system

Abstract

The system according to this embodiment aims to set the optimal price by taking into account market trends, competitive pricing, and customer purchase history. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, and an application unit. The collection unit collects market trends, competitive prices, and customer purchase history. The analysis unit analyzes the data collected by the collection unit and sets the optimal price. The application unit applies the price set by the analysis unit.

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, it is difficult to set an optimal price considering market trends, competitive prices, and customer purchase histories, and there is room for improvement.

[0005] The system according to the embodiment aims to set an optimal price in consideration of market trends, competitive prices, and customer purchase histories.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an application unit. The collection unit collects market trends, competitive prices, and customer purchase histories. The analysis unit analyzes the data collected by the collection unit and sets an optimal price. The application unit applies the price set by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can set an optimal price by taking into account market trends, competitive pricing, and customer purchase history. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is an AI-powered system designed to solve challenges faced by merchants, such as pricing difficulties, high return rates, and inefficient shipping options. This system provides three main functions: a dynamic pricing agent, return forecasting and management, and personalized shipping options. The dynamic pricing agent uses AI to analyze market trends, competitor pricing, and customer purchase history in real time, automatically setting the optimal price. This allows merchants to maximize revenue while maintaining competitiveness. Return forecasting and management uses an AI model to predict the likelihood of product returns and suggest improvements. Based on past return data, it identifies which products are prone to returns and analyzes the causes. Personalized shipping options analyze customers' past shipping choices and behavior to suggest the optimal shipping option. This improves customer satisfaction and optimizes shipping costs. These functions are expected to improve merchant revenue, customer satisfaction, online shop operational efficiency, and platform reputation and revenue. For example, the dynamic pricing agent uses AI to analyze market trends, competitor pricing, and customer purchase history in real time, automatically setting the optimal price. This allows merchants to maximize revenue while maintaining competitiveness. Return prediction and management uses AI models to predict the likelihood of product returns and suggest improvements. Based on historical return data, it identifies which products are most likely to be returned and analyzes the causes. Personalized shipping options analyze customers' past shipping choices and behavior to suggest the optimal shipping option. This improves customer satisfaction and optimizes shipping costs. As a result, the system can help merchants increase revenue, improve customer satisfaction, enhance online store operational efficiency, and improve platform reputation and revenue.

[0029] The system according to this embodiment comprises a data collection unit, an analysis unit, and an application unit. The data collection unit collects market trends, competitor prices, and customer purchase history. The data collection unit can collect data from, for example, publicly available data on the internet, competitor price information, and customer purchase history databases. The data collection unit can also dynamically adjust the scope and priority of the data to be collected using AI. The analysis unit analyzes the data collected by the data collection unit and sets the optimal price. The analysis unit can, for example, use AI to analyze the collected data in real time and perform demand forecasting and competitor analysis. The analysis unit can also estimate user sentiment and adjust the pricing algorithm based on the estimated user sentiment. The application unit applies the price set by the analysis unit. The application unit can, for example, use AI to automatically apply the set price and adjust the price display method and notification method. The application unit can also dynamically change the price according to specific time periods or seasons. As a result, the system can collect market trends, competitor prices, and customer purchase history, and set and apply the optimal price to maximize revenue while maintaining competitiveness.

[0030] The data collection unit collects market trends, competitor pricing, and customer purchase history. Specifically, it can collect data from publicly available data on the internet, competitor pricing information, and customer purchase history databases. Publicly available data on the internet includes information from news sites, blogs, and social media, and this information is automatically collected using crawling technology. Competitor pricing information is obtained from websites and online stores using scraping technology. Customer purchase history databases are accessed from customer management systems and sales management systems owned by companies to obtain past purchase history and customer attribute information. The data collection unit can also use AI to dynamically adjust the scope and priority of the data to be collected. For example, the AI ​​evaluates the quality and reliability of the collected data and prioritizes the collection of high-priority data. It can also optimize the frequency and timing of collection to obtain the latest information in real time. This allows the data collection unit to efficiently collect data from a wide range of data sources and improve the accuracy and reliability of the overall system. Furthermore, the data collection unit preprocesses the data, removing noise and missing values ​​to prepare it in a format that is easy for the analysis unit to use. This allows the data collection unit to handle everything from data collection to preprocessing in a consistent manner, thereby improving the overall data quality of the system.

[0031] The analysis department analyzes data collected by the data collection department to set optimal prices. Specifically, it uses AI to analyze collected data in real time and perform demand forecasting and competitive analysis. In demand forecasting, future demand is predicted based on past purchase history and market trend data. The AI ​​uses time series analysis and machine learning algorithms to learn demand fluctuation patterns and make highly accurate predictions. In competitive analysis, the price information and market share of competitors are analyzed to optimize the company's pricing strategy. The AI ​​can monitor competitor price fluctuations and promotional activities in real time and respond quickly. Furthermore, the analysis department can estimate user sentiment and adjust the pricing algorithm based on the estimated user sentiment. For example, it analyzes text data from social media and review sites to classify user sentiment as positive, negative, or neutral. The AI ​​uses natural language processing technology to extract sentiment from the text data and adjusts pricing according to changes in sentiment. This allows the analysis department to set optimal prices by combining demand forecasting, competitive analysis, and sentiment estimation. Furthermore, the analysis department monitors the effect of the set price and readjusts the pricing as needed. This allows the analysis department to constantly adapt to market changes and maintain the optimal pricing strategy.

[0032] The application unit applies the prices set by the analysis unit. Specifically, it can use AI to automatically apply the set prices and adjust how prices are displayed and notified. For example, in an online store, it can reflect the set prices on product pages in real time, providing customers with the latest price information. The AI ​​can also personalize price displays based on customers' browsing and purchase history. For example, it can increase purchasing intent by offering exclusive discounts or special offers to specific customers. The application unit can also dynamically change prices according to specific times of day or seasons. For example, it can maximize revenue by raising prices during times of high demand or seasons and lowering prices during times of low demand. Furthermore, the application unit can adjust how price changes are notified to effectively communicate information to customers. For example, it can use email or push notifications to inform customers of price changes or special offers. This allows the application unit to quickly and effectively apply the set prices and provide customers with optimal price information. In addition, the application unit monitors the effectiveness of price application and readjusts the pricing strategy as needed. This allows the application unit to constantly respond to market changes and maintain an optimal pricing strategy.

[0033] The data collection unit can collect product return data. For example, when a customer requests a return, the unit collects data such as the reason for the return, the date and time of the return, and the returned product. The data collection unit can also use AI to dynamically adjust the scope and priority of the data it collects. The data collection unit can also analyze past return data to predict return trends for specific products. When collecting return data, the data collection unit can also add customer social media activity and online reviews. This allows the data collection unit to identify the causes of returns and