system

Abstract

An object of the system according to the embodiment is to provide an environment in which the burden of operating the EC site is reduced and the operator can concentrate on providing high-quality products and improving services.SOLUTION: A system includes a promotion automation part, a site analysis automation part, and a customer dealing automation part. The promotion automation component automates the planning and execution of product promotions. The site analysis automation unit analyzes access data and user behavior data of the EC site and evaluates the performance of the site. The customer handling automation unit automatically responds to an inquiry from a customer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, operating an e-commerce site required a lot of work, such as product promotion and site analysis, which prevented operators from concentrating on their primary objective of providing products and improving services.

[0005] The system according to the embodiment aims to reduce the burden of operating an e-commerce site and provide an environment in which operators can concentrate on providing high-quality products and improving services. [Means for solving the problem]

[0006] The system according to the embodiment includes a promotion automation unit, a site analysis automation unit, and a customer support automation unit. The promotion automation unit automates the planning and execution of product promotions. The site analysis automation unit analyzes access data and user behavior data from the e-commerce site to evaluate site performance. The customer support automation unit automatically responds to inquiries from customers. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden of operating an e-commerce site and provide an environment in which operators can concentrate on providing high-quality products and improving services. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The e-commerce site operation system according to an embodiment of the present invention utilizes AI technology to reduce the operational burden of an e-commerce site, providing an environment in which operators can focus on providing high-quality products and improving services. This system significantly reduces the operator's burden through the automation of product promotion, site analysis, and customer support. As a result, the e-commerce site operation system can provide an environment in which operators can focus on their original purpose: "delivering products to consumers."

[0029] An e-commerce website operation system according to an embodiment includes a promotion automation unit, a site analysis automation unit, and a customer support automation unit. The promotion automation unit automates the planning and execution of product promotions. For example, the generation AI analyzes past sales data and customer purchase histories to propose optimal promotion strategies. The generation AI can also predict when a particular product will sell well and automatically set up discount campaigns and advertisements to suit that period. The generation AI also generates promotion strategies based on prompts containing instructions regarding the promotion's purpose and target products. The site analysis automation unit analyzes access data and user behavior data of the e-commerce website to evaluate site performance. For example, the generation AI analyzes which pages are most visited and which products have the highest purchase rates, and reports the results to the operator. The generation AI can also generate analysis results based on prompts containing instructions regarding the data to be analyzed. The customer support automation unit automatically responds to customer inquiries. For example, the generation AI understands customer questions and generates appropriate answers. The generation AI can also automatically respond to inquiries regarding product inventory and delivery status. Furthermore, the generation AI generates answers based on prompts containing instructions related to customer inquiries. As a result, the e-commerce site operation system according to the embodiment can provide an environment in which operators can focus on providing high-quality products and improving services. For example, operators can focus on developing new products and improving the quality of existing products by reducing their operational burden with the generation AI. They can also work on improving services to increase customer satisfaction.

[0030] The promotion automation unit analyzes sales data and customer purchasing history to propose optimal promotion strategies. For example, the generation AI in the promotion automation unit analyzes customers' social media activity to identify the interests of each individual customer. For example, it analyzes the content of posts that customers frequently "like" or share, and proposes optimal promotions based on that information. The generation AI in the promotion automation unit also analyzes past sales data to predict when a particular product will sell well. For example, the generation AI predicts based on past sales data that a particular product will sell well in the summer and sets up discount campaigns to suit that time of year. The generation AI in the promotion automation unit also analyzes customer purchasing history to propose optimal promotions for each individual customer. For example, the generation AI proposes promotions related to products that a particular customer has previously purchased based on the customer's purchasing history. This allows the effectiveness of product promotions to be maximized by proposing optimal promotion strategies.

[0031] The site analysis automation unit can analyze page visits and product purchase rates and report the results to the operator. For example, the generation AI analyzes access data for an e-commerce site to determine which pages are most visited. For example, the generation AI analyzes access logs and reports that certain pages are visited more frequently than others. The site analysis automation unit also analyzes user behavior data to determine which products have a high purchase rate. For example, the generation AI analyzes user clickstream data and reports that certain products have a higher purchase rate than others. The site analysis automation unit also builds a system in which the generation AI reports the analysis results to the operator. For example, the generation AI displays the analysis results on a dashboard, allowing the operator to understand the site's performance in real time. This allows the operator to quickly understand the site's performance and identify areas for improvement.

[0032] The customer response automation department can understand customer questions and generate appropriate answers. For example, in the customer response automation department, the generation AI analyzes customer questions and generates appropriate answers. For example, in response to a customer question such as "What is the product's inventory status?", the generation AI replies "We currently have it in stock" based on inventory data. The customer response automation department also builds a system in which the generation AI analyzes customer inquiries and generates appropriate answers. For example, in response to a customer question such as "What is the delivery status?", the generation AI replies "The product is currently being delivered" based on delivery data. The customer response automation department also builds a system in which the generation AI can handle complex customer questions. For example, in response to a customer question such as "How do I return a product?", the generation AI replies with detailed instructions based on the return policy. This reduces the time spent responding to customers and allows the department to focus on other important tasks.

[0033] The promotion automation unit analyzes customers' social media activities and can propose optimal promotions to individual customers in real time. In the promotion automation unit, for example, the generation AI analyzes customers' social media activities and identifies the interests and concerns of individual customers. For example, the generation AI analyzes the content of posts that customers frequently "like" or share, and proposes optimal promotions based on that information. The promotion automation unit also builds a system in which the generation AI monitors customers' social media activities in real time and proposes optimal promotions. For example, immediately after a customer posts about a specific product, the generation AI proposes a discount coupon related to that product. The promotion automation unit also builds a system in which the generation AI proposes optimal promotions to individual customers based on the customer's social media activities. For example, if a customer shows interest in a specific brand, the generation AI proposes promotions related to products from that brand. This makes it possible to propose promotions based on customers' interests and concerns in real time.

