transmits it to a central database. This ensures that the entire system operates based on the latest information and can provide the best possible service to the customer. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection to respond flexibly to specific situations and conditions. For example, during peak hours, the frequency of data collection can be increased to track the behavior of each individual customer in detail. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit uses data mining techniques to analyze the information. Specifically, it analyzes customer purchase history and behavioral data to extract customer preferences and purchasing patterns. This allows for the identification of products and services that customers are interested in. Furthermore, it can also analyze information using statistical analysis techniques. For example, it can statistically analyze data such as customer age, gender, and purchase history to formulate effective marketing strategies for specific customer segments. It can also analyze information using machine learning algorithms. For example, it can build models to predict future purchasing behavior based on customer behavioral data. This allows for the prediction of products customers are likely to purchase next and the recommendation of those products at the appropriate time. By combining these techniques, the analysis unit can analyze collected data from multiple perspectives and accurately understand customer needs and preferences. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. For example, it can analyze seasonal purchasing trends and purchasing behavior during specific events to formulate future marketing strategies. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Advice Department provides advice based on the analysis results obtained by the Analysis Department. For example, the Advice Department proposes customer service methods. Specifically, it proposes the optimal customer service method based on customer preferences and purchase history. For example, it recommends related products based on products that customers have previously purchased or shown interest in. It can also identify products and services that customers are likely to be interested in based on customer behavior data and propose effective customer service methods. Furthermore, the Advice Department can also make product recommendations. For example, it recommends products that customers are likely to be interested in based on their purchase history and preferences. This allows for the effective suggestion of products that customers are likely to purchase. The Advice Department can also point out areas for improvement in customer service. For example, it identifies areas for improvement in customer service methods and responses based on customer feedback and behavior data and provides specific advice to staff. This improves staff customer service skills and increases customer satisfaction. The Advice Department provides this advice in real time to support staff in responding quickly and appropriately. Furthermore, the Advice Department can also develop long-term customer service strategies by utilizing historical data and statistical information. For example, by analyzing effective customer service methods and product recommendation patterns for specific customer segments, the system can develop future customer service strategies. This allows the advisory department to not only provide real-time advice but also to develop long-term customer service strategies, maximizing the overall effectiveness of the system.

[0033] The generation unit generates customer service scripts based on advice provided by the advice unit. For example, the generation unit generates customer service scripts that include the flow of conversation. Specifically, it designs effective conversation flows based on customer preferences and purchase history, supporting staff in providing smooth customer service. For example, it generates conversation scripts that include explanations of products and services that customers might be interested in, and suggestions for related products. It can also generate customer service scripts that include recommended phrases. For example, it can suggest effective phrases and questions to customers to pique their interest. Furthermore, it can generate customer service scripts that include customer service procedures. For example, it shows appropriate response procedures to customer requests and questions, supporting staff in responding quickly and appropriately. The generation unit generates these scripts in real time and provides them to staff for immediate use. In addition, the generation unit can continuously improve the accuracy and effectiveness of the scripts by utilizing past data and statistical information. For example, it can review the content and structure of the scripts based on customer feedback and behavioral data to generate more effective scripts. The generation unit can also simulate multiple scenarios and select the most effective script. This allows the generation unit to support staff in providing effective customer service and increasing customer satisfaction.

[0034] The display unit displays customer information based on customer service scripts generated by the generation unit. For example, the display unit presents customer-facing information on a tablet. Specifically, it displays the customer's name, purchase history, and product information that might interest them on the tablet, helping staff to provide effective customer service. It can also utilize AR data glasses to display customer facial recognition and purchase history. For example, by having staff wear AR data glasses, the system can recognize the customer's face and display their past purchase history and preferences in real time. This allows staff to instantly grasp customer information and provide individualized service. Furthermore, the display unit can also display the customer's name and purchase history. For example, when a customer enters a store, the display unit can show the customer's name and past purchase history, enabling staff to provide personalized customer service. The display unit updates this information in real time, supporting customer service based on the latest information. In addition, the display unit can collect customer feedback and continuously improve the accuracy and effectiveness of the displayed content. For example, it can review the displayed content and layout based on customer reactions and behavioral data to provide more effective information presentation. The display unit can also link multiple devices to centrally manage information. For example, by linking devices such as tablets, AR data glasses, and smartphones, customer information can be centrally managed and accessed by staff from any device. This allows the display unit to support staff in providing effective customer service and increase customer satisfaction.

[0035] The advice unit can provide advice to staff members via their earphones. The advice unit can provide advice using, for example, Bluetooth® earphones. The advice unit can also provide advice using, for example, earphones with noise-canceling capabilities. The advice unit can also provide advice using, for example, wireless earphones. This allows staff members to receive advice in real time. Some or all of the above-described processes in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can provide advice using an AI model that takes the analysis results obtained by the analysis unit as input and outputs advice.

[0036] The display unit can present customer information on a tablet. The display unit can, for example, present customer information using a tablet. By displaying customer information on a tablet, it is possible to provide customers with appropriate information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can present customer information using an AI model that takes a customer service script generated by the generation unit as input and outputs customer information.

[0037] The display unit can use AR data glasses to display customer facial recognition and purchase history. This allows for instant access to customer information by utilizing AR data glasses. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can take a customer service script generated by the generation unit as input and display customer information using an AI model that outputs customer facial recognition and purchase history.

[0038] The generation unit can automatically generate the optimal customer service script using AI. The generation unit can generate the customer service script using, for example, deep learning technology. The generation unit can also generate the customer service script using, for example, natural language processing technology. The generation unit can also generate the customer service script using, for example, machine learning algorithms. This allows the optimal customer service script to be automatically generated by using AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate a customer service script using a generation AI model that takes advice provided by the advice unit as input and outputs a customer service script.

