system

Abstract

The system according to this embodiment aims to streamline customer service operations in supermarkets and improve customer satisfaction. [Solution] The system according to this embodiment comprises a guidance unit, a response unit, a guidance unit, and an analysis unit. The guidance unit provides store information. The response unit responds to customer inquiries. The guidance unit provides information and guidance about in-store events. The analysis unit analyzes the content handled by the robot.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the shortage of manpower in supermarkets has become apparent, the response to customers visiting the store has become inadequate, and the decline in service quality has become an issue.

[0005] The system according to the embodiment aims to improve the efficiency of customer service in supermarkets and enhance customer satisfaction.

Means for Solving the Problems

[0006] The system according to the embodiment includes a guiding unit, a responding unit, a guiding and inducing unit, and an analyzing unit. The guiding unit provides in-store guidance. The responding unit responds to customer inquiries. The guiding and inducing unit guides and induces in-store events. The analyzing unit analyzes the content handled by the robot. [Effects of the Invention]

[0007] The system according to this embodiment can streamline customer service operations in supermarkets and improve customer satisfaction. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 supermarket operations support system according to an embodiment of the present invention is a system that provides services combining robots and generative AI to solve the problem of labor shortages in supermarkets. This system has four main functions: store guidance, customer service, guidance and instruction for in-store events, and business analysis. First, the robot provides store guidance in various locations within the store. For example, if a customer asks where a specific product is located, the robot will guide them to that location. Next, the robot responds to customer inquiries. For example, if a customer asks about detailed product information or inventory status, the robot uses generative AI to provide an appropriate answer immediately. Furthermore, the robot provides guidance and instruction for in-store events (time-limited sales, food samples, etc.). For example, by providing information on the start time of time-limited sales or the location of the food sample corner, customers can participate in events efficiently. Finally, the generative AI analyzes the content handled by the robot and uses it to improve operations. For example, by analyzing the content of customer inquiries and the response time, it is possible to improve the efficiency of operations and the quality of service. This mechanism enables improved customer satisfaction and operational efficiency, and reduces personnel costs. For example, the robot provides store guidance in various locations within the store. For example, if a customer asks where a specific product is located, the robot will guide them to that location. This allows customers to quickly find the product they are looking for. Next, the robot responds to customer inquiries. For example, if asked about product details or stock availability, the robot uses generative AI to provide an immediate and appropriate answer. This allows customers to quickly obtain the information they need. Furthermore, the robot guides and directs customers to in-store events (time-limited offers, samples, etc.). For example, by providing information on the start time of a time-limited offer or the location of the sample corner, customers can efficiently participate in the event. Finally, the generative AI analyzes the content of the robot's interactions and uses it to improve operations. For example, by analyzing the content of customer inquiries and the response time, it can be used to improve operational efficiency and service quality. This system solves the problem of labor shortages in supermarkets and achieves improved customer satisfaction and operational efficiency.Furthermore, by using generation AI, it is possible to provide immediate and appropriate answers to customer questions, and to provide appropriate answers by pre-learning information about the sales floor. This also eliminates individual differences in the content of the answers. As a result, supermarket business support systems can improve customer satisfaction and operational efficiency.

[0029] The supermarket business support system according to this embodiment comprises a guidance unit, a response unit, a navigation unit, and an analysis unit. The guidance unit provides store layout guidance. For example, the guidance unit guides customers to the location of a specific product. For example, the guidance unit displays a map of the store to make it easier for customers to find the product they are looking for. The guidance unit can also use voice guidance to guide customers to the desired store. For example, the guidance unit provides voice guidance to the location of the product the customer inquired about. The response unit responds to customer inquiries. For example, if asked about detailed product information or stock status, the response unit uses a generating AI to provide an immediate and appropriate answer. For example, the response unit provides detailed explanations of product features and usage. The response unit can also check and provide real-time stock status of products to customers. For example, the response unit immediately answers whether a product is in stock. The navigation unit provides guidance and directions for in-store events. For example, the navigation unit guides customers to the start time of a limited-time offer or the location of a sample corner. For example, the navigation unit displays in-store event information to make it easier for customers to participate. Furthermore, the guidance unit can also announce the start time of an event via voice. For example, the guidance unit can announce the start time of a limited-time offer via voice. The analysis unit uses a generation AI to analyze the content handled by the robot and utilizes it for business improvement. For example, the analysis unit analyzes customer inquiries and response times to improve operational efficiency and service quality. For example, the analysis unit classifies customer inquiries and identifies frequently asked questions. The analysis unit can also analyze response times to improve operational efficiency. For example, if the response time is long, the analysis unit identifies the cause and proposes improvement measures. As a result, the supermarket business support system according to this embodiment can achieve improved customer satisfaction and operational efficiency.

[0030] The information desk provides store layout guidance. For example, it can guide customers to the location of specific products. Specifically, the information desk displays a store map and provides an interface to make it easier for customers to find the products they are looking for. For example, touch-panel displays could be installed throughout the store, allowing customers to enter product names or categories, and the location of those products would be displayed on the map. The information desk can also use voice guidance to guide customers to their desired section. For example, it could utilize a speaker system installed in the store to provide voice guidance to customers about the location of products they inquire about. Furthermore, the information desk can be linked with a smartphone app, allowing customers to search for products through the app and display the results on their smartphone screen. This enables customers to navigate the store efficiently using their smartphones. The information desk can also be equipped with accessibility features such as Braille displays and sign language videos to accommodate visually impaired and hearing impaired customers. This ensures that all customers can enjoy a comfortable shopping experience.

[0031] The customer support department handles customer inquiries. For example, when asked about product details or stock availability, the department uses generative AI to provide immediate and appropriate answers. Specifically, the generative AI uses natural language processing technology to understand customer questions and generate appropriate responses. For example, if a customer asks, "How many calories are in this product?", the generative AI refers to the product's nutritional information database and provides accurate calorie information. The customer support department can also check product stock availability in real time and provide this information to customers. For example, to immediately answer whether a product is in stock, the department can obtain stock data by linking with the store's inventory management system. Furthermore, the customer support department can also provide detailed explanations of product features and usage. For example, the generative AI extracts information from product instruction manuals and the manufacturer's official website and explains it to customers in an easy-to-understand manner. The customer support department can also save customer inquiry history so that future inquiries can be handled quickly. This allows customers to receive consistent service and improves customer satisfaction.

[0032] The guidance unit provides information and directions to in-store events. For example, it can guide customers to the start time of a limited-time offer or the location of a tasting corner. Specifically, the guidance unit uses in-store displays and digital signage to visually display event information. For example, it can display the start time of a limited-time offer or the location of a tasting corner on a map so that customers can easily find them. The guidance unit can also use voice guidance to announce the start time of an event. For example, it can announce the start time of a limited-time offer via the in-store speaker system. Furthermore, the guidance unit can be linked with a smartphone app to provide customers with event information via push notifications. This allows customers to receive event information in real time, making it easier for them to participate. The guidance unit can also monitor the participation status of events in real time and adjust the guidance content according to the crowd situation. For example, if a tasting corner is crowded, it can guide customers to the location of another tasting corner to reduce their waiting time. In this way, the guidance unit can support customers in smoothly participating in events and improve customer satisfaction.