[0034] The promotion automation unit can analyze competitors' promotional strategies and automatically generate optimal counter-promotions. For example, the generation AI in the promotion automation unit analyzes competitors' promotional campaigns and evaluates their effectiveness. For example, the generation AI analyzes competitors' discount campaigns and advertising strategies and proposes counter-promotions. The promotion automation unit also builds a system in which the generation AI automatically generates optimal promotions based on competitors' promotional strategies. For example, the generation AI analyzes promotions that competitors are running for specific products and proposes counter-discount campaigns. The promotion automation unit also builds a system in which the generation AI monitors competitors' promotional strategies in real time and automatically generates counter-promotions. For example, the generation AI proposes counter-promotions immediately after a competitor launches a new promotion. This makes it possible to automatically generate optimal promotions to counter competitors' promotional strategies.

[0035] The promotion automation unit can monitor the effectiveness of promotions in real time and automatically adjust the promotion content as needed. For example, the generation AI in the promotion automation unit monitors the effectiveness of promotions in real time and automatically adjusts the promotion content if the effectiveness is low. For example, the generation AI changes the discount rate or extends the promotion period. The promotion automation unit also builds a system in which the generation AI automatically adjusts the optimal promotion content based on the effectiveness of the promotion. For example, the generation AI changes the target audience for advertising if the effectiveness of the promotion is low. The promotion automation unit also builds a system in which the generation AI monitors the effectiveness of promotions in real time and automatically adjusts the promotion content as needed. For example, the generation AI changes the promotion content if the effectiveness of the promotion is low. This makes it possible to monitor the effectiveness of promotions in real time and automatically adjust the promotion content as needed.

[0036] The site analysis automation unit can analyze a user's mouse and touch movements and automatically suggest improvements to the user interface. In this case, for example, the generation AI analyzes a user's mouse and touch movements and identifies improvements to the user interface. For example, the generation AI makes suggestions to improve the position or design of buttons with low click rates. The site analysis automation unit also builds a system in which the generation AI automatically suggests improvements to the user interface based on user operation data. For example, the generation AI makes suggestions to improve the layout of pages that users frequently scroll through. The site analysis automation unit also builds a system in which the generation AI monitors a user's mouse and touch movements in real time and automatically suggests improvements to the user interface. For example, if a user frequently clicks in a specific area, the generation AI makes suggestions to improve the design of that area. This makes it possible to automatically suggest improvements to the user interface based on user operation data.

[0037] The site analysis automation unit can evaluate the quality of content on a site and automatically make suggestions to improve low-rated content. In this case, for example, the generation AI analyzes content on a site and identifies low-quality content. For example, the generation AI evaluates pages with short viewing times and pages with high bounce rates. The site analysis automation unit also builds a system in which the generation AI automatically makes suggestions to improve low-rated content based on the quality of the content. For example, the generation AI suggests enriching the content or improving the design for low-rated content. The site analysis automation unit also builds a system in which the generation AI monitors content on a site in real time and automatically makes suggestions to improve low-rated content. For example, the generation AI suggests optimizing keywords or adding images for low-rated content. This makes it possible to evaluate the quality of content on a site and automatically make suggestions to improve low-rated content.

[0038] The site analysis automation unit can monitor site performance in real time and automatically issue alerts when problems occur. For example, the site analysis automation unit builds a system in which a generation AI monitors site performance in real time and automatically issues alerts when problems occur. For example, the generation AI issues an alert if the server response time becomes slow. The site analysis automation unit also builds a system in which a generation AI automatically issues alerts when problems occur based on site performance. For example, the generation AI analyzes error logs and issues an alert when a specific error occurs. The site analysis automation unit also builds a system in which a generation AI monitors site performance in real time and automatically issues alerts when problems occur. For example, the generation AI detects a decline in performance and issues an alert to the operator. This makes it possible to monitor site performance in real time and automatically issue alerts when problems occur.

[0039] The site analysis automation unit can analyze site performance on different devices and browsers and make optimization suggestions. In the site analysis automation unit, for example, the generation AI analyzes site performance on different devices and browsers and makes optimization suggestions. For example, the generation AI evaluates display speed on smartphones and tablets. The site analysis automation unit also builds a system in which the generation AI makes optimization suggestions based on site performance on different devices and browsers. For example, if the display speed on a specific browser is slow, the generation AI makes optimization suggestions for that browser. The site analysis automation unit also builds a system in which the generation AI monitors site performance on different devices and browsers in real time and makes optimization suggestions. For example, if the display speed on a specific device is slow, the generation AI makes optimization suggestions for that device. This makes it possible to analyze site performance on different devices and browsers and make optimization suggestions.

[0040] The customer response automation department can analyze a customer's past inquiry history and automatically generate the optimal response for each individual customer. In the customer response automation department, for example, a generation AI analyzes a customer's past inquiry history and automatically generates the optimal response for each individual customer. For example, the generation AI proposes the optimal response based on the past inquiry content and response history. The customer response automation department also builds a system in which the generation AI automatically generates the optimal response based on the customer's inquiry history. For example, the generation AI proposes the optimal response to a similar inquiry based on the content of a specific customer's past inquiries. The customer response automation department also builds a system in which the generation AI monitors a customer's past inquiry history in real time and automatically generates the optimal response. For example, the generation AI proposes the optimal response to a current inquiry based on the content of the customer's past inquiries. This makes it possible to analyze a customer's past inquiry history and automatically generate the optimal response for each individual customer.

