system

Abstract

The system according to this embodiment aims to provide product recommendations, real-time support, and after-sales support by utilizing user behavior data. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a recommendation unit, a support unit, and a guidance unit. The data collection unit collects user behavior data. The analysis unit analyzes the data collected by the data collection unit. The recommendation unit recommends products based on the analysis results obtained by the analysis unit. The support unit answers user questions in real time. The guidance unit provides after-sales support and guidance on return and exchange procedures.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 conventional technology, recommendations of products using user behavior data, real-time support during the purchase process, and after-sales support are not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize user behavior data to provide product recommendations, real-time support, and after-sales support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, a support unit, and a guidance unit. The data collection unit collects user behavior data. The analysis unit analyzes the data collected by the data collection unit. The recommendation unit recommends products based on the analysis results obtained by the analysis unit. The support unit answers user questions in real time. The guidance unit provides after-sales support and guidance on return and exchange procedures. [Effects of the Invention]

[0007] The system according to this embodiment can utilize user behavior data to provide product recommendations, real-time support, and after-sales support. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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 online store purchase support services according to an embodiment of the present invention is a system that collects and analyzes user behavior data to recommend products, provides real-time support for questions and uncertainties during the purchase process, and provides guidance on after-sales support and return / exchange procedures. The AI ​​assistant for online store purchase support services can improve the user's purchasing experience by collecting and analyzing user behavior data and providing product recommendations, real-time support, after-sales support, and guidance on return / exchange procedures. For example, the AI ​​assistant for online store purchase support services collects user behavior data. The data collected includes the user's browsing history and purchase history. For example, information on products that the user has previously viewed and purchased is collected. This data is collected by the collection unit. Next, the collected data is analyzed. The analysis unit analyzes the collected data to identify the user's interests and preferences. For example, by analyzing the product categories that the user frequently views and the trends in products that the user purchases, the user's interests and preferences can be understood. Based on the analysis results, relevant products are recommended. The recommendation unit recommends products relevant to the user based on the analysis results. For example, it can recommend products related to products that the user has previously purchased or products in the same category. Furthermore, it provides real-time answers to user questions. The support department uses natural language processing technology to answer user questions in real time. For example, if a user enters a question or point of confusion during the purchase process, the AI ​​analyzes the question and provides an appropriate answer. It also provides after-sales support and guidance on return and exchange procedures. The guidance department collects the information necessary for users to return or exchange items and guides them through the process. For example, it provides information on how to return items and the necessary documents, enabling users to complete the process smoothly. In this way, the AI ​​assistant online store purchase support service can improve the user's purchasing experience by collecting and analyzing user behavior data and providing product recommendations, real-time support, and guidance on after-sales support and return / exchange procedures.This allows AI assistants in online store purchase support services to improve the user's shopping experience.

[0029] The AI ​​assistant for online store purchase support services according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, a support unit, and a guidance unit. The data collection unit collects user behavior data. User behavior data includes, but is not limited to, browsing history and purchase history. For example, the data collection unit collects information on products that the user has previously viewed or purchased. The data collection unit can also collect user click data and search history. For example, the data collection unit collects data on links that the user has clicked and keywords that have been searched. Furthermore, the data collection unit can collect user behavior data in real time. For example, the data collection unit collects information on the page that the user is currently viewing. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit analyzes the user's browsing frequency and purchase frequency to identify the user's interests and preferences. The analysis unit can also analyze user behavior patterns. For example, the analysis unit analyzes the product categories that the user frequently views and the trends in the products that the user purchases. Furthermore, the analytics unit can analyze user behavior data in real time. For example, the analytics unit analyzes information on the page the user is currently viewing to identify the user's interests and preferences. The recommendation unit recommends products based on the analysis results obtained by the analytics unit. The recommendation unit recommends products using, for example, collaborative filtering or content-based recommendation algorithms. For example, the recommendation unit recommends products related to products the user has previously purchased. The recommendation unit can also recommend products in product categories that the user frequently views. Furthermore, the recommendation unit can analyze user behavior data in real time and recommend relevant products. For example, the recommendation unit recommends products based on information on the page the user is currently viewing. The support unit answers user questions in real time. For example, the support unit uses natural language processing technology to analyze user questions and provide appropriate answers. For example, when a user enters questions or uncertainties during the purchase process, the AI ​​analyzes the questions and provides appropriate answers.Furthermore, the support unit can provide real-time chat support to users' questions. For example, when a user enters a question via chat, the AI ​​analyzes the question and provides a real-time answer. The guidance unit provides information on after-sales support and return / exchange procedures. For example, the guidance unit collects the information necessary when a user is returning or exchanging an item and guides them through the process. For example, the guidance unit provides information on how to return an item and the necessary documents. The guidance unit can also provide information necessary when a user is receiving after-sales support. For example, the guidance unit provides information on the warranty period and the support provided. As a result, the AI ​​assistant for online store purchase support services according to this embodiment can collect and analyze user behavior data and provide product recommendations, real-time support, and guidance on after-sales support and return / exchange procedures.

[0030] The data collection unit collects user behavior data. This data includes, but is not limited to, browsing and purchase history. For example, the unit collects information on products that users have previously viewed or purchased. Specifically, it collects data such as which pages users viewed and for how long, which links they clicked, and which product detail pages they viewed. The data collection unit can also collect user click data and search history. For example, it collects data on links users clicked and keywords they searched for. This allows the unit to understand what products users are interested in. Furthermore, the data collection unit can collect user behavior data in real time. For example, it collects information on the page a user is currently viewing. This allows the unit to instantly understand what products or categories a user is currently interested in. The data collection unit centrally manages this data and makes it accessible to the analytics and recommendation units. Data collection is carried out with respect for user privacy and with appropriate security measures in place. For example, data is encrypted and stored on secure servers. Data is also collected only with the user's consent, and users are given the option to refuse data collection. This allows the data collection unit to efficiently and securely collect user behavior data, thereby improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses methods such as statistical analysis and machine learning algorithms to analyze the data. Specifically, it analyzes user browsing and purchase frequencies to identify user interests and preferences. For example, if a user frequently browses products in a particular category, it determines that the user has a high level of interest in that category. The analysis unit can also analyze user behavior patterns. For example, it analyzes the product categories a user frequently browses and the trends in products they purchase. This allows for an understanding of what products users are interested in and what purchasing behavior they exhibit. Furthermore, the analysis unit can analyze user behavior data in real time. For example, it analyzes information from the page a user is currently viewing to identify their interests and preferences. This allows for immediate identification of products and categories that a user is currently interested in. Based on these analysis results, the analysis unit provides information to the recommendation and support units. Deep learning and natural language processing technologies may also be used in the analysis, enabling highly accurate analysis. For example, deep learning can be used to learn user behavior patterns and identify interests and preferences with greater accuracy. Furthermore, natural language processing technology is used to analyze the user's search keywords and identify the information the user is seeking. This allows the analysis unit to quickly and accurately analyze user behavior data, improving the overall system performance.

[0032] The recommendation unit recommends products based on the analysis results obtained by the analysis unit. The recommendation unit uses methods such as collaborative filtering and content-based recommendation algorithms to make product recommendations. Specifically, it recommends products related to items the user has previously purchased. For example, it recommends products in the same category as items the user has previously purchased, or related accessories. The recommendation unit can also recommend products in categories the user frequently views. For example, it can prioritize displaying products in categories the user frequently views to attract user interest. Furthermore, the recommendation unit can analyze user behavior data in real time and recommend relevant products. For example, it can recommend products based on information from the page the user is currently viewing. This allows for immediate recommendations of products and categories related to the user's current interests. The recommendation unit provides these recommendations to the user, making it easier for them to find products they are interested in. Recommendations can utilize not only the user's past behavior data but also the behavior data of other users. For example, by recommending products purchased by other users who viewed the same product, it can recommend products that the user is likely to be interested in. The recommendation unit can also collect user feedback to continuously improve the accuracy of its recommendation algorithms. For example, if a user purchases a recommended product, the recommendation algorithm is adjusted based on that data to provide more accurate recommendations. This allows the recommendation system to recommend appropriate products to users and increase their purchasing intent.

