system

Abstract

An object of a system according to an embodiment is to generate an optimal UIUX based on a purchase behavior of a customer and optimize information of a search result or a product detail page.SOLUTION: A system includes a purchase action analysis part, a UIUX generation part, a retrieval result optimization part, a commodity detail optimization part, and an information emphasis part. The purchase behavior analysis unit analyzes the purchase behavior of the customer. A UIUX generation part generates an optimum UIUX on the basis of the result analyzed by the purchase action analysis part. A retrieval result optimization part optimizes the arrangement order of the retrieval result based on the UIUX generated by the UIUX generation part. The product detail optimization unit optimizes the information configuration of the product detail page based on the UIUX generated by the UIUX generation unit. The information emphasis part determines information to be emphasized on the basis of the UIUX generated by the UIUX generation part.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] Conventional technologies have had the problem of not adequately generating optimal UI / UX based on customer purchasing behavior and optimizing information on search results and product detail pages.

[0005] The system according to the embodiment aims to generate optimal UIUX based on customer purchasing behavior and to optimize search results and information on product detail pages. [Means for solving the problem]

[0006] The system according to the embodiment includes a purchasing behavior analysis unit, a UIUX generation unit, a search result optimization unit, a product detail optimization unit, and an information highlighting unit. The purchasing behavior analysis unit analyzes customer purchasing behavior. The UIUX generation unit generates an optimal UIUX based on the results of the analysis by the purchasing behavior analysis unit. The search result optimization unit optimizes the order of search results based on the UIUX generated by the UIUX generation unit. The product detail optimization unit optimizes the information configuration of the product detail page based on the UIUX generated by the UIUX generation unit. The information highlighting unit determines information to be highlighted based on the UIUX generated by the UIUX generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate optimal UIUX based on customer purchasing behavior and optimize search results and information on product detail pages. [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 EC site system according to the embodiment of the present invention is a system that automatically generates an optimal UIUX based on customer purchasing behavior and maximizes the purchase rate. This allows the EC site system to provide an optimal UIUX for each customer and maximize the purchase rate.

[0029] An e-commerce site system according to an embodiment includes a purchasing behavior analysis unit, a UIUX generation unit, a search result optimization unit, a product detail optimization unit, and an information highlighting unit. The purchasing behavior analysis unit analyzes customer purchasing behavior. For example, the purchasing behavior analysis unit collects and analyzes customer past purchase history and on-site behavior data. The purchasing behavior analysis unit can also analyze what products customers search for, which pages they view, and what products they purchase. The purchasing behavior analysis unit can also identify customer preferences and interests based on the customer behavior data. The UIUX generation unit generates an optimal UIUX based on the results of the analysis by the purchasing behavior analysis unit. For example, if a customer prioritizes price, the UIUX generation unit emphasizes price information, and if a customer prioritizes reviews, the UIUX generation unit emphasizes review information. The UIUX generation unit can also optimize the sorting of search results and the information configuration of product detail pages based on the customer's interests. The search result optimization unit optimizes the sorting of search results based on the UIUX generated by the UIUX generation unit. For example, the search result optimization unit displays products similar to products the customer has previously purchased or viewed at the top of the results. In addition, if a customer has a preference for a particular brand or price range, the search result optimization unit can also sort the search results accordingly. The product detail optimization unit optimizes the information configuration of the product detail page based on the UIUX generated by the UIUX generation unit. For example, if a customer prioritizes specifications, the product detail optimization unit displays detailed specification information, and if a customer prioritizes images, the product detail optimization unit displays many images. In addition, if a customer prioritizes reviews, the product detail optimization unit can place review information in a prominent position. The information highlighting unit determines information to be emphasized based on the UIUX generated by the UIUX generation unit. For example, if a customer prioritizes price, the information highlighting unit displays price information in a large size and emphasizes discount information. In addition, if a customer prioritizes brand, the information highlighting unit can place a brand logo or brand story in a prominent position. As a result, the e-commerce site system according to the embodiment can provide an optimal UIUX based on customer purchasing behavior and maximize purchase rates.For example, an e-commerce site system can provide a UIUX that emphasizes price information to customers who prioritize price, and a UIUX that emphasizes review information to customers who prioritize reviews. Also, for customers who prefer a particular brand, the products of that brand can be displayed in a prominent position. This can improve customer satisfaction and maximize the purchase rate.

[0030] The purchasing behavior analysis unit can analyze comments on social media or review sites in addition to customer purchasing behavior data. For example, the purchasing behavior analysis unit collects comments on social media in addition to customer purchasing behavior data and analyzes them using natural language processing technology. For example, it analyzes posts on Twitter or Facebook to understand customer interests. The purchasing behavior analysis unit can also collect and analyze comments on review sites. For example, it can analyze comments on Amazon reviews or Yelp reviews to identify customer preferences. This allows for a more detailed understanding of customer preferences.

[0031] The purchasing behavior analysis unit monitors customer purchasing behavior in real time and can respond immediately to changes in behavior. The purchasing behavior analysis unit, for example, builds a system that monitors customer behavior on a site in real time and responds immediately to changes in behavior. For example, if a customer views a particular product for a long time, information related to that product can be displayed immediately. The purchasing behavior analysis unit can also display recommended products related to a product that a customer adds to their cart. Furthermore, if a customer frequently views products in a particular category, the purchasing behavior analysis unit can display products in that category on the homepage. This allows for immediate response to changes in customer behavior.

[0032] The purchasing behavior analysis unit can integrate customer purchasing behavior data with purchasing data from other e-commerce sites and physical stores to analyze a wider range of purchasing behavior patterns. For example, the purchasing behavior analysis unit can integrate customer purchasing behavior data with data from other e-commerce sites to analyze a wider range of purchasing behavior patterns. For example, it can integrate data from Amazon and Rakuten to understand customers' overall purchasing trends. The purchasing behavior analysis unit can also integrate purchasing data from physical stores to analyze online and offline purchasing behavior. For example, it can analyze POS data and customer survey data to understand customer purchasing behavior comprehensively. This makes it possible to analyze a wider range of purchasing behavior patterns.