[0041] The customer response automation unit can analyze the content of customer inquiries in real time and automatically provide the optimal answer. In the customer response automation unit, for example, the generation AI analyzes the content of customer inquiries in real time and automatically provides the optimal answer. For example, the generation AI instantly generates answers to questions about product details and usage methods. The customer response automation unit also builds a system in which the generation AI automatically provides the optimal answer based on the content of customer inquiries. For example, when a customer asks a question about a specific product, the generation AI generates an answer based on detailed information about that product. The customer response automation unit also builds a system in which the generation AI monitors the content of customer inquiries in real time and automatically provides the optimal answer. For example, when a customer asks a question about a specific service, the generation AI generates an answer based on detailed information about that service. This makes it possible to analyze the content of customer inquiries in real time and automatically provide the optimal answer.

[0042] The customer response automation department can monitor customer response performance in real time and automatically adjust response content as needed. For example, the customer response automation department builds a system in which a generation AI monitors customer response performance in real time and automatically adjusts response content as needed. For example, the generation AI suggests a quick response if response times become long. The customer response automation department also builds a system in which a generation AI automatically adjusts optimal response content based on customer response performance. For example, the generation AI makes suggestions to improve response content if customer satisfaction is low. The customer response automation department also builds a system in which a generation AI monitors customer response performance in real time and automatically adjusts response content as needed. For example, the generation AI suggests a quick response if response times to customer inquiries become long. This makes it possible to monitor customer response performance in real time and automatically adjust response content as needed.

[0043] The customer response automation unit can automatically generate customer responses in different languages, thereby realizing global customer response. For example, the customer response automation unit builds a system in which a generation AI automatically generates customer responses in different languages. For example, the generation AI automatically generates responses in multiple languages, such as English, French, and Chinese. The customer response automation unit also builds a system in which the generation AI realizes global customer response based on customer responses in different languages. For example, the generation AI proposes the optimal response in a specific language in response to an inquiry in that language. The customer response automation unit also builds a system in which the generation AI monitors customer responses in different languages ​​in real time, thereby realizing global customer response. For example, the generation AI proposes a quick and appropriate response to an inquiry in a specific language. This makes it possible to automatically generate customer responses in different languages ​​and realize global customer response.

[0044] The system can use a generative AI to analyze customer feedback and automatically suggest improvements to products and services. For example, the generative AI analyzes customer feedback and automatically suggests improvements to products and services. For example, the generative AI identifies areas for improvement based on customer reviews and comments. The system also builds a system in which the generative AI automatically suggests improvements to products and services based on customer feedback. For example, the generative AI analyzes customer survey results and suggests areas for improvement to specific products. The system also builds a system in which the generative AI monitors customer feedback in real time and automatically suggests improvements to products and services. For example, the generative AI analyzes customer comments and suggests areas for improvement to services. In this way, it is possible to analyze customer feedback and automatically suggest improvements to products and services.

[0045] The system can use generative AI to analyze competitors' products and services and automatically generate optimal improvement measures to compete with them. For example, the generative AI analyzes competitors' products and services and automatically generates optimal improvement measures to compete with them. For example, the generative AI analyzes competitors' new features and services and proposes competitive improvement measures. The system also builds a system in which the generative AI automatically generates optimal improvement measures based on competitors' products and services. For example, the generative AI analyzes competitors' product reviews and proposes competitive improvement measures. The system also builds a system in which the generative AI monitors competitors' products and services in real time and automatically generates optimal improvement measures to compete with them. For example, the generative AI proposes competitive improvement measures immediately after a competitor launches a new service. This makes it possible to analyze competitors' products and services and automatically generate optimal improvement measures to compete with them.

[0046] The system uses generative AI to automate the development process of new products and bring them to market quickly. For example, the system builds a system in which generative AI automates the development process of new products and brings them to market quickly. For example, generative AI automates everything from product concept design to prototype creation. The system also builds a system in which generative AI uses the new product development process to bring them to market quickly. For example, generative AI automates the process from product design to manufacturing. The system also builds a system in which generative AI monitors the development process of new products in real time and brings them to market quickly. For example, generative AI grasps the development status of products in real time and achieves rapid market launch. This allows the development process of new products to be automated and bring them to market quickly.

[0047] The system uses generative AI to automatically generate improvement measures for products and services tailored to different markets and customer segments, thereby enabling customized offerings. For example, the generative AI analyzes data from different markets and customer segments and automatically generates improvement measures for products and services tailored to the data. For example, the generative AI proposes product improvements tailored to specific market needs. The system also builds a system in which the generative AI uses data from different markets and customer segments to enable customized offerings. For example, the generative AI proposes service improvements tailored to specific customer segments. The system also builds a system in which the generative AI monitors data from different markets and customer segments in real time and automatically generates improvement measures for products and services tailored to the data. For example, the generative AI proposes product and service improvements based on customer feedback in a specific market. This enables customized offerings to be achieved by automatically generating improvement measures for products and services tailored to different markets and customer segments.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The e-commerce site operation system can also provide interactive elements to further increase users' purchasing motivation. For example, the acquisition unit can display a pop-up of related products when a user is viewing a product page. The provision unit can also display other users' reviews and ratings in real time when a user adds a specific product to their cart. Furthermore, the determination unit can recommend the most suitable products for each individual user based on the user's past purchasing history. This can increase users' purchasing motivation and increase sales.

[0050] The site analysis automation unit can also optimize site navigation based on user behavior data. For example, the acquisition unit can analyze the pages a user frequently visits and the links they click to provide an optimal navigation menu. The provision unit can also display help messages and guides when a user is lost on a particular page. Furthermore, the determination unit can automatically adjust the layout and design of the site based on the user behavior data. This can improve user convenience and optimize site performance.