[0033] The support department provides real-time answers to user questions. For example, it uses natural language processing technology to analyze user questions and provide appropriate answers. Specifically, when a user enters a question or point of confusion during the purchase process, the AI ​​analyzes the question and provides an appropriate answer. For example, if a user asks about product details or delivery status, the AI ​​analyzes the question and provides relevant information. The support department can also provide real-time chat support for user questions. For example, when a user enters a question via chat, the AI ​​analyzes the question and provides a real-time answer. This allows users to quickly resolve their questions and proceed smoothly through the purchase process. The support department can continuously improve the accuracy of its answers to user questions. For example, it adjusts the content and format of answers based on user feedback to provide more appropriate responses. Furthermore, the support department can support multiple languages. For example, even if a user enters a question in a different language, the AI ​​analyzes the question and provides an answer in the appropriate language. This allows the support department to provide fast and appropriate support to users globally. Furthermore, the support department can refer to a user's past question history to provide more appropriate answers. For example, if a user has asked the same question before, the support department can quickly provide an answer based on that history. This allows the support department to provide users with prompt and appropriate support, thereby improving user satisfaction.

[0034] The Guidance Department provides after-sales support and guidance on return and exchange procedures. For example, the Guidance Department collects the information necessary for users to return or exchange products and guides them through the process. Specifically, it provides the necessary documents and procedures for users to return products. For example, it provides information on how to return products and the necessary documents. The Guidance Department can also provide information necessary for users to receive after-sales support. For example, it provides information on the warranty period and support details. This allows users to quickly obtain the necessary information and proceed with the process smoothly. The Guidance Department can collect user feedback and continuously improve the accuracy and effectiveness of its guidance. For example, it adjusts the guidance based on feedback after users have completed a procedure, providing more appropriate information. The Guidance Department can also reliably transmit information using multiple communication methods. For example, it uses a combination of email, SMS, and in-app notifications to ensure that important information is delivered reliably. This allows the Guidance Department to provide information to users quickly and reliably and proceed with the process smoothly. Furthermore, the Guidance Department can refer to the user's past procedure history to provide more appropriate guidance. For example, if a user has performed the same procedure in the past, it can provide guidance quickly based on that history. This allows the guidance unit to provide users with quick and appropriate instructions, thereby improving user satisfaction.

[0035] The data collection unit can collect the user's browsing history and purchase history. For example, the data collection unit can collect data on pages the user has previously viewed and the time spent viewing them. The data collection unit can also collect information on products the user has purchased and the date and time of purchase. For example, the data collection unit can collect detailed information and purchase history of products the user has purchased. Furthermore, the data collection unit can collect the user's browsing history and purchase history in real time. For example, the data collection unit can collect information on the page the user is currently viewing and information on products they have purchased in real time. By collecting the user's browsing history and purchase history, it is possible to understand the user's interests and preferences. 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 browsing history and purchase history data into a generating AI and have the generating AI perform the data collection.

[0036] The analysis unit can analyze the collected data and identify user interests and preferences. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. For instance, it can analyze user browsing and purchasing frequency to identify user interests and preferences. The analysis unit can also analyze user behavior patterns. For example, it can analyze the product categories users frequently browse and the trends in products they purchase. Furthermore, the analysis unit can analyze user behavior data in real time. For example, it can analyze the information on the page a user is currently viewing to identify user interests and preferences. This allows the analysis of collected data to identify user interests and preferences, enabling the recommendation of more appropriate products. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0037] The recommendation unit can recommend relevant products based on the analysis results. The recommendation unit recommends products using, for example, collaborative filtering or content-based recommendation algorithms. For example, the recommendation unit recommends products related to products the user has purchased in the past. The recommendation unit can also recommend products in product categories that the user frequently views. Furthermore, the recommendation unit can analyze user behavior data in real time and recommend relevant products. For example, the recommendation unit recommends products based on information from the page the user is currently viewing. This allows the recommendation unit to provide products that match the user's interests and preferences by recommending relevant products based on the analysis results. Some or all of the above processes in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the analysis results into a generating AI and have the generating AI perform product recommendations.

[0038] The support department can answer user questions in real time using natural language processing technology. For example, the support department can analyze user questions using natural language processing technology and provide appropriate answers. For example, when a user enters a question or point of confusion during the purchase process, the AI ​​analyzes the question and provides an appropriate answer. The support department can also provide real-time chat support for user questions. For example, when a user enters a question via chat, the AI ​​analyzes the question and provides an answer in real time. This allows for the rapid resolution of questions and points of confusion during the purchase process by providing real-time answers to user questions. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input a user's question into a generating AI and have the generating AI generate an answer.

[0039] The guidance unit can collect the information necessary for users to return or exchange items and guide them through the process. For example, the guidance unit can collect the information necessary for users to return or exchange items and guide them through the process. For example, the guidance unit can provide information on how to return items and the necessary documents. The guidance unit can also provide information necessary for users to receive after-sales support. For example, the guidance unit can provide information on the warranty period and support details. This helps users to complete return or exchange procedures smoothly. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input information about the user's return or exchange procedure into a generating AI and have the generating AI execute the procedure guidance.

[0040] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can identify the time periods when the user frequently accessed the system in the past and collect data during those times. The data collection unit can also analyze the devices the user has used in the past and collect data from the most suitable devices. Furthermore, the data collection unit can analyze the user's past behavioral patterns and select the most efficient data collection method. For example, the data collection unit can select the optimal data collection method based on data from the time periods and devices the user frequently accessed the system in the past. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data. 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 past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current interests and preferences during data collection. For example, the data collection unit can prioritize collecting data related to product categories that the user is currently interested in. The data collection unit can also filter data based on keywords that the user has recently searched for. Furthermore, the data collection unit can collect data related to the page the user is currently viewing. For example, the data collection unit can filter data based on information about product categories that the user is currently interested in, recently searched keywords, and the page the user is currently viewing. This allows for the collection of more relevant data by filtering data based on the user's current interests and preferences. 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 data on the user's current interests and preferences into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of data around their home. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. This allows for the priority collection of highly relevant data by considering the user's geographical location information. 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 and have the generating AI perform the collection of highly relevant data.

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

[0044] The analysis unit can optimize its analysis algorithm by referring to the user's past behavioral data during analysis. For example, the analysis unit can optimize its analysis algorithm based on data of products the user has frequently purchased in the past. The analysis unit can also refer to the user's past browsing history to perform analysis focused on categories of interest. Furthermore, the analysis unit can analyze the user's past purchase history to perform analysis based on purchase trends. For example, the analysis unit optimizes its analysis algorithm based on data of products the user has frequently purchased in the past, past browsing history, and purchase history. This allows the analysis algorithm to be optimized by referring to the user's past behavioral data, providing more accurate 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 past behavioral data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0045] The analysis unit can apply different analysis methods based on the user's interests and preferences during analysis. For example, the analysis unit can perform a detailed analysis on product categories that the user is interested in. It can also apply specific analysis methods to brands that the user has shown interest in. Furthermore, the analysis unit can apply different analysis methods based on data of products that the user frequently views. For example, the analysis unit can apply different analysis methods based on data of product categories that the user is interested in, brands that the user has shown interest in, and products that the user frequently views. This allows for the provision of more appropriate analysis results by applying different analysis methods based on the user's interests and preferences. 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 data on the user's interests and preferences into a generating AI and have the generating AI execute the application of different analysis methods.

[0046] The analysis unit can determine the priority of analysis based on when the user's behavioral data was submitted. For example, the analysis unit may prioritize analyzing data recently submitted by the user. It can also prioritize analyzing data submitted by the user during a specific time period. Furthermore, it can prioritize analyzing data that the user frequently submits. For example, the analysis unit may determine the priority of analysis based on recently submitted data, data submitted during a specific time period, and frequently submitted data. By determining the priority of analysis based on when the user's behavioral data was submitted, it is possible to provide more timely analysis results. 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 may input data on when the user's behavioral data was submitted into a generating AI and have the generating AI perform the determination of the analysis priority.

[0047] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced. The analysis unit can also perform analysis by referring to the latest research in fields that the user is interested in. Furthermore, the analysis unit can improve the accuracy of its analysis based on literature that the user frequently references. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced, the latest research in fields that the user is interested in, and frequently referenced literature. In this way, the accuracy of the analysis can be improved by referring to the user's relevant literature. 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 relevant literature into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0048] The recommendation system can recommend the most suitable products by referring to the user's past purchase history. For example, the recommendation system can recommend products related to products the user has previously purchased. It can also analyze the user's past purchase history and recommend products in the same category. Furthermore, the recommendation system can recommend products from brands that the user frequently purchases. For example, the recommendation system can recommend the most suitable products based on products the user has previously purchased, past purchase history, and brands that the user frequently purchases. This allows for the recommendation of more relevant products by referring to the user's past purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input data from the user's past purchase history into a generating AI and have the generating AI perform the optimal product recommendation.