[0033] The analytics department uses a generation AI to analyze the content handled by robots and utilize it for business improvement. Specifically, the analytics department analyzes customer inquiries and response times to improve operational efficiency and service quality. For example, the generation AI categorizes customer inquiries and identifies frequently asked questions. This allows for the preparation of answers to common questions in advance, shortening response times. The analytics department can also analyze response times to improve operational efficiency. For example, if response times are long, it can identify the causes and propose solutions. Furthermore, the analytics department can collect customer feedback to improve services. For example, the generation AI analyzes customer feedback and extracts specific areas for improvement. This allows supermarkets to respond quickly to customer needs and improve service quality. The analytics department can also compile this data into regular reports and provide them to management to support strategic decision-making. In this way, the analytics department can achieve operational efficiency and improved service quality, thereby improving the overall performance of the supermarket.

[0034] The response unit can provide an appropriate response immediately based on information pre-trained by a generative AI. For example, if asked about product details or usage, the response unit can provide an appropriate response immediately using the generative AI. For example, the response unit can explain the product's features and usage in detail. The response unit can also check the product's inventory status in real time and provide this information to the customer. For example, the response unit can immediately answer whether the product is in stock. In this way, by using generative AI, appropriate answers can be provided immediately to customer questions. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can provide an appropriate response immediately to customer questions based on information pre-trained by a generative AI.

[0035] The analysis department can analyze customer inquiries and response times to improve operational efficiency and service quality. For example, the analysis department can classify customer inquiries and identify frequently asked questions. For example, the analysis department can classify inquiries about product details and usage and identify frequently asked questions. The analysis department can also analyze response times to improve operational efficiency. For example, if response times are long, the analysis department can identify the cause and propose solutions. In this way, by analyzing customer inquiries and response times, operational efficiency and service quality can be improved. Some or all of the above processes in the analysis department may be performed using, for example, generative AI, or not. For example, the analysis department can use generative AI to analyze customer inquiries and response times to improve operational efficiency and service quality.

[0036] The information desk can guide customers to the location of specific products. For example, the information desk can tell customers where a particular product is located. The information desk can, for example, display a map of the store to make it easier for customers to find the product they are looking for. The information desk can also use voice guidance to guide customers to the desired section of the store. For example, the information desk can provide voice guidance to the location of the product the customer is asking about. This allows customers to quickly find the product they are looking for. Some or all of the above processes in the information desk may be performed using AI, for example, or not using AI. For example, the information desk can use AI to tell customers where a particular product is located.

[0037] The guidance unit can provide information about the start time of a limited-time offer and the location of the tasting corner. For example, the guidance unit can provide information about the start time of a limited-time offer and the location of the tasting corner. The guidance unit can also display information about in-store events to make it easier for customers to participate. The guidance unit can also provide voice announcements about the start time of events. For example, the guidance unit can announce the start time of a limited-time offer by voice. This allows customers to participate in events efficiently. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to provide information about the start time of a limited-time offer and the location of the tasting corner.

[0038] The guidance system can provide the optimal guidance route by referring to the customer's past purchase history during guidance. For example, the guidance system can guide customers to the sales floor of related products based on products they have purchased in the past. For example, the guidance system can prioritize guiding customers to frequently visited sales floors based on their past purchase history. The guidance system can also analyze the customer's purchase history and suggest the most efficient route. For example, the guidance system can suggest the most efficient route based on the customer's purchase history. This improves customer convenience by providing the optimal guidance route based on the customer's past purchase history. Some or all of the above processing in the guidance system may be performed using AI, for example, or without AI. For example, the guidance system can use AI to refer to the customer's past purchase history and provide the optimal guidance route.

[0039] The guidance unit can acquire the customer's current location information in real time during guidance and guide them along the shortest route. For example, the guidance unit can guide the customer along the shortest route from their current location to the desired product section. For example, the guidance unit can update the customer's current location in real time while they are moving and suggest the optimal route. Furthermore, if the customer gets lost, the guidance unit can update their current location in real time and provide guidance again. For example, if the customer gets lost, the guidance unit will update their current location in real time and provide guidance again. This makes the customer's movement more efficient by guiding them along the shortest route based on their current location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can acquire the customer's current location information in real time using AI and guide them along the shortest route.

[0040] The information desk can customize the content of the information given to customers based on their age and gender. For example, the information desk can provide visually easy-to-understand information for children. For example, the information desk can provide information at a slower pace for elderly people. The information desk can also prioritize showing customers products or areas that are of interest to them based on their gender. For example, the information desk prioritizes showing customers products or areas that are of interest to them based on their gender. By providing information tailored to the customer's age and gender, customer satisfaction is improved. Some or all of the above processing in the information desk may be performed using AI, for example, or not using AI. For example, the information desk can use AI to customize the content of the information given to customers based on their age and gender.

[0041] The guidance unit can analyze the customer's purchase history when providing guidance and provide promotional information on relevant products. For example, the guidance unit can provide promotional information related to products the customer has purchased in the past. For example, the guidance unit can guide the customer to promotional information on products that may be of interest based on their purchase history. The guidance unit can also provide discount information on specific products based on the customer's purchase history. For example, the guidance unit can provide discount information on specific products based on the customer's purchase history. This increases the customer's willingness to purchase by providing relevant promotional information based on their purchase history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to analyze the customer's purchase history when providing guidance and provide promotional information on relevant products.

[0042] The support unit can provide the best possible answer by referring to the customer's past inquiry history during support. For example, the support unit can provide relevant information based on the content of past inquiries the customer has made. For example, the support unit can prioritize answering frequently asked questions based on the customer's past inquiry history. The support unit can also analyze the customer's inquiry history and provide the most appropriate answer. For example, the support unit analyzes the customer's inquiry history and provides the most appropriate answer. This improves customer satisfaction by providing the best possible answer based on the customer's past inquiry history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use AI to refer to the customer's past inquiry history during support and provide the best possible answer.

[0043] The response unit can acquire the customer's current purchasing status in real time during a response and provide relevant information. For example, the response unit can provide information related to the products the customer is currently purchasing. For example, the response unit can suggest the most suitable products based on the customer's current purchasing status. The response unit can also provide detailed information about products the customer is considering purchasing. For example, the response unit can provide detailed information about products the customer is considering purchasing. By providing relevant information based on the customer's current purchasing status, customer satisfaction is improved. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can use AI to acquire the customer's current purchasing status in real time during a response and provide relevant information.

[0044] The response unit can customize the content of its responses based on the customer's age and gender during the interaction. For example, the response unit can provide visually easy-to-understand answers for children. For example, the response unit can provide answers at a slower pace for elderly people. The response unit can also prioritize providing answers about products and services that are of interest to the customer based on their gender. For example, the response unit prioritizes providing answers about products and services that are of interest to the customer based on their gender. This improves customer satisfaction by providing answers that are tailored to the customer's age and gender. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can use AI to customize the content of its responses based on the customer's age and gender during the interaction.