[0051] The promotion automation unit can also predict the user's purchasing behavior and provide promotions based on the prediction. For example, the acquisition unit can analyze the user's past purchase history and browsing history to predict which products the user is likely to purchase next. The provision unit can provide the user with special discounts and coupons based on the prediction. Furthermore, the determination unit can monitor the user's purchasing behavior in real time and provide optimal promotions. This makes it possible to predict the user's purchasing behavior and provide effective promotions.

[0052] The site analysis automation unit can also optimize site navigation based on user behavior data. For example, the acquisition unit can analyze the pages a user frequently visits and the links they click to provide an optimal navigation menu. The provision unit can also display help messages and guides when a user is lost on a particular page. Furthermore, the determination unit can automatically adjust the layout and design of the site based on the user behavior data. This can improve user convenience and optimize site performance.

[0053] The promotion automation unit can also predict the user's purchasing behavior and provide promotions based on the prediction. For example, the acquisition unit can analyze the user's past purchase history and browsing history to predict which products the user is likely to purchase next. The provision unit can provide the user with special discounts and coupons based on the prediction. Furthermore, the determination unit can monitor the user's purchasing behavior in real time and provide optimal promotions. This makes it possible to predict the user's purchasing behavior and provide effective promotions.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The promotion automation unit automates the planning and execution of product promotions. The generation AI analyzes past sales data and customer purchase history to propose optimal promotion strategies. It also predicts when specific products will sell well and automatically sets up discount campaigns and advertisements to suit those periods. It also generates promotion strategies based on prompts that include instructions on the promotion's purpose and target products. Step 2: The site analysis automation unit analyzes the e-commerce site's access data and user behavior data to evaluate the site's performance. The generation AI analyzes which pages are most visited and which products have the highest purchase rates, and reports the results to the operator. It can also generate analysis results based on prompts containing instructions about the data to be analyzed. Step 3: The customer response automation department automatically responds to customer inquiries. The generation AI understands customer questions and generates appropriate answers. It can also automatically respond to inquiries about product inventory and delivery status. It also generates answers based on prompts containing instructions about the customer's inquiry.

[0056] (Example 2) The e-commerce site operation system according to an embodiment of the present invention utilizes AI technology to reduce the operational burden of an e-commerce site, providing an environment in which operators can focus on providing high-quality products and improving services. This system significantly reduces the operator's burden through the automation of product promotion, site analysis, and customer support. As a result, the e-commerce site operation system can provide an environment in which operators can focus on their original purpose: "delivering products to consumers."

[0057] An e-commerce website operation system according to an embodiment includes a promotion automation unit, a site analysis automation unit, and a customer support automation unit. The promotion automation unit automates the planning and execution of product promotions. For example, the generation AI analyzes past sales data and customer purchase histories to propose optimal promotion strategies. The generation AI can also predict when a particular product will sell well and automatically set up discount campaigns and advertisements to suit that period. The generation AI also generates promotion strategies based on prompts containing instructions regarding the promotion's purpose and target products. The site analysis automation unit analyzes access data and user behavior data of the e-commerce website to evaluate site performance. For example, the generation AI analyzes which pages are most visited and which products have the highest purchase rates, and reports the results to the operator. The generation AI can also generate analysis results based on prompts containing instructions regarding the data to be analyzed. The customer support automation unit automatically responds to customer inquiries. For example, the generation AI understands customer questions and generates appropriate answers. The generation AI can also automatically respond to inquiries regarding product inventory and delivery status. Furthermore, the generation AI generates answers based on prompts containing instructions related to customer inquiries. As a result, the e-commerce site operation system according to the embodiment can provide an environment in which operators can focus on providing high-quality products and improving services. For example, operators can focus on developing new products and improving the quality of existing products by reducing their operational burden with the generation AI. They can also work on improving services to increase customer satisfaction.

[0058] The promotion automation unit analyzes sales data and customer purchasing history to propose optimal promotion strategies. For example, the generation AI in the promotion automation unit analyzes customers' social media activity to identify the interests of each individual customer. For example, it analyzes the content of posts that customers frequently "like" or share, and proposes optimal promotions based on that information. The generation AI in the promotion automation unit also analyzes past sales data to predict when a particular product will sell well. For example, the generation AI predicts based on past sales data that a particular product will sell well in the summer and sets up discount campaigns to suit that time of year. The generation AI in the promotion automation unit also analyzes customer purchasing history to propose optimal promotions for each individual customer. For example, the generation AI proposes promotions related to products that a particular customer has previously purchased based on the customer's purchasing history. This allows the effectiveness of product promotions to be maximized by proposing optimal promotion strategies.

[0059] The site analysis automation unit can analyze page visits and product purchase rates and report the results to the operator. For example, the generation AI analyzes access data for an e-commerce site to determine which pages are most visited. For example, the generation AI analyzes access logs and reports that certain pages are visited more frequently than others. The site analysis automation unit also analyzes user behavior data to determine which products have a high purchase rate. For example, the generation AI analyzes user clickstream data and reports that certain products have a higher purchase rate than others. The site analysis automation unit also builds a system in which the generation AI reports the analysis results to the operator. For example, the generation AI displays the analysis results on a dashboard, allowing the operator to understand the site's performance in real time. This allows the operator to quickly understand the site's performance and identify areas for improvement.

[0060] The customer response automation department can understand customer questions and generate appropriate answers. For example, in the customer response automation department, the generation AI analyzes customer questions and generates appropriate answers. For example, in response to a customer question such as "What is the product's inventory status?", the generation AI replies "We currently have it in stock" based on inventory data. The customer response automation department also builds a system in which the generation AI analyzes customer inquiries and generates appropriate answers. For example, in response to a customer question such as "What is the delivery status?", the generation AI replies "The product is currently being delivered" based on delivery data. The customer response automation department also builds a system in which the generation AI can handle complex customer questions. For example, in response to a customer question such as "How do I return a product?", the generation AI replies with detailed instructions based on the return policy. This reduces the time spent responding to customers and allows the department to focus on other important tasks.

