system

Abstract

The system according to this embodiment aims to analyze a customer's purchase history and browsing history and recommend the most suitable products. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a presentation unit, and a support unit. The collection unit collects the customer's purchase history or browsing history. The analysis unit analyzes the data collected by the collection unit. The presentation unit presents products to the customer based on the analysis results by the analysis unit. The support unit supports one of the following procedures related to the products presented by the presentation unit: purchase procedure, return procedure, or exchange procedure.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully achieved to effectively utilize a customer's purchase history and browsing history to recommend products, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a customer's purchase history and browsing history and recommend optimal products.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a presentation unit, and a support unit. The collection unit collects the customer's purchase history or browsing history. The analysis unit analyzes the data collected by the collection unit. The presentation unit presents products to the customer based on the analysis results from the analysis unit. The support unit supports one of the following procedures related to the products presented by the presentation unit: purchase procedure, return procedure, or exchange procedure. [Effects of the Invention]

[0007] The system according to this embodiment can analyze a customer's purchase history and browsing history and recommend the most suitable products. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI assistant for product purchase support on a shopping site according to an embodiment of the present invention is a system that presents recommended products and sale information based on the customer's purchase history and browsing history. This system collects the customer's purchase history or browsing history, analyzes the collected data, and presents products to recommend to the customer. Furthermore, it supports at least one of the purchase procedure, return procedure, or exchange procedure for the presented products. This mechanism allows customers to easily compare products and check detailed information and reviews. It also enables smooth purchase procedures and return / exchange procedures. For example, it collects information about products that the customer has added to their cart or products that have been purchased. This information is collected by the collection unit. Next, it analyzes the collected data. The analysis unit analyzes the collected data to identify the customer's purchasing patterns and interests. For example, it analyzes whether there are products that the customer often purchases when they are on sale. Based on the analysis results, it presents products to recommend to the customer. The presentation unit recommends the most suitable products to the customer based on the analysis results. For example, it presents information about products on sale, detailed product information, product review information, etc. Furthermore, it supports at least one of the purchase procedure, return procedure, or exchange procedure for the presented products. The support department assists with these procedures via chat. For example, it provides necessary information to customers as they proceed with the purchase process, ensuring a smooth transaction. This system allows customers to easily compare products and view detailed information and reviews. It also streamlines the purchase process and return / exchange procedures. For instance, if a customer has a problem with a purchased product, the chat-based return support allows for a quick resolution. Furthermore, the data collection department can estimate the user's emotions and adjust the timing of collecting purchase and browsing history based on these estimated emotions. This allows for the recommendation of optimal products at the moment when the customer's purchase intent is highest. As a result, the AI ​​assistant supporting product purchases on shopping sites can recommend appropriate products based on the customer's purchase and browsing history and support the purchase, return, and exchange processes.

[0029] The AI ​​assistant for supporting product purchases on a shopping site according to this embodiment comprises a collection unit, an analysis unit, a presentation unit, and a support unit. The collection unit collects the customer's purchase history or browsing history. For example, the collection unit collects information about products that the customer has added to their cart or products that have been purchased. The collection unit can also collect information such as the type of products the customer has viewed, the date and time of viewing, and the number of times they have been viewed. For example, the collection unit collects information about products that the customer has added to their cart in real time. The collection unit can also periodically collect information about products that the customer has purchased. Furthermore, the collection unit can save information about products that the customer has viewed as history and use it for analysis later. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to identify the customer's purchasing patterns and interests. The analysis unit can analyze whether there are products that the customer often purchases when they are on sale. For example, the analysis unit can identify the customer's interest in a specific product category based on their purchase history. The analysis unit can also analyze the trends of products that the customer is interested in based on their browsing history. Furthermore, the analysis unit can analyze customer purchasing patterns and identify sale items. The presentation unit presents recommended products to the customer based on the analysis results from the analysis unit. The presentation unit, for example, presents information about sale items. The presentation unit can also present detailed product information and reviews. The presentation unit presents products based on the analysis results to recommend the most suitable product to the customer. The presentation unit can also present sale information and detailed product information in real time. Furthermore, the presentation unit can recommend relevant products based on the customer's interests. The support unit supports at least one of the purchase, return, or exchange procedures for products presented by the presentation unit. The support unit supports these procedures, for example, in a chat format. The support unit provides necessary information to customers as they proceed with the purchase process, enabling them to proceed smoothly. The support unit supports the return process in a chat format, for example, if there is a problem with the product the customer purchased.Furthermore, the support department can guide customers through the necessary procedures if they wish to exchange an item. In addition, the support department can provide support until the customer completes the purchase process. As a result, the AI ​​assistant for product purchase support on the shopping site according to this embodiment can recommend appropriate products based on the customer's purchase history and browsing history, and support the purchase process and return / exchange procedures.

[0030] The data collection unit collects customer purchase or browsing history. For example, it collects information about items added to a customer's cart and items purchased. Specifically, it can collect information about items added to a customer's cart in real time and information about purchased items periodically. Furthermore, the data collection unit can also collect information such as the types of products viewed by customers, the date and time of viewing, and the number of viewings. This allows for a detailed understanding of customer interests and purchasing patterns. The data collection unit saves information about products viewed by customers as a history, which can then be used for analysis. For example, if a customer frequently views products in a particular category, it can be determined that they have a high level of interest in that category. The data collection unit can also identify products that customers are likely to repurchase based on information about products they have previously purchased. This allows the data collection unit to track customer purchasing behavior in detail and provide foundational data for recommending the most suitable products to individual customers. Furthermore, the data collection unit centrally manages customer data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and presentation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to identify customer purchasing patterns and interests. Specifically, it can analyze whether there are products that customers tend to purchase when they are on sale. Based on the customer's purchase history, the analysis unit can identify interests in specific product categories. It can also analyze trends in products of interest based on the customer's browsing history. Furthermore, the analysis unit can analyze customer purchasing patterns and identify products that are on sale. In AI-based analysis, machine learning algorithms are used to analyze customer behavior data in detail and extract patterns and trends. For example, clustering methods can be used to group customers based on different purchasing patterns and recommend the most suitable products for each group. It is also possible to use deep learning-based image recognition technology to identify designs, colors, brands, etc., that customers are interested in from images of products they have viewed. As a result, the analysis unit can quickly and accurately analyze the collected data and gain a detailed understanding of customer interests and purchasing patterns. Furthermore, the analysis unit can also use historical data and statistical information to perform long-term trend analysis and predictions. For example, based on past sales data, it's possible to predict sales fluctuations during specific periods and develop effective marketing strategies. Furthermore, anomaly detection algorithms can be used to detect unusual purchasing patterns and abnormal data, enabling early intervention. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term strategy planning and anomaly detection, improving the overall reliability and effectiveness of the system.