[0033] The purchasing behavior analysis unit can analyze customer purchasing behavior data based on seasons or events, and provide the optimal UIUX for each season. For example, the purchasing behavior analysis unit can analyze customer purchasing behavior data by season, and provide the optimal UIUX for each season. For example, it can emphasize information about summer products in the summer and winter products in the winter. The purchasing behavior analysis unit can also analyze purchasing behavior data based on events such as sales periods and holiday seasons, and provide the optimal UIUX for each event. For example, it can emphasize special offers and discount information during Black Friday and Christmas sales. This makes it possible to provide the optimal UIUX for each season.

[0034] The UIUX generation unit can individually customize the UIUX generation algorithm based on customer purchasing behavior data and provide a different UIUX for each customer. The UIUX generation unit builds a system that individually customizes the UIUX generation algorithm based on, for example, customer purchasing behavior data. For example, if a customer places importance on price, the UIUX generation unit can provide a UIUX that emphasizes price information. Also, if a customer places importance on reviews, the UIUX generation unit can provide a UIUX that emphasizes review information. The UIUX generation unit can also adjust the design and layout of the UIUX based on the customer's preferences and interests. This makes it possible to provide a different UIUX for each customer.

[0035] The UIUX generation unit can generate a UIUX by taking into account customer demographic information in addition to customer purchasing behavior data. The UIUX generation unit builds a system that generates a UIUX by taking into account demographic information in addition to customer purchasing behavior data, for example. For example, a UIUX is provided according to age and gender. The UIUX generation unit can also provide a UIUX according to region and occupation. The UIUX generation unit can also adjust the design and layout of the UIUX based on the customer's demographic information. This makes it possible to generate a UIUX by taking into account customer demographic information.

[0036] The UIUX generation unit can generate a UIUX optimized for different devices based on customer purchasing behavior data. The UIUX generation unit, for example, builds a system that generates a UIUX optimized for smartphones based on customer purchasing behavior data. For example, it provides layouts and content that fit the screen size of smartphones. The UIUX generation unit can also generate a UIUX optimized for tablets and PCs. The UIUX generation unit can also adjust the UIUX design and layout according to the characteristics of each device. This makes it possible to generate a UIUX optimized for different devices.

[0037] The UIUX generation unit can generate UIUX corresponding to different languages ​​and cultural spheres based on customer purchasing behavior data. The UIUX generation unit, for example, builds a system that generates UIUX corresponding to different languages ​​based on customer purchasing behavior data. For example, it provides UIUX corresponding to languages ​​such as English, French, and Chinese. The UIUX generation unit can also generate UIUX corresponding to different cultural spheres. For example, it provides designs and content tailored to Asian and Western cultures. The UIUX generation unit can also adjust the design and layout of the UIUX according to the characteristics of each language and cultural sphere. This makes it possible to generate UIUX corresponding to different languages ​​and cultural spheres.

[0038] The search result optimization unit dynamically changes the order of search results based on customer search behavior data, enabling optimization each time a customer's interests change. The search result optimization unit, for example, analyzes customer search behavior data in real time and builds a system that dynamically changes the order of search results. For example, if a customer shows interest in a particular product category, products in that category are displayed at the top. The search result optimization unit can also optimize the order of search results each time a customer's interests change. For example, it sorts search results to match a product category in which the customer has newly become interested. This makes it possible to optimize the order of search results each time a customer's interests change.

[0039] The search result optimization unit can refer to the search behavior data of other customers in addition to the customer's search behavior data, and refer to search results of customers with similar interests. The search result optimization unit, for example, builds a system that refers to the search behavior data of other customers in addition to the customer's search behavior data, and refers to search results of customers with similar interests. For example, the search results are optimized based on search results of customers who are interested in the same product category. The search result optimization unit can also provide search results tailored to the customer's interests based on the search behavior data of other customers. This makes it possible to refer to search results of customers with similar interests.

[0040] The search result optimization unit can provide the optimal sort order of search results for each different category and subcategory based on customer search behavior data. The search result optimization unit, for example, builds a system that provides the optimal sort order of search results for each different category based on customer search behavior data. For example, the search result optimization unit may prioritize price in the home appliance category and design in the fashion category. The search result optimization unit can also provide the optimal sort order of search results for each subcategory. For example, the search results are optimized for each subcategory, such as smartphones, laptops, and tablets. This makes it possible to provide the optimal sort order of search results for each different category and subcategory.

[0041] The search result optimization unit can optimize the order of search results according to the time of day and day of the week based on customer search behavior data. The search result optimization unit, for example, builds a system that optimizes the order of search results according to the time of day and day of the week based on customer search behavior data. For example, business-related products are displayed at the top during the daytime on weekdays, and leisure-related products are displayed at the top on weekends. The search result optimization unit can also adjust the order of search results according to the time of day and day of the week. For example, breakfast-related products are emphasized in the morning, and information about products with a relaxing effect is emphasized in the evening. This makes it possible to optimize the order of search results according to the time of day and day of the week.

[0042] The product detail optimization unit dynamically changes the information configuration of the product detail page based on customer browsing behavior data, and can optimize it every time the customer's interests change. The product detail optimization unit, for example, analyzes customer browsing behavior data in real time and builds a system that dynamically changes the information configuration of the product detail page. For example, if a customer places importance on specification information, the specification information is displayed in detail. Furthermore, if a customer places importance on images, the product detail optimization unit can also display many images. Furthermore, if a customer places importance on reviews, the product detail optimization unit can also place review information in a prominent position. In this way, the information configuration of the product detail page can be optimized every time the customer's interests change.