[0061] The promotion automation unit analyzes customers' social media activities and can propose optimal promotions to individual customers in real time. In the promotion automation unit, for example, the generation AI analyzes customers' social media activities and identifies the interests and concerns of individual customers. For example, the generation AI analyzes the content of posts that customers frequently "like" or share, and proposes optimal promotions based on that information. The promotion automation unit also builds a system in which the generation AI monitors customers' social media activities in real time and proposes optimal promotions. For example, immediately after a customer posts about a specific product, the generation AI proposes a discount coupon related to that product. The promotion automation unit also builds a system in which the generation AI proposes optimal promotions to individual customers based on the customer's social media activities. For example, if a customer shows interest in a specific brand, the generation AI proposes promotions related to products from that brand. This makes it possible to propose promotions based on customers' interests and concerns in real time.

[0062] The promotion automation unit can analyze competitors' promotional strategies and automatically generate optimal counter-promotions. For example, the generation AI in the promotion automation unit analyzes competitors' promotional campaigns and evaluates their effectiveness. For example, the generation AI analyzes competitors' discount campaigns and advertising strategies and proposes counter-promotions. The promotion automation unit also builds a system in which the generation AI automatically generates optimal promotions based on competitors' promotional strategies. For example, the generation AI analyzes promotions that competitors are running for specific products and proposes counter-discount campaigns. The promotion automation unit also builds a system in which the generation AI monitors competitors' promotional strategies in real time and automatically generates counter-promotions. For example, the generation AI proposes counter-promotions immediately after a competitor launches a new promotion. This makes it possible to automatically generate optimal promotions to counter competitors' promotional strategies.

[0063] The promotion automation unit can analyze a customer's emotional state and automatically generate promotions that elicit positive emotions. In the promotion automation unit, for example, the generation AI analyzes a customer's emotional state and suggests promotions that elicit positive emotions. For example, if a customer is feeling stressed, the generation AI suggests products that have a relaxing effect. The promotion automation unit also builds a system in which the generation AI automatically generates promotions that elicit positive emotions based on the customer's emotional state. For example, the generation AI suggests promotions that make customers feel happy. The promotion automation unit also builds a system in which the generation AI monitors a customer's emotional state in real time and automatically generates promotions that elicit positive emotions. For example, the generation AI suggests a promotion related to a particular product immediately after a customer expresses positive emotions about that product. This makes it possible to automatically generate promotions based on the customer's emotional state.

[0064] The promotion automation unit can monitor the effectiveness of promotions in real time and automatically adjust the promotion content as needed. For example, the generation AI in the promotion automation unit monitors the effectiveness of promotions in real time and automatically adjusts the promotion content if the effectiveness is low. For example, the generation AI changes the discount rate or extends the promotion period. The promotion automation unit also builds a system in which the generation AI automatically adjusts the optimal promotion content based on the effectiveness of the promotion. For example, the generation AI changes the target audience for advertising if the effectiveness of the promotion is low. The promotion automation unit also builds a system in which the generation AI monitors the effectiveness of promotions in real time and automatically adjusts the promotion content as needed. For example, the generation AI changes the promotion content if the effectiveness of the promotion is low. This makes it possible to monitor the effectiveness of promotions in real time and automatically adjust the promotion content as needed.

[0065] The promotion automation unit uses the emotion estimation function to evaluate the effectiveness of a promotion from an emotional perspective and identify the most effective promotion method. In the promotion automation unit, for example, the generation AI uses the emotion estimation function to evaluate the effectiveness of a promotion from an emotional perspective. For example, the generation AI analyzes customers' emotional reactions and identifies a promotion that elicits positive emotions. The promotion automation unit also builds a system in which the generation AI uses the emotion estimation function to identify the most effective promotion method. For example, the generation AI identifies that a particular promotion method is more effective than other methods based on customers' emotional reactions. The promotion automation unit also builds a system in which the generation AI uses the emotion estimation function to evaluate the effectiveness of a promotion in real time and identify the most effective promotion method. For example, the generation AI monitors customers' emotional reactions while a promotion is being implemented and identifies the most effective method. This makes it possible to evaluate the effectiveness of a promotion from an emotional perspective and identify the most effective promotion method.

[0066] The site analysis automation unit can analyze a user's mouse and touch movements and automatically suggest improvements to the user interface. In this case, for example, the generation AI analyzes a user's mouse and touch movements and identifies improvements to the user interface. For example, the generation AI makes suggestions to improve the position or design of buttons with low click rates. The site analysis automation unit also builds a system in which the generation AI automatically suggests improvements to the user interface based on user operation data. For example, the generation AI makes suggestions to improve the layout of pages that users frequently scroll through. The site analysis automation unit also builds a system in which the generation AI monitors a user's mouse and touch movements in real time and automatically suggests improvements to the user interface. For example, if a user frequently clicks in a specific area, the generation AI makes suggestions to improve the design of that area. This makes it possible to automatically suggest improvements to the user interface based on user operation data.

[0067] The site analysis automation unit can evaluate the quality of content on a site and automatically make suggestions to improve low-rated content. In this case, for example, the generation AI analyzes content on a site and identifies low-quality content. For example, the generation AI evaluates pages with short viewing times and pages with high bounce rates. The site analysis automation unit also builds a system in which the generation AI automatically makes suggestions to improve low-rated content based on the quality of the content. For example, the generation AI suggests enriching the content or improving the design for low-rated content. The site analysis automation unit also builds a system in which the generation AI monitors content on a site in real time and automatically makes suggestions to improve low-rated content. For example, the generation AI suggests optimizing keywords or adding images for low-rated content. This makes it possible to evaluate the quality of content on a site and automatically make suggestions to improve low-rated content.