[0032] The display unit presents products recommended to the customer based on the analysis results from the analysis unit. Specifically, it presents information about products on sale. The display unit can also present detailed product information and reviews. For example, it presents products based on the analysis results to recommend the most suitable product to the customer. It can also present sale information and detailed product information in real time. Furthermore, the display unit can recommend related products based on the customer's interests. Based on the customer's purchase history and browsing history, the display unit selects the most suitable products for each individual customer and provides personalized recommendations. For example, it can attract the customer's interest by recommending products similar to products the customer has purchased in the past or products in the same category. It also supports the customer's purchase decision by presenting review information and ratings of products the customer has viewed. The display unit can use AI to analyze the customer's interests and purchasing patterns and make recommendations at the optimal time. For example, if a customer views a particular product multiple times, it will be determined that the customer has a high level of interest in that product, and will promote purchase by presenting sale information and discount coupons. Furthermore, the display unit can provide customers with the latest information based on data updated in real time. This allows the display unit to quickly provide customers with appropriate product information and increase their purchase intent. In addition, the display unit can collect customer feedback and continuously improve the accuracy and effectiveness of recommendations. For example, if a customer purchases a recommended product, that data is fed back to the analysis unit and reflected in the next recommendation. Also, if a customer does not show interest in a recommended product, the reason can be analyzed and used to improve the next recommendation. In this way, the display unit can always provide customers with the most optimal product information and improve the purchasing experience.

[0033] The support department assists with at least one of the following procedures related to products presented by the presentation department: purchase, return, or exchange. Specifically, it provides support for these procedures via chat. The support department provides necessary information to customers as they proceed with the purchase process, ensuring a smooth process. For example, if a customer has a problem with a purchased product, the support department assists with the return process via chat. It can also guide customers through the necessary procedures if they wish to exchange the product. Furthermore, the support department can support customers until they complete the purchase process. The support department utilizes AI-powered chatbots to quickly respond to customer questions and problems. For example, if a customer has a question during the purchase process, the chatbot can provide an immediate answer, ensuring a smooth process. It also handles questions regarding returns and exchanges, guiding customers through the necessary procedures. The support department can collect customer feedback and continuously improve the quality of its services. For example, it can evaluate whether customers were satisfied with the chatbot's response and use the results to improve the quality of service. It can also analyze what kind of support customers are seeking for specific issues and provide more appropriate support. This allows the support department to provide customers with quick and appropriate support, improving the purchasing experience. Furthermore, the support department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only chat but also voice calls, email, and SMS in combination. This allows the support department to provide prompt and reliable support to customers, improving the purchasing experience.

[0034] The display unit can present information about sale items. For example, the display unit can identify sale items and present that information to the customer. The display unit can prioritize displaying items that are on sale or items with high discount rates. For example, the display unit can update sale items in real time to provide customers with the latest information. The display unit can also customize sale information based on customer interests. For example, the display unit can recommend sale items based on information about items the customer has previously purchased or viewed. This allows the display unit to offer attractive products to customers by presenting sale item information. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input sale item information into a generating AI, which can analyze the sale information and recommend the most suitable products to the customer.

[0035] The analysis unit can analyze which products customers frequently purchase when they go on sale. For example, the analysis unit can identify sale items based on the customer's purchase history. The analysis unit can identify sale items by analyzing data on products the customer has purchased in the past. For example, the analysis unit can analyze which products customers frequently purchase during sale periods. The analysis unit can also identify which products customers frequently purchase at a specific discount rate. If there are specific products that customers frequently purchase when they go on sale compared to other products, the presentation unit will prioritize displaying sale information for those specific products over other products. For example, the presentation unit will prioritize displaying specific products based on data on products the customer has purchased in the past. By prioritizing the display of sale items, the presentation unit can increase the customer's purchasing intent. For example, the presentation unit will prioritize displaying specific products based on data on products the customer has purchased in the past. Furthermore, by prioritizing the display of sale items, the presentation unit can increase the customer's purchasing intent. This allows the presentation unit to prioritize displaying products that customers are likely to purchase, thereby increasing their purchasing intent. Some or all of the above-described processes in the analysis unit and presentation unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the customer's purchase history into a generating AI, the generating AI can identify sale items, and the presentation unit can present that information to the customer.

[0036] The presentation unit can present either detailed product information or product review information based on the analysis results from the analysis unit. For example, the presentation unit can present detailed product information to the customer. The presentation unit can provide detailed information such as product specifications and usage instructions. For example, the presentation unit can present product review information to the customer. The presentation unit can provide information such as user reviews and rating scores. For example, the presentation unit can update detailed product information and review information in real time to provide the customer with the latest information. The presentation unit can also customize relevant product information based on the customer's interests. For example, the presentation unit can recommend relevant product information based on information about products the customer has previously purchased or viewed. This can support purchasing decisions by providing customers with detailed product information and review information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input detailed product information and review information into a generating AI, which can analyze that information and recommend the most suitable product information to the customer.

[0037] The data collection unit can collect information about products that customers add to their cart or products that customers purchase. For example, the data collection unit can collect information about products that customers add to their cart in real time. The data collection unit can also collect information about products that customers purchase periodically. For example, the data collection unit can save information about products that customers add to their cart as a history and use it for analysis later. The data collection unit can also save information about products that customers purchase as a history and use it for analysis later. This allows the collection unit to collect appropriate product information based on the customer's cart and purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about products that customers add to their cart into a generating AI, which can analyze that information and recommend the most suitable products to the customer.

[0038] The support department can provide support for at least one of the following procedures: purchase, return, or exchange, via chat. For example, the support department can provide necessary information to customers as they proceed with the purchase process, ensuring a smooth process. For example, if a customer has a problem with a purchased item, the support department can provide support for the return process via chat. The support department can also guide customers through the necessary procedures if they wish to exchange an item. Furthermore, the support department can provide support until the customer completes the purchase process. This allows customers to complete the process smoothly through chat-based support. Some or all of the above processes performed by the support department may be carried out using AI, or not. For example, the support department can input customer inquiries into a generating AI, which can then generate appropriate answers and provide them to the customer.

[0039] The data collection unit can analyze a user's past purchase history and select an efficient collection method. For example, the data collection unit can identify product categories that a user frequently purchases and prioritize collecting history related to those categories. If a user tends to make purchases during specific time periods, the data collection unit can concentrate collection during those times. If a user frequently makes purchases during sales periods, the data collection unit can focus on collecting history from those sales periods. This enables efficient data collection by analyzing past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's past purchase history into a generating AI, which can then select an efficient collection method.

[0040] The data collection unit can filter the collected purchase history based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the data collection unit can prioritize collecting the purchase history of products related to that hobby. If a user moves, the data collection unit can collect the purchase history of products related to the new address. If a user plans to attend a specific event, the data collection unit can collect the purchase history of products related to that event. This allows for the collection of highly relevant data based on the user's lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location when collecting purchase history. For example, if the user lives in a specific region, the data collection unit can prioritize the collection of popular products in that region. If the user is traveling, the data collection unit can prioritize the collection of purchase history at the travel destination. If the user frequently uses a specific store, the data collection unit can prioritize the collection of purchase history at that store. This allows for the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant purchase history.

[0042] The data collection unit can analyze a user's social media activity and collect relevant history when collecting purchase history. For example, if a user mentions a specific product on social media, the data collection unit can collect history related to that product. The data collection unit can prioritize collecting product history of brands that the user follows on social media. The data collection unit can collect product history related to groups and communities that the user participates in on social media. This allows for the collection of highly relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on the user's social media activity into a generating AI, which can then collect relevant history.