[0043] The product detail optimization unit can refer to the browsing behavior data of other customers in addition to the customer's browsing behavior data, and use this data to refer to the information configurations of customers with similar interests. The product detail optimization unit, for example, builds a system that refers to the browsing behavior data of other customers in addition to the customer's browsing behavior data, and uses this data to refer to the information configurations of customers with similar interests. For example, the information configuration is optimized based on the information configurations of customers who are interested in the same product category. The product detail optimization unit can also provide an information configuration tailored to the customer's interests based on the browsing behavior data of other customers. This makes it possible to refer to the information configurations of customers with similar interests.

[0044] The product detail optimization unit can provide information configurations optimized for different devices based on customer browsing behavior data. For example, the product detail optimization unit builds a system that provides information configurations optimized for smartphones based on customer browsing behavior data. For example, it provides layouts and content that match the screen size of smartphones. The product detail optimization unit can also provide information configurations optimized for tablets and PCs. The product detail optimization unit can also adjust the information configuration according to the characteristics of each device. This makes it possible to provide information configurations optimized for different devices.

[0045] The product detail optimization unit can provide information configurations that correspond to different languages ​​and cultural spheres based on customer browsing behavior data. The product detail optimization unit, for example, builds a system that provides information configurations that correspond to different languages ​​based on customer browsing behavior data. For example, it provides information configurations that correspond to languages ​​such as English, French, and Chinese. The product detail optimization unit can also provide information configurations that correspond to different cultural spheres. For example, it provides designs and content that are suited to Asian and Western cultures. The product detail optimization unit can also adjust the information configuration according to the characteristics of each language and cultural sphere. This makes it possible to provide information configurations that correspond to different languages ​​and cultural spheres.

[0046] The information emphasis unit dynamically changes the information to be emphasized based on customer purchasing behavior data, and can optimize the information each time the customer's interests change. The information emphasis unit, for example, analyzes customer purchasing behavior data in real time and builds a system that dynamically changes the information to be emphasized. For example, if a customer places importance on price, price information is emphasized. Furthermore, if a customer places importance on reviews, the information emphasis unit can also emphasize review information. Furthermore, the information emphasis unit can also optimize the information to be emphasized each time the customer's interests change. This makes it possible to optimize the information to be emphasized each time the customer's interests change.

[0047] The information highlighting unit can refer to the purchasing behavior data of other customers in addition to the customer's purchasing behavior data, and refer to the emphasized information of customers with similar interests. The information highlighting unit, for example, builds a system that refers to the purchasing behavior data of other customers in addition to the customer's purchasing behavior data, and refers to the emphasized information of customers with similar interests. For example, information is optimized based on the emphasized information of customers who are interested in the same product category. The information highlighting unit can also provide emphasized information tailored to the customer's interests based on the purchasing behavior data of other customers. This makes it possible to refer to the emphasized information of customers with similar interests.

[0048] The information highlighting unit can provide information to be emphasized for different categories and subcategories based on customer purchasing behavior data. The information highlighting unit, for example, builds a system that provides information to be emphasized for different categories based on customer purchasing behavior data. For example, it emphasizes specification information in the home appliance category and design information in the fashion category. The information highlighting unit can also provide information to be emphasized for each subcategory. For example, it optimizes information for each subcategory, such as smartphones, laptops, and tablets. This makes it possible to provide information to be emphasized for each category and subcategory.

[0049] The information highlighting unit can optimize the information to be emphasized according to the time of day and day of the week based on customer purchasing behavior data. The information highlighting unit, for example, builds a system that optimizes the information to be emphasized according to the time of day and day of the week based on customer purchasing behavior data. For example, business-related information is emphasized during the daytime on weekdays, and leisure-related information is emphasized on weekends. The information highlighting unit can also adjust the information according to the time of day and day of the week. For example, breakfast-related information is emphasized in the morning, and information that has a relaxing effect is emphasized in the evening. This makes it possible to optimize the information to be emphasized according to the time of day and day of the week.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The e-commerce site system can also estimate a customer's health status based on their purchasing behavior data and provide a UIUX that matches their health status. For example, if a customer frequently searches for health-related products, it can highlight health-related information and products. If a customer purchases fitness-related products, it can provide fitness-related content. If a customer enters the results of a health check, it can suggest the most suitable products based on the results. This makes it possible to provide a UIUX that matches the customer's health status.

[0052] The e-commerce site system can also estimate a customer's lifestyle based on their purchasing behavior data and provide a UIUX that suits their lifestyle. For example, if a customer frequently purchases outdoor-related products, it can emphasize outdoor information and products. If a customer frequently purchases household goods, it can provide content related to household goods. If a customer frequently purchases travel-related products, it can suggest travel-related information and products. This makes it possible to provide a UIUX that suits each customer's lifestyle.

[0053] The EC site system can also infer a customer's hobbies and interests based on their purchasing behavior data, and provide a UIUX that matches those hobbies and interests. For example, if a customer frequently purchases music-related products, it can emphasize music-related information and products. If a customer purchases sports-related products, it can provide sports-related content. If a customer purchases art-related products, it can suggest art-related information and products. This makes it possible to provide a UIUX that matches a customer's hobbies and interests.

[0054] The e-commerce site system can also estimate a customer's lifestyle based on their purchasing behavior data and provide a UIUX that suits their lifestyle. For example, if a customer frequently visits the site at night, it can provide designs and content that are suitable for the night. If a customer visits the site in the morning, it can emphasize information and products that are suitable for the morning. If a customer visits the site on the weekend, it can provide offers and content that are suitable for the weekend. This makes it possible to provide a UIUX that suits the customer's lifestyle.