[0049] The recommendation system can optimize its recommendation algorithm based on the user's current interests. For example, it can optimize the recommendation algorithm based on the product categories the user is currently interested in. It can also optimize the recommendation algorithm based on keywords the user has recently searched for. Furthermore, it can optimize the recommendation algorithm based on the page the user is currently viewing. For example, it can optimize the recommendation algorithm based on information about the product categories the user is currently interested in, recently searched keywords, and the page the user is currently viewing. This allows for the recommendation of more appropriate products by optimizing the recommendation algorithm based on the user's current interests. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input data on the user's current interests into a generating AI and have the generating AI perform the optimization of the recommendation algorithm.

[0050] The recommendation system can prioritize recommending highly relevant products by considering the user's geographical location. For example, if the user is in a specific region, the recommendation system will prioritize recommending products related to that region. Furthermore, if the user is traveling, the recommendation system can prioritize recommending products related to their travel destination. Additionally, if the user is at home, the recommendation system can prioritize collecting data about their surroundings. For example, if the recommendation system is in a specific region, it will prioritize recommending products related to that region. This allows the system to prioritize recommending highly relevant products by considering the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input the user's geographical location into a generating AI and have the generating AI recommend highly relevant products.

[0051] The recommendation unit can analyze a user's social media activity and recommend relevant products. For example, it can recommend products related to products the user has shared on social media. It can also recommend products related to brands the user follows on social media. Furthermore, it can recommend products related to posts the user has "liked" on social media. For example, it can recommend products related to products the user has shared on social media, brands they follow, and posts they have "liked." In this way, it can recommend relevant products by analyzing the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input data on the user's social media activity into a generating AI and have the generating AI perform the recommendation of relevant products.

[0052] The support unit can provide the best possible answer by referring to the user's past question history during support. For example, the support unit can provide relevant answers based on the questions the user has asked in the past. The support unit can also analyze the user's past question history and provide answers to frequently asked questions. Furthermore, the support unit can provide the best possible answer by referring to the support the user has received in the past. For example, the support unit can provide the best possible answer based on the questions the user has asked in the past, their past question history, and the support they have received in the past. This allows the support unit to provide more appropriate answers by referring to the user's past question history. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input data from the user's past question history into a generating AI and have the generating AI perform the task of providing the best possible answer.

[0053] The support unit can customize the support provided based on the user's current situation. For example, the support unit can provide appropriate support by considering which stage of the purchase process the user is currently in. It can also provide the optimal support method based on the device the user is currently using. Furthermore, the support unit can provide relevant support by considering the user's current geographical location. For example, the support unit customizes the support based on the user's current stage of the purchase process, the device they are currently using, and their current geographical location. This allows for more appropriate support to be provided by customizing the support based on the user's current situation. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input data on the user's current situation into a generating AI and have the generating AI perform the customization of the support.

[0054] The support unit can provide optimal support by considering the user's geographical location information during support. For example, if the user is in a specific region, the support unit can provide support relevant to that region. Furthermore, if the user is traveling, the support unit can provide support relevant to their travel destination. Additionally, if the user is at home, the support unit can provide support based on information about their surroundings. For example, if the support unit is in a specific region, it can provide support relevant to that region. This allows for the provision of more appropriate support by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI provide optimal support.

[0055] The support department can analyze a user's social media activity and provide relevant support during support sessions. For example, the support department can provide support related to issues the user has shared on social media. It can also provide support related to brands the user follows on social media. Furthermore, the support department can provide support related to posts the user has "liked" on social media. For example, the support department can provide support related to issues the user has shared on social media, brands they follow, and posts they have "liked." This allows for the provision of more appropriate support by analyzing the user's social media activity. Some or all of the above processing 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 and have the generating AI perform the provision of relevant support.

[0056] The guidance unit can provide optimal guidance by referring to the user's past return and exchange history. For example, the guidance unit can provide appropriate guidance based on information about products the user has returned in the past. The guidance unit can also guide the user through the optimal exchange procedure by referring to the user's past exchange history. Furthermore, the guidance unit can provide optimal guidance based on the content of guidance the user has received in the past. For example, the guidance unit can provide optimal guidance based on information about products the user has returned in the past, past exchange history, and content of guidance received in the past. This allows for the provision of more appropriate guidance by referring to the user's past return and exchange history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the user's past return and exchange history into a generating AI and have the generating AI perform the task of providing optimal guidance.

[0057] The guidance unit can customize the guidance content based on the user's current situation. For example, the guidance unit can provide appropriate guidance by considering which stage of the purchase process the user is currently in. The guidance unit can also provide the optimal guidance method based on the device the user is currently using. Furthermore, the guidance unit can provide relevant guidance by considering the user's current geographical location. For example, the guidance unit customizes the guidance content based on the user's current stage of the purchase process, the device they are currently using, and their current geographical location. This allows for the provision of more appropriate guidance by customizing the guidance content based on the user's current situation. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the user's current situation into a generating AI and have the generating AI perform the customization of the guidance content.

[0058] The guidance unit can provide optimal guidance by considering the user's geographical location information during guidance. For example, if the user is in a specific region, the guidance unit can provide guidance relevant to that region. Furthermore, if the user is traveling, the guidance unit can provide guidance relevant to the travel destination. Additionally, if the user is at home, the guidance unit can provide guidance based on information about the area around the user's home. For example, if the guidance unit is in a specific region, it can provide guidance relevant to that region. This allows for the provision of more appropriate guidance by considering the user's geographical location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's geographical location information into a generating AI and have the generating AI provide optimal guidance.

[0059] The guidance unit can analyze the user's social media activity and provide relevant guidance during the guidance process. For example, the guidance unit can provide guidance related to issues the user has shared on social media. It can also provide guidance related to brands the user follows on social media. Furthermore, the guidance unit can provide guidance related to posts the user has "liked" on social media. For example, the guidance unit can provide guidance related to issues the user has shared on social media, brands they follow, and posts they have "liked." This allows for the provision of more appropriate guidance by analyzing the user's social media activity. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the user's social media activity into a generating AI and have the generating AI provide relevant guidance.

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

[0061] An AI assistant for online store purchase support services can collect user behavior data while considering the user's geographical location. For example, if the user is in a specific region, the data collection unit can prioritize collecting browsing and purchase history of products related to that region. If the user is traveling, it can also collect data on products related to their travel destination. Furthermore, if the user is at home, it can collect data on products around their home. This allows for the collection of more 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, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI prioritize data collection.

[0062] The analysis unit can analyze user behavior data while taking into account the user's social media activity. For example, the analysis unit can analyze data related to products shared by the user on social media and brands followed by the user. It can also analyze data related to posts that the user "likes" on social media. Furthermore, it can analyze the content of comments made by the user on social media to identify the user's interests and concerns. By considering the user's social media activity, it is possible to provide more accurate analysis results. 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 social media activity into a generating AI and have the generating AI perform the data analysis.

[0063] The data collection unit can collect user behavior data while considering the user's past purchase history. For example, the data collection unit can prioritize collecting data related to products the user has purchased in the past. It can also collect product data from categories that the user frequently purchases. Furthermore, if the user frequently purchases products from a particular brand, it can collect data related to that brand. This allows for the collection of more relevant data by considering the user's 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 data from the user's past purchase history into a generating AI and have the generating AI prioritize data collection.

[0064] The analysis unit can analyze user behavior data while taking into account the user's current interests and preferences. For example, the analysis unit can prioritize analyzing data related to product categories that the user is currently interested in. It can also analyze data related to keywords the user has recently searched for. Furthermore, it can analyze data related to the page the user is currently viewing. By considering the user's current interests and preferences, it is possible to provide more accurate analysis results. 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 current interests and preferences into a generating AI and have the generating AI perform the data analysis.