[0043] The analysis unit can improve the accuracy of its analysis by referring to the user's past purchasing patterns during the analysis process. For example, the analysis unit can analyze patterns of products the user has purchased in the past to improve the accuracy of recommending similar products. If the user prefers a particular brand, the analysis unit can prioritize the analysis of products from that brand. If the user tends to purchase products in a particular price range, the analysis unit can focus on analyzing products in that price range. This improves the accuracy of the analysis by referring to past purchasing patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past purchasing patterns into a generating AI, which can then improve the accuracy of the analysis.

[0044] The analysis unit can customize its analysis methods based on the user's current living situation during analysis. For example, if the user has started a new job, the analysis unit will prioritize analyzing products related to that job. If the user has moved, the analysis unit can analyze products related to the new address. If the user has plans to attend a specific event, the analysis unit can analyze products related to that event. By customizing the analysis method based on the user's living situation, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's living situation into a generating AI, which can then customize the analysis method.

[0045] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, if the user lives in a specific region, the analysis unit can prioritize analyzing products popular in that region. If the user is traveling, the analysis unit can analyze their purchasing patterns at their travel destination. If the user frequently uses a specific store, the analysis unit can analyze their purchasing patterns at that store. By considering geographical location information, the analysis unit can provide highly relevant analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during the analysis process. For example, if a user mentions a specific product on social media, the analysis unit will analyze data related to that product. The analysis unit can prioritize the analysis of products from brands that the user follows on social media. The analysis unit can analyze data on products related to groups and communities that the user participates in on social media. This improves the accuracy of the analysis by referring to social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data on the user's social media activity into a generating AI, which can then improve the accuracy of the analysis.

[0047] The display unit can adjust the level of detail in recommendations based on the importance of the product at the time of display. For example, for expensive products, the display unit can provide recommendations that include detailed information and reviews. For everyday products, the display unit can provide concise information. For new or limited-edition products, the display unit can provide recommendations that include special promotional information. This allows for the provision of appropriate information by adjusting the level of detail in recommendations according to the importance of the product. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input product importance data into a generating AI, which can then adjust the level of detail in the recommendations.

[0048] The display unit can apply different recommendation algorithms depending on the product category at the time of display. For example, in the case of electronic devices, the display unit can provide recommendations that emphasize technical specifications and reviews. In the case of fashion items, the display unit can provide recommendations that include trend information and styling suggestions. In the case of food products, the display unit can provide recommendations that include nutritional information and recipe suggestions. This allows for the provision of optimal recommendations according to the product category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input product category data into a generating AI, and the generating AI can apply different recommendation algorithms.

[0049] The display unit can determine the recommendation priority based on the product submission date at the time of display. For example, the display unit can prioritize recommendations for new products or limited-edition products. The display unit can prioritize recommendations for products during sales periods. The display unit can recommend seasonal products or event-related products at the appropriate time. This allows information to be provided at the appropriate time by determining the recommendation priority based on the product submission date. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input product submission date data into a generating AI, and the generating AI can determine the recommendation priority.

[0050] The display unit can adjust the order of recommendations based on the relevance of the products at the time of display. For example, the display unit can prioritize recommending products that are highly relevant to products the user has previously purchased. The display unit can prioritize recommending products that are highly relevant to products the user has viewed. The display unit can prioritize recommending products that are highly relevant to products the user has added to their cart. In this way, by adjusting the order of recommendations based on the relevance of the products, more relevant information can be provided. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input product relevance data into a generating AI, and the generating AI can adjust the order of recommendations.

[0051] The support department can analyze a user's past purchasing behavior to select the most suitable support method during support. For example, the support department can prioritize providing support methods that the user has frequently used in the past. The support department can refer to the support history of products the user has previously purchased and provide support for similar products. The support department can prioritize providing support channels (chat, phone, etc.) that the user has used in the past. This allows the support department to provide the most suitable support method by analyzing past purchasing behavior. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input data on the user's past purchasing behavior into a generating AI, which can then select the most suitable support method.

[0052] The support unit can customize the means of support provided based on the user's current living situation. For example, if a user starts a new job, the support unit can prioritize support for products related to that job. If a user moves, the support unit can provide support for products related to their new address. If a user plans to attend a specific event, the support unit can provide support for products related to that event. By customizing the means of support based on the user's living situation, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input user living situation data into a generating AI, which can then customize the means of support.

[0053] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user lives in a specific region, the support unit can provide support options available in that region. If the user is traveling, the support unit can provide support options available at the travel destination. If the user frequently uses a specific store, the support unit can provide support options at that store. In this way, the optimal support method can be provided by considering geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's geographical location information into a generating AI, which can then select the optimal support method.

[0054] The support department can provide optimal support by analyzing the user's social media activity during support sessions. For example, if a user mentions a specific product on social media, the support department can provide support related to that product. The support department can provide support for products from brands that the user follows on social media. The support department can provide support for products related to groups and communities that the user participates in on social media. In this way, optimal support can be provided by analyzing social media activity. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input data on the user's social media activity into a generating AI, which can then provide optimal support.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] An AI assistant for supporting product purchases on shopping websites can provide incentives to increase customer purchasing intent based on their purchase and browsing history. For example, if a customer views a specific product multiple times, a discount coupon for that product can be offered. Also, if a customer frequently purchases products in a specific category, points can be awarded for products in that category. Furthermore, if a customer spends above a certain amount during a sale, a coupon that can be used for their next purchase can be offered. This can increase customer purchasing intent and encourage repeat purchases. The provision of incentives may be done using AI or not. For example, data related to incentive provision can be input into a generating AI, which can then determine the optimal incentive.

[0057] An AI assistant for supporting product purchases on shopping websites can predict customer purchasing behavior based on their purchase and browsing history, and secure inventory in advance. For example, if a customer frequently purchases a particular product, the AI ​​can secure inventory of that product in advance. Also, if a customer tends to purchase a particular product during a specific season, the AI ​​can increase inventory for that season. Furthermore, if a customer purchases products related to a specific event, the AI ​​can secure inventory for that event. This prevents stockouts and provides customers with a smooth purchasing experience. Inventory securing can be done using AI or not. For example, data on inventory securing can be input into a generating AI, which can then determine the optimal inventory quantity.

[0058] An AI assistant for supporting product purchases on shopping websites can predict customer purchasing behavior based on their purchase and browsing history and provide personalized advertisements. For example, if a customer frequently purchases products from a particular brand, the AI ​​can provide advertisements for new products and sales from that brand. Similarly, if a customer frequently browses products in a specific category, the AI ​​can provide advertisements related to those products. Furthermore, if a customer purchases products related to a specific event, the AI ​​can provide advertisements related to that event. This allows for the provision of highly relevant advertisements to customers, thereby increasing their purchase intent. Advertisement delivery may be performed using AI or without AI. For example, data related to advertisement delivery can be input into a generating AI, which can then determine the most appropriate advertisement.