[0068] The site analysis automation unit uses the emotion estimation function to analyze a user's emotional response and identify and improve elements that cause negative emotions. In the site analysis automation unit, for example, the generation AI uses the emotion estimation function to analyze a user's emotional response and identify elements that cause negative emotions. For example, the generation AI identifies pages or elements that cause users to feel stressed. In addition, the site analysis automation unit uses the emotion estimation function to build a system that improves elements that cause negative emotions. For example, the generation AI identifies elements that cause users to feel dissatisfied and makes suggestions to improve those elements. In addition, the site analysis automation unit uses the emotion estimation function to build a system that monitors a user's emotional response in real time and identifies and improves elements that cause negative emotions. For example, if a user expresses negative emotions on a particular page, the generation AI makes suggestions to improve the design or content of that page. This makes it possible to analyze a user's emotional response and identify and improve elements that cause negative emotions.

[0069] The site analysis automation unit can monitor site performance in real time and automatically issue alerts when problems occur. For example, the site analysis automation unit builds a system in which a generation AI monitors site performance in real time and automatically issues alerts when problems occur. For example, the generation AI issues an alert if the server response time becomes slow. The site analysis automation unit also builds a system in which a generation AI automatically issues alerts when problems occur based on site performance. For example, the generation AI analyzes error logs and issues an alert when a specific error occurs. The site analysis automation unit also builds a system in which a generation AI monitors site performance in real time and automatically issues alerts when problems occur. For example, the generation AI detects a decline in performance and issues an alert to the operator. This makes it possible to monitor site performance in real time and automatically issue alerts when problems occur.

[0070] The site analysis automation unit can analyze site performance on different devices and browsers and make optimization suggestions. In the site analysis automation unit, for example, the generation AI analyzes site performance on different devices and browsers and makes optimization suggestions. For example, the generation AI evaluates display speed on smartphones and tablets. The site analysis automation unit also builds a system in which the generation AI makes optimization suggestions based on site performance on different devices and browsers. For example, if the display speed on a specific browser is slow, the generation AI makes optimization suggestions for that browser. The site analysis automation unit also builds a system in which the generation AI monitors site performance on different devices and browsers in real time and makes optimization suggestions. For example, if the display speed on a specific device is slow, the generation AI makes optimization suggestions for that device. This makes it possible to analyze site performance on different devices and browsers and make optimization suggestions.

[0071] The site analysis automation unit can use the emotion estimation function to optimize site design and content based on the user's emotional response. In the site analysis automation unit, for example, the generation AI uses the emotion estimation function to optimize site design based on the user's emotional response. For example, the generation AI emphasizes design elements that indicate positive emotions in the user. The site analysis automation unit also builds a system in which the generation AI optimizes site design and content based on the emotion estimation function. For example, the generation AI makes suggestions to improve elements that indicate negative emotions in the user. The site analysis automation unit also builds a system in which the generation AI monitors the user's emotional response in real time using the emotion estimation function to optimize site design and content. For example, if the user expresses positive emotions on a particular page, the generation AI makes suggestions to emphasize the design of that page. This makes it possible to optimize site design and content based on the user's emotional response.

[0072] The customer response automation department can analyze a customer's past inquiry history and automatically generate the optimal response for each individual customer. In the customer response automation department, for example, a generation AI analyzes a customer's past inquiry history and automatically generates the optimal response for each individual customer. For example, the generation AI proposes the optimal response based on the past inquiry content and response history. The customer response automation department also builds a system in which the generation AI automatically generates the optimal response based on the customer's inquiry history. For example, the generation AI proposes the optimal response to a similar inquiry based on the content of a specific customer's past inquiries. The customer response automation department also builds a system in which the generation AI monitors a customer's past inquiry history in real time and automatically generates the optimal response. For example, the generation AI proposes the optimal response to a current inquiry based on the content of the customer's past inquiries. This makes it possible to analyze a customer's past inquiry history and automatically generate the optimal response for each individual customer.

[0073] The customer response automation unit can analyze the content of customer inquiries in real time and automatically provide the optimal answer. In the customer response automation unit, for example, the generation AI analyzes the content of customer inquiries in real time and automatically provides the optimal answer. For example, the generation AI instantly generates answers to questions about product details and usage methods. The customer response automation unit also builds a system in which the generation AI automatically provides the optimal answer based on the content of customer inquiries. For example, when a customer asks a question about a specific product, the generation AI generates an answer based on detailed information about that product. The customer response automation unit also builds a system in which the generation AI monitors the content of customer inquiries in real time and automatically provides the optimal answer. For example, when a customer asks a question about a specific service, the generation AI generates an answer based on detailed information about that service. This makes it possible to analyze the content of customer inquiries in real time and automatically provide the optimal answer.

[0074] The customer response automation department can monitor customer response performance in real time and automatically adjust response content as needed. For example, the customer response automation department builds a system in which a generation AI monitors customer response performance in real time and automatically adjusts response content as needed. For example, the generation AI suggests a quick response if response times become long. The customer response automation department also builds a system in which a generation AI automatically adjusts optimal response content based on customer response performance. For example, the generation AI makes suggestions to improve response content if customer satisfaction is low. The customer response automation department also builds a system in which a generation AI monitors customer response performance in real time and automatically adjusts response content as needed. For example, the generation AI suggests a quick response if response times to customer inquiries become long. This makes it possible to monitor customer response performance in real time and automatically adjust response content as needed.