[0039] The data collection unit can acquire input from a camera and a microphone. The data collection unit can acquire input using, for example, an HD camera. The data collection unit can also acquire input using, for example, a 360-degree camera. The data collection unit can also acquire input using, for example, a directional microphone. The data collection unit can also acquire input using, for example, a noise-canceling microphone. This makes information collection more efficient by acquiring input from a camera and a microphone. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can collect information using an AI model that takes data acquired from a camera and a microphone as input and outputs information.

[0040] The data collection unit can analyze a customer's past visit history and select the optimal information collection method. For example, the data collection unit can prioritize collecting information on relevant products based on products the customer has purchased in the past. The data collection unit can also analyze the time periods when a customer has visited in the past and collect information accordingly. The data collection unit can also collect information on relevant services based on services the customer has used in the past. This allows the optimal information collection method to be selected by analyzing the customer's past visit history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's past visit history data into a generating AI and have the generating AI select the optimal information collection method.

[0041] The data collection unit can filter information based on the customer's current purchasing intent and areas of interest during the information collection process. For example, the data collection unit can filter information based on product categories that the customer is currently interested in. For example, if the customer has a high purchasing intent, the data collection unit can prioritize the collection of information on related products. The data collection unit can also filter and collect relevant information based on the customer's areas of interest. This allows for the collection of highly relevant information by filtering information based on the customer's purchasing intent 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 the customer's current purchasing intent data into a generating AI and have the generating AI perform the information filtering.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the customer's geographical location during data collection. For example, the data collection unit can prioritize the collection of information about stores and services related to the customer's current location. For example, the data collection unit can also collect information about nearby events based on the customer's geographical location. For example, the data collection unit can also collect information about special offers at the nearest store based on the customer's location. This allows for the priority collection of highly relevant information by 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 example, the data collection unit can input the customer's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0043] The data collection unit can analyze the customer's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information on products and services that the customer has shown interest in on social media. The data collection unit can also analyze the content of the customer's social media posts and collect relevant information. The data collection unit can also collect information related to brands and influencers that the customer follows. In this way, relevant information can be collected by analyzing the customer's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's social media activity data into a generating AI and have the generating AI collect relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can also perform a concise analysis on information of low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, for product information, the analysis unit can apply an analysis algorithm based on sales data. For customer information, the analysis unit can also apply an analysis algorithm based on purchase history. For service information, the analysis unit can also apply an analysis algorithm based on customer satisfaction data. This enables efficient analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also analyze older information as needed. The analysis unit may also adjust the priority of analysis according to the timing of information collection. This enables efficient analysis by determining the priority of analysis based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The advice unit can adjust the level of detail of its advice based on the importance of the information it provides. For example, it can provide detailed advice for highly important information, and concise advice for less important information. The advice unit can also adjust the depth of its advice according to the importance of the information. This allows for more efficient advice by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input information importance data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0049] The advice unit can apply different advice algorithms depending on the category of information when providing advice. For example, for product information, the advice unit can apply an advice algorithm based on sales data. For customer information, the advice unit can also apply an advice algorithm based on purchase history. For service information, the advice unit can also apply an advice algorithm based on customer satisfaction data. By applying different advice algorithms depending on the category of information, efficient advice becomes possible. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input information category data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0050] The advice unit can determine the priority of advice based on when the information was collected when providing advice. For example, the advice unit will prioritize advice based on the most recent information. For example, the advice unit can also provide advice on older information as needed. The advice unit can also adjust the priority of advice according to when the information was collected. This enables efficient advice by determining the priority of advice based on when the information was collected. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input information collection time data into a generating AI and have the generating AI perform the determination of the priority of advice.

[0051] The advice unit can adjust the order of advice based on the relevance of the information when providing advice. For example, the advice unit can prioritize advice on highly relevant information. For example, the advice unit can postpone advice on less relevant information. The advice unit can also adjust the order of advice according to the relevance of the information. This allows for efficient advice by adjusting the order of advice based on the relevance of the information. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0052] The generation unit can adjust the level of detail in customer service scripts based on the importance of the information. For example, the generation unit can generate detailed scripts for highly important information. For example, the generation unit can also generate concise scripts for less important information. The generation unit can also adjust the depth of the script according to the importance of the information. This makes it possible to generate efficient customer service scripts by adjusting the level of detail in the script based on the importance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the script.

[0053] The generation unit can apply different generation algorithms depending on the information category when generating customer service scripts. For example, for product information, the generation unit can apply a generation algorithm based on sales data. For customer information, the generation unit can also apply a generation algorithm based on purchase history. For service information, the generation unit can also apply a generation algorithm based on customer satisfaction data. By applying different generation algorithms depending on the information category, efficient customer service script generation becomes possible. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0054] The generation unit can determine the priority of customer service scripts based on the timing of information collection when generating them. For example, the generation unit prioritizes reflecting the latest information in the script. For example, the generation unit can also reflect older information in the script as needed. For example, the generation unit can adjust the script priority according to the timing of information collection. This enables the efficient generation of customer service scripts by determining the script priority based on the timing of information collection. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information collection timing data into a generation AI and have the generation AI perform the determination of script priority.

[0055] The generation unit can adjust the order of customer service scripts based on the relevance of the information when generating them. For example, the generation unit can prioritize reflecting highly relevant information in the script. For example, the generation unit can postpone less relevant information. For example, the generation unit can adjust the order of the scripts according to the relevance of the information. This makes it possible to generate efficient customer service scripts by adjusting the order of the scripts based on the relevance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the script order.

[0056] The display unit can adjust the level of detail of the display based on the importance of the information during display. For example, the display unit can display highly important information in detail. For example, the display unit can also display less important information in a concise manner. The display unit can also adjust the depth of the display according to the importance of the information. This allows for efficient information display by adjusting the level of detail of the display based on the importance of the information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0057] The display unit can apply different display algorithms depending on the category of information during display. For example, for product information, the display unit can apply a display algorithm based on sales data. For customer information, the display unit can also apply a display algorithm based on purchase history. For service information, the display unit can also apply a display algorithm based on customer satisfaction data. By applying different display algorithms depending on the category of information, efficient information display becomes possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input information category data into a generating AI and have the generating AI execute the application of the display algorithm.