[0045] The response unit can analyze the customer's purchase history at the time of interaction and provide promotional information for relevant products. For example, the response unit can provide promotional information related to products the customer has purchased in the past. For example, the response unit can guide the customer to promotional information for products that may be of interest based on their purchase history. The response unit can also provide discount information for specific products based on the customer's purchase history. For example, the response unit can provide discount information for specific products based on the customer's purchase history. This increases the customer's willingness to purchase by providing relevant promotional information based on their purchase history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can use AI to analyze the customer's purchase history at the time of interaction and provide promotional information for relevant products.

[0046] The guidance unit can provide the optimal guidance route by referring to the customer's past event participation history during guidance. For example, the guidance unit guides customers to relevant events based on events they have previously attended. For example, the guidance unit prioritizes guiding customers to events that are likely to be of interest based on their past event participation history. The guidance unit can also analyze the customer's event participation history and propose the most efficient guidance route. For example, the guidance unit analyzes the customer's event participation history and proposes the most efficient guidance route. This improves customer convenience by providing the optimal guidance route based on the customer's past event participation history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to refer to the customer's past event participation history during guidance and provide the optimal guidance route.

[0047] The guidance unit can acquire the customer's current location information in real time during guidance and guide them along the shortest route. For example, the guidance unit can guide the customer along the shortest route from their current location to the event venue. For example, the guidance unit can update the customer's current location in real time while they are moving and suggest the optimal route. Furthermore, if the customer gets lost, the guidance unit can update their current location in real time and guide them again. For example, if the customer gets lost, the guidance unit will update their current location in real time and guide them again. This makes the customer's travel more efficient by guiding them along the shortest route based on their current location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to acquire the customer's current location information in real time during guidance and guide them along the shortest route.

[0048] The guidance unit can customize the guidance content based on the customer's age and gender during guidance. For example, the guidance unit provides visually easy-to-understand guidance for children. For example, the guidance unit provides guidance at a slower pace for the elderly. The guidance unit can also prioritize guiding customers to products or events that are of interest to them, depending on their gender. For example, the guidance unit prioritizes guiding customers to products or events that are of interest to them, depending on their gender. By providing guidance tailored to the customer's age and gender, customer satisfaction is improved. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to customize the guidance content based on the customer's age and gender during guidance.

[0049] The guidance unit can analyze the customer's purchase history during guidance and provide relevant event information. For example, the guidance unit can provide event information related to products the customer has purchased in the past. For example, the guidance unit can guide the customer to event information that may be of interest based on their purchase history. The guidance unit can also provide guidance to specific events based on the customer's purchase history. For example, the guidance unit can provide guidance to specific events based on the customer's purchase history. This allows the guidance unit to attract the customer's interest by providing relevant event information based on their purchase history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to analyze the customer's purchase history during guidance and provide relevant event information.

[0050] The analysis unit can apply the optimal analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can select the optimal algorithm based on past analysis data and perform the analysis. For example, the analysis unit can extract similar cases from past data and utilize them in the analysis. The analysis unit can also apply efficient analysis methods based on past analysis results. For example, the analysis unit can apply efficient analysis methods based on past analysis results. This improves the accuracy of the analysis by applying the optimal algorithm based on past analysis data. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can apply the optimal analysis algorithm by referring to past analysis data using generative AI.

[0051] The analysis department can customize the analysis results by considering customer attribute information during the analysis. For example, the analysis department can customize the analysis results by considering the customer's age and gender. For example, the analysis department can customize the analysis results based on the customer's purchase history. The analysis department can also customize the analysis results by considering the customer's past inquiry history. For example, the analysis department can customize the analysis results by considering the customer's past inquiry history. This allows for the provision of more appropriate analysis results by considering customer attribute information. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or without generative AI. For example, the analysis department can use generative AI to customize the analysis results by considering customer attribute information during the analysis.

[0052] The analysis department can customize the perspective of the analysis based on the customer's age and gender during the analysis. For example, the analysis department can provide visually easy-to-understand analysis results for children. For example, the analysis department can provide analysis results at a slower pace for the elderly. The analysis department can also provide analysis results that are likely to be of interest depending on gender. For example, the analysis department can provide analysis results that are likely to be of interest depending on gender. This allows for the provision of more appropriate analysis results by performing analysis according to the customer's age and gender. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or not. For example, the analysis department can use generative AI to customize the perspective of the analysis based on the customer's age and gender during the analysis.

[0053] The analysis unit can provide analysis results by referring to the customer's purchase history during analysis. For example, the analysis unit can provide analysis results related to products the customer has purchased in the past. For example, the analysis unit can provide analysis results that may be of interest to the customer based on their purchase history. The analysis unit can also provide analysis results for specific products based on the customer's purchase history. For example, the analysis unit can provide analysis results for specific products based on the customer's purchase history. This allows the analysis unit to attract the customer's interest by providing analysis results based on their purchase history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can provide analysis results by referring to the customer's purchase history during analysis using generative AI.

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

[0055] Supermarket business support systems can also be equipped with personalized recommendation features based on customer purchase history. For example, they can recommend related or new products based on items a customer has purchased in the past. This makes it easier for customers to find products that suit their preferences. The recommendation function can also suggest products according to the season or event. For example, it can suggest cold drinks and ice cream in the summer, and hot drinks and ingredients for hot pot dishes in the winter. Furthermore, the recommendation function can analyze the customer's purchase history, list frequently purchased items, and display them as reminders for the next shopping trip. This helps customers remember to purchase the items they need.

[0056] Supermarket business support systems can also incorporate a function that combines customer purchase history with current inventory levels to prioritize recommending items with low stock. For example, if a customer frequently purchases an item that is about to run out of stock, the system can prioritize recommending that item. This ensures that customers can purchase the items they need. The inventory-based recommendation function can also prioritize recommending items that are on sale. For example, it can list sale items and notify customers. Furthermore, the inventory-based recommendation function can also prioritize recommending seasonal or limited-edition items. This allows customers to purchase items in a timely manner.

[0057] The supermarket business support system can also incorporate a function that combines customer purchase history and current location information to suggest the optimal shopping route. For example, based on products a customer has purchased in the past, it can suggest a route that efficiently visits the sections of related products. This allows customers to complete their shopping efficiently. The location-based shopping route suggestion function can also guide customers to the shortest route from their current location to the desired product section. For example, it can update the customer's current location in real time while they are moving and suggest the optimal route. Furthermore, if a customer gets lost, the location-based shopping route suggestion function can update their current location in real time and provide guidance again. This allows customers to find the products they are looking for efficiently.

[0058] The supermarket business support system can also incorporate a function that combines customer purchase history with current weather information to suggest the most suitable products. For example, it can suggest related products based on products a customer has purchased in the past and the current weather. This makes it easier for customers to find products that are appropriate for the weather. Furthermore, the weather-based product suggestion function can prioritize suggesting products suitable for specific weather conditions. For example, it can suggest umbrellas and raincoats on rainy days, and sunglasses and sunscreen on sunny days. In addition, the weather-based product suggestion function can prioritize suggesting seasonal or limited-edition products. This allows customers to purchase products in a timely manner.