[0059] An AI assistant for supporting product purchases on a shopping site can predict customer purchasing behavior based on their purchase and browsing history and provide personalized newsletters. For example, if a customer frequently purchases products from a particular brand, a newsletter containing information on new products and sales from that brand can be provided. Similarly, if a customer frequently browses products in a specific category, a newsletter related to products in that category can be provided. Furthermore, if a customer purchases products related to a specific event, a newsletter related to that event can be provided. This allows for the provision of highly relevant information to customers, thereby increasing their purchasing intent. The delivery of newsletters may be done using AI or not. For example, data related to newsletter delivery can be input into a generating AI, which can then determine the most suitable newsletter.

[0060] An AI assistant for supporting product purchases on shopping websites can predict customer purchasing behavior based on their purchase and browsing history, and provide personalized gift suggestions. For example, if a customer frequently purchases products from a particular brand, products from that brand can be suggested as gifts. Similarly, if a customer frequently browses products in a particular category, products from that category can be suggested as gifts. Furthermore, if a customer purchases products related to a specific event, products related to that event can be suggested as gifts. This allows for highly relevant gift suggestions to customers, thereby increasing their purchasing intent. Gift suggestions may be made using AI or not. For example, data related to gift suggestions can be input into a generating AI, which can then determine the most suitable gift.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects the customer's purchase or browsing history. For example, the data collection unit collects information about products the customer has added to their cart or products they have purchased. The data collection unit can also collect information such as the type of products the customer has viewed, the date and time of viewing, and the number of times they have viewed. For example, the data collection unit collects information about products the customer has added to their cart in real time. The data collection unit can also periodically collect information about products the customer has purchased. Furthermore, the data collection unit can save information about products the customer has viewed as a history and use it for analysis later. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data to identify, for example, customer purchasing patterns and interests. The analysis unit can analyze whether there are products that customers tend to purchase when they are on sale. For example, the analysis unit can identify customer interests in specific product categories based on their purchase history. The analysis unit can also analyze trends in products that customers are interested in based on their browsing history. Furthermore, the analysis unit can analyze customer purchasing patterns and identify products that are on sale. Step 3: The display unit presents products to the customer based on the analysis results from the analysis unit. The display unit may, for example, present information about products on sale. The display unit can also present detailed product information and reviews. For example, the display unit presents products based on the analysis results to recommend the most suitable product to the customer. The display unit can also present sale information and detailed product information in real time. Furthermore, the display unit can recommend related products based on the customer's interests. Step 4: The support department will assist with at least one of the following procedures related to the products presented by the presentation department: purchase, return, or exchange. The support department will provide support for these procedures, for example, via chat. The support department will provide the necessary information to help customers proceed with the purchase process smoothly. For example, if there is a problem with the product the customer purchased, the support department will assist with the return process via chat. The support department can also guide customers through the necessary procedures if they wish to exchange the product. Furthermore, the support department can provide support until the customer completes the purchase process.

[0063] (Example of form 2) An AI assistant for product purchase support on a shopping site according to an embodiment of the present invention is a system that presents recommended products and sale information based on the customer's purchase history and browsing history. This system collects the customer's purchase history or browsing history, analyzes the collected data, and presents products to recommend to the customer. Furthermore, it supports at least one of the purchase procedure, return procedure, or exchange procedure for the presented products. This mechanism allows customers to easily compare products and check detailed information and reviews. It also enables smooth purchase procedures and return / exchange procedures. For example, it collects information about products that the customer has added to their cart or products that have been purchased. This information is collected by the collection unit. Next, it analyzes the collected data. The analysis unit analyzes the collected data to identify the customer's purchasing patterns and interests. For example, it analyzes whether there are products that the customer often purchases when they are on sale. Based on the analysis results, it presents products to recommend to the customer. The presentation unit recommends the most suitable products to the customer based on the analysis results. For example, it presents information about products on sale, detailed product information, product review information, etc. Furthermore, it supports at least one of the purchase procedure, return procedure, or exchange procedure for the presented products. The support department assists with these procedures via chat. For example, it provides necessary information to customers as they proceed with the purchase process, ensuring a smooth transaction. This system allows customers to easily compare products and view detailed information and reviews. It also streamlines the purchase process and return / exchange procedures. For instance, if a customer has a problem with a purchased product, the chat-based return support allows for a quick resolution. Furthermore, the data collection department can estimate the user's emotions and adjust the timing of collecting purchase and browsing history based on these estimated emotions. This allows for the recommendation of optimal products at the moment when the customer's purchase intent is highest. As a result, the AI ​​assistant supporting product purchases on shopping sites can recommend appropriate products based on the customer's purchase and browsing history and support the purchase, return, and exchange processes.

[0064] The AI ​​assistant for supporting product purchases on a shopping site according to this embodiment comprises a collection unit, an analysis unit, a presentation unit, and a support unit. The collection unit collects the customer's purchase history or browsing history. For example, the collection unit collects information about products that the customer has added to their cart or products that have been purchased. The collection unit can also collect information such as the type of products the customer has viewed, the date and time of viewing, and the number of times they have been viewed. For example, the collection unit collects information about products that the customer has added to their cart in real time. The collection unit can also periodically collect information about products that the customer has purchased. Furthermore, the collection unit can save information about products that the customer has viewed as history and use it for analysis later. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to identify the customer's purchasing patterns and interests. The analysis unit can analyze whether there are products that the customer often purchases when they are on sale. For example, the analysis unit can identify the customer's interest in a specific product category based on their purchase history. The analysis unit can also analyze the trends of products that the customer is interested in based on their browsing history. Furthermore, the analysis unit can analyze customer purchasing patterns and identify sale items. The presentation unit presents recommended products to the customer based on the analysis results from the analysis unit. The presentation unit, for example, presents information about sale items. The presentation unit can also present detailed product information and reviews. The presentation unit presents products based on the analysis results to recommend the most suitable product to the customer. The presentation unit can also present sale information and detailed product information in real time. Furthermore, the presentation unit can recommend relevant products based on the customer's interests. The support unit supports at least one of the purchase, return, or exchange procedures for products presented by the presentation unit. The support unit supports these procedures, for example, in a chat format. The support unit provides necessary information to customers as they proceed with the purchase process, enabling them to proceed smoothly. The support unit supports the return process in a chat format, for example, if there is a problem with the product the customer purchased.Furthermore, the support department can guide customers through the necessary procedures if they wish to exchange an item. In addition, the support department can provide support until the customer completes the purchase process. As a result, the AI ​​assistant for product purchase support on the shopping site according to this embodiment can recommend appropriate products based on the customer's purchase history and browsing history, and support the purchase process and return / exchange procedures.