[0055] The EC site system can also estimate a customer's purchasing intent based on their purchasing behavior data and provide a UIUX that matches their purchasing intent. For example, if a customer shows a high level of purchasing intent, it can highlight special offers and discount information. On the other hand, if a customer shows a low level of purchasing intent, it can provide content to stimulate their purchasing intent. Furthermore, if a customer shows a high interest in a particular product, it can suggest information and products related to that product. This makes it possible to provide a UIUX that matches a customer's purchasing intent.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The purchasing behavior analysis unit analyzes customer purchasing behavior. For example, it collects the customer's past purchase history and behavioral data on the site, and analyzes what products they searched for, which pages they viewed, and what products they purchased. It can also identify customer preferences and interests based on customer behavioral data. Step 2: The UIUX generation unit generates an optimal UIUX based on the results of the analysis by the purchasing behavior analysis unit. For example, if the customer prioritizes price, it will emphasize price information, and if the customer prioritizes reviews, it will emphasize review information. It can also optimize the order of search results and the information structure on product detail pages based on customer interests. Step 3: The search result optimization unit optimizes the order of search results based on the UIUX generated by the UIUX generation unit. For example, products similar to those purchased or viewed by the customer are displayed at the top. Also, if a customer has a preference for a specific brand or price range, the search results can be sorted accordingly. Step 4: The product details optimization unit optimizes the information configuration of the product details page based on the UIUX generated by the UIUX generation unit. For example, if a customer prioritizes specs, it displays detailed spec information, and if they prioritize images, it displays many images. Also, if a customer prioritizes reviews, it can place review information in a prominent position. Step 5: The information highlighting unit determines the information to be highlighted based on the UIUX generated by the UIUX generation unit. For example, if the customer places importance on price, the price information can be displayed in a large size and discount information can be highlighted. Also, if the customer places importance on the brand, the brand logo and brand story can be placed in a prominent position.

[0058] (Example 2) The EC site system according to the embodiment of the present invention is a system that automatically generates an optimal UIUX based on customer purchasing behavior and maximizes the purchase rate. This allows the EC site system to provide an optimal UIUX for each customer and maximize the purchase rate.

[0059] An e-commerce site system according to an embodiment includes a purchasing behavior analysis unit, a UIUX generation unit, a search result optimization unit, a product detail optimization unit, and an information highlighting unit. The purchasing behavior analysis unit analyzes customer purchasing behavior. For example, the purchasing behavior analysis unit collects and analyzes customer past purchase history and on-site behavior data. The purchasing behavior analysis unit can also analyze what products customers search for, which pages they view, and what products they purchase. The purchasing behavior analysis unit can also identify customer preferences and interests based on the customer behavior data. The UIUX generation unit generates an optimal UIUX based on the results of the analysis by the purchasing behavior analysis unit. For example, if a customer prioritizes price, the UIUX generation unit emphasizes price information, and if a customer prioritizes reviews, the UIUX generation unit emphasizes review information. The UIUX generation unit can also optimize the sorting of search results and the information configuration of product detail pages based on the customer's interests. The search result optimization unit optimizes the sorting of search results based on the UIUX generated by the UIUX generation unit. For example, the search result optimization unit displays products similar to products the customer has previously purchased or viewed at the top of the results. In addition, if a customer has a preference for a particular brand or price range, the search result optimization unit can also sort the search results accordingly. The product detail optimization unit optimizes the information configuration of the product detail page based on the UIUX generated by the UIUX generation unit. For example, if a customer prioritizes specifications, the product detail optimization unit displays detailed specification information, and if a customer prioritizes images, the product detail optimization unit displays many images. In addition, if a customer prioritizes reviews, the product detail optimization unit can place review information in a prominent position. The information highlighting unit determines information to be emphasized based on the UIUX generated by the UIUX generation unit. For example, if a customer prioritizes price, the information highlighting unit displays price information in a large size and emphasizes discount information. In addition, if a customer prioritizes brand, the information highlighting unit can place a brand logo or brand story in a prominent position. As a result, the e-commerce site system according to the embodiment can provide an optimal UIUX based on customer purchasing behavior and maximize purchase rates.For example, an e-commerce site system can provide a UIUX that emphasizes price information to customers who prioritize price, and a UIUX that emphasizes review information to customers who prioritize reviews. Also, for customers who prefer a particular brand, the products of that brand can be displayed in a prominent position. This can improve customer satisfaction and maximize the purchase rate.

[0060] The purchasing behavior analysis unit can analyze comments on social media or review sites in addition to customer purchasing behavior data. For example, the purchasing behavior analysis unit collects comments on social media in addition to customer purchasing behavior data and analyzes them using natural language processing technology. For example, it analyzes posts on Twitter or Facebook to understand customer interests. The purchasing behavior analysis unit can also collect and analyze comments on review sites. For example, it can analyze comments on Amazon reviews or Yelp reviews to identify customer preferences. This allows for a more detailed understanding of customer preferences.

[0061] The purchasing behavior analysis unit monitors customer purchasing behavior in real time and can respond immediately to changes in behavior. The purchasing behavior analysis unit, for example, builds a system that monitors customer behavior on a site in real time and responds immediately to changes in behavior. For example, if a customer views a particular product for a long time, information related to that product can be displayed immediately. The purchasing behavior analysis unit can also display recommended products related to a product that a customer adds to their cart. Furthermore, if a customer frequently views products in a particular category, the purchasing behavior analysis unit can display products in that category on the homepage. This allows for immediate response to changes in customer behavior.

[0062] The purchasing behavior analysis unit can use the emotion estimation function to analyze the emotions of customers during purchasing behavior and adjust the UIUX based on changes in emotions. The purchasing behavior analysis unit, for example, uses the emotion estimation function to analyze the emotions of customers during purchasing behavior in real time and builds a system that adjusts the UIUX based on changes in emotions. For example, if a customer shows positive emotions, content to maintain those emotions can be displayed. In addition, if a customer shows negative emotions, the purchasing behavior analysis unit can also display content to improve those emotions. In addition, the purchasing behavior analysis unit can adjust the design and layout of the UIUX based on the customer's emotion data. This makes it possible to adjust the UIUX based on the customer's emotions.