[0059] The supermarket business support system can also include a function that combines customer purchase history with current promotion information to suggest the most suitable promotions. For example, it can provide promotion information related to products that customers have purchased in the past. This makes it easier for customers to find promotions that suit their preferences. Furthermore, the promotion-based suggestion function can also prioritize suggesting products that are currently on sale. For example, it can list products that are on sale and notify customers. In addition, the promotion-based suggestion function can prioritize suggesting promotions that are appropriate for the season or events. This allows customers to receive promotion information in a timely manner.

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

[0061] Step 1: The information desk provides store directions. For example, it guides customers to the location of specific products and displays a store map to help customers find their desired items. It can also use voice guidance to lead customers to their desired section. For example, it can provide voice guidance to the location of a product the customer inquired about. Step 2: The support department handles customer inquiries. For example, if asked about product details or stock availability, it uses AI generation to provide an immediate and appropriate answer. It explains product features and usage in detail and checks and provides real-time stock availability information to the customer. For example, it can instantly answer whether a product is in stock. Step 3: The guidance unit guides and directs customers to in-store events. For example, it provides information on the start time of time-limited offers and the location of sample corners, and displays in-store event information to make it easier for customers to participate. It can also announce event start times by voice. For example, it can announce the start time of time-limited offers by voice. Step 4: The analysis department uses AI to analyze the content handled by the robot and utilizes it for business improvement. For example, it analyzes customer inquiries and response times to improve operational efficiency and service quality. It categorizes customer inquiries and identifies frequently asked questions. It can also analyze response times to improve operational efficiency. For example, if response times are long, it identifies the cause and proposes improvement measures.

[0062] (Example of form 2) The supermarket operations support system according to an embodiment of the present invention is a system that provides services combining robots and generative AI to solve the problem of labor shortages in supermarkets. This system has four main functions: store guidance, customer service, guidance and instruction for in-store events, and business analysis. First, the robot provides store guidance in various locations within the store. For example, if a customer asks where a specific product is located, the robot will guide them to that location. Next, the robot responds to customer inquiries. For example, if a customer asks about detailed product information or inventory status, the robot uses generative AI to provide an appropriate answer immediately. Furthermore, the robot provides guidance and instruction for in-store events (time-limited sales, food samples, etc.). For example, by providing information on the start time of time-limited sales or the location of the food sample corner, customers can participate in events efficiently. Finally, the generative AI analyzes the content handled by the robot and uses it to improve operations. For example, by analyzing the content of customer inquiries and the response time, it is possible to improve the efficiency of operations and the quality of service. This mechanism enables improved customer satisfaction and operational efficiency, and reduces personnel costs. For example, the robot provides store guidance in various locations within the store. For example, if a customer asks where a specific product is located, the robot will guide them to that location. This allows customers to quickly find the product they are looking for. Next, the robot responds to customer inquiries. For example, if asked about product details or stock availability, the robot uses generative AI to provide an immediate and appropriate answer. This allows customers to quickly obtain the information they need. Furthermore, the robot guides and directs customers to in-store events (time-limited offers, samples, etc.). For example, by providing information on the start time of a time-limited offer or the location of the sample corner, customers can efficiently participate in the event. Finally, the generative AI analyzes the content of the robot's interactions and uses it to improve operations. For example, by analyzing the content of customer inquiries and the response time, it can be used to improve operational efficiency and service quality. This system solves the problem of labor shortages in supermarkets and achieves improved customer satisfaction and operational efficiency.Furthermore, by using generation AI, it is possible to provide immediate and appropriate answers to customer questions, and to provide appropriate answers by pre-learning information about the sales floor. This also eliminates individual differences in the content of the answers. As a result, supermarket business support systems can improve customer satisfaction and operational efficiency.

[0063] The supermarket business support system according to this embodiment comprises a guidance unit, a response unit, a navigation unit, and an analysis unit. The guidance unit provides store layout guidance. For example, the guidance unit guides customers to the location of a specific product. For example, the guidance unit displays a map of the store to make it easier for customers to find the product they are looking for. The guidance unit can also use voice guidance to guide customers to the desired store. For example, the guidance unit provides voice guidance to the location of the product the customer inquired about. The response unit responds to customer inquiries. For example, if asked about detailed product information or stock status, the response unit uses a generating AI to provide an immediate and appropriate answer. For example, the response unit provides detailed explanations of product features and usage. The response unit can also check and provide real-time stock status of products to customers. For example, the response unit immediately answers whether a product is in stock. The navigation unit provides guidance and directions for in-store events. For example, the navigation unit guides customers to the start time of a limited-time offer or the location of a sample corner. For example, the navigation unit displays in-store event information to make it easier for customers to participate. Furthermore, the guidance unit can also announce the start time of an event via voice. For example, the guidance unit can announce the start time of a limited-time offer via voice. The analysis unit uses a generation AI to analyze the content handled by the robot and utilizes it for business improvement. For example, the analysis unit analyzes customer inquiries and response times to improve operational efficiency and service quality. For example, the analysis unit classifies customer inquiries and identifies frequently asked questions. The analysis unit can also analyze response times to improve operational efficiency. For example, if the response time is long, the analysis unit identifies the cause and proposes improvement measures. As a result, the supermarket business support system according to this embodiment can achieve improved customer satisfaction and operational efficiency.

[0064] The information desk provides store layout guidance. For example, it can guide customers to the location of specific products. Specifically, the information desk displays a store map and provides an interface to make it easier for customers to find the products they are looking for. For example, touch-panel displays could be installed throughout the store, allowing customers to enter product names or categories, and the location of those products would be displayed on the map. The information desk can also use voice guidance to guide customers to their desired section. For example, it could utilize a speaker system installed in the store to provide voice guidance to customers about the location of products they inquire about. Furthermore, the information desk can be linked with a smartphone app, allowing customers to search for products through the app and display the results on their smartphone screen. This enables customers to navigate the store efficiently using their smartphones. The information desk can also be equipped with accessibility features such as Braille displays and sign language videos to accommodate visually impaired and hearing impaired customers. This ensures that all customers can enjoy a comfortable shopping experience.

[0065] The customer support department handles customer inquiries. For example, when asked about product details or stock availability, the department uses generative AI to provide immediate and appropriate answers. Specifically, the generative AI uses natural language processing technology to understand customer questions and generate appropriate responses. For example, if a customer asks, "How many calories are in this product?", the generative AI refers to the product's nutritional information database and provides accurate calorie information. The customer support department can also check product stock availability in real time and provide this information to customers. For example, to immediately answer whether a product is in stock, the department can obtain stock data by linking with the store's inventory management system. Furthermore, the customer support department can also provide detailed explanations of product features and usage. For example, the generative AI extracts information from product instruction manuals and the manufacturer's official website and explains it to customers in an easy-to-understand manner. The customer support department can also save customer inquiry history so that future inquiries can be handled quickly. This allows customers to receive consistent service and improves customer satisfaction.

[0066] The guidance unit provides information and directions to in-store events. For example, it can guide customers to the start time of a limited-time offer or the location of a tasting corner. Specifically, the guidance unit uses in-store displays and digital signage to visually display event information. For example, it can display the start time of a limited-time offer or the location of a tasting corner on a map so that customers can easily find them. The guidance unit can also use voice guidance to announce the start time of an event. For example, it can announce the start time of a limited-time offer via the in-store speaker system. Furthermore, the guidance unit can be linked with a smartphone app to provide customers with event information via push notifications. This allows customers to receive event information in real time, making it easier for them to participate. The guidance unit can also monitor the participation status of events in real time and adjust the guidance content according to the crowd situation. For example, if a tasting corner is crowded, it can guide customers to the location of another tasting corner to reduce their waiting time. In this way, the guidance unit can support customers in smoothly participating in events and improve customer satisfaction.