[0065] The recommendation unit can recommend the most suitable products by referring to the user's past purchase history. For example, it can recommend products related to products the user has purchased in the past. It can also analyze the user's past purchase history and recommend products in the same category. Furthermore, it can recommend products from brands that the user frequently purchases. This allows for the recommendation of more relevant products by referring to the user's past purchase history. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input data on the user's past purchase history into a generating AI and have the generating AI perform the optimal product recommendation.

[0066] The support department can provide the best possible answers by referring to the user's past question history. For example, it can provide relevant answers based on questions the user has asked in the past. It can also analyze the user's past question history and provide answers to frequently asked questions. Furthermore, it can provide the best possible answers by referring to the support the user has received in the past. This allows for the provision of more appropriate answers by referring to the user's past question history. 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 from the user's past question history into a generating AI and have the generating AI perform the task of providing the best possible answers.

[0067] The guidance unit can provide optimal guidance by referring to the user's past return and exchange history. For example, it can provide appropriate guidance based on information about products the user has returned in the past. It can also guide the user through the optimal exchange procedure by referring to the user's past exchange history. Furthermore, it can provide optimal guidance based on the guidance the user has received in the past. In this way, by referring to the user's past return and exchange history, more appropriate guidance can be provided. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the user's past return and exchange history into a generating AI and have the generating AI perform the task of providing optimal guidance.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The collection unit collects user behavior data. User behavior data includes, but is not limited to, browsing history and purchase history. For example, the collection unit collects information on products the user has previously viewed or purchased. The collection unit can also collect user click data and search history. For example, the collection unit collects data on links the user has clicked and keywords the user has searched for. Furthermore, the collection unit can collect user behavior data in real time. For example, the collection unit collects information on the page the user is currently viewing. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit analyzes users' browsing and purchasing frequencies to identify users' interests and preferences. The analysis unit can also analyze users' behavior patterns. For example, the analysis unit analyzes the product categories that users frequently browse and the trends in the products they purchase. Furthermore, the analysis unit can analyze user behavior data in real time. For example, the analysis unit analyzes the information on the page that a user is currently viewing to identify users' interests and preferences. Step 3: The recommendation unit recommends products based on the analysis results obtained by the analysis unit. The recommendation unit recommends products using, for example, collaborative filtering or content-based recommendation algorithms. For example, the recommendation unit recommends products related to products the user has purchased in the past. The recommendation unit can also recommend products in product categories that the user frequently views. Furthermore, the recommendation unit can analyze user behavior data in real time and recommend relevant products. For example, the recommendation unit recommends products based on information from the page the user is currently viewing. Step 4: The support team provides real-time answers to user questions. The support team uses natural language processing technology, for example, to analyze user questions and provide appropriate answers. For instance, when a user enters a question or point of confusion during the purchase process, the AI ​​analyzes the question and provides an appropriate answer. The support team can also provide real-time chat support for user questions. For example, when a user enters a question via chat, the AI ​​analyzes the question and provides an answer in real time. Step 5: The Guidance Department provides after-sales support and guidance on return and exchange procedures. For example, the Guidance Department collects the information necessary for users to return or exchange products and guides them through the process. For instance, the Guidance Department provides information on how to return products and the necessary documents. The Guidance Department can also provide information necessary for users to receive after-sales support. For example, the Guidance Department provides information on the warranty period and support services.

[0070] (Example of form 2) An AI assistant for online store purchase support services according to an embodiment of the present invention is a system that collects and analyzes user behavior data to recommend products, provides real-time support for questions and uncertainties during the purchase process, and provides guidance on after-sales support and return / exchange procedures. The AI ​​assistant for online store purchase support services can improve the user's purchasing experience by collecting and analyzing user behavior data and providing product recommendations, real-time support, after-sales support, and guidance on return / exchange procedures. For example, the AI ​​assistant for online store purchase support services collects user behavior data. The data collected includes the user's browsing history and purchase history. For example, information on products that the user has previously viewed and purchased is collected. This data is collected by the collection unit. Next, the collected data is analyzed. The analysis unit analyzes the collected data to identify the user's interests and preferences. For example, by analyzing the product categories that the user frequently views and the trends in products that the user purchases, the user's interests and preferences can be understood. Based on the analysis results, relevant products are recommended. The recommendation unit recommends products relevant to the user based on the analysis results. For example, it can recommend products related to products that the user has previously purchased or products in the same category. Furthermore, it provides real-time answers to user questions. The support department uses natural language processing technology to answer user questions in real time. For example, if a user enters a question or point of confusion during the purchase process, the AI ​​analyzes the question and provides an appropriate answer. It also provides after-sales support and guidance on return and exchange procedures. The guidance department collects the information necessary for users to return or exchange items and guides them through the process. For example, it provides information on how to return items and the necessary documents, enabling users to complete the process smoothly. In this way, the AI ​​assistant online store purchase support service can improve the user's purchasing experience by collecting and analyzing user behavior data and providing product recommendations, real-time support, and guidance on after-sales support and return / exchange procedures.This allows AI assistants in online store purchase support services to improve the user's shopping experience.

[0071] The AI ​​assistant for online store purchase support services according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, a support unit, and a guidance unit. The data collection unit collects user behavior data. User behavior data includes, but is not limited to, browsing history and purchase history. For example, the data collection unit collects information on products that the user has previously viewed or purchased. The data collection unit can also collect user click data and search history. For example, the data collection unit collects data on links that the user has clicked and keywords that have been searched. Furthermore, the data collection unit can collect user behavior data in real time. For example, the data collection unit collects information on the page that the user is currently viewing. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit analyzes the user's browsing frequency and purchase frequency to identify the user's interests and preferences. The analysis unit can also analyze user behavior patterns. For example, the analysis unit analyzes the product categories that the user frequently views and the trends in the products that the user purchases. Furthermore, the analytics unit can analyze user behavior data in real time. For example, the analytics unit analyzes information on the page the user is currently viewing to identify the user's interests and preferences. The recommendation unit recommends products based on the analysis results obtained by the analytics unit. The recommendation unit recommends products using, for example, collaborative filtering or content-based recommendation algorithms. For example, the recommendation unit recommends products related to products the user has previously purchased. The recommendation unit can also recommend products in product categories that the user frequently views. Furthermore, the recommendation unit can analyze user behavior data in real time and recommend relevant products. For example, the recommendation unit recommends products based on information on the page the user is currently viewing. The support unit answers user questions in real time. For example, the support unit uses natural language processing technology to analyze user questions and provide appropriate answers. For example, when a user enters questions or uncertainties during the purchase process, the AI ​​analyzes the questions and provides appropriate answers.Furthermore, the support unit can provide real-time chat support to users' questions. For example, when a user enters a question via chat, the AI ​​analyzes the question and provides a real-time answer. The guidance unit provides information on after-sales support and return / exchange procedures. For example, the guidance unit collects the information necessary when a user is returning or exchanging an item and guides them through the process. For example, the guidance unit provides information on how to return an item and the necessary documents. The guidance unit can also provide information necessary when a user is receiving after-sales support. For example, the guidance unit provides information on the warranty period and the support provided. As a result, the AI ​​assistant for online store purchase support services according to this embodiment can collect and analyze user behavior data and provide product recommendations, real-time support, and guidance on after-sales support and return / exchange procedures.

[0072] The data collection unit collects user behavior data. This data includes, but is not limited to, browsing and purchase history. For example, the unit collects information on products that users have previously viewed or purchased. Specifically, it collects data such as which pages users viewed and for how long, which links they clicked, and which product detail pages they viewed. The data collection unit can also collect user click data and search history. For example, it collects data on links users clicked and keywords they searched for. This allows the unit to understand what products users are interested in. Furthermore, the data collection unit can collect user behavior data in real time. For example, it collects information on the page a user is currently viewing. This allows the unit to instantly understand what products or categories a user is currently interested in. The data collection unit centrally manages this data and makes it accessible to the analytics and recommendation units. Data collection is carried out with respect for user privacy and with appropriate security measures in place. For example, data is encrypted and stored on secure servers. Data is also collected only with the user's consent, and users are given the option to refuse data collection. This allows the data collection unit to efficiently and securely collect user behavior data, thereby improving the overall system performance.