[0065] The data collection unit collects customer purchase or browsing history. For example, it collects information about items added to a customer's cart and items purchased. Specifically, it can collect information about items added to a customer's cart in real time and information about purchased items periodically. Furthermore, the data collection unit can also collect information such as the types of products viewed by customers, the date and time of viewing, and the number of viewings. This allows for a detailed understanding of customer interests and purchasing patterns. The data collection unit saves information about products viewed by customers as a history, which can then be used for analysis. For example, if a customer frequently views products in a particular category, it can be determined that they have a high level of interest in that category. The data collection unit can also identify products that customers are likely to repurchase based on information about products they have previously purchased. This allows the data collection unit to track customer purchasing behavior in detail and provide foundational data for recommending the most suitable products to individual customers. Furthermore, the data collection unit centrally manages customer data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and presentation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to identify customer purchasing patterns and interests. Specifically, it can analyze whether there are products that customers tend to purchase when they are on sale. Based on the customer's purchase history, the analysis unit can identify interests in specific product categories. It can also analyze trends in products of interest based on the customer's browsing history. Furthermore, the analysis unit can analyze customer purchasing patterns and identify products that are on sale. In AI-based analysis, machine learning algorithms are used to analyze customer behavior data in detail and extract patterns and trends. For example, clustering methods can be used to group customers based on different purchasing patterns and recommend the most suitable products for each group. It is also possible to use deep learning-based image recognition technology to identify designs, colors, brands, etc., that customers are interested in from images of products they have viewed. As a result, the analysis unit can quickly and accurately analyze the collected data and gain a detailed understanding of customer interests and purchasing patterns. Furthermore, the analysis unit can also use historical data and statistical information to perform long-term trend analysis and predictions. For example, based on past sales data, it's possible to predict sales fluctuations during specific periods and develop effective marketing strategies. Furthermore, anomaly detection algorithms can be used to detect unusual purchasing patterns and abnormal data, enabling early intervention. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term strategy planning and anomaly detection, improving the overall reliability and effectiveness of the system.

[0067] The display unit presents products recommended to the customer based on the analysis results from the analysis unit. Specifically, it presents information about products on sale. The display unit can also present detailed product information and reviews. For example, it presents products based on the analysis results to recommend the most suitable product to the customer. It can also present sale information and detailed product information in real time. Furthermore, the display unit can recommend related products based on the customer's interests. Based on the customer's purchase history and browsing history, the display unit selects the most suitable products for each individual customer and provides personalized recommendations. For example, it can attract the customer's interest by recommending products similar to products the customer has purchased in the past or products in the same category. It also supports the customer's purchase decision by presenting review information and ratings of products the customer has viewed. The display unit can use AI to analyze the customer's interests and purchasing patterns and make recommendations at the optimal time. For example, if a customer views a particular product multiple times, it will be determined that the customer has a high level of interest in that product, and will promote purchase by presenting sale information and discount coupons. Furthermore, the display unit can provide customers with the latest information based on data updated in real time. This allows the display unit to quickly provide customers with appropriate product information and increase their purchase intent. In addition, the display unit can collect customer feedback and continuously improve the accuracy and effectiveness of recommendations. For example, if a customer purchases a recommended product, that data is fed back to the analysis unit and reflected in the next recommendation. Also, if a customer does not show interest in a recommended product, the reason can be analyzed and used to improve the next recommendation. In this way, the display unit can always provide customers with the most optimal product information and improve the purchasing experience.

[0068] The support department assists with at least one of the following procedures related to products presented by the presentation department: purchase, return, or exchange. Specifically, it provides support for these procedures via chat. The support department provides necessary information to customers as they proceed with the purchase process, ensuring a smooth process. For example, if a customer has a problem with a purchased product, the support department assists with the return process via chat. It can also guide customers through the necessary procedures if they wish to exchange the product. Furthermore, the support department can support customers until they complete the purchase process. The support department utilizes AI-powered chatbots to quickly respond to customer questions and problems. For example, if a customer has a question during the purchase process, the chatbot can provide an immediate answer, ensuring a smooth process. It also handles questions regarding returns and exchanges, guiding customers through the necessary procedures. The support department can collect customer feedback and continuously improve the quality of its services. For example, it can evaluate whether customers were satisfied with the chatbot's response and use the results to improve the quality of service. It can also analyze what kind of support customers are seeking for specific issues and provide more appropriate support. This allows the support department to provide customers with quick and appropriate support, improving the purchasing experience. Furthermore, the support department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only chat but also voice calls, email, and SMS in combination. This allows the support department to provide prompt and reliable support to customers, improving the purchasing experience.

[0069] The display unit can present information about sale items. For example, the display unit can identify sale items and present that information to the customer. The display unit can prioritize displaying items that are on sale or items with high discount rates. For example, the display unit can update sale items in real time to provide customers with the latest information. The display unit can also customize sale information based on customer interests. For example, the display unit can recommend sale items based on information about items the customer has previously purchased or viewed. This allows the display unit to offer attractive products to customers by presenting sale item information. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input sale item information into a generating AI, which can analyze the sale information and recommend the most suitable products to the customer.

[0070] The analysis unit can analyze which products customers frequently purchase when they go on sale. For example, the analysis unit can identify sale items based on the customer's purchase history. The analysis unit can identify sale items by analyzing data on products the customer has purchased in the past. For example, the analysis unit can analyze which products customers frequently purchase during sale periods. The analysis unit can also identify which products customers frequently purchase at a specific discount rate. If there are specific products that customers frequently purchase when they go on sale compared to other products, the presentation unit will prioritize displaying sale information for those specific products over other products. For example, the presentation unit will prioritize displaying specific products based on data on products the customer has purchased in the past. By prioritizing the display of sale items, the presentation unit can increase the customer's purchasing intent. For example, the presentation unit will prioritize displaying specific products based on data on products the customer has purchased in the past. Furthermore, by prioritizing the display of sale items, the presentation unit can increase the customer's purchasing intent. This allows the presentation unit to prioritize displaying products that customers are likely to purchase, thereby increasing their purchasing intent. Some or all of the above-described processes in the analysis unit and presentation unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the customer's purchase history into a generating AI, the generating AI can identify sale items, and the presentation unit can present that information to the customer.

[0071] The presentation unit can present either detailed product information or product review information based on the analysis results from the analysis unit. For example, the presentation unit can present detailed product information to the customer. The presentation unit can provide detailed information such as product specifications and usage instructions. For example, the presentation unit can present product review information to the customer. The presentation unit can provide information such as user reviews and rating scores. For example, the presentation unit can update detailed product information and review information in real time to provide the customer with the latest information. The presentation unit can also customize relevant product information based on the customer's interests. For example, the presentation unit can recommend relevant product information based on information about products the customer has previously purchased or viewed. This can support purchasing decisions by providing customers with detailed product information and review information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input detailed product information and review information into a generating AI, which can analyze that information and recommend the most suitable product information to the customer.

[0072] The data collection unit can collect information about products that customers add to their cart or products that customers purchase. For example, the data collection unit can collect information about products that customers add to their cart in real time. The data collection unit can also collect information about products that customers purchase periodically. For example, the data collection unit can save information about products that customers add to their cart as a history and use it for analysis later. The data collection unit can also save information about products that customers purchase as a history and use it for analysis later. This allows the collection unit to collect appropriate product information based on the customer's cart and purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about products that customers add to their cart into a generating AI, which can analyze that information and recommend the most suitable products to the customer.