[0067] The analytics department uses a generation AI to analyze the content handled by robots and utilize it for business improvement. Specifically, the analytics department analyzes customer inquiries and response times to improve operational efficiency and service quality. For example, the generation AI categorizes customer inquiries and identifies frequently asked questions. This allows for the preparation of answers to common questions in advance, shortening response times. The analytics department can also analyze response times to improve operational efficiency. For example, if response times are long, it can identify the causes and propose solutions. Furthermore, the analytics department can collect customer feedback to improve services. For example, the generation AI analyzes customer feedback and extracts specific areas for improvement. This allows supermarkets to respond quickly to customer needs and improve service quality. The analytics department can also compile this data into regular reports and provide them to management to support strategic decision-making. In this way, the analytics department can achieve operational efficiency and improved service quality, thereby improving the overall performance of the supermarket.

[0068] The response unit can provide an appropriate response immediately based on information pre-trained by a generative AI. For example, if asked about product details or usage, the response unit can provide an appropriate response immediately using the generative AI. For example, the response unit can explain the product's features and usage in detail. The response unit can also check the product's inventory status in real time and provide this information to the customer. For example, the response unit can immediately answer whether the product is in stock. In this way, by using generative AI, appropriate answers can be provided immediately to customer questions. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can provide an appropriate response immediately to customer questions based on information pre-trained by a generative AI.

[0069] The analysis department can analyze customer inquiries and response times to improve operational efficiency and service quality. For example, the analysis department can classify customer inquiries and identify frequently asked questions. For example, the analysis department can classify inquiries about product details and usage and identify frequently asked questions. The analysis department can also analyze response times to improve operational efficiency. For example, if response times are long, the analysis department can identify the cause and propose solutions. In this way, by analyzing customer inquiries and response times, operational efficiency and service quality can be improved. Some or all of the above processes in the analysis department may be performed using, for example, generative AI, or not. For example, the analysis department can use generative AI to analyze customer inquiries and response times to improve operational efficiency and service quality.

[0070] The information desk can guide customers to the location of specific products. For example, the information desk can tell customers where a particular product is located. The information desk can, for example, display a map of the store to make it easier for customers to find the product they are looking for. The information desk can also use voice guidance to guide customers to the desired section of the store. For example, the information desk can provide voice guidance to the location of the product the customer is asking about. This allows customers to quickly find the product they are looking for. Some or all of the above processes in the information desk may be performed using AI, for example, or not using AI. For example, the information desk can use AI to tell customers where a particular product is located.

[0071] The guidance unit can provide information about the start time of a limited-time offer and the location of the tasting corner. For example, the guidance unit can provide information about the start time of a limited-time offer and the location of the tasting corner. The guidance unit can also display information about in-store events to make it easier for customers to participate. The guidance unit can also provide voice announcements about the start time of events. For example, the guidance unit can announce the start time of a limited-time offer by voice. This allows customers to participate in events efficiently. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to provide information about the start time of a limited-time offer and the location of the tasting corner.

[0072] The guidance system can estimate the customer's emotions and adjust the way it presents the guidance based on those emotions. For example, if the customer is stressed, the guidance system can provide simple and easy-to-understand guidance. If the customer is relaxed, the guidance system can provide guidance that includes detailed explanations. The guidance system can also provide concise and quick guidance if the customer is in a hurry. For example, if the guidance system is in a hurry, it can provide concise and quick guidance. By providing guidance that is tailored to the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance system may be performed using AI or not. For example, the guidance system can use AI to estimate the customer's emotions and adjust the way it presents the guidance based on those emotions.

[0073] The guidance system can provide the optimal guidance route by referring to the customer's past purchase history during guidance. For example, the guidance system can guide customers to the sales floor of related products based on products they have purchased in the past. For example, the guidance system can prioritize guiding customers to frequently visited sales floors based on their past purchase history. The guidance system can also analyze the customer's purchase history and suggest the most efficient route. For example, the guidance system can suggest the most efficient route based on the customer's purchase history. This improves customer convenience by providing the optimal guidance route based on the customer's past purchase history. Some or all of the above processing in the guidance system may be performed using AI, for example, or without AI. For example, the guidance system can use AI to refer to the customer's past purchase history and provide the optimal guidance route.

[0074] The guidance unit can acquire the customer's current location information in real time during guidance and guide them along the shortest route. For example, the guidance unit can guide the customer along the shortest route from their current location to the desired product section. For example, the guidance unit can update the customer's current location in real time while they are moving and suggest the optimal route. Furthermore, if the customer gets lost, the guidance unit can update their current location in real time and provide guidance again. For example, if the customer gets lost, the guidance unit will update their current location in real time and provide guidance again. This makes the customer's movement more efficient by guiding them along the shortest route based on their current location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can acquire the customer's current location information in real time using AI and guide them along the shortest route.

[0075] The guidance system can estimate the customer's emotions and prioritize guidance based on those emotions. For example, if the customer is in a hurry, the guidance system will prioritize providing the most important information. If the customer is relaxed, the guidance system will provide guidance that includes detailed information. The guidance system can also prioritize simple and easy-to-understand guidance if the customer is stressed. For example, if the guidance system is stressed, it will prioritize simple and easy-to-understand guidance. By prioritizing guidance according to the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance system may be performed using AI or not. For example, the guidance system can estimate the customer's emotions using AI and prioritize guidance based on those estimated emotions.

[0076] The information desk can customize the content of the information given to customers based on their age and gender. For example, the information desk can provide visually easy-to-understand information for children. For example, the information desk can provide information at a slower pace for elderly people. The information desk can also prioritize showing customers products or areas that are of interest to them based on their gender. For example, the information desk prioritizes showing customers products or areas that are of interest to them based on their gender. By providing information tailored to the customer's age and gender, customer satisfaction is improved. Some or all of the above processing in the information desk may be performed using AI, for example, or not using AI. For example, the information desk can use AI to customize the content of the information given to customers based on their age and gender.

[0077] The guidance unit can analyze the customer's purchase history when providing guidance and provide promotional information on relevant products. For example, the guidance unit can provide promotional information related to products the customer has purchased in the past. For example, the guidance unit can guide the customer to promotional information on products that may be of interest based on their purchase history. The guidance unit can also provide discount information on specific products based on the customer's purchase history. For example, the guidance unit can provide discount information on specific products based on the customer's purchase history. This increases the customer's willingness to purchase by providing relevant promotional information based on their purchase history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to analyze the customer's purchase history when providing guidance and provide promotional information on relevant products.

[0078] The response unit can estimate the customer's emotions and adjust the tone of its response based on the estimated emotions. For example, if the customer is nervous, the response unit will respond in a calm tone. For example, if the customer is relaxed, the response unit will respond in a cheerful tone. The response unit can also respond in a quick and concise tone if the customer is in a hurry. For example, if the customer is in a hurry, the response unit will respond in a quick and concise tone. By responding in a tone appropriate to the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can estimate the customer's emotions using AI and adjust the tone of its response based on the estimated emotions.