[0073] The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses methods such as statistical analysis and machine learning algorithms to analyze the data. Specifically, it analyzes user browsing and purchase frequencies to identify user interests and preferences. For example, if a user frequently browses products in a particular category, it determines that the user has a high level of interest in that category. The analysis unit can also analyze user behavior patterns. For example, it analyzes the product categories a user frequently browses and the trends in products they purchase. This allows for an understanding of what products users are interested in and what purchasing behavior they exhibit. Furthermore, the analysis unit can analyze user behavior data in real time. For example, it analyzes information from the page a user is currently viewing to identify their interests and preferences. This allows for immediate identification of products and categories that a user is currently interested in. Based on these analysis results, the analysis unit provides information to the recommendation and support units. Deep learning and natural language processing technologies may also be used in the analysis, enabling highly accurate analysis. For example, deep learning can be used to learn user behavior patterns and identify interests and preferences with greater accuracy. Furthermore, natural language processing technology is used to analyze the user's search keywords and identify the information the user is seeking. This allows the analysis unit to quickly and accurately analyze user behavior data, improving the overall system performance.

[0074] The recommendation unit recommends products based on the analysis results obtained by the analysis unit. The recommendation unit uses methods such as collaborative filtering and content-based recommendation algorithms to make product recommendations. Specifically, it recommends products related to items the user has previously purchased. For example, it recommends products in the same category as items the user has previously purchased, or related accessories. The recommendation unit can also recommend products in categories the user frequently views. For example, it can prioritize displaying products in categories the user frequently views to attract user interest. Furthermore, the recommendation unit can analyze user behavior data in real time and recommend relevant products. For example, it can recommend products based on information from the page the user is currently viewing. This allows for immediate recommendations of products and categories related to the user's current interests. The recommendation unit provides these recommendations to the user, making it easier for them to find products they are interested in. Recommendations can utilize not only the user's past behavior data but also the behavior data of other users. For example, by recommending products purchased by other users who viewed the same product, it can recommend products that the user is likely to be interested in. The recommendation unit can also collect user feedback to continuously improve the accuracy of its recommendation algorithms. For example, if a user purchases a recommended product, the recommendation algorithm is adjusted based on that data to provide more accurate recommendations. This allows the recommendation system to recommend appropriate products to users and increase their purchasing intent.

[0075] The support department provides real-time answers to user questions. For example, it uses natural language processing technology to analyze user questions and provide appropriate answers. Specifically, when a user enters a question or point of confusion during the purchase process, the AI ​​analyzes the question and provides an appropriate answer. For example, if a user asks about product details or delivery status, the AI ​​analyzes the question and provides relevant information. The support department can also provide real-time chat support for user questions. For example, when a user enters a question via chat, the AI ​​analyzes the question and provides a real-time answer. This allows users to quickly resolve their questions and proceed smoothly through the purchase process. The support department can continuously improve the accuracy of its answers to user questions. For example, it adjusts the content and format of answers based on user feedback to provide more appropriate responses. Furthermore, the support department can support multiple languages. For example, even if a user enters a question in a different language, the AI ​​analyzes the question and provides an answer in the appropriate language. This allows the support department to provide fast and appropriate support to users globally. Furthermore, the support department can refer to a user's past question history to provide more appropriate answers. For example, if a user has asked the same question before, the support department can quickly provide an answer based on that history. This allows the support department to provide users with prompt and appropriate support, thereby improving user satisfaction.

[0076] The Guidance Department provides after-sales support and guidance on return and exchange procedures. For example, the Guidance Department collects the information necessary for users to return or exchange products and guides them through the process. Specifically, it provides the necessary documents and procedures for users to return products. For example, it provides information on how to return products and the necessary documents. The Guidance Department can also provide information necessary for users to receive after-sales support. For example, it provides information on the warranty period and support details. This allows users to quickly obtain the necessary information and proceed with the process smoothly. The Guidance Department can collect user feedback and continuously improve the accuracy and effectiveness of its guidance. For example, it adjusts the guidance based on feedback after users have completed a procedure, providing more appropriate information. The Guidance Department can also reliably transmit information using multiple communication methods. For example, it uses a combination of email, SMS, and in-app notifications to ensure that important information is delivered reliably. This allows the Guidance Department to provide information to users quickly and reliably and proceed with the process smoothly. Furthermore, the Guidance Department can refer to the user's past procedure history to provide more appropriate guidance. For example, if a user has performed the same procedure in the past, it can provide guidance quickly based on that history. This allows the guidance unit to provide users with quick and appropriate instructions, thereby improving user satisfaction.

[0077] The data collection unit can collect the user's browsing history and purchase history. For example, the data collection unit can collect data on pages the user has previously viewed and the time spent viewing them. The data collection unit can also collect information on products the user has purchased and the date and time of purchase. For example, the data collection unit can collect detailed information and purchase history of products the user has purchased. Furthermore, the data collection unit can collect the user's browsing history and purchase history in real time. For example, the data collection unit can collect information on the page the user is currently viewing and information on products they have purchased in real time. By collecting the user's browsing history and purchase history, it is possible to understand the user's interests and preferences. 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 browsing history and purchase history data into a generating AI and have the generating AI perform the data collection.

[0078] The analysis unit can analyze the collected data and identify user interests and preferences. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. For instance, it can analyze user browsing and purchasing frequency to identify user interests and preferences. The analysis unit can also analyze user behavior patterns. For example, it can analyze the product categories users frequently browse and the trends in products they purchase. Furthermore, the analysis unit can analyze user behavior data in real time. For example, it can analyze the information on the page a user is currently viewing to identify user interests and preferences. This allows the analysis of collected data to identify user interests and preferences, enabling the recommendation of more appropriate products. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0079] The recommendation unit can recommend relevant products based on the analysis results. The recommendation unit recommends products using, for example, collaborative filtering or content-based recommendation algorithms. For example, the recommendation unit recommends products related to products the user has purchased in the past. The recommendation unit can also recommend products in product categories that the user frequently views. Furthermore, the recommendation unit can analyze user behavior data in real time and recommend relevant products. For example, the recommendation unit recommends products based on information from the page the user is currently viewing. This allows the recommendation unit to provide products that match the user's interests and preferences by recommending relevant products based on the analysis results. Some or all of the above processes in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the analysis results into a generating AI and have the generating AI perform product recommendations.

[0080] The support department can answer user questions in real time using natural language processing technology. For example, the support department can analyze user questions using natural language processing technology and provide appropriate answers. For example, when a user enters a question or point of confusion during the purchase process, the AI ​​analyzes the question and provides an appropriate answer. The support department can also provide real-time chat support for user questions. For example, when a user enters a question via chat, the AI ​​analyzes the question and provides an answer in real time. This allows for the rapid resolution of questions and points of confusion during the purchase process by providing real-time answers to user questions. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input a user's question into a generating AI and have the generating AI generate an answer.

[0081] The guidance unit can collect the information necessary for users to return or exchange items and guide them through the process. For example, the guidance unit can collect the information necessary for users to return or exchange items and guide them through the process. For example, the guidance unit can provide information on how to return items and the necessary documents. The guidance unit can also provide information necessary for users to receive after-sales support. For example, the guidance unit can provide information on the warranty period and support details. This helps users to complete return or exchange procedures smoothly. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input information about the user's return or exchange procedure into a generating AI and have the generating AI execute the procedure guidance.

[0082] The recommendation system can estimate a user's emotions and recommend products based on those emotions. The recommendation system estimates user emotions using emotion estimation technologies such as facial expression analysis, voice analysis, and text analysis. For example, the recommendation system can capture a user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. It can also record a user's voice and estimate their emotions using voice analysis technology. Furthermore, the recommendation system can analyze a user's text data and estimate their emotions. For example, it can analyze text data entered by a user and estimate their emotions. This allows for the provision of more appropriate products by recommending products based on user emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation system may be performed using AI, or without AI. For example, the recommendation system can input user sentiment data into a generative AI and have the AI ​​generate product recommendations.

[0083] The data collection unit can estimate the user's emotions and adjust the timing of collecting browsing and purchase history based on the estimated emotions. The data collection unit estimates the user's emotions using emotion estimation technologies such as facial expression analysis, voice analysis, and text analysis. For example, the data collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the data collection unit can analyze the user's text data and estimate the emotion. For example, the data collection unit can analyze text data entered by the user and estimate the emotion. This allows for the collection of more appropriate data by adjusting the timing of data collection based on 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-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI execute the data collection process at the appropriate time.