[0073] The support department can provide support for at least one of the following procedures: purchase, return, or exchange, via chat. For example, the support department can provide necessary information to customers as they proceed with the purchase process, ensuring a smooth process. For example, if a customer has a problem with a purchased item, the support department can provide support for the return process via chat. The support department can also guide customers through the necessary procedures if they wish to exchange an item. Furthermore, the support department can provide support until the customer completes the purchase process. This allows customers to complete the process smoothly through chat-based support. Some or all of the above processes performed by the support department may be carried out using AI, or not. For example, the support department can input customer inquiries into a generating AI, which can then generate appropriate answers and provide them to the customer.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of collecting purchase and browsing history based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the data when the user is relaxed. If the user is excited, the data collection unit can immediately collect the purchase history and send it to the analysis unit in real time. If the user is tired, the data collection unit can adjust the collection timing and collect the data after the user has rested. In this way, by adjusting the collection timing according to the user's emotions, data can be collected at the optimal time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can analyze the emotions and adjust the collection timing.

[0075] The data collection unit can analyze a user's past purchase history and select an efficient collection method. For example, the data collection unit can identify product categories that a user frequently purchases and prioritize collecting history related to those categories. If a user tends to make purchases during specific time periods, the data collection unit can concentrate collection during those times. If a user frequently makes purchases during sales periods, the data collection unit can focus on collecting history from those sales periods. This enables efficient data collection by analyzing past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's past purchase history into a generating AI, which can then select an efficient collection method.

[0076] The data collection unit can filter the collected purchase history based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the data collection unit can prioritize collecting the purchase history of products related to that hobby. If a user moves, the data collection unit can collect the purchase history of products related to the new address. If a user plans to attend a specific event, the data collection unit can collect the purchase history of products related to that event. This allows for the collection of highly relevant data based on the user's lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of purchase history to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting the most recent purchase history. If the user is relaxed, the data collection unit can collect detailed past purchase history. If the user is stressed, the data collection unit can prioritize collecting the history of products related to stress reduction. This enables efficient data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can analyze the emotions and determine the priority of purchase history to collect.

[0078] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location when collecting purchase history. For example, if the user lives in a specific region, the data collection unit can prioritize the collection of popular products in that region. If the user is traveling, the data collection unit can prioritize the collection of purchase history at the travel destination. If the user frequently uses a specific store, the data collection unit can prioritize the collection of purchase history at that store. This allows for the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant purchase history.

[0079] The data collection unit can analyze a user's social media activity and collect relevant history when collecting purchase history. For example, if a user mentions a specific product on social media, the data collection unit can collect history related to that product. The data collection unit can prioritize collecting product history of brands that the user follows on social media. The data collection unit can collect product history related to groups and communities that the user participates in on social media. This allows for the collection of highly relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on the user's social media activity into a generating AI, which can then collect relevant history.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate recommendations. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide immediate recommendations. If the user is excited, the analysis unit can adjust the analysis algorithm to provide visually appealing recommendations. In this way, by adjusting the analysis algorithm according to the user's emotions, the optimal analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI, which can analyze the emotions and adjust the analysis algorithm.

[0081] The analysis unit can improve the accuracy of its analysis by referring to the user's past purchasing patterns during the analysis process. For example, the analysis unit can analyze patterns of products the user has purchased in the past to improve the accuracy of recommending similar products. If the user prefers a particular brand, the analysis unit can prioritize the analysis of products from that brand. If the user tends to purchase products in a particular price range, the analysis unit can focus on analyzing products in that price range. This improves the accuracy of the analysis by referring to past purchasing patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past purchasing patterns into a generating AI, which can then improve the accuracy of the analysis.

[0082] The analysis unit can customize its analysis methods based on the user's current living situation during analysis. For example, if the user has started a new job, the analysis unit will prioritize analyzing products related to that job. If the user has moved, the analysis unit can analyze products related to the new address. If the user has plans to attend a specific event, the analysis unit can analyze products related to that event. By customizing the analysis method based on the user's living situation, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's living situation into a generating AI, which can then customize the analysis method.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a concise display method. In this way, by adjusting the display method according to the user's emotions, highly visible analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, which can analyze the emotions and adjust the display method.

[0084] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, if the user lives in a specific region, the analysis unit can prioritize analyzing products popular in that region. If the user is traveling, the analysis unit can analyze their purchasing patterns at their travel destination. If the user frequently uses a specific store, the analysis unit can analyze their purchasing patterns at that store. By considering geographical location information, the analysis unit can provide highly relevant analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then perform the analysis.

[0085] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during the analysis process. For example, if a user mentions a specific product on social media, the analysis unit will analyze data related to that product. The analysis unit can prioritize the analysis of products from brands that the user follows on social media. The analysis unit can analyze data on products related to groups and communities that the user participates in on social media. This improves the accuracy of the analysis by referring to social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data on the user's social media activity into a generating AI, which can then improve the accuracy of the analysis.

[0086] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated emotions. For example, if the user is relaxed, the recommendation unit can provide recommendations with detailed explanations. If the user is in a hurry, the recommendation unit can provide concise and to-the-point recommendations. If the user is excited, the recommendation unit can provide visually appealing recommendations. This allows for more effective recommendations by adjusting the way recommendations are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user emotion data into a generative AI, which can analyze the emotions and adjust the way recommendations are presented.

[0087] The display unit can adjust the level of detail in recommendations based on the importance of the product at the time of display. For example, for expensive products, the display unit can provide recommendations that include detailed information and reviews. For everyday products, the display unit can provide concise information. For new or limited-edition products, the display unit can provide recommendations that include special promotional information. This allows for the provision of appropriate information by adjusting the level of detail in recommendations according to the importance of the product. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input product importance data into a generating AI, which can then adjust the level of detail in the recommendations.

[0088] The display unit can apply different recommendation algorithms depending on the product category at the time of display. For example, in the case of electronic devices, the display unit can provide recommendations that emphasize technical specifications and reviews. In the case of fashion items, the display unit can provide recommendations that include trend information and styling suggestions. In the case of food products, the display unit can provide recommendations that include nutritional information and recipe suggestions. This allows for the provision of optimal recommendations according to the product category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input product category data into a generating AI, and the generating AI can apply different recommendation algorithms.

[0089] The recommendation unit can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is in a hurry, the recommendation unit can provide short, concise recommendations. If the user is relaxed, the recommendation unit can provide longer recommendations with detailed explanations. If the user is excited, the recommendation unit can provide recommendations with visually stimulating effects. This allows for the provision of an appropriate amount of information by adjusting the length of recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not using AI. For example, the recommendation unit can input user emotion data into a generative AI, which can analyze the emotions and adjust the length of recommendations.

[0090] The display unit can determine the recommendation priority based on the product submission date at the time of display. For example, the display unit can prioritize recommendations for new products or limited-edition products. The display unit can prioritize recommendations for products during sales periods. The display unit can recommend seasonal products or event-related products at the appropriate time. This allows information to be provided at the appropriate time by determining the recommendation priority based on the product submission date. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input product submission date data into a generating AI, and the generating AI can determine the recommendation priority.