[0079] The support unit can provide the best possible answer by referring to the customer's past inquiry history during support. For example, the support unit can provide relevant information based on the content of past inquiries the customer has made. For example, the support unit can prioritize answering frequently asked questions based on the customer's past inquiry history. The support unit can also analyze the customer's inquiry history and provide the most appropriate answer. For example, the support unit analyzes the customer's inquiry history and provides the most appropriate answer. This improves customer satisfaction by providing the best possible answer based on the customer's past inquiry history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use AI to refer to the customer's past inquiry history during support and provide the best possible answer.

[0080] The response unit can acquire the customer's current purchasing status in real time during a response and provide relevant information. For example, the response unit can provide information related to the products the customer is currently purchasing. For example, the response unit can suggest the most suitable products based on the customer's current purchasing status. The response unit can also provide detailed information about products the customer is considering purchasing. For example, the response unit can provide detailed information about products the customer is considering purchasing. By providing relevant information based on the customer's current purchasing status, customer satisfaction is improved. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can use AI to acquire the customer's current purchasing status in real time during a response and provide relevant information.

[0081] The response unit can estimate the customer's emotions and prioritize responses based on those emotions. For example, if the customer is in a hurry, the response unit will prioritize answering the most important questions. If the customer is relaxed, the response unit will provide answers that include detailed information. The response unit can also prioritize simple and easy-to-understand answers if the customer is stressed. For example, if the response unit is stressed, it will prioritize simple and easy-to-understand answers. By prioritizing responses according to the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the response unit may be performed using AI or not using AI. For example, the response unit can estimate the customer's emotions using AI and prioritize responses based on those estimated emotions.

[0082] The response unit can customize the content of its responses based on the customer's age and gender during the interaction. For example, the response unit can provide visually easy-to-understand answers for children. For example, the response unit can provide answers at a slower pace for elderly people. The response unit can also prioritize providing answers about products and services that are of interest to the customer based on their gender. For example, the response unit prioritizes providing answers about products and services that are of interest to the customer based on their gender. This improves customer satisfaction by providing answers that are tailored to the customer's age and gender. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can use AI to customize the content of its responses based on the customer's age and gender during the interaction.

[0083] The response unit can analyze the customer's purchase history at the time of interaction and provide promotional information for relevant products. For example, the response unit can provide promotional information related to products the customer has purchased in the past. For example, the response unit can guide the customer to promotional information for products that may be of interest based on their purchase history. The response unit can also provide discount information for specific products based on the customer's purchase history. For example, the response unit can provide discount information for specific products based on the customer's purchase history. This increases the customer's willingness to purchase by providing relevant promotional information based on their purchase history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can use AI to analyze the customer's purchase history at the time of interaction and provide promotional information for relevant products.

[0084] The guidance unit can estimate the customer's emotions and adjust the way it guides based on the estimated emotions. For example, if the customer is nervous, the guidance unit will guide in a calm tone. If the customer is relaxed, the guidance unit will guide in a bright tone. The guidance unit can also guide in a quick and concise tone if the customer is in a hurry. For example, if the guidance unit is in a hurry, the guidance unit will guide in a quick and concise tone. By providing guidance that is appropriate to the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can estimate the customer's emotions using AI and adjust the way it guides based on the estimated emotions.

[0085] The guidance unit can provide the optimal guidance route by referring to the customer's past event participation history during guidance. For example, the guidance unit guides customers to relevant events based on events they have previously attended. For example, the guidance unit prioritizes guiding customers to events that are likely to be of interest based on their past event participation history. The guidance unit can also analyze the customer's event participation history and propose the most efficient guidance route. For example, the guidance unit analyzes the customer's event participation history and proposes the most efficient guidance route. This improves customer convenience by providing the optimal guidance route based on the customer's past event participation history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to refer to the customer's past event participation history during guidance and provide the optimal guidance route.

[0086] The guidance unit can acquire the customer's current location information in real time during guidance and guide them along the shortest route. For example, the guidance unit can guide the customer along the shortest route from their current location to the event venue. For example, the guidance unit can update the customer's current location in real time while they are moving and suggest the optimal route. Furthermore, if the customer gets lost, the guidance unit can update their current location in real time and guide them again. For example, if the customer gets lost, the guidance unit will update their current location in real time and guide them again. This makes the customer's travel more efficient by guiding them along the shortest route based on their current location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to acquire the customer's current location information in real time during guidance and guide them along the shortest route.

[0087] The guidance unit can estimate the customer's emotions and determine the priority of guidance based on the estimated emotions. For example, if the customer is in a hurry, the guidance unit will prioritize guiding them with the most important information. If the customer is relaxed, the guidance unit will provide guidance that includes detailed information. The guidance unit can also prioritize simple and easy-to-understand guidance if the customer is stressed. For example, if the guidance unit is stressed, it will prioritize simple and easy-to-understand guidance. This improves customer satisfaction by determining the priority of guidance according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can estimate the customer's emotions using AI and determine the priority of guidance based on the estimated emotions.

[0088] The guidance unit can customize the guidance content based on the customer's age and gender during guidance. For example, the guidance unit provides visually easy-to-understand guidance for children. For example, the guidance unit provides guidance at a slower pace for the elderly. The guidance unit can also prioritize guiding customers to products or events that are of interest to them, depending on their gender. For example, the guidance unit prioritizes guiding customers to products or events that are of interest to them, depending on their gender. By providing guidance tailored to the customer's age and gender, customer satisfaction is improved. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to customize the guidance content based on the customer's age and gender during guidance.

[0089] The guidance unit can analyze the customer's purchase history during guidance and provide relevant event information. For example, the guidance unit can provide event information related to products the customer has purchased in the past. For example, the guidance unit can guide the customer to event information that may be of interest based on their purchase history. The guidance unit can also provide guidance to specific events based on the customer's purchase history. For example, the guidance unit can provide guidance to specific events based on the customer's purchase history. This allows the guidance unit to attract the customer's interest by providing relevant event information based on their purchase history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to analyze the customer's purchase history during guidance and provide relevant event information.

[0090] The analysis unit can estimate the customer's emotions and adjust the perspective of the analysis based on the estimated emotions. For example, if the customer is stressed, the analysis unit can perform an analysis to identify the stressors. For example, if the customer is relaxed, the analysis unit can perform an analysis to identify the relaxation factors. The analysis unit can also perform an analysis to identify factors that require a quick response if the customer is in a hurry. For example, if the analysis unit is in a hurry, the analysis unit can perform an analysis to identify factors that require a quick response. By performing an analysis that is tailored to the customer's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. 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 estimate the customer's emotions using AI and adjust the perspective of the analysis based on the estimated emotions.

[0091] The analysis unit can apply the optimal analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can select the optimal algorithm based on past analysis data and perform the analysis. For example, the analysis unit can extract similar cases from past data and utilize them in the analysis. The analysis unit can also apply efficient analysis methods based on past analysis results. For example, the analysis unit can apply efficient analysis methods based on past analysis results. This improves the accuracy of the analysis by applying the optimal algorithm based on past analysis data. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can apply the optimal analysis algorithm by referring to past analysis data using generative AI.