[0084] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can identify the time periods when the user frequently accessed the system in the past and collect data during those times. The data collection unit can also analyze the devices the user has used in the past and collect data from the most suitable devices. Furthermore, the data collection unit can analyze the user's past behavioral patterns and select the most efficient data collection method. For example, the data collection unit can select the optimal data collection method based on data from the time periods and devices the user frequently accessed the system in the past. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data. 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 past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0085] The data collection unit can filter data based on the user's current interests and preferences during data collection. For example, the data collection unit can prioritize collecting data related to product categories that the user is currently interested in. The data collection unit can also filter data based on keywords that the user has recently searched for. Furthermore, the data collection unit can collect data related to the page the user is currently viewing. For example, the data collection unit can filter data based on information about product categories that the user is currently interested in, recently searched keywords, and the page the user is currently viewing. This allows for the collection of more relevant data by filtering data based on the user's current interests and preferences. 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 data on the user's current interests and preferences into a generating AI and have the generating AI perform data filtering.

[0086] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. The data collection unit estimates the user's emotions using emotion estimation techniques such as facial expression analysis, voice analysis, and text analysis. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis techniques. Furthermore, the data collection unit can analyze the user's text data and estimate their emotions. For example, the data collection unit analyzes text data entered by the user and estimates their emotions. This allows for the priority of data collection based on the user's emotions, thereby prioritizing the collection of more important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data.

[0087] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of data around their home. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. This allows for the priority collection of highly relevant data by considering the user's geographical location information. 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 and have the generating AI perform the collection of highly relevant data.

[0088] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to products that the user has shared on social media. The data collection unit can also collect data related to brands that the user follows on social media. Furthermore, the data collection unit can collect data related to posts that the user has "liked" on social media. For example, the data collection unit can collect data related to products that the user has shared on social media, brands that the user follows, and posts that the user has "liked". In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0089] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The analysis unit estimates the user's emotions using emotion estimation techniques such as facial expression analysis, voice analysis, and text analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis techniques. Furthermore, the analysis unit can analyze the user's text data and estimate the emotions. For example, the analysis unit can analyze text data entered by the user and estimate the emotions. By adjusting the data analysis method based on the user's emotions, more appropriate 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the data analysis method.

[0090] The analysis unit can optimize its analysis algorithm by referring to the user's past behavioral data during analysis. For example, the analysis unit can optimize its analysis algorithm based on data of products the user has frequently purchased in the past. The analysis unit can also refer to the user's past browsing history to perform analysis focused on categories of interest. Furthermore, the analysis unit can analyze the user's past purchase history to perform analysis based on purchase trends. For example, the analysis unit optimizes its analysis algorithm based on data of products the user has frequently purchased in the past, past browsing history, and purchase history. This allows the analysis algorithm to be optimized by referring to the user's past behavioral data, providing more accurate 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 past behavioral data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0091] The analysis unit can apply different analysis methods based on the user's interests and preferences during analysis. For example, the analysis unit can perform a detailed analysis on product categories that the user is interested in. It can also apply specific analysis methods to brands that the user has shown interest in. Furthermore, the analysis unit can apply different analysis methods based on data of products that the user frequently views. For example, the analysis unit can apply different analysis methods based on data of product categories that the user is interested in, brands that the user has shown interest in, and products that the user frequently views. This allows for the provision of more appropriate analysis results by applying different analysis methods based on the user's interests and preferences. 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 data on the user's interests and preferences into a generating AI and have the generating AI execute the application of different analysis methods.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using emotion estimation techniques such as facial expression analysis, voice analysis, and text analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis techniques. Furthermore, the analysis unit can analyze the user's text data and estimate the emotions. For example, the analysis unit can analyze text data entered by the user and estimate the emotions. By adjusting the display method of the analysis results based on the user's emotions, it is possible to provide results that are easier to see. 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-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust how the analysis results are displayed.

[0093] The analysis unit can determine the priority of analysis based on when the user's behavioral data was submitted. For example, the analysis unit may prioritize analyzing data recently submitted by the user. It can also prioritize analyzing data submitted by the user during a specific time period. Furthermore, it can prioritize analyzing data that the user frequently submits. For example, the analysis unit may determine the priority of analysis based on recently submitted data, data submitted during a specific time period, and frequently submitted data. By determining the priority of analysis based on when the user's behavioral data was submitted, it is possible to provide more timely analysis results. 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 may input data on when the user's behavioral data was submitted into a generating AI and have the generating AI perform the determination of the analysis priority.

[0094] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced. The analysis unit can also perform analysis by referring to the latest research in fields that the user is interested in. Furthermore, the analysis unit can improve the accuracy of its analysis based on literature that the user frequently references. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced, the latest research in fields that the user is interested in, and frequently referenced literature. In this way, the accuracy of the analysis can be improved by referring to the user's relevant literature. 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 relevant literature into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0095] The recommendation system can estimate the user's emotions and adjust its product recommendation method based on those estimated emotions. The recommendation system estimates the user's emotions using emotion estimation technologies such as facial expression analysis, voice analysis, and text analysis. For example, the recommendation system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the recommendation system can analyze the user's text data and estimate their emotions. For example, it can analyze text data entered by the user and estimate their emotions. This allows the system to provide more appropriate products by adjusting its product recommendation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation system may be performed using AI, or without AI. For example, the recommendation department can input user sentiment data into a generative AI and have the AI ​​adjust how products are recommended.

[0096] The recommendation system can recommend the most suitable products by referring to the user's past purchase history. For example, the recommendation system can recommend products related to products the user has previously purchased. It can also analyze the user's past purchase history and recommend products in the same category. Furthermore, the recommendation system can recommend products from brands that the user frequently purchases. For example, the recommendation system can recommend the most suitable products based on products the user has previously purchased, past purchase history, and brands that the user frequently purchases. This allows for the recommendation of more relevant products by referring to the user's past purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input data from the user's past purchase history into a generating AI and have the generating AI perform the optimal product recommendation.

[0097] The recommendation system can optimize its recommendation algorithm based on the user's current interests. For example, it can optimize the recommendation algorithm based on the product categories the user is currently interested in. It can also optimize the recommendation algorithm based on keywords the user has recently searched for. Furthermore, it can optimize the recommendation algorithm based on the page the user is currently viewing. For example, it can optimize the recommendation algorithm based on information about the product categories the user is currently interested in, recently searched keywords, and the page the user is currently viewing. This allows for the recommendation of more appropriate products by optimizing the recommendation algorithm based on the user's current interests. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input data on the user's current interests into a generating AI and have the generating AI perform the optimization of the recommendation algorithm.

[0098] The recommendation system can estimate the user's emotions and determine the priority of recommended products based on those estimated emotions. The recommendation system estimates the user's emotions using emotion estimation technologies such as facial expression analysis, voice analysis, and text analysis. For example, the recommendation system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the recommendation system can analyze the user's text data and estimate their emotions. For example, it can analyze text data entered by the user and estimate their emotions. This allows the system to provide more appropriate products by prioritizing recommended products based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation system may be performed using AI, or without AI. For example, the recommendation department can input user sentiment data into a generative AI and have the AI ​​determine the priority of recommended products.

[0099] The recommendation system can prioritize recommending highly relevant products by considering the user's geographical location. For example, if the user is in a specific region, the recommendation system will prioritize recommending products related to that region. Furthermore, if the user is traveling, the recommendation system can prioritize recommending products related to their travel destination. Additionally, if the user is at home, the recommendation system can prioritize collecting data about their surroundings. For example, if the recommendation system is in a specific region, it will prioritize recommending products related to that region. This allows the system to prioritize recommending highly relevant products by considering the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input the user's geographical location into a generating AI and have the generating AI recommend highly relevant products.

[0100] The recommendation unit can analyze a user's social media activity and recommend relevant products. For example, it can recommend products related to products the user has shared on social media. It can also recommend products related to brands the user follows on social media. Furthermore, it can recommend products related to posts the user has "liked" on social media. For example, it can recommend products related to products the user has shared on social media, brands they follow, and posts they have "liked." In this way, it can recommend relevant products by analyzing the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input data on the user's social media activity into a generating AI and have the generating AI perform the recommendation of relevant products.

[0101] The support unit can estimate the user's emotions and adjust the method of providing support based on the estimated emotions. The support unit estimates the user's emotions using emotion estimation technologies such as facial expression analysis, voice analysis, and text analysis. For example, the support unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The support unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the support unit can analyze the user's text data and estimate the emotions. For example, the support unit can analyze text data entered by the user and estimate the emotions. This allows for the provision of more appropriate support by adjusting the method of providing support based on 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-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​adjust the method of providing support.