[0091] The display unit can adjust the order of recommendations based on the relevance of the products at the time of display. For example, the display unit can prioritize recommending products that are highly relevant to products the user has previously purchased. The display unit can prioritize recommending products that are highly relevant to products the user has viewed. The display unit can prioritize recommending products that are highly relevant to products the user has added to their cart. In this way, by adjusting the order of recommendations based on the relevance of the products, more relevant information can be provided. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input product relevance data into a generating AI, and the generating AI can adjust the order of recommendations.

[0092] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is nervous, the support unit can provide support in a calm tone. If the user is relaxed, the support unit can provide support in a friendly tone. If the user is in a hurry, the support unit can provide quick and concise support. This allows for more appropriate support to be provided by adjusting the support methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI, which can analyze the emotions and adjust the support methods.

[0093] The support department can analyze a user's past purchasing behavior to select the most suitable support method during support. For example, the support department can prioritize providing support methods that the user has frequently used in the past. The support department can refer to the support history of products the user has previously purchased and provide support for similar products. The support department can prioritize providing support channels (chat, phone, etc.) that the user has used in the past. This allows the support department to provide the most suitable support method by analyzing past purchasing behavior. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input data on the user's past purchasing behavior into a generating AI, which can then select the most suitable support method.

[0094] The support unit can customize the means of support provided based on the user's current living situation. For example, if a user starts a new job, the support unit can prioritize support for products related to that job. If a user moves, the support unit can provide support for products related to their new address. If a user plans to attend a specific event, the support unit can provide support for products related to that event. By customizing the means of support based on the user's living situation, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input user living situation data into a generating AI, which can then customize the means of support.

[0095] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is nervous, the support unit can provide quick support. If the user is relaxed, the support unit can provide detailed support. If the user is in a hurry, the support unit can provide concise and quick support. This allows for the provision of quick and appropriate support by determining the priority of support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI, which can analyze the emotions and determine the priority of support.

[0096] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user lives in a specific region, the support unit can provide support options available in that region. If the user is traveling, the support unit can provide support options available at the travel destination. If the user frequently uses a specific store, the support unit can provide support options at that store. In this way, the optimal support method can be provided by considering geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's geographical location information into a generating AI, which can then select the optimal support method.

[0097] The support department can provide optimal support by analyzing the user's social media activity during support sessions. For example, if a user mentions a specific product on social media, the support department can provide support related to that product. The support department can provide support for products from brands that the user follows on social media. The support department can provide support for products related to groups and communities that the user participates in on social media. In this way, optimal support can be provided by analyzing social media activity. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input data on the user's social media activity into a generating AI, which can then provide optimal support.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] An AI assistant for supporting product purchases on shopping websites can provide incentives to increase customer purchasing intent based on their purchase and browsing history. For example, if a customer views a specific product multiple times, a discount coupon for that product can be offered. Also, if a customer frequently purchases products in a specific category, points can be awarded for products in that category. Furthermore, if a customer spends above a certain amount during a sale, a coupon that can be used for their next purchase can be offered. This can increase customer purchasing intent and encourage repeat purchases. The provision of incentives may be done using AI or not. For example, data related to incentive provision can be input into a generating AI, which can then determine the optimal incentive.

[0100] An AI assistant for supporting product purchases on shopping websites can predict customer purchasing behavior based on their purchase and browsing history, and secure inventory in advance. For example, if a customer frequently purchases a particular product, the AI ​​can secure inventory of that product in advance. Also, if a customer tends to purchase a particular product during a specific season, the AI ​​can increase inventory for that season. Furthermore, if a customer purchases products related to a specific event, the AI ​​can secure inventory for that event. This prevents stockouts and provides customers with a smooth purchasing experience. Inventory securing can be done using AI or not. For example, data on inventory securing can be input into a generating AI, which can then determine the optimal inventory quantity.

[0101] An AI assistant for supporting product purchases on shopping websites can predict customer purchasing behavior based on their purchase and browsing history and provide personalized advertisements. For example, if a customer frequently purchases products from a particular brand, the AI ​​can provide advertisements for new products and sales from that brand. Similarly, if a customer frequently browses products in a specific category, the AI ​​can provide advertisements related to those products. Furthermore, if a customer purchases products related to a specific event, the AI ​​can provide advertisements related to that event. This allows for the provision of highly relevant advertisements to customers, thereby increasing their purchase intent. Advertisement delivery may be performed using AI or without AI. For example, data related to advertisement delivery can be input into a generating AI, which can then determine the most appropriate advertisement.

[0102] An AI assistant for supporting product purchases on a shopping site can predict customer purchasing behavior based on their purchase and browsing history and provide personalized newsletters. For example, if a customer frequently purchases products from a particular brand, a newsletter containing information on new products and sales from that brand can be provided. Similarly, if a customer frequently browses products in a specific category, a newsletter related to products in that category can be provided. Furthermore, if a customer purchases products related to a specific event, a newsletter related to that event can be provided. This allows for the provision of highly relevant information to customers, thereby increasing their purchasing intent. The delivery of newsletters may be done using AI or not. For example, data related to newsletter delivery can be input into a generating AI, which can then determine the most suitable newsletter.

[0103] An AI assistant for supporting product purchases on shopping websites can predict customer purchasing behavior based on their purchase and browsing history, and provide personalized gift suggestions. For example, if a customer frequently purchases products from a particular brand, products from that brand can be suggested as gifts. Similarly, if a customer frequently browses products in a particular category, products from that category can be suggested as gifts. Furthermore, if a customer purchases products related to a specific event, products related to that event can be suggested as gifts. This allows for highly relevant gift suggestions to customers, thereby increasing their purchasing intent. Gift suggestions may be made using AI or not. For example, data related to gift suggestions can be input into a generating AI, which can then determine the most suitable gift.

[0104] An AI assistant for supporting product purchases on a shopping site can estimate a customer's emotions and, based on those emotions, recommend relaxation products to alleviate the customer's stress. For example, if a customer is stressed, it can recommend relaxing aromatherapy oils or massage devices. If a customer is tired, it can recommend relaxing bath salts or cushions. Furthermore, if a customer is anxious, it can recommend relaxing music or meditation apps. By recommending relaxation products according to the customer's emotions, it is possible to reduce customer stress and increase their willingness to purchase. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Recommendation of relaxation products may be done using AI or not. For example, data related to relaxation product recommendations can be input into a generative AI, which can then determine the most suitable product.

[0105] An AI assistant for supporting product purchases on a shopping site can estimate a customer's emotions and, based on those emotions, recommend entertainment products to improve the customer's mood. For example, if a customer is feeling down, it can recommend movies or music to lift their spirits. If a customer is bored, it can recommend kits or games to start a new hobby. Furthermore, if a customer is feeling lonely, it can recommend apps or social games to join online communities. By recommending entertainment products according to the customer's emotions, it is possible to improve the customer's mood and increase their willingness to purchase. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Recommendation of entertainment products may be done using AI or not. For example, data related to entertainment product recommendations can be input into a generative AI, and the generative AI can determine the most suitable product.