[0092] The analysis department can customize the analysis results by considering customer attribute information during the analysis. For example, the analysis department can customize the analysis results by considering the customer's age and gender. For example, the analysis department can customize the analysis results based on the customer's purchase history. The analysis department can also customize the analysis results by considering the customer's past inquiry history. For example, the analysis department can customize the analysis results by considering the customer's past inquiry history. This allows for the provision of more appropriate analysis results by considering customer attribute information. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or without generative AI. For example, the analysis department can use generative AI to customize the analysis results by considering customer attribute information during the analysis.

[0093] The analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the customer is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the customer is relaxed, the analysis unit can provide a display method that includes detailed information. The analysis unit can also provide a concise display method if the customer is in a hurry. For example, if the analysis unit is in a hurry, it can provide a concise display method. This deepens the understanding of the analysis results by providing a display method that is appropriate to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. 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 estimate the customer's emotions using AI and adjust the display method of the analysis results based on the estimated emotions.

[0094] The analysis department can customize the perspective of the analysis based on the customer's age and gender during the analysis. For example, the analysis department can provide visually easy-to-understand analysis results for children. For example, the analysis department can provide analysis results at a slower pace for the elderly. The analysis department can also provide analysis results that are likely to be of interest depending on gender. For example, the analysis department can provide analysis results that are likely to be of interest depending on gender. This allows for the provision of more appropriate analysis results by performing analysis according to the customer's age and gender. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or not. For example, the analysis department can use generative AI to customize the perspective of the analysis based on the customer's age and gender during the analysis.

[0095] The analysis unit can provide analysis results by referring to the customer's purchase history during analysis. For example, the analysis unit can provide analysis results related to products the customer has purchased in the past. For example, the analysis unit can provide analysis results that may be of interest to the customer based on their purchase history. The analysis unit can also provide analysis results for specific products based on the customer's purchase history. For example, the analysis unit can provide analysis results for specific products based on the customer's purchase history. This allows the analysis unit to attract the customer's interest by providing analysis results based on their purchase history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can provide analysis results by referring to the customer's purchase history during analysis using generative AI.

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

[0097] Supermarket business support systems can also be equipped with personalized recommendation features based on customer purchase history. For example, they can recommend related or new products based on items a customer has purchased in the past. This makes it easier for customers to find products that suit their preferences. The recommendation function can also suggest products according to the season or event. For example, it can suggest cold drinks and ice cream in the summer, and hot drinks and ingredients for hot pot dishes in the winter. Furthermore, the recommendation function can analyze the customer's purchase history, list frequently purchased items, and display them as reminders for the next shopping trip. This helps customers remember to purchase the items they need.

[0098] Supermarket business support systems can also be equipped with a function to estimate customer emotions and make recommendations based on those emotions. For example, if a customer is feeling stressed, the system can suggest products or services that have a relaxing effect. This makes it easier for customers to find products that match their mood. The emotion-based recommendation function can also suggest products or services that enhance enjoyment if the customer is relaxed. For example, it can suggest desserts, sweets, or entertainment-related products to a relaxed customer. Furthermore, if the customer is in a hurry, the emotion-based recommendation function can suggest products or services that can be purchased quickly. This allows customers to complete their shopping efficiently.

[0099] Supermarket business support systems can also incorporate a function that combines customer purchase history with current inventory levels to prioritize recommending items with low stock. For example, if a customer frequently purchases an item that is about to run out of stock, the system can prioritize recommending that item. This ensures that customers can purchase the items they need. The inventory-based recommendation function can also prioritize recommending items that are on sale. For example, it can list sale items and notify customers. Furthermore, the inventory-based recommendation function can also prioritize recommending seasonal or limited-edition items. This allows customers to purchase items in a timely manner.

[0100] The supermarket operations support system can also be equipped with a function to estimate customer emotions and adjust the in-store music and lighting based on those emotions. For example, if a customer is feeling stressed, relaxing music can be played and the lighting softened. This allows customers to relax and enjoy their shopping. The emotion-based music and lighting adjustment function can also play bright and cheerful music and brighten the lighting if the customer is relaxed. For example, pop or jazz music can be played for relaxed customers. Furthermore, if a customer is in a hurry, the emotion-based music and lighting adjustment function can brighten the lighting and dim the music to allow them to shop efficiently. This allows customers to complete their shopping efficiently.

[0101] The supermarket business support system can also incorporate a function that combines customer purchase history and current location information to suggest the optimal shopping route. For example, based on products a customer has purchased in the past, it can suggest a route that efficiently visits the sections of related products. This allows customers to complete their shopping efficiently. The location-based shopping route suggestion function can also guide customers to the shortest route from their current location to the desired product section. For example, it can update the customer's current location in real time while they are moving and suggest the optimal route. Furthermore, if a customer gets lost, the location-based shopping route suggestion function can update their current location in real time and provide guidance again. This allows customers to find the products they are looking for efficiently.

[0102] The supermarket operations support system can also be equipped with a function to estimate customer emotions and adjust product displays based on those emotions. For example, if a customer is feeling stressed, it can provide a simple and highly visible display, allowing the customer to choose products without feeling stressed. The emotion-based display adjustment function can also provide displays with more detailed information when the customer is relaxed. For example, it can display detailed product descriptions and usage examples to a relaxed customer. Furthermore, the emotion-based display adjustment function can provide a concise display when the customer is in a hurry, allowing the customer to choose products efficiently.

[0103] The supermarket business support system can also incorporate a function that combines customer purchase history with current weather information to suggest the most suitable products. For example, it can suggest related products based on products a customer has purchased in the past and the current weather. This makes it easier for customers to find products that are appropriate for the weather. Furthermore, the weather-based product suggestion function can prioritize suggesting products suitable for specific weather conditions. For example, it can suggest umbrellas and raincoats on rainy days, and sunglasses and sunscreen on sunny days. In addition, the weather-based product suggestion function can prioritize suggesting seasonal or limited-edition products. This allows customers to purchase products in a timely manner.

[0104] The supermarket operations support system can also be equipped with a function to estimate customer emotions and reduce checkout waiting times based on those emotions. For example, if a customer is feeling stressed, they can be given priority at the checkout. This allows customers to complete their shopping without feeling stressed. The emotion-based checkout waiting time reduction function can also allow customers who are relaxed to pass through the checkout in the normal waiting time. For example, relaxed customers can be passed through the checkout in the normal waiting time. Furthermore, if a customer is in a hurry, they can be passed through the checkout quickly. This allows customers to complete their shopping efficiently.

[0105] The supermarket business support system can also include a function that combines customer purchase history with current promotion information to suggest the most suitable promotions. For example, it can provide promotion information related to products that customers have purchased in the past. This makes it easier for customers to find promotions that suit their preferences. Furthermore, the promotion-based suggestion function can also prioritize suggesting products that are currently on sale. For example, it can list products that are on sale and notify customers. In addition, the promotion-based suggestion function can prioritize suggesting promotions that are appropriate for the season or events. This allows customers to receive promotion information in a timely manner.