[0102] The support unit can provide the best possible answer by referring to the user's past question history during support. For example, the support unit can provide relevant answers based on the questions the user has asked in the past. The support unit can also analyze the user's past question history and provide answers to frequently asked questions. Furthermore, the support unit can provide the best possible answer by referring to the support the user has received in the past. For example, the support unit can provide the best possible answer based on the questions the user has asked in the past, their past question history, and the support they have received in the past. This allows the support unit to provide more appropriate answers by referring to the user's past question history. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input data from the user's past question history into a generating AI and have the generating AI perform the task of providing the best possible answer.

[0103] The support unit can customize the support provided based on the user's current situation. For example, the support unit can provide appropriate support by considering which stage of the purchase process the user is currently in. It can also provide the optimal support method based on the device the user is currently using. Furthermore, the support unit can provide relevant support by considering the user's current geographical location. For example, the support unit customizes the support based on the user's current stage of the purchase process, the device they are currently using, and their current geographical location. This allows for more appropriate support to be provided by customizing the support based on the user's current situation. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input data on the user's current situation into a generating AI and have the generating AI perform the customization of the support.

[0104] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. The support unit estimates the user's emotions using emotion estimation technologies such as facial expression analysis, voice analysis, and text analysis. For example, the support unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The support unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the support unit can analyze the user's text data and estimate the emotions. For example, the support unit analyzes the text data entered by the user and estimates the emotions. This allows for the provision of more appropriate support by determining the priority of support based on 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-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​determine the priority of support requests.

[0105] The support unit can provide optimal support by considering the user's geographical location information during support. For example, if the user is in a specific region, the support unit can provide support relevant to that region. Furthermore, if the user is traveling, the support unit can provide support relevant to their travel destination. Additionally, if the user is at home, the support unit can provide support based on information about their surroundings. For example, if the support unit is in a specific region, it can provide support relevant to that region. This allows for the provision of more appropriate support by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI provide optimal support.

[0106] The support department can analyze a user's social media activity and provide relevant support during support sessions. For example, the support department can provide support related to issues the user has shared on social media. It can also provide support related to brands the user follows on social media. Furthermore, the support department can provide support related to posts the user has "liked" on social media. For example, the support department can provide support related to issues the user has shared on social media, brands they follow, and posts they have "liked." This allows for the provision of more appropriate support by analyzing the user's social media activity. Some or all of the above processing 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 and have the generating AI perform the provision of relevant support.

[0107] The guidance unit can estimate the user's emotions and adjust the method of providing guidance based on the estimated user emotions. The guidance unit estimates the user's emotions using emotion estimation techniques such as facial expression analysis, voice analysis, and text analysis. For example, the guidance unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The guidance unit can also record the user's voice and estimate their emotions using voice analysis techniques. Furthermore, the guidance unit can analyze the user's text data and estimate their emotions. For example, the guidance unit analyzes text data entered by the user and estimates their emotions. By doing so, the guidance unit can provide more appropriate guidance by adjusting the method of providing guidance based on 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-described processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input user emotion data into a generating AI and have the generating AI adjust how the guidance is provided.

[0108] The guidance unit can provide optimal guidance by referring to the user's past return and exchange history. For example, the guidance unit can provide appropriate guidance based on information about products the user has returned in the past. The guidance unit can also guide the user through the optimal exchange procedure by referring to the user's past exchange history. Furthermore, the guidance unit can provide optimal guidance based on the content of guidance the user has received in the past. For example, the guidance unit can provide optimal guidance based on information about products the user has returned in the past, past exchange history, and content of guidance received in the past. This allows for the provision of more appropriate guidance by referring to the user's past return and exchange history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the user's past return and exchange history into a generating AI and have the generating AI perform the task of providing optimal guidance.

[0109] The guidance unit can customize the guidance content based on the user's current situation. For example, the guidance unit can provide appropriate guidance by considering which stage of the purchase process the user is currently in. The guidance unit can also provide the optimal guidance method based on the device the user is currently using. Furthermore, the guidance unit can provide relevant guidance by considering the user's current geographical location. For example, the guidance unit customizes the guidance content based on the user's current stage of the purchase process, the device they are currently using, and their current geographical location. This allows for the provision of more appropriate guidance by customizing the guidance content based on the user's current situation. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the user's current situation into a generating AI and have the generating AI perform the customization of the guidance content.

[0110] The guidance unit can estimate the user's emotions and determine the priority of guidance based on the estimated user emotions. The guidance unit estimates the user's emotions using emotion estimation techniques such as facial expression analysis, voice analysis, and text analysis. For example, the guidance unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The guidance unit can also record the user's voice and estimate their emotions using voice analysis techniques. Furthermore, the guidance unit can analyze the user's text data and estimate their emotions. For example, the guidance unit analyzes text data entered by the user and estimates their emotions. By doing so, more appropriate guidance can be provided by determining the priority of guidance based on the user's emotions. Emotion estimation is implemented 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-described processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input user emotion data into a generating AI and have the generating AI determine the priority of the guidance.

[0111] The guidance unit can provide optimal guidance by considering the user's geographical location information during guidance. For example, if the user is in a specific region, the guidance unit can provide guidance relevant to that region. Furthermore, if the user is traveling, the guidance unit can provide guidance relevant to the travel destination. Additionally, if the user is at home, the guidance unit can provide guidance based on information about the area around the user's home. For example, if the guidance unit is in a specific region, it can provide guidance relevant to that region. This allows for the provision of more appropriate guidance by considering the user's geographical location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's geographical location information into a generating AI and have the generating AI provide optimal guidance.

[0112] The guidance unit can analyze the user's social media activity and provide relevant guidance during the guidance process. For example, the guidance unit can provide guidance related to issues the user has shared on social media. It can also provide guidance related to brands the user follows on social media. Furthermore, the guidance unit can provide guidance related to posts the user has "liked" on social media. For example, the guidance unit can provide guidance related to issues the user has shared on social media, brands they follow, and posts they have "liked." This allows for the provision of more appropriate guidance by analyzing the user's social media activity. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the user's social media activity into a generating AI and have the generating AI provide relevant guidance.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] An AI assistant for online store purchase support services can collect user behavior data while considering the user's geographical location. For example, if the user is in a specific region, the data collection unit can prioritize collecting browsing and purchase history of products related to that region. If the user is traveling, it can also collect data on products related to their travel destination. Furthermore, if the user is at home, it can collect data on products around their home. This allows for the collection of more 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, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI prioritize data collection.

[0115] The analysis unit can analyze user behavior data while taking into account the user's social media activity. For example, the analysis unit can analyze data related to products shared by the user on social media and brands followed by the user. It can also analyze data related to posts that the user "likes" on social media. Furthermore, it can analyze the content of comments made by the user on social media to identify the user's interests and concerns. By considering the user's social media activity, it is possible to provide more accurate analysis results. 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 social media activity into a generating AI and have the generating AI perform the data analysis.

[0116] The recommendation unit can estimate the user's emotions and adjust its product recommendation method based on those emotions. For example, if the user is stressed, it can recommend products that have a relaxing effect. If the user is happy, it can recommend products that will make them even happier. Furthermore, if the user is sad, it can recommend products that will lift their spirits. By adjusting the product recommendation method based on the user's emotions, it is possible to provide more appropriate products. 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 and have the generative AI adjust the product recommendation method.

[0117] The support unit can estimate the user's emotions and adjust the way support is provided based on those emotions. For example, if the user is feeling anxious, it can provide more attentive and reassuring support. If the user is in a hurry, it can provide faster support. Furthermore, if the user is satisfied, it can provide additional information or options. In this way, by adjusting the way support is provided based on the user's emotions, more appropriate support 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 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 and have the generative AI adjust the way support is provided.

[0118] The guidance unit can estimate the user's emotions and adjust the way guidance is provided based on the estimated emotions. For example, if the user is confused, it can provide more detailed and easy-to-understand guidance. If the user is confident, it can provide concise guidance. Furthermore, if the user is dissatisfied, it can provide a quick and courteous response. In this way, by adjusting the way guidance is provided based on the user's emotions, more appropriate guidance 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 guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input user emotion data into the generative AI and have the generative AI adjust the way guidance is provided.