[0075] The customer response automation unit can automatically generate customer responses in different languages, thereby realizing global customer response. For example, the customer response automation unit builds a system in which a generation AI automatically generates customer responses in different languages. For example, the generation AI automatically generates responses in multiple languages, such as English, French, and Chinese. The customer response automation unit also builds a system in which the generation AI realizes global customer response based on customer responses in different languages. For example, the generation AI proposes the optimal response in a specific language in response to an inquiry in that language. The customer response automation unit also builds a system in which the generation AI monitors customer responses in different languages ​​in real time, thereby realizing global customer response. For example, the generation AI proposes a quick and appropriate response to an inquiry in a specific language. This makes it possible to automatically generate customer responses in different languages ​​and realize global customer response.

[0076] The customer response automation unit can use the emotion estimation function to optimize response content based on the customer's emotional reaction, thereby improving customer satisfaction. In the customer response automation unit, for example, the generation AI uses the emotion estimation function to optimize response content based on the customer's emotional reaction. For example, if a customer is dissatisfied, the generation AI suggests a quick and courteous response. The customer response automation unit also builds a system in which the generation AI uses the emotion estimation function to automatically generate response content that improves customer satisfaction. For example, the generation AI suggests special offers that will satisfy the customer. The customer response automation unit also builds a system in which the generation AI uses the emotion estimation function to monitor customers' emotional reactions in real time and optimize response content. For example, if a customer shows positive emotions in response to a specific inquiry, the generation AI suggests the optimal response to that inquiry. This makes it possible to optimize response content based on the customer's emotional reaction, thereby improving customer satisfaction.

[0077] The system can use a generative AI to analyze customer feedback and automatically suggest improvements to products and services. For example, the generative AI analyzes customer feedback and automatically suggests improvements to products and services. For example, the generative AI identifies areas for improvement based on customer reviews and comments. The system also builds a system in which the generative AI automatically suggests improvements to products and services based on customer feedback. For example, the generative AI analyzes customer survey results and suggests areas for improvement to specific products. The system also builds a system in which the generative AI monitors customer feedback in real time and automatically suggests improvements to products and services. For example, the generative AI analyzes customer comments and suggests areas for improvement to services. In this way, it is possible to analyze customer feedback and automatically suggest improvements to products and services.

[0078] The system can use generative AI to analyze competitors' products and services and automatically generate optimal improvement measures to compete with them. For example, the generative AI analyzes competitors' products and services and automatically generates optimal improvement measures to compete with them. For example, the generative AI analyzes competitors' new features and services and proposes competitive improvement measures. The system also builds a system in which the generative AI automatically generates optimal improvement measures based on competitors' products and services. For example, the generative AI analyzes competitors' product reviews and proposes competitive improvement measures. The system also builds a system in which the generative AI monitors competitors' products and services in real time and automatically generates optimal improvement measures to compete with them. For example, the generative AI proposes competitive improvement measures immediately after a competitor launches a new service. This makes it possible to analyze competitors' products and services and automatically generate optimal improvement measures to compete with them.

[0079] The system can use the emotion estimation function to analyze customers' emotional reactions and automatically generate improvement measures for products and services that elicit positive emotions. For example, the generation AI in the system uses the emotion estimation function to analyze customers' emotional reactions and propose improvement measures for products and services that elicit positive emotions. For example, the generation AI proposes adding new functions that will make customers feel happy. The system also builds a system in which the generation AI uses the emotion estimation function to automatically generate improvement measures that elicit positive emotions. For example, the generation AI proposes service improvements that will satisfy customers. The system also builds a system in which the generation AI uses the emotion estimation function to monitor customers' emotional reactions in real time and automatically generate improvement measures that elicit positive emotions. For example, if a customer expresses positive emotions toward a particular product, the generation AI proposes improvement measures for that product. In this way, it is possible to analyze customers' emotional reactions and automatically generate improvement measures for products and services that elicit positive emotions.

[0080] The system uses generative AI to automate the development process of new products and bring them to market quickly. For example, the system builds a system in which generative AI automates the development process of new products and brings them to market quickly. For example, generative AI automates everything from product concept design to prototype creation. The system also builds a system in which generative AI uses the new product development process to bring them to market quickly. For example, generative AI automates the process from product design to manufacturing. The system also builds a system in which generative AI monitors the development process of new products in real time and brings them to market quickly. For example, generative AI grasps the development status of products in real time and achieves rapid market launch. This allows the development process of new products to be automated and bring them to market quickly.

[0081] The system uses generative AI to automatically generate improvement measures for products and services tailored to different markets and customer segments, thereby enabling customized offerings. For example, the generative AI analyzes data from different markets and customer segments and automatically generates improvement measures for products and services tailored to the data. For example, the generative AI proposes product improvements tailored to specific market needs. The system also builds a system in which the generative AI uses data from different markets and customer segments to enable customized offerings. For example, the generative AI proposes service improvements tailored to specific customer segments. The system also builds a system in which the generative AI monitors data from different markets and customer segments in real time and automatically generates improvement measures for products and services tailored to the data. For example, the generative AI proposes product and service improvements based on customer feedback in a specific market. This enables customized offerings to be achieved by automatically generating improvement measures for products and services tailored to different markets and customer segments.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The e-commerce site operation system can also provide interactive elements to further increase users' purchasing motivation. For example, the acquisition unit can display a pop-up of related products when a user is viewing a product page. The provision unit can also display other users' reviews and ratings in real time when a user adds a specific product to their cart. Furthermore, the determination unit can recommend the most suitable products for each individual user based on the user's past purchasing history. This can increase users' purchasing motivation and increase sales.

[0084] The promotion automation unit can also estimate the user's emotions and provide promotions based on those emotions. For example, the determination unit can analyze the user's facial expressions and voice while browsing the site, and if the user is excited, provide limited-time sales or special offers. If the user is feeling stressed, the provision unit can recommend products with a relaxing effect. Furthermore, the acquisition unit can monitor the user's emotional state in real time and provide optimal promotions. This makes it possible to provide promotions based on the user's emotions and improve customer satisfaction.