[0063] The purchasing behavior analysis unit can integrate customer purchasing behavior data with purchasing data from other e-commerce sites and physical stores to analyze a wider range of purchasing behavior patterns. For example, the purchasing behavior analysis unit can integrate customer purchasing behavior data with data from other e-commerce sites to analyze a wider range of purchasing behavior patterns. For example, it can integrate data from Amazon and Rakuten to understand customers' overall purchasing trends. The purchasing behavior analysis unit can also integrate purchasing data from physical stores to analyze online and offline purchasing behavior. For example, it can analyze POS data and customer survey data to understand customer purchasing behavior comprehensively. This makes it possible to analyze a wider range of purchasing behavior patterns.

[0064] The purchasing behavior analysis unit can analyze customer purchasing behavior data based on seasons or events, and provide the optimal UIUX for each season. For example, the purchasing behavior analysis unit can analyze customer purchasing behavior data by season, and provide the optimal UIUX for each season. For example, it can emphasize information about summer products in the summer and winter products in the winter. The purchasing behavior analysis unit can also analyze purchasing behavior data based on events such as sales periods and holiday seasons, and provide the optimal UIUX for each event. For example, it can emphasize special offers and discount information during Black Friday and Christmas sales. This makes it possible to provide the optimal UIUX for each season.

[0065] The purchasing behavior analysis unit can use the emotion estimation function to analyze the emotions of customers while they are browsing products in real time, and provide a UIUX that elicits positive emotions. The purchasing behavior analysis unit, for example, uses the emotion estimation function to analyze the emotions of customers while they are browsing products in real time, and builds a system that provides a UIUX that elicits positive emotions. For example, if a customer is excited, a special offer can be displayed. The purchasing behavior analysis unit can also provide designs and content that have a relaxing effect if the customer is relaxed. The purchasing behavior analysis unit can also adjust the design and layout of the UIUX based on the customer's emotion data. This makes it possible to provide a UIUX that elicits positive emotions in customers.

[0066] The UIUX generation unit can individually customize the UIUX generation algorithm based on customer purchasing behavior data and provide a different UIUX for each customer. The UIUX generation unit builds a system that individually customizes the UIUX generation algorithm based on, for example, customer purchasing behavior data. For example, if a customer places importance on price, the UIUX generation unit can provide a UIUX that emphasizes price information. Also, if a customer places importance on reviews, the UIUX generation unit can provide a UIUX that emphasizes review information. The UIUX generation unit can also adjust the design and layout of the UIUX based on the customer's preferences and interests. This makes it possible to provide a different UIUX for each customer.

[0067] The UIUX generation unit can generate a UIUX by taking into account customer demographic information in addition to customer purchasing behavior data. The UIUX generation unit builds a system that generates a UIUX by taking into account demographic information in addition to customer purchasing behavior data, for example. For example, a UIUX is provided according to age and gender. The UIUX generation unit can also provide a UIUX according to region and occupation. The UIUX generation unit can also adjust the design and layout of the UIUX based on the customer's demographic information. This makes it possible to generate a UIUX by taking into account customer demographic information.

[0068] The UIUX generation unit uses the emotion estimation function to generate a UIUX that corresponds to the customer's emotions, thereby providing a design that is easy to empathize with emotionally. The UIUX generation unit, for example, uses the emotion estimation function to build a system that generates a UIUX that corresponds to the customer's emotions. For example, if a customer shows positive emotions, the UIUX generation unit can provide a design that maintains those emotions. In addition, if a customer shows negative emotions, the UIUX generation unit can also provide a design that improves those emotions. In addition, the UIUX generation unit can provide a design that is easy to empathize with emotionally based on customer emotion data. In this way, it is possible to generate a UIUX that corresponds to the customer's emotions, thereby providing a design that is easy to empathize with emotionally.

[0069] The UIUX generation unit can generate a UIUX optimized for different devices based on customer purchasing behavior data. The UIUX generation unit, for example, builds a system that generates a UIUX optimized for smartphones based on customer purchasing behavior data. For example, it provides layouts and content that fit the screen size of smartphones. The UIUX generation unit can also generate a UIUX optimized for tablets and PCs. The UIUX generation unit can also adjust the UIUX design and layout according to the characteristics of each device. This makes it possible to generate a UIUX optimized for different devices.

[0070] The UIUX generation unit can generate UIUX corresponding to different languages ​​and cultural spheres based on customer purchasing behavior data. The UIUX generation unit, for example, builds a system that generates UIUX corresponding to different languages ​​based on customer purchasing behavior data. For example, it provides UIUX corresponding to languages ​​such as English, French, and Chinese. The UIUX generation unit can also generate UIUX corresponding to different cultural spheres. For example, it provides designs and content tailored to Asian and Western cultures. The UIUX generation unit can also adjust the design and layout of the UIUX according to the characteristics of each language and cultural sphere. This makes it possible to generate UIUX corresponding to different languages ​​and cultural spheres.

[0071] The UIUX generation unit can use the emotion estimation function to provide the optimal UIUX for the time period when the customer is most relaxed. For example, the UIUX generation unit uses the emotion estimation function to identify the time period when the customer is most relaxed, and builds a system that provides the optimal UIUX for that time period. For example, for customers who are relaxing at night or on weekends, it provides a calming design and content that has a relaxing effect. The UIUX generation unit can also adjust the design and layout of the UIUX according to the customer's level of relaxation. This makes it possible to provide the optimal UIUX for the time period when the customer is relaxed.

[0072] The search result optimization unit dynamically changes the order of search results based on customer search behavior data, enabling optimization each time a customer's interests change. The search result optimization unit, for example, analyzes customer search behavior data in real time and builds a system that dynamically changes the order of search results. For example, if a customer shows interest in a particular product category, products in that category are displayed at the top. The search result optimization unit can also optimize the order of search results each time a customer's interests change. For example, it sorts search results to match a product category in which the customer has newly become interested. This makes it possible to optimize the order of search results each time a customer's interests change.