[0119] The data collection unit can collect user behavior data while considering the user's past purchase history. For example, the data collection unit can prioritize collecting data related to products the user has purchased in the past. It can also collect product data from categories that the user frequently purchases. Furthermore, if the user frequently purchases products from a particular brand, it can collect data related to that brand. This allows for the collection of more relevant data by considering the user's 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 data from the user's past purchase history into a generating AI and have the generating AI prioritize data collection.

[0120] The analysis unit can analyze user behavior data while taking into account the user's current interests and preferences. For example, the analysis unit can prioritize analyzing data related to product categories that the user is currently interested in. It can also analyze data related to keywords the user has recently searched for. Furthermore, it can analyze data related to the page the user is currently viewing. By considering the user's current interests and preferences, it is possible to provide more accurate analysis results. 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 current interests and preferences into a generating AI and have the generating AI perform the data analysis.

[0121] The recommendation unit can recommend the most suitable products by referring to the user's past purchase history. For example, it can recommend products related to products the user has purchased in the past. It can also analyze the user's past purchase history and recommend products in the same category. Furthermore, it can recommend products from brands that the user frequently purchases. This allows for the recommendation of more relevant products by referring to the user's past purchase history. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input data on the user's past purchase history into a generating AI and have the generating AI perform the optimal product recommendation.

[0122] The support department can provide the best possible answers by referring to the user's past question history. For example, it can provide relevant answers based on questions the user has asked in the past. It can also analyze the user's past question history and provide answers to frequently asked questions. Furthermore, it can provide the best possible answers by referring to the support the user has received in the past. This allows for the provision of more appropriate answers by referring to the user's past question history. 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 from the user's past question history into a generating AI and have the generating AI perform the task of providing the best possible answers.

[0123] The guidance unit can provide optimal guidance by referring to the user's past return and exchange history. For example, it can provide appropriate guidance based on information about products the user has returned in the past. It can also guide the user through the optimal exchange procedure by referring to the user's past exchange history. Furthermore, it can provide optimal guidance based on the guidance the user has received in the past. In this way, by referring to the user's past return and exchange history, more appropriate guidance can be provided. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the user's past return and exchange history into a generating AI and have the generating AI perform the task of providing optimal guidance.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The collection unit collects user behavior data. User behavior data includes, but is not limited to, browsing history and purchase history. For example, the collection unit collects information on products the user has previously viewed or purchased. The collection unit can also collect user click data and search history. For example, the collection unit collects data on links the user has clicked and keywords the user has searched for. Furthermore, the collection unit can collect user behavior data in real time. For example, the collection unit collects information on the page the user is currently viewing. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit analyzes users' browsing and purchasing frequencies to identify users' interests and preferences. The analysis unit can also analyze users' behavior patterns. For example, the analysis unit analyzes the product categories that users frequently browse and the trends in the products they purchase. Furthermore, the analysis unit can analyze user behavior data in real time. For example, the analysis unit analyzes the information on the page that a user is currently viewing to identify users' interests and preferences. Step 3: The recommendation unit recommends products based on the analysis results obtained by the analysis unit. The recommendation unit recommends products using, for example, collaborative filtering or content-based recommendation algorithms. For example, the recommendation unit recommends products related to products the user has purchased in the past. The recommendation unit can also recommend products in product categories that the user frequently views. Furthermore, the recommendation unit can analyze user behavior data in real time and recommend relevant products. For example, the recommendation unit recommends products based on information from the page the user is currently viewing. Step 4: The support team provides real-time answers to user questions. The support team uses natural language processing technology, for example, to analyze user questions and provide appropriate answers. For instance, when a user enters a question or point of confusion during the purchase process, the AI ​​analyzes the question and provides an appropriate answer. The support team can also provide real-time chat support for user questions. For example, when a user enters a question via chat, the AI ​​analyzes the question and provides an answer in real time. Step 5: The Guidance Department provides after-sales support and guidance on return and exchange procedures. For example, the Guidance Department collects the information necessary for users to return or exchange products and guides them through the process. For instance, the Guidance Department provides information on how to return products and the necessary documents. The Guidance Department can also provide information necessary for users to receive after-sales support. For example, the Guidance Department provides information on the warranty period and support services.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] For example, the data collection unit can collect user behavior data using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends products based on the analysis results. For example, the support unit is implemented by the control unit 46A of the smart device 14 and answers user questions in real time. For example, the guidance unit is implemented by the specific processing unit 290 of the data processing device 12 and guides users through after-sales support and return / exchange procedures. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] For example, the data collection unit can collect user behavior data using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends products based on the analysis results. For example, the support unit is implemented by the control unit 46A of the smart glasses 214 and answers user questions in real time. For example, the guidance unit is implemented by the specific processing unit 290 of the data processing device 12 and guides users through after-sales support and return / exchange procedures. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

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

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

[0156] In 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.

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

[0158] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0160] The data processing system 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.

[0161] For example, the data collection unit can collect user behavior data using the camera 42 and microphone 238 of the headset terminal 314. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends products based on the analysis results. For example, the support unit is implemented by the control unit 46A of the headset terminal 314 and answers user questions in real time. For example, the guidance unit is implemented by the specific processing unit 290 of the data processing device 12 and guides users through after-sales support and return / exchange procedures. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] For example, the data collection unit can collect user behavior data using the camera 42 and microphone 238 of the robot 414. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends products based on the analysis results. For example, the support unit is implemented by the control unit 46A of the robot 414 and answers user questions in real time. For example, the guidance unit is implemented by the specific processing unit 290 of the data processing device 12 and guides users through after-sales support and return / exchange procedures. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) A data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, A recommendation unit that recommends products based on the analysis results obtained by the aforementioned analysis unit, A support department that answers user questions in real time, It includes a guidance section that provides after-sales support and instructions for returns and exchanges. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects users' browsing and purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recommendation department, Based on the analysis results, we recommend relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is It uses natural language processing technology to provide real-time answers to user questions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned guidance unit, Collect the information necessary for users to return or exchange items and guide them through the process. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recommendation department, It estimates the user's emotions and recommends products based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting browsing and purchase history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze users' past behavioral data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analytical methods are applied based on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the user's behavioral data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation department, It estimates the user's emotions and adjusts the product recommendation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, the system refers to the user's past purchase history to suggest the most suitable products. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation department, When making recommendations, the recommendation algorithm is optimized based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation department, It estimates the user's emotions and prioritizes recommended products based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation department, When making recommendations, the system prioritizes recommending highly relevant products by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recommendation department, When making recommendations, the system analyzes the user's social media activity and recommends relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is We estimate the user's emotions and adjust how support is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is When providing support, we refer to the user's past question history to provide the best possible answer. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is During support, customize the support content based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) 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 30) The aforementioned support unit is When providing support, we take the user's geographical location into consideration to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is During support, we analyze the user's social media activity and provide relevant support. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned guidance unit, It estimates the user's emotions and adjusts how guidance is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned guidance unit, During guidance, the system will refer to the user's past return and exchange history to provide the most appropriate guidance. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned guidance unit, During guidance, customize the guidance content based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned guidance unit, It estimates the user's emotions and determines the priority of guidance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned guidance unit, When providing guidance, the system takes the user's geographical location into consideration to provide optimal guidance. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned guidance unit, During guidance, the system analyzes the user's social media activity and provides relevant guidance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 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 data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, A recommendation unit that recommends products based on the analysis results obtained by the aforementioned analysis unit, A support department that answers user questions in real time, It includes a guidance section that provides after-sales support and instructions for returns and exchanges. A system characterized by the following features.

2. The aforementioned collection unit is Collects users' browsing and purchase history. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to identify the user's interests and preferences. The system according to feature 1.

4. The aforementioned recommendation department, Based on the analysis results, we recommend relevant products. The system according to feature 1.

5. The aforementioned support unit is It uses natural language processing technology to provide real-time answers to user questions. The system according to feature 1.

6. The aforementioned guidance unit, Collect the information necessary for users to return or exchange items and guide them through the process. The system according to feature 1.

7. The aforementioned recommendation department, It estimates the user's emotions and recommends products based on those estimated emotions. The system according to feature 1.

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

9. The aforementioned collection unit is Analyze users' past behavioral data to select the optimal data collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current interests and preferences. The system according to feature 1.

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