[0106] An AI assistant for supporting product purchases on a shopping site can estimate a customer's emotions and, based on those emotions, recommend self-improvement products to boost their motivation. For example, if a customer is feeling discouraged, it can recommend books or seminars to boost their motivation. If a customer is working towards achieving a goal, it can recommend planners or apps to support that goal. Furthermore, if a customer is seeking personal growth, it can recommend online courses or learning materials to acquire new skills. By recommending self-improvement products according to the customer's emotions, it is possible to increase customer motivation and improve their willingness to purchase. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Recommendation of self-improvement products may be done using AI or not. For example, data related to self-improvement product recommendations can be input into a generative AI, which can then determine the most suitable product.

[0107] An AI assistant for supporting product purchases on a shopping site can estimate a customer's emotions and, based on those emotions, recommend healthcare products to support the customer's health. For example, if a customer is feeling stressed, it can recommend stress-reducing supplements or fitness equipment. If a customer is tired, it can recommend relaxing massagers or sleep improvement products. Furthermore, if a customer is health-conscious, it can recommend health management apps or wearable devices. By recommending healthcare products according to the customer's emotions, it is possible to support the customer's health and increase their willingness to purchase. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Healthcare product recommendations may be performed using AI or not. For example, data related to healthcare product recommendations can be input into a generative AI, which can then determine the most suitable product.

[0108] An AI assistant for supporting product purchases on a shopping site can estimate customer emotions and, based on those estimates, provide customer support that is sensitive to the customer's feelings. For example, if a customer is dissatisfied, it can provide prompt and courteous assistance to help resolve the problem. If a customer is satisfied, it can express gratitude and offer incentives to encourage repeat purchases. Furthermore, if a customer is confused, it can provide clear explanations and reassurance. By providing customer support that is sensitive to the customer's feelings, it is possible to improve customer satisfaction and promote repeat purchases. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Customer support may be performed using AI or not. For example, customer support data can be input into a generative AI, which can then determine the optimal support method.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects the customer's purchase or browsing history. For example, the data collection unit collects information about products the customer has added to their cart or products they have purchased. The data collection unit can also collect information such as the type of products the customer has viewed, the date and time of viewing, and the number of times they have viewed. For example, the data collection unit collects information about products the customer has added to their cart in real time. The data collection unit can also periodically collect information about products the customer has purchased. Furthermore, the data collection unit can save information about products the customer has viewed as a history and use it for analysis later. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data to identify, for example, customer purchasing patterns and interests. The analysis unit can analyze whether there are products that customers tend to purchase when they are on sale. For example, the analysis unit can identify customer interests in specific product categories based on their purchase history. The analysis unit can also analyze trends in products that customers are interested in based on their browsing history. Furthermore, the analysis unit can analyze customer purchasing patterns and identify products that are on sale. Step 3: The display unit presents products to the customer based on the analysis results from the analysis unit. The display unit may, for example, present information about products on sale. The display unit can also present detailed product information and reviews. For example, the display unit presents products based on the analysis results to recommend the most suitable product to the customer. The display unit can also present sale information and detailed product information in real time. Furthermore, the display unit can recommend related products based on the customer's interests. Step 4: The support department will assist with at least one of the following procedures related to the products presented by the presentation department: purchase, return, or exchange. The support department will provide support for these procedures, for example, via chat. The support department will provide the necessary information to help customers proceed with the purchase process smoothly. For example, if there is a problem with the product the customer purchased, the support department will assist with the return process via chat. The support department can also guide customers through the necessary procedures if they wish to exchange the product. Furthermore, the support department can provide support until the customer completes the purchase process.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] For example, the data collection unit can collect customer purchase history and browsing history using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data to identify customer purchase patterns and interests. The presentation unit is implemented by the control unit 46A of the smart device 14, which recommends the most suitable products to the customer based on the analysis results. The support unit is implemented by the control unit 46A of the smart device 14, which supports purchase procedures and return / exchange procedures in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] For example, the data collection unit can collect customer purchase and browsing history using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data to identify customer purchase patterns and interests. The presentation unit is implemented by the control unit 46A of the smart glasses 214, which recommends the most suitable products to the customer based on the analysis results. The support unit is implemented by the control unit 46A of the smart glasses 214, which supports purchase procedures and return / exchange procedures in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] For example, the data collection unit can collect customer purchase and browsing history using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data to identify customer purchase patterns and interests. The presentation unit is implemented by the control unit 46A of the headset terminal 314, which recommends the most suitable products to the customer based on the analysis results. The support unit is implemented by the control unit 46A of the headset terminal 314, which supports purchase procedures and return / exchange procedures in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] For example, the data collection unit can collect customer purchase and browsing history using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data to identify customer purchasing patterns and interests. The presentation unit is implemented by the control unit 46A of the robot 414, which recommends the most suitable products to the customer based on the analysis results. The support unit is implemented by the control unit 46A of the robot 414, which supports purchase procedures and return / exchange procedures in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A collection unit that collects customer purchase history or browsing history, An analysis unit analyzes the data collected by the aforementioned collection unit, A presentation unit that presents products to the customer based on the analysis results from the analysis unit, The system includes a support unit that supports one of the following procedures: purchase procedure, return procedure, or exchange procedure for the product presented by the presentation unit. A system characterized by the following features. (Note 2) The aforementioned display unit is, Provide information about items included in the sale. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the products that customers frequently purchase when they are on sale. The aforementioned display unit is, If there is a particular product that customers frequently purchase when it goes on sale, compared to other products, then the sale information for that particular product will be prioritized over that of the other products when displaying sales information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is, Based on the analysis results from the aforementioned analysis unit, either detailed product information or product review information will be presented. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We collect information about items that customers add to their cart or items that customers purchase. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is We provide support for at least one of the following processes via chat: purchase, return, or exchange. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting purchase and browsing history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past purchase history and select the most efficient data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of purchase history to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting purchase history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting purchase history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the user's past purchasing patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting, adjust the level of detail in recommendations based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, When presenting products, different recommendation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, It estimates the user's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting, the recommendation priority is determined based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, When presenting products, the order of recommendations is adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is During support, we analyze the user's past purchasing behavior to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During support, customize the support methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During support, we analyze the user's social media activity to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects customer purchase history or browsing history, An analysis unit analyzes the data collected by the aforementioned collection unit, A presentation unit that presents products to the customer based on the analysis results from the analysis unit, The system includes a support unit that supports one of the following procedures: purchase procedure, return procedure, or exchange procedure for the product presented by the presentation unit. A system characterized by the following features.

2. The aforementioned display unit is, Provide information about items included in the sale. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the products that the aforementioned customer frequently purchases when they are on sale. The aforementioned display unit is, If there is a particular product that customers frequently purchase when it goes on sale, compared to other products, then the sale information for that particular product will be prioritized over that of the other products when displaying sales information. The system according to feature 1.

4. The aforementioned display unit is, Based on the analysis results from the aforementioned analysis unit, either detailed product information or product review information will be presented. The system according to feature 1.

5. The aforementioned collection unit is The collection of information regarding the products the customer has added to their cart, or the products the customer has purchased. The system according to feature 1.

6. The aforementioned support unit is We provide support for at least one of the following processes via chat: purchase, return, or exchange. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting purchase and browsing history based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past purchase history and select the most efficient data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of purchase history to collect based on those estimated emotions. The system according to feature 1.

Images