[0073] The search result optimization unit can refer to the search behavior data of other customers in addition to the customer's search behavior data, and refer to search results of customers with similar interests. The search result optimization unit, for example, builds a system that refers to the search behavior data of other customers in addition to the customer's search behavior data, and refers to search results of customers with similar interests. For example, the search results are optimized based on search results of customers who are interested in the same product category. The search result optimization unit can also provide search results tailored to the customer's interests based on the search behavior data of other customers. This makes it possible to refer to search results of customers with similar interests.

[0074] The search result optimization unit can use the emotion estimation function to optimize the order of search results so that customers feel positive emotions toward the search results. The search result optimization unit, for example, uses the emotion estimation function to build a system that optimizes the order of search results so that customers feel positive emotions toward the search results. For example, if a customer is excited, search results that maintain that emotion are displayed at the top. Furthermore, if a customer expresses satisfaction, the search result optimization unit can also provide search results that maintain that emotion. Furthermore, the search result optimization unit can adjust the order of search results based on customer emotion data. In this way, the order can be optimized so that customers feel positive emotions toward the search results.

[0075] The search result optimization unit can provide the optimal sort order of search results for each different category and subcategory based on customer search behavior data. The search result optimization unit, for example, builds a system that provides the optimal sort order of search results for each different category based on customer search behavior data. For example, the search result optimization unit may prioritize price in the home appliance category and design in the fashion category. The search result optimization unit can also provide the optimal sort order of search results for each subcategory. For example, the search results are optimized for each subcategory, such as smartphones, laptops, and tablets. This makes it possible to provide the optimal sort order of search results for each different category and subcategory.

[0076] The search result optimization unit can optimize the order of search results according to the time of day and day of the week based on customer search behavior data. The search result optimization unit, for example, builds a system that optimizes the order of search results according to the time of day and day of the week based on customer search behavior data. For example, business-related products are displayed at the top during the daytime on weekdays, and leisure-related products are displayed at the top on weekends. The search result optimization unit can also adjust the order of search results according to the time of day and day of the week. For example, breakfast-related products are emphasized in the morning, and information about products with a relaxing effect is emphasized in the evening. This makes it possible to optimize the order of search results according to the time of day and day of the week.

[0077] The search result optimization unit can use the emotion estimation function to analyze the emotions of customers when they are viewing search results in real time, and provide a sort order that elicits positive emotions. The search result optimization unit, for example, uses the emotion estimation function to analyze the emotions of customers when they are viewing search results in real time, and builds a system that provides a sort order that elicits positive emotions. For example, if a customer is excited, search results that maintain that emotion are displayed at the top. Furthermore, if a customer expresses satisfaction, the search result optimization unit can also provide search results that maintain that emotion. Furthermore, the search result optimization unit can adjust the sort order of search results based on customer emotion data. In this way, it is possible to analyze the emotions of customers when they are viewing search results in real time, and provide a sort order that elicits positive emotions.

[0078] The product detail optimization unit dynamically changes the information configuration of the product detail page based on customer browsing behavior data, and can optimize it every time the customer's interests change. The product detail optimization unit, for example, analyzes customer browsing behavior data in real time and builds a system that dynamically changes the information configuration of the product detail page. For example, if a customer places importance on specification information, the specification information is displayed in detail. Furthermore, if a customer places importance on images, the product detail optimization unit can also display many images. Furthermore, if a customer places importance on reviews, the product detail optimization unit can also place review information in a prominent position. In this way, the information configuration of the product detail page can be optimized every time the customer's interests change.

[0079] The product detail optimization unit can refer to the browsing behavior data of other customers in addition to the customer's browsing behavior data, and use this data to refer to the information configurations of customers with similar interests. The product detail optimization unit, for example, builds a system that refers to the browsing behavior data of other customers in addition to the customer's browsing behavior data, and uses this data to refer to the information configurations of customers with similar interests. For example, the information configuration is optimized based on the information configurations of customers who are interested in the same product category. The product detail optimization unit can also provide an information configuration tailored to the customer's interests based on the browsing behavior data of other customers. This makes it possible to refer to the information configurations of customers with similar interests.

[0080] The product detail optimization unit can use the emotion estimation function to optimize the information configuration so that customers feel positive emotions toward the product detail page. The product detail optimization unit, for example, uses the emotion estimation function to build a system that optimizes the information configuration so that customers feel positive emotions toward the product detail page. For example, if a customer is excited, the product detail optimization unit can emphasize information to maintain that emotion. Furthermore, if a customer expresses satisfaction, the product detail optimization unit can also provide information to maintain that emotion. Furthermore, the product detail optimization unit can adjust the information configuration based on customer emotion data. This makes it possible to optimize the information configuration so that customers feel positive emotions toward the product detail page.

[0081] The product detail optimization unit can provide information configurations optimized for different devices based on customer browsing behavior data. For example, the product detail optimization unit builds a system that provides information configurations optimized for smartphones based on customer browsing behavior data. For example, it provides layouts and content that match the screen size of smartphones. The product detail optimization unit can also provide information configurations optimized for tablets and PCs. The product detail optimization unit can also adjust the information configuration according to the characteristics of each device. This makes it possible to provide information configurations optimized for different devices.

[0082] The product detail optimization unit can provide information configurations that correspond to different languages ​​and cultural spheres based on customer browsing behavior data. The product detail optimization unit, for example, builds a system that provides information configurations that correspond to different languages ​​based on customer browsing behavior data. For example, it provides information configurations that correspond to languages ​​such as English, French, and Chinese. The product detail optimization unit can also provide information configurations that correspond to different cultural spheres. For example, it provides designs and content that are suited to Asian and Western cultures. The product detail optimization unit can also adjust the information configuration according to the characteristics of each language and cultural sphere. This makes it possible to provide information configurations that correspond to different languages ​​and cultural spheres.