[0106] The supermarket operations support system can also be equipped with a function to estimate customer emotions and adjust in-store signage based on those emotions. For example, if a customer is feeling stressed, it can provide simple and highly visible signage, allowing the customer to move around the store without feeling stressed. The emotion-based signage adjustment function can also provide detailed signage when a customer is relaxed. For example, it can display detailed product descriptions and usage examples to a relaxed customer. Furthermore, the emotion-based signage adjustment function can provide concise signage when a customer is in a hurry, allowing the customer to move around the store efficiently.

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

[0108] Step 1: The information desk provides store directions. For example, it guides customers to the location of specific products and displays a store map to help customers find their desired items. It can also use voice guidance to lead customers to their desired section. For example, it can provide voice guidance to the location of a product the customer inquired about. Step 2: The support department handles customer inquiries. For example, if asked about product details or stock availability, it uses AI generation to provide an immediate and appropriate answer. It explains product features and usage in detail and checks and provides real-time stock availability information to the customer. For example, it can instantly answer whether a product is in stock. Step 3: The guidance unit guides and directs customers to in-store events. For example, it provides information on the start time of time-limited offers and the location of sample corners, and displays in-store event information to make it easier for customers to participate. It can also announce event start times by voice. For example, it can announce the start time of time-limited offers by voice. Step 4: The analysis department uses AI to analyze the content handled by the robot and utilizes it for business improvement. For example, it analyzes customer inquiries and response times to improve operational efficiency and service quality. It categorizes customer inquiries and identifies frequently asked questions. It can also analyze response times to improve operational efficiency. For example, if response times are long, it identifies the cause and proposes improvement measures.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the guidance unit, response unit, guidance unit, and analysis unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the guidance unit is implemented by the control unit 46A of the smart device 14 and provides voice guidance to the location of the product the customer inquired about. The response unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses generated AI to provide immediate and appropriate answers regarding product details and inventory status. The guidance unit is implemented, for example, by the control unit 46A of the smart device 14 and provides voice guidance to the start time of time-limited offers and the location of the tasting corner. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the content of customer inquiries and response times to help improve operational efficiency and service quality. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice 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.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the guidance unit, response unit, guidance unit, and analysis unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the guidance unit is implemented by the control unit 46A of the smart glasses 214 and provides voice guidance to the location of the product the customer inquired about. The response unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses generated AI to provide immediate and appropriate answers regarding product details and inventory status. The guidance unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides voice guidance to the start time of time-limited offers and the location of the tasting corner. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the content of customer inquiries and response times to help improve operational efficiency and service quality. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice 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.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the guidance unit, response unit, guidance unit, and analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the guidance unit is implemented by the control unit 46A of the headset terminal 314 and provides voice guidance to the location of the product the customer inquired about. The response unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses generated AI to provide immediate and appropriate answers regarding product details and inventory status. The guidance unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides voice guidance to the start time of time-limited offers and the location of the tasting corner. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the content of customer inquiries and response times to help improve operational efficiency and service quality. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice 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.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the guidance unit, response unit, guidance unit, and analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the guidance unit is implemented by the control unit 46A of the robot 414 and provides voice guidance to the location of the product the customer inquired about. The response unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses generated AI to provide immediate and appropriate answers regarding product details and inventory status. The guidance unit is implemented by, for example, the control unit 46A of the robot 414 and provides voice guidance to the start time of time-limited offers and the location of the tasting corner. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the content of customer inquiries and response times to help improve operational efficiency and service quality. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The information desk provides guidance to customers in the sales area, The customer support department handles customer inquiries, The guidance department, which provides information and directions for in-store events, The analysis department analyzes the content handled by the robot, Equipped with A system characterized by the following features. (Note 2) The corresponding part is, The generation AI provides an appropriate answer instantly based on pre-trained information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze customer inquiries and response times to improve operational efficiency and service quality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned guide section is To guide customers to the location of specific products. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned induction unit is Provide information on the start time of the limited-time offer and the location of the tasting corner. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned guide section is The system estimates the customer's emotions and adjusts the way information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned guide section is When providing directions, the system uses the customer's past purchase history to provide the most suitable route. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned guide section is The system acquires the customer's current location information in real time during guidance and provides guidance along the shortest route. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned guide section is The system estimates customer emotions and determines the priority of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned guide section is Customize the guidance provided based on the customer's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned guide section is During the consultation, the system analyzes the customer's purchase history and provides promotional information for related products. The system described in Appendix 1, characterized by the features described herein. (Note 12) The corresponding part is, The system estimates the customer's emotions and adjusts the tone of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The corresponding part is, When responding to a customer, we refer to their past inquiry history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 14) The corresponding part is, When responding to a customer, the system obtains their current purchasing status in real time and provides relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The corresponding part is, The system estimates customer emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The corresponding part is, Customize the response based on the customer's age and gender during the interaction. The system described in Appendix 1, characterized by the features described herein. (Note 17) The corresponding part is, During customer interaction, the system analyzes the customer's purchase history and provides promotional information for related products. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned induction unit is We estimate customer emotions and adjust the way we present persuasive messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned induction unit is During guidance, the system provides the optimal guidance route by referring to the customer's past event participation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned induction unit is During guidance, the system acquires the customer's current location information in real time and guides them along the shortest route. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned induction unit is The system estimates customer emotions and determines guidance priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned induction unit is The guidance provided will be customized based on the customer's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned induction unit is During the guidance process, the system analyzes the customer's purchase history and provides relevant event information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is We estimate customer emotions and adjust the analytical perspective based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is During analysis, past analysis data is referenced to apply the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is Customize the analysis results by considering customer attribute information during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is It estimates customer emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is Customize the analytical perspective based on the customer's age and gender during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During analysis, customer purchase history is referenced to provide analysis results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information desk provides guidance to customers in the sales area, The customer support department handles customer inquiries, The guidance team is responsible for guiding and directing people to in-store events, The analysis department analyzes the content handled by the robot, Equipped with A system characterized by the following features.

2. The corresponding part is, The generation AI provides an appropriate answer instantly based on pre-trained information. The system according to feature 1.

3. The aforementioned analysis unit is We analyze customer inquiries and response times to improve operational efficiency and service quality. The system according to feature 1.

4. The aforementioned guide section is To guide customers to the location of specific products. The system according to feature 1.

5. The aforementioned induction unit is Provide information on the start time of the limited-time offer and the location of the tasting corner. The system according to feature 1.

6. The aforementioned guide section is The system estimates the customer's emotions and adjusts the way information is presented based on those estimated emotions. The system according to feature 1.

7. The aforementioned guide section is When providing directions, the system uses the customer's past purchase history to provide the most suitable route. The system according to feature 1.

8. The aforementioned guide section is The system acquires the customer's current location information in real time during guidance and provides guidance along the shortest route. The system according to feature 1.

9. The aforementioned guide section is The system estimates customer emotions and determines the priority of guidance based on those estimated emotions. The system according to feature 1.

10. The aforementioned guide section is Customize the guidance provided based on the customer's age and gender. The system according to feature 1.

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