[0085] The site analysis automation unit can also optimize site navigation based on user behavior data. For example, the acquisition unit can analyze the pages a user frequently visits and the links they click to provide an optimal navigation menu. The provision unit can also display help messages and guides when a user is lost on a particular page. Furthermore, the determination unit can automatically adjust the layout and design of the site based on the user behavior data. This can improve user convenience and optimize site performance.

[0086] The customer response automation unit can also estimate the user's emotions and provide a response based on those emotions. For example, the determination unit can analyze the user's facial expressions and voice when making an inquiry, and if the user is angry, provide a prompt and courteous response. Furthermore, if the user is satisfied, the provision unit can provide additional services or benefits. Furthermore, the acquisition unit can monitor the user's emotional state in real time and provide an optimal response. This allows for a response based on the user's emotions and improves customer satisfaction.

[0087] The promotion automation unit can also predict the user's purchasing behavior and provide promotions based on the prediction. For example, the acquisition unit can analyze the user's past purchase history and browsing history to predict which products the user is likely to purchase next. The provision unit can provide the user with special discounts and coupons based on the prediction. Furthermore, the determination unit can monitor the user's purchasing behavior in real time and provide optimal promotions. This makes it possible to predict the user's purchasing behavior and provide effective promotions.

[0088] The promotion automation unit can also estimate the user's emotions and provide promotions based on those emotions. For example, the determination unit can analyze the user's facial expressions and voice while browsing the site, and if the user is excited, provide limited-time sales or special offers. If the user is feeling stressed, the provision unit can recommend products with a relaxing effect. Furthermore, the acquisition unit can monitor the user's emotional state in real time and provide optimal promotions. This makes it possible to provide promotions based on the user's emotions and improve customer satisfaction.

[0089] The site analysis automation unit can also optimize site navigation based on user behavior data. For example, the acquisition unit can analyze the pages a user frequently visits and the links they click to provide an optimal navigation menu. The provision unit can also display help messages and guides when a user is lost on a particular page. Furthermore, the determination unit can automatically adjust the layout and design of the site based on the user behavior data. This can improve user convenience and optimize site performance.

[0090] The customer response automation unit can also estimate the user's emotions and provide a response based on those emotions. For example, the determination unit can analyze the user's facial expressions and voice when making an inquiry, and if the user is angry, provide a prompt and courteous response. Furthermore, if the user is satisfied, the provision unit can provide additional services or benefits. Furthermore, the acquisition unit can monitor the user's emotional state in real time and provide an optimal response. This allows for a response based on the user's emotions and improves customer satisfaction.

[0091] The promotion automation unit can also predict the user's purchasing behavior and provide promotions based on the prediction. For example, the acquisition unit can analyze the user's past purchase history and browsing history to predict which products the user is likely to purchase next. The provision unit can provide the user with special discounts and coupons based on the prediction. Furthermore, the determination unit can monitor the user's purchasing behavior in real time and provide optimal promotions. This makes it possible to predict the user's purchasing behavior and provide effective promotions.

[0092] The site analysis automation unit can also estimate the user's emotions and make site improvement suggestions based on those emotions. For example, the determination unit can analyze the user's facial expressions and voice while browsing the site, and if the user is dissatisfied, make suggestions to improve the site's design and content. If the user is satisfied, the provision unit can make suggestions to emphasize those elements. Furthermore, the acquisition unit can monitor the user's emotional state in real time and make optimal improvement suggestions. This makes it possible to make site improvement suggestions based on the user's emotions and improve the user experience.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The promotion automation unit automates the planning and execution of product promotions. The generation AI analyzes past sales data and customer purchase history to propose optimal promotion strategies. It also predicts when specific products will sell well and automatically sets up discount campaigns and advertisements to suit those periods. It also generates promotion strategies based on prompts that include instructions on the promotion's purpose and target products. Step 2: The site analysis automation unit analyzes the e-commerce site's access data and user behavior data to evaluate the site's performance. The generation AI analyzes which pages are most visited and which products have the highest purchase rates, and reports the results to the operator. It can also generate analysis results based on prompts containing instructions about the data to be analyzed. Step 3: The customer response automation department automatically responds to customer inquiries. The generation AI understands customer questions and generates appropriate answers. It can also automatically respond to inquiries about product inventory and delivery status. It also generates answers based on prompts containing instructions about the customer's inquiry.

[0095] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 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 the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a 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.

[0130] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] 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.

[0154] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, a promotion automation department that automates the planning and execution of product promotions; The Site Analysis Automation Department analyzes access data and user behavior data from EC sites and evaluates site performance. A customer response automation unit that automatically responds to inquiries from customers. A system characterized by:

2. The promotion automation unit Monitor promotional effectiveness in real time and automatically adjust promotions as needed 2. The system of claim 1.

3. The site analysis automation unit Analyzes user mouse and touch movements and automatically suggests improvements to the user interface 2. The system of claim 1.

4. The customer response automation unit Analyzing the inquiry history of the customer and automatically generating the optimal response for each customer.

2. The system of claim 1.

5. The promotion automation unit Evaluate promotional effectiveness from an emotional perspective and identify the most effective promotional methods 2. The system of claim 1.

6. The site analysis automation unit Analyze users' emotional responses, identify factors that cause negative emotions, and improve them 2. The system of claim 1.

7. The customer response automation unit Analyze the emotional state of the customer and automatically generate responses that elicit positive emotions.

2. The system of claim 1.

8. The system comprises: Analyze the customer's emotional responses and automatically generate improvement measures for products and services that elicit positive emotions.

2. The system of claim 1.

Images