[0083] The product detail optimization unit can use the emotion estimation function to provide the optimal information configuration for the time period when the customer is most relaxed. For example, the product detail optimization unit uses the emotion estimation function to identify the time period when the customer is most relaxed, and builds a system that provides the optimal information configuration for that time period. For example, calm designs and content with a relaxing effect are provided to customers who are relaxing at night or on weekends. The product detail optimization unit can also adjust the information configuration according to the customer's level of relaxation. This makes it possible to provide the optimal information configuration for the time period when the customer is relaxed.

[0084] The information emphasis unit dynamically changes the information to be emphasized based on customer purchasing behavior data, and can optimize the information each time the customer's interests change. The information emphasis unit, for example, analyzes customer purchasing behavior data in real time and builds a system that dynamically changes the information to be emphasized. For example, if a customer places importance on price, price information is emphasized. Furthermore, if a customer places importance on reviews, the information emphasis unit can also emphasize review information. Furthermore, the information emphasis unit can also optimize the information to be emphasized each time the customer's interests change. This makes it possible to optimize the information to be emphasized each time the customer's interests change.

[0085] The information highlighting unit can refer to the purchasing behavior data of other customers in addition to the customer's purchasing behavior data, and refer to the emphasized information of customers with similar interests. The information highlighting unit, for example, builds a system that refers to the purchasing behavior data of other customers in addition to the customer's purchasing behavior data, and refers to the emphasized information of customers with similar interests. For example, information is optimized based on the emphasized information of customers who are interested in the same product category. The information highlighting unit can also provide emphasized information tailored to the customer's interests based on the purchasing behavior data of other customers. This makes it possible to refer to the emphasized information of customers with similar interests.

[0086] The information emphasis unit can use the emotion estimation function to optimize information so that customers feel positive emotions toward the emphasized information. For example, the information emphasis unit uses the emotion estimation function to build a system that optimizes information so that customers feel positive emotions toward the emphasized information. For example, if a customer is excited, the information emphasis unit emphasizes information to maintain that emotion. Furthermore, if a customer expresses satisfaction, the information emphasis unit can also provide information to maintain that emotion. Furthermore, the information emphasis unit can adjust information based on customer emotion data. This makes it possible to optimize information so that customers feel positive emotions toward the emphasized information.

[0087] The information highlighting unit can provide information to be emphasized for different categories and subcategories based on customer purchasing behavior data. The information highlighting unit, for example, builds a system that provides information to be emphasized for different categories based on customer purchasing behavior data. For example, it emphasizes specification information in the home appliance category and design information in the fashion category. The information highlighting unit can also provide information to be emphasized for each subcategory. For example, it optimizes information for each subcategory, such as smartphones, laptops, and tablets. This makes it possible to provide information to be emphasized for each category and subcategory.

[0088] The information highlighting unit can optimize the information to be emphasized according to the time of day and day of the week based on customer purchasing behavior data. The information highlighting unit, for example, builds a system that optimizes the information to be emphasized according to the time of day and day of the week based on customer purchasing behavior data. For example, business-related information is emphasized during the daytime on weekdays, and leisure-related information is emphasized on weekends. The information highlighting unit can also adjust the information according to the time of day and day of the week. For example, breakfast-related information is emphasized in the morning, and information that has a relaxing effect is emphasized in the evening. This makes it possible to optimize the information to be emphasized according to the time of day and day of the week.

[0089] The information emphasis unit can use the emotion estimation function to analyze the emotion of a customer when viewing the highlighted information in real time, and provide information to elicit positive emotions. For example, the information emphasis unit can use the emotion estimation function to analyze the emotion of a customer when viewing the highlighted information in real time, and build a system to provide information to elicit positive emotions. For example, if a customer is excited, the information emphasis unit can emphasize information to maintain that emotion. Furthermore, if a customer expresses satisfaction, the information emphasis unit can also provide information to maintain that emotion. Furthermore, the information emphasis unit can adjust information based on the customer's emotion data. This makes it possible to analyze the emotion of a customer when viewing the highlighted information in real time, and provide information to elicit positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The e-commerce site system can also estimate a customer's health status based on their purchasing behavior data and provide a UIUX that matches their health status. For example, if a customer frequently searches for health-related products, it can highlight health-related information and products. If a customer purchases fitness-related products, it can provide fitness-related content. If a customer enters the results of a health check, it can suggest the most suitable products based on the results. This makes it possible to provide a UIUX that matches the customer's health status.

[0092] The e-commerce site system can also estimate a customer's lifestyle based on their purchasing behavior data and provide a UIUX that suits their lifestyle. For example, if a customer frequently purchases outdoor-related products, it can emphasize outdoor information and products. If a customer frequently purchases household goods, it can provide content related to household goods. If a customer frequently purchases travel-related products, it can suggest travel-related information and products. This makes it possible to provide a UIUX that suits each customer's lifestyle.

[0093] The EC site system can also infer a customer's hobbies and interests based on their purchasing behavior data, and provide a UIUX that matches those hobbies and interests. For example, if a customer frequently purchases music-related products, it can emphasize music-related information and products. If a customer purchases sports-related products, it can provide sports-related content. If a customer purchases art-related products, it can suggest art-related information and products. This makes it possible to provide a UIUX that matches a customer's hobbies and interests.

[0094] The e-commerce site system can also estimate a customer's lifestyle based on their purchasing behavior data and provide a UIUX that suits their lifestyle. For example, if a customer frequently visits the site at night, it can provide designs and content that are suitable for the night. If a customer visits the site in the morning, it can emphasize information and products that are suitable for the morning. If a customer visits the site on the weekend, it can provide offers and content that are suitable for the weekend. This makes it possible to provide a UIUX that suits the customer's lifestyle.