[0058] The display unit can determine the display priority based on the information collection timing when displaying information. For example, the display unit may prioritize displaying the latest information. The display unit may also display older information as needed. The display unit may also adjust the display priority according to the information collection timing. This enables efficient information display by determining the display priority based on the information collection timing. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input information collection timing data into a generating AI and have the generating AI perform the determination of the display priority.

[0059] The display unit can adjust the display order based on the relevance of the information during display. For example, the display unit can prioritize the display of highly relevant information. For example, the display unit can postpone the display of less relevant information. The display unit can also adjust the display order according to the relevance of the information. This allows for efficient information display by adjusting the display order based on the relevance of the information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can estimate customer preferences based on their purchase history and collect information based on those estimated preferences. For example, the data collection unit can analyze trends in products that customers have purchased in the past and prioritize collecting information on related products. For example, if a customer prefers a particular brand, the data collection unit can also collect information on new products from that brand. For example, if a customer frequently purchases products from a particular category, the data collection unit can also collect information on new products from that category. By collecting information based on customer preferences, the system can provide customers with more relevant information.

[0062] The data collection unit can analyze customer purchasing patterns based on their purchase history and select the optimal information collection method. For example, if a customer tends to purchase certain products during a particular season, the data collection unit will prioritize collecting information on products related to that season. For example, if a customer tends to purchase certain products before a particular event, the data collection unit can also collect information on products related to that event. For example, if a customer frequently purchases products from a particular brand, the data collection unit can also collect information on new products from that brand. By collecting information based on customer purchasing patterns, the system can provide customers with more relevant information.

[0063] The analysis unit can analyze customer purchasing trends based on their purchase history and select the most appropriate analysis method. For example, if a customer frequently purchases products in a particular category, the analysis unit will prioritize analyzing products related to that category. For example, if a customer prefers products from a particular brand, the analysis unit can also analyze products related to that brand. For example, if a customer tends to purchase products in a particular price range, the analysis unit can also analyze products related to that price range. By performing analysis based on customer purchasing trends, the system can provide customers with more relevant information.

[0064] The advisory department can analyze customer purchasing trends based on their purchase history and provide optimal advice. For example, if a customer frequently purchases items in a particular category, the advisory department will prioritize advising on products related to that category. For example, if a customer prefers products from a particular brand, the advisory department can also advise on products related to that brand. For example, if a customer tends to purchase items in a particular price range, the advisory department can also advise on products related to that price range. By providing advice based on customer purchasing trends, the advisory department can provide customers with more relevant information.

[0065] The display unit can analyze customer purchasing trends based on their purchase history and select the optimal display method. For example, if a customer frequently purchases products in a particular category, the display unit will prioritize displaying information related to that category. For example, if a customer prefers products from a particular brand, the display unit can also display information related to products from that brand. For example, if a customer tends to purchase products in a particular price range, the display unit can also display information related to products in that price range. By displaying information based on customer purchasing trends, the system can provide customers with more relevant information.

[0066] The generation unit can analyze customer purchasing trends based on their purchase history and generate optimal customer service scripts. For example, if a customer frequently purchases products in a particular category, the generation unit can generate a script that includes suggestions for products related to that category. For example, if a customer prefers products from a particular brand, the generation unit can also generate a script that includes suggestions for products related to that brand. For example, if a customer tends to purchase products in a particular price range, the generation unit can also generate a script that includes suggestions for products related to that price range. This allows for the generation of customer service scripts based on customer purchasing trends, thereby providing customers with more relevant information.

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

[0068] Step 1: The collection unit collects information. For example, the collection unit can acquire input from a camera and microphone and collect customer facial recognition, purchase history, and behavioral data. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using, for example, data mining techniques, statistical analysis techniques, and machine learning algorithms. Step 3: The advice unit provides advice based on the analysis results obtained by the analysis unit. For example, the advice unit can suggest customer service methods, recommend products, and point out areas for improvement in customer service. Step 4: The generation unit generates a customer service script based on the advice provided by the advice unit. The generation unit can generate a customer service script that includes, for example, the flow of conversation, recommended phrases, and customer service procedures. Step 5: The display unit displays customer information based on the customer service script generated by the generation unit. The display unit can, for example, present customer information on a tablet and use AR data glasses to display customer facial recognition, purchase history, customer name, and purchase history.

[0069] (Example of form 2) The customer service support system according to an embodiment of the present invention is a system that improves the quality of customer service by utilizing an AI agent. This customer service support system uses AI to acquire input from a camera and microphone and provide advice in real time to the staff member's earphone. It also displays customer-oriented information on a tablet. This allows staff members to receive optimal suggestions and sales pitches during customer service. Furthermore, it utilizes AR data glasses to display customer facial recognition and purchase history, and to support staff in real time. This allows staff members to instantly grasp customer information and respond appropriately. In addition, the AI ​​automatically generates an optimal customer service script, enabling all staff members to provide consistent quality customer service. This eliminates variations in the quality of customer service, allowing all staff members to perform at the same level. This system enables all staff members to provide consistently high-quality customer service, improving customer satisfaction and sales. It also reduces the burden on staff and improves the working environment. For example, the customer service support system allows staff members to receive advice in real time during customer service. For example, the customer service support system displays customer facial recognition and purchase history, enabling staff members to instantly grasp customer information. For example, the customer service support system uses AI to automatically generate an optimal customer service script, enabling all staff members to provide consistent quality customer service. This customer service support system enables all staff to provide consistently high-quality service, leading to increased customer satisfaction and sales. Furthermore, it reduces staff workload and improves the working environment.

[0070] The customer service support system according to the embodiment comprises a collection unit, an analysis unit, an advice unit, a generation unit, and a display unit. The collection unit collects information. The collection unit acquires input from, for example, a camera and a microphone. The collection unit can, for example, collect customer facial recognition and purchase history. The collection unit can also, for example, collect customer behavior data. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using, for example, data mining techniques. The analysis unit can also, for example, analyze the information using statistical analysis techniques. The analysis unit can also, for example, analyze the information using machine learning algorithms. The advice unit provides advice based on the analysis results obtained by the analysis unit. The advice unit can, for example, suggest customer service methods. The advice unit can also, for example, recommend products. The advice unit can also, for example, point out areas for improvement in customer service. The generation unit generates a customer service script based on the advice provided by the advice unit. The generation unit generates a customer service script that includes, for example, the flow of a conversation. The generation unit can also, for example, generate a customer service script that includes recommended phrases. The generation unit can, for example, generate a customer service script that includes customer service procedures. The display unit displays customer information based on the customer service script generated by the generation unit. The display unit can, for example, present customer information on a tablet. The display unit can also, for example, use AR data glasses to display customer facial recognition and purchase history. The display unit can also, for example, display the customer's name and purchase history. As a result, the customer service support system according to the embodiment can efficiently collect, analyze, provide advice on, generate customer service scripts, and display customer information.

[0071] The data collection unit collects information. For example, it obtains input from cameras and microphones. Specifically, the camera recognizes the customer's face and captures their expressions and movements in real time. This allows for inferring the customer's emotions and interests. The microphone collects the customer's voice and converts the conversation into text data using speech recognition technology. This allows for an accurate understanding of the customer's requests and questions. Furthermore, the data collection unit can retrieve the customer's purchase history from a database and analyze past purchase patterns and preferences. For example, it collects information on products the customer has previously purchased and products they have frequently viewed to understand their preferences. It can also collect customer behavior data. For example, it can record movement routes within the store, time spent there, and actions at specific product shelves using sensors and beacons. This allows for a more detailed understanding of the customer's interests and concerns. The data collection unit collects this diverse data in real time and transmits it to a central database. This ensures that the entire system operates based on the latest information and can provide the best possible service to the customer. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection to respond flexibly to specific situations and conditions. For example, during peak hours, the frequency of data collection can be increased to track the behavior of each individual customer in detail. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0072] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit uses data mining techniques to analyze the information. Specifically, it analyzes customer purchase history and behavioral data to extract customer preferences and purchasing patterns. This allows for the identification of products and services that customers are interested in. Furthermore, it can also analyze information using statistical analysis techniques. For example, it can statistically analyze data such as customer age, gender, and purchase history to formulate effective marketing strategies for specific customer segments. It can also analyze information using machine learning algorithms. For example, it can build models to predict future purchasing behavior based on customer behavioral data. This allows for the prediction of products customers are likely to purchase next and the recommendation of those products at the appropriate time. By combining these techniques, the analysis unit can analyze collected data from multiple perspectives and accurately understand customer needs and preferences. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. For example, it can analyze seasonal purchasing trends and purchasing behavior during specific events to formulate future marketing strategies. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0073] The Advice Department provides advice based on the analysis results obtained by the Analysis Department. For example, the Advice Department proposes customer service methods. Specifically, it proposes the optimal customer service method based on customer preferences and purchase history. For example, it recommends related products based on products that customers have previously purchased or shown interest in. It can also identify products and services that customers are likely to be interested in based on customer behavior data and propose effective customer service methods. Furthermore, the Advice Department can also make product recommendations. For example, it recommends products that customers are likely to be interested in based on their purchase history and preferences. This allows for the effective suggestion of products that customers are likely to purchase. The Advice Department can also point out areas for improvement in customer service. For example, it identifies areas for improvement in customer service methods and responses based on customer feedback and behavior data and provides specific advice to staff. This improves staff customer service skills and increases customer satisfaction. The Advice Department provides this advice in real time to support staff in responding quickly and appropriately. Furthermore, the Advice Department can also develop long-term customer service strategies by utilizing historical data and statistical information. For example, by analyzing effective customer service methods and product recommendation patterns for specific customer segments, the system can develop future customer service strategies. This allows the advisory department to not only provide real-time advice but also to develop long-term customer service strategies, maximizing the overall effectiveness of the system.

[0074] The generation unit generates customer service scripts based on advice provided by the advice unit. For example, the generation unit generates customer service scripts that include the flow of conversation. Specifically, it designs effective conversation flows based on customer preferences and purchase history, supporting staff in providing smooth customer service. For example, it generates conversation scripts that include explanations of products and services that customers might be interested in, and suggestions for related products. It can also generate customer service scripts that include recommended phrases. For example, it can suggest effective phrases and questions to customers to pique their interest. Furthermore, it can generate customer service scripts that include customer service procedures. For example, it shows appropriate response procedures to customer requests and questions, supporting staff in responding quickly and appropriately. The generation unit generates these scripts in real time and provides them to staff for immediate use. In addition, the generation unit can continuously improve the accuracy and effectiveness of the scripts by utilizing past data and statistical information. For example, it can review the content and structure of the scripts based on customer feedback and behavioral data to generate more effective scripts. The generation unit can also simulate multiple scenarios and select the most effective script. This allows the generation unit to support staff in providing effective customer service and increasing customer satisfaction.

[0075] The display unit displays customer information based on customer service scripts generated by the generation unit. For example, the display unit presents customer-facing information on a tablet. Specifically, it displays the customer's name, purchase history, and product information that might interest them on the tablet, helping staff to provide effective customer service. It can also utilize AR data glasses to display customer facial recognition and purchase history. For example, by having staff wear AR data glasses, the system can recognize the customer's face and display their past purchase history and preferences in real time. This allows staff to instantly grasp customer information and provide individualized service. Furthermore, the display unit can also display the customer's name and purchase history. For example, when a customer enters a store, the display unit can show the customer's name and past purchase history, enabling staff to provide personalized customer service. The display unit updates this information in real time, supporting customer service based on the latest information. In addition, the display unit can collect customer feedback and continuously improve the accuracy and effectiveness of the displayed content. For example, it can review the displayed content and layout based on customer reactions and behavioral data to provide more effective information presentation. The display unit can also link multiple devices to centrally manage information. For example, by linking devices such as tablets, AR data glasses, and smartphones, customer information can be centrally managed and accessed by staff from any device. This allows the display unit to support staff in providing effective customer service and increase customer satisfaction.

[0076] The advice unit can provide advice to staff members via their earphones. The advice unit can provide advice using, for example, Bluetooth earphones. The advice unit can also provide advice using, for example, earphones with noise-canceling capabilities. The advice unit can also provide advice using, for example, wireless earphones. This allows staff members to receive advice in real time. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can provide advice using an AI model that takes the analysis results obtained by the analysis unit as input and outputs advice.

[0077] The display unit can present customer information on a tablet. The display unit can also present customer information using a tablet, for example. This allows for the provision of appropriate information to customers by displaying customer information on a tablet. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can present customer information using an AI model that takes a customer service script generated by the generation unit as input and outputs customer information.

[0078] The display unit can use AR data glasses to display customer facial recognition and purchase history. The display unit can also use, for example, Vuzix Blade to display customer facial recognition and purchase history. This allows for instant acquisition of customer information by utilizing AR data glasses. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can take a customer service script generated by the generation unit as input and display customer information using an AI model that outputs customer facial recognition and purchase history.

[0079] The generation unit can automatically generate the optimal customer service script using AI. The generation unit can generate the customer service script using, for example, deep learning technology. The generation unit can also generate the customer service script using, for example, natural language processing technology. The generation unit can also generate the customer service script using, for example, machine learning algorithms. This allows the optimal customer service script to be automatically generated by using AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate a customer service script using a generation AI model that takes advice provided by the advice unit as input and outputs a customer service script.

[0080] The data collection unit can acquire input from a camera and a microphone. The data collection unit can acquire input using, for example, an HD camera. The data collection unit can also acquire input using, for example, a 360-degree camera. The data collection unit can also acquire input using, for example, a directional microphone. The data collection unit can also acquire input using, for example, a noise-canceling microphone. This makes information collection more efficient by acquiring input from a camera and a microphone. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can collect information using an AI model that takes data acquired from a camera and a microphone as input and outputs information.

[0081] The data collection unit can estimate the emotions of staff members and adjust the timing of information collection based on the estimated emotions. For example, if a staff member is stressed, the data collection unit can reduce the frequency of information collection and increase it if they are relaxed. For example, if a staff member is busy, the data collection unit can delay the timing of information collection and speed it up if they have time. For example, if a staff member is tired, the data collection unit can adjust the timing of information collection to allow for break time. This allows for efficient information collection by adjusting the timing of information collection according to the emotions of staff members. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input staff facial expression data into a generative AI and have the generative AI perform the estimation of the staff member's emotions.

[0082] The data collection unit can analyze a customer's past visit history and select the optimal information collection method. For example, the data collection unit can prioritize collecting information on relevant products based on products the customer has purchased in the past. The data collection unit can also analyze the time periods when a customer has visited in the past and collect information accordingly. The data collection unit can also collect information on relevant services based on services the customer has used in the past. This allows the optimal information collection method to be selected by analyzing the customer's past visit history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's past visit history data into a generating AI and have the generating AI select the optimal information collection method.

[0083] The data collection unit can filter information based on the customer's current purchasing intent and areas of interest during the information collection process. For example, the data collection unit can filter information based on product categories that the customer is currently interested in. For example, if the customer has a high purchasing intent, the data collection unit can prioritize the collection of information on related products. The data collection unit can also filter and collect relevant information based on the customer's areas of interest. This allows for the collection of highly relevant information by filtering information based on the customer's purchasing intent 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 the customer's current purchasing intent data into a generating AI and have the generating AI perform the information filtering.

[0084] The data collection unit can estimate the emotions of staff members and determine the priority of information to collect based on the estimated emotions. For example, if a staff member is stressed, the data collection unit will prioritize collecting information of high importance. For example, if a staff member is relaxed, the data collection unit can also collect detailed information. For example, if a staff member is busy, the data collection unit can also prioritize collecting only the essential information. This enables efficient information collection by prioritizing information according to the emotions of the staff member. 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input staff facial expression data into a generative AI and have the generative AI perform the estimation of the staff member's emotions.

[0085] The data collection unit can prioritize the collection of highly relevant information by considering the customer's geographical location during data collection. For example, the data collection unit can prioritize the collection of information about stores and services related to the customer's current location. For example, the data collection unit can also collect information about nearby events based on the customer's geographical location. For example, the data collection unit can also collect information about special offers at the nearest store based on the customer's location. This allows for the priority collection of highly relevant information by 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 example, the data collection unit can input the customer's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0086] The data collection unit can analyze the customer's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information on products and services that the customer has shown interest in on social media. The data collection unit can also analyze the content of the customer's social media posts and collect relevant information. The data collection unit can also collect information related to brands and influencers that the customer follows. In this way, relevant information can be collected by analyzing the customer's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's social media activity data into a generating AI and have the generating AI collect relevant information.

[0087] The analysis unit can estimate the emotions of staff members and adjust the presentation of the analysis based on the estimated emotions. For example, if a staff member is tense, the analysis unit provides a simple and easy-to-understand analysis result. For example, if a staff member is relaxed, the analysis unit can also provide a detailed analysis result. For example, if a staff member is tired, the analysis unit can also provide a concise analysis result that gets straight to the point. This allows for efficient analysis by adjusting the presentation of the analysis according to the emotions of the staff member. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input staff member facial expression data into a generative AI and have the generative AI perform the estimation of the staff member's emotions.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can also perform a concise analysis on information of low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, for product information, the analysis unit can apply an analysis algorithm based on sales data. For customer information, the analysis unit can also apply an analysis algorithm based on purchase history. For service information, the analysis unit can also apply an analysis algorithm based on customer satisfaction data. This enables efficient analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0090] The analysis unit can estimate the emotions of staff members and adjust the length of the analysis based on the estimated emotions. For example, if a staff member is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if a staff member is relaxed, the analysis unit can also provide a detailed analysis result. For example, if a staff member is tired, the analysis unit can also provide a brief analysis result. This allows for efficient analysis by adjusting the length of the analysis according to the emotions of the staff member. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input staff member facial expression data into a generative AI and have the generative AI perform the estimation of the staff member's emotions.

[0091] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also analyze older information as needed. The analysis unit may also adjust the priority of analysis according to the timing of information collection. This enables efficient analysis by determining the priority of analysis based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI and have the generating AI determine the priority of analysis.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0093] The advice unit can estimate the staff's emotions and adjust the way it expresses advice based on the estimated emotions. For example, if the staff is tense, the advice unit will provide simple and easy-to-understand advice. For example, if the staff is relaxed, the advice unit may provide detailed advice. For example, if the staff is tired, the advice unit may provide concise advice that gets straight to the point. This allows for efficient advice by adjusting the way it expresses advice according to the staff'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 advice unit may be performed using AI or not using AI. For example, the advice unit can input staff's facial expression data into the generative AI and have the generative AI perform the estimation of the staff's emotions.

[0094] The advice unit can adjust the level of detail of its advice based on the importance of the information it provides. For example, it can provide detailed advice for highly important information, and concise advice for less important information. The advice unit can also adjust the depth of its advice according to the importance of the information. This allows for more efficient advice by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input information importance data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0095] The advice unit can apply different advice algorithms depending on the category of information when providing advice. For example, for product information, the advice unit can apply an advice algorithm based on sales data. For customer information, the advice unit can also apply an advice algorithm based on purchase history. For service information, the advice unit can also apply an advice algorithm based on customer satisfaction data. By applying different advice algorithms depending on the category of information, efficient advice becomes possible. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input information category data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0096] The advice unit can estimate the staff's emotions and adjust the length of the advice based on the estimated emotions. For example, if the staff is in a hurry, the advice unit will provide short, concise advice. If the staff is relaxed, the advice unit may also provide detailed advice. If the staff is tired, the advice unit may also provide brief advice. This allows for efficient advice by adjusting the length of the advice according to the staff's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input staff facial expression data into the generative AI and have the generative AI perform the estimation of the staff's emotions.

[0097] The advice unit can determine the priority of advice based on when the information was collected when providing advice. For example, the advice unit will prioritize advice based on the most recent information. For example, the advice unit can also provide advice on older information as needed. The advice unit can also adjust the priority of advice according to when the information was collected. This enables efficient advice by determining the priority of advice based on when the information was collected. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input information collection time data into a generating AI and have the generating AI perform the determination of the priority of advice.

[0098] The advice unit can adjust the order of advice based on the relevance of the information when providing advice. For example, the advice unit can prioritize advice on highly relevant information. For example, the advice unit can postpone advice on less relevant information. The advice unit can also adjust the order of advice according to the relevance of the information. This allows for efficient advice by adjusting the order of advice based on the relevance of the information. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0099] The generation unit can estimate the emotions of staff members and adjust the method of generating customer service scripts based on the estimated emotions. For example, if a staff member is nervous, the generation unit will generate a simple and easy-to-read script. For example, if a staff member is relaxed, the generation unit can also generate a detailed script. For example, if a staff member is tired, the generation unit can also generate a concise script that gets straight to the point. This allows for the efficient generation of customer service scripts by adjusting the method of generating them according to the emotions of the staff members. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input staff member facial expression data into the generation AI and have the generation AI perform the estimation of the staff member's emotions.

[0100] The generation unit can adjust the level of detail in customer service scripts based on the importance of the information. For example, the generation unit can generate detailed scripts for highly important information. For example, the generation unit can also generate concise scripts for less important information. The generation unit can also adjust the depth of the script according to the importance of the information. This makes it possible to generate efficient customer service scripts by adjusting the level of detail in the script based on the importance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the script.

[0101] The generation unit can apply different generation algorithms depending on the information category when generating customer service scripts. For example, for product information, the generation unit can apply a generation algorithm based on sales data. For customer information, the generation unit can also apply a generation algorithm based on purchase history. For service information, the generation unit can also apply a generation algorithm based on customer satisfaction data. By applying different generation algorithms depending on the information category, efficient customer service script generation becomes possible. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0102] The generation unit can estimate the emotions of staff members and adjust the length of the customer service script based on the estimated emotions. For example, if a staff member is in a hurry, the generation unit can generate a short, concise script. For example, if a staff member is relaxed, the generation unit can also generate a detailed script. For example, if a staff member is tired, the generation unit can also generate a brief script. This allows for the efficient generation of customer service scripts by adjusting the length of the script according to the emotions of the staff member. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input staff member facial expression data into the generation AI and have the generation AI perform the estimation of the staff member's emotions.

[0103] The generation unit can determine the priority of customer service scripts based on the timing of information collection when generating them. For example, the generation unit prioritizes reflecting the latest information in the script. For example, the generation unit can also reflect older information in the script as needed. For example, the generation unit can adjust the script priority according to the timing of information collection. This enables the efficient generation of customer service scripts by determining the script priority based on the timing of information collection. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information collection timing data into a generation AI and have the generation AI perform the determination of script priority.

[0104] The generation unit can adjust the order of customer service scripts based on the relevance of the information when generating them. For example, the generation unit can prioritize reflecting highly relevant information in the script. For example, the generation unit can postpone less relevant information. For example, the generation unit can adjust the order of the scripts according to the relevance of the information. This makes it possible to generate efficient customer service scripts by adjusting the order of the scripts based on the relevance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the script order.

[0105] The display unit can estimate the emotions of staff members and adjust the display method based on the estimated emotions. For example, if a staff member is tense, the display unit can provide a simple and highly visible display method. For example, if a staff member is relaxed, the display unit can also provide a display method that includes detailed information. For example, if a staff member is tired, the display unit can also provide a concise display method that gets straight to the point. This allows for efficient information display by adjusting the display method according to the emotions of the staff member. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input staff member facial expression data into the generative AI and have the generative AI perform the estimation of the staff member's emotions.

[0106] The display unit can adjust the level of detail of the display based on the importance of the information during display. For example, the display unit can display highly important information in detail. For example, the display unit can also display less important information in a concise manner. The display unit can also adjust the depth of the display according to the importance of the information. This allows for efficient information display by adjusting the level of detail of the display based on the importance of the information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0107] The display unit can apply different display algorithms depending on the category of information during display. For example, for product information, the display unit can apply a display algorithm based on sales data. For customer information, the display unit can also apply a display algorithm based on purchase history. For service information, the display unit can also apply a display algorithm based on customer satisfaction data. By applying different display algorithms depending on the category of information, efficient information display becomes possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input information category data into a generating AI and have the generating AI execute the application of the display algorithm.

[0108] The display unit can estimate the emotions of staff members and adjust the length of the display based on the estimated emotions. For example, if a staff member is in a hurry, the display unit can display a short, concise message. If a staff member is relaxed, the display unit can display a detailed message. If a staff member is tired, the display unit can display a simple message. This allows for efficient information display by adjusting the length of the display according to the emotions of the staff member. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input staff member facial expression data into the generative AI and have the generative AI perform the estimation of the staff member's emotions.

[0109] The display unit can determine the display priority based on the information collection timing when displaying information. For example, the display unit may prioritize displaying the latest information. The display unit may also display older information as needed. The display unit may also adjust the display priority according to the information collection timing. This enables efficient information display by determining the display priority based on the information collection timing. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input information collection timing data into a generating AI and have the generating AI perform the determination of the display priority.

[0110] The display unit can adjust the display order based on the relevance of the information during display. For example, the display unit can prioritize the display of highly relevant information. For example, the display unit can postpone the display of less relevant information. The display unit can also adjust the display order according to the relevance of the information. This allows for efficient information display by adjusting the display order based on the relevance of the information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The analysis unit can estimate the customer's emotions and adjust the analysis results based on those estimated emotions. For example, if the customer is excited, the analysis unit will emphasize positive analysis results. For example, if the customer is feeling anxious, the analysis unit can also provide reassuring analysis results. For example, if the customer is satisfied, the analysis unit can also provide analysis results that include further suggestions. In this way, by adjusting the analysis results according to the customer's emotions, more appropriate information can be provided to the customer.

[0113] The data collection unit can estimate customer preferences based on their purchase history and collect information based on those estimated preferences. For example, the data collection unit can analyze trends in products that customers have purchased in the past and prioritize collecting information on related products. For example, if a customer prefers a particular brand, the data collection unit can also collect information on new products from that brand. For example, if a customer frequently purchases products from a particular category, the data collection unit can also collect information on new products from that category. By collecting information based on customer preferences, the system can provide customers with more relevant information.

[0114] The advice department can estimate the customer's emotions and adjust the content of the advice based on those emotions. For example, if the customer is excited, the advice department will offer proactive suggestions. For example, if the customer is feeling anxious, the advice department can also provide reassuring advice. For example, if the customer is satisfied, the advice department can also provide advice that includes further suggestions. In this way, by adjusting the content of the advice according to the customer's emotions, it is possible to provide more appropriate advice to the customer.

[0115] The display unit can estimate the customer's emotions and adjust the displayed content based on those emotions. For example, if the customer is excited, the display unit will emphasize positive information. For example, if the customer is feeling anxious, the display unit can also display reassuring information. For example, if the customer is satisfied, the display unit can also display information including further suggestions. In this way, by adjusting the displayed content according to the customer's emotions, more appropriate information can be provided to the customer.

[0116] The generation unit can estimate the customer's emotions and adjust the content of the customer service script based on those emotions. For example, if the customer is excited, the generation unit will generate a script that includes proactive suggestions. For example, if the customer is feeling anxious, the generation unit can also generate a script that provides reassurance. For example, if the customer is satisfied, the generation unit can also generate a script that includes further suggestions. This allows for more appropriate customer service by adjusting the content of the customer service script according to the customer's emotions.

[0117] The data collection unit can analyze customer purchasing patterns based on their purchase history and select the optimal information collection method. For example, if a customer tends to purchase certain products during a particular season, the data collection unit will prioritize collecting information on products related to that season. For example, if a customer tends to purchase certain products before a particular event, the data collection unit can also collect information on products related to that event. For example, if a customer frequently purchases products from a particular brand, the data collection unit can also collect information on new products from that brand. By collecting information based on customer purchasing patterns, the system can provide customers with more relevant information.

[0118] The analysis unit can analyze customer purchasing trends based on their purchase history and select the most appropriate analysis method. For example, if a customer frequently purchases products in a particular category, the analysis unit will prioritize analyzing products related to that category. For example, if a customer prefers products from a particular brand, the analysis unit can also analyze products related to that brand. For example, if a customer tends to purchase products in a particular price range, the analysis unit can also analyze products related to that price range. By performing analysis based on customer purchasing trends, the system can provide customers with more relevant information.

[0119] The advisory department can analyze customer purchasing trends based on their purchase history and provide optimal advice. For example, if a customer frequently purchases items in a particular category, the advisory department will prioritize advising on products related to that category. For example, if a customer prefers products from a particular brand, the advisory department can also advise on products related to that brand. For example, if a customer tends to purchase items in a particular price range, the advisory department can also advise on products related to that price range. By providing advice based on customer purchasing trends, the advisory department can provide customers with more relevant information.

[0120] The display unit can analyze customer purchasing trends based on their purchase history and select the optimal display method. For example, if a customer frequently purchases products in a particular category, the display unit will prioritize displaying information related to that category. For example, if a customer prefers products from a particular brand, the display unit can also display information related to products from that brand. For example, if a customer tends to purchase products in a particular price range, the display unit can also display information related to products in that price range. By displaying information based on customer purchasing trends, the system can provide customers with more relevant information.

[0121] The generation unit can analyze customer purchasing trends based on their purchase history and generate optimal customer service scripts. For example, if a customer frequently purchases products in a particular category, the generation unit can generate a script that includes suggestions for products related to that category. For example, if a customer prefers products from a particular brand, the generation unit can also generate a script that includes suggestions for products related to that brand. For example, if a customer tends to purchase products in a particular price range, the generation unit can also generate a script that includes suggestions for products related to that price range. This allows for the generation of customer service scripts based on customer purchasing trends, thereby providing customers with more relevant information.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The collection unit collects information. For example, the collection unit can acquire input from a camera and microphone and collect customer facial recognition, purchase history, and behavioral data. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using, for example, data mining techniques, statistical analysis techniques, and machine learning algorithms. Step 3: The advice unit provides advice based on the analysis results obtained by the analysis unit. For example, the advice unit can suggest customer service methods, recommend products, and point out areas for improvement in customer service. Step 4: The generation unit generates a customer service script based on the advice provided by the advice unit. The generation unit can generate a customer service script that includes, for example, the flow of conversation, recommended phrases, and customer service procedures. Step 5: The display unit displays customer information based on the customer service script generated by the generation unit. The display unit can, for example, present customer information on a tablet and use AR data glasses to display customer facial recognition, purchase history, customer name, and purchase history.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, advice unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides advice based on the analysis results. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a customer service script based on the advice. The display unit is implemented in the control unit 46A of the smart device 14 and displays customer information based on the generated customer service script. 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, advice unit, generation unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information. The advice unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides advice based on the analysis results. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates a customer service script based on the advice. The display unit is implemented, for example, in the control unit 46A of the smart glasses 214, and displays customer information based on the generated customer service script. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, advice unit, generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides advice based on the analysis results. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a customer service script based on the advice. The display unit is implemented in the control unit 46A of the headset terminal 314 and displays customer information based on the generated customer service script. 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.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, advice unit, generation unit, and display unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides advice based on the analysis results. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a customer service script based on the advice. The display unit is implemented, for example, by the control unit 46A of the robot 414 and displays customer information based on the generated customer service script. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, An advice unit provides advice based on the analysis results obtained by the aforementioned analysis unit, A generation unit that generates a customer service script based on the advice provided by the aforementioned advice unit, The system includes a display unit that displays customer information based on the customer service script generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned advice section, Providing advice to staff via earphones The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is Display customer information on a tablet. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is AR data glasses are used to recognize customer faces and display their purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is AI automatically generates the optimal customer service script. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Get input from the camera and microphone The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the emotions of the staff and adjust the timing of information gathering based on the estimated emotions of the staff. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the customer's past visit history and select the most suitable method for gathering information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on the customer's current purchasing intent and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Estimate the emotions of the staff and prioritize the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, we prioritize collecting highly relevant information by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze customers' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of the staff and adjust the representation of the analysis based on the estimated emotions of the staff. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the emotions of the staff and adjusts the length of the analysis based on the estimated emotions of the staff. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, The system estimates the emotions of the staff and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, The system estimates the staff's emotions and adjusts the length of advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, When providing advice, prioritize the advice based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When providing advice, adjust the order of advice based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is The system estimates the emotions of the staff and adjusts how customer service scripts are generated based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating customer service scripts, adjust the level of detail in the script based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating customer service scripts, different generation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is The system estimates the emotions of the staff and adjusts the length of the customer service script based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating customer service scripts, prioritize the scripts based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating customer service scripts, the script order is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is The system estimates the emotions of the staff and adjusts the display method based on the estimated emotions of the staff. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is When displaying information, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned display unit is When displaying information, different display algorithms are applied depending on the category of the information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned display unit is The system estimates the staff's emotions and adjusts the display length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned display unit is When displaying information, the display priority is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned display unit is When displaying information, adjust the display order based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, An advice unit provides advice based on the analysis results obtained by the aforementioned analysis unit, A generation unit that generates a customer service script based on the advice provided by the aforementioned advice unit, The system includes a display unit that displays customer information based on the customer service script generated by the generation unit. A system characterized by the following features.

2. The aforementioned advice section, Providing advice to staff via earphones The system according to feature 1.

3. The aforementioned display unit is Display customer information on a tablet. The system according to feature 1.

4. The aforementioned display unit is AR data glasses are used to recognize customer faces and display their purchase history. The system according to feature 1.

5. The generating unit is AI is used to automatically generate the optimal customer service script. The system according to feature 1.

6. The aforementioned collection unit is Get input from the camera and microphone The system according to feature 1.

7. The aforementioned collection unit is We estimate the emotions of the staff and adjust the timing of information gathering based on the estimated emotions of the staff. The system according to feature 1.

8. The aforementioned collection unit is Analyze the customer's past visit history and select the most suitable method for gathering information. The system according to feature 1.

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