[0095] The EC site system can also estimate a customer's purchasing intent based on their purchasing behavior data and provide a UIUX that matches their purchasing intent. For example, if a customer shows a high level of purchasing intent, it can highlight special offers and discount information. On the other hand, if a customer shows a low level of purchasing intent, it can provide content to stimulate their purchasing intent. Furthermore, if a customer shows a high interest in a particular product, it can suggest information and products related to that product. This makes it possible to provide a UIUX that matches a customer's purchasing intent.

[0096] E-commerce site systems can also estimate customer emotions based on customer purchasing behavior data and provide UIUX that reflects those emotions. For example, if a customer expresses positive emotions, they can provide designs and content to maintain those emotions. If a customer expresses negative emotions, they can highlight information or products to improve those emotions. If a customer is excited, they can provide special offers or discount information to maintain those emotions. This allows them to provide UIUX that reflects customer emotions.

[0097] The e-commerce site system can also estimate a customer's stress level based on their purchasing behavior data and provide a UIUX that corresponds to the stress level. For example, if a customer indicates a high stress level, it can provide designs and content that have a relaxing effect. If a customer indicates a low stress level, it can emphasize information and products to help maintain that state. Furthermore, if a customer is feeling stressed, it can provide special offers and discount information to improve their emotions. In this way, it is possible to provide a UIUX that corresponds to the customer's stress level.

[0098] The e-commerce site system can also estimate a customer's happiness level based on their purchasing behavior data and provide a UIUX that corresponds to their happiness level. For example, if a customer indicates a high level of happiness, it can provide designs and content to maintain that emotion. If a customer indicates a low level of happiness, it can highlight information and products to improve that emotion. Furthermore, if a customer is feeling happy, it can provide special offers and discount information to maintain that emotion. This makes it possible to provide a UIUX that corresponds to the customer's happiness level.

[0099] The e-commerce site system can also estimate a customer's level of excitement based on their purchasing behavior data and provide a UI / UX that corresponds to their level of excitement. For example, if a customer shows a high level of excitement, it can provide designs and content that maintain that emotion. If a customer shows a low level of excitement, it can emphasize information or products that will elicit that emotion. If a customer is excited, it can also provide special offers or discount information to maintain that emotion. This allows it to provide a UI / UX that corresponds to the customer's level of excitement.

[0100] The e-commerce site system can also estimate customer satisfaction levels based on customer purchasing behavior data and provide UIUX that reflects that level of satisfaction. For example, if a customer indicates high satisfaction, it can provide designs and content to maintain that level of satisfaction. If a customer indicates low satisfaction, it can highlight information or products to improve that level of satisfaction. Furthermore, if a customer is satisfied, it can provide special offers or discount information to maintain that level of satisfaction. This makes it possible to provide UIUX that reflects customer satisfaction.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The purchasing behavior analysis unit analyzes customer purchasing behavior. For example, it collects the customer's past purchase history and behavioral data on the site, and analyzes what products they searched for, which pages they viewed, and what products they purchased. It can also identify customer preferences and interests based on customer behavioral data. Step 2: The UIUX generation unit generates an optimal UIUX based on the results of the analysis by the purchasing behavior analysis unit. For example, if the customer prioritizes price, it will emphasize price information, and if the customer prioritizes reviews, it will emphasize review information. It can also optimize the order of search results and the information structure on product detail pages based on customer interests. Step 3: The search result optimization unit optimizes the order of search results based on the UIUX generated by the UIUX generation unit. For example, products similar to those purchased or viewed by the customer are displayed at the top. Also, if a customer has a preference for a specific brand or price range, the search results can be sorted accordingly. Step 4: The product details optimization unit optimizes the information configuration of the product details page based on the UIUX generated by the UIUX generation unit. For example, if a customer prioritizes specs, it displays detailed spec information, and if they prioritize images, it displays many images. Also, if a customer prioritizes reviews, it can place review information in a prominent position. Step 5: The information highlighting unit determines the information to be highlighted based on the UIUX generated by the UIUX generation unit. For example, if the customer places importance on price, the price information can be displayed in a large size and discount information can be highlighted. Also, if the customer places importance on the brand, the brand logo and brand story can be placed in a prominent position.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 purchasing behavior analysis unit that analyzes customer purchasing behavior; a UIUX generation unit that generates an optimal UIUX based on the results of the analysis by the purchasing behavior analysis unit; a search result optimization unit that optimizes the order of search results based on the UIUX generated by the UIUX generation unit; a product detail optimization unit that optimizes the information configuration of the product detail page based on the UIUX generated by the UIUX generation unit; an information highlighting unit that determines information to be highlighted based on the UIUX generated by the UIUX generating unit; A system characterized by:

2. The purchasing behavior analysis unit In addition to the customer's purchasing behavior data, analyze their social media posts or comments on review sites.

2. The system of claim 1.

3. The UIUX generation unit The UIUX generation algorithm is individually customized based on the purchasing behavior data of the customer, and a different UIUX is provided for each customer.

2. The system of claim 1.

4. The search result optimization unit Dynamically changing the order of the search results based on the customer's search behavior data, and optimizing the order whenever the customer's interests change.

2. The system of claim 1.

5. The product detail optimization unit The information configuration of the product detail page is dynamically changed based on the browsing behavior data of the customer, and the optimization is performed whenever the customer's interests change.

2. The system of claim 1.

6. The information highlighting unit The information to be emphasized is dynamically changed based on the purchasing behavior data of the customer, and the optimization is performed whenever the interest of the customer changes.

2. The system of claim 1.

7. The purchasing behavior analysis unit Using emotion estimation function, analyze the emotion of the customer during the purchasing behavior and adjust the UIUX based on the emotion change.

2. The system of claim 1.

8. The UIUX generation unit Using emotion estimation function, the UI / UX is generated according to the customer's emotions, providing a design that is easy to empathize with emotionally.

2. The system of claim 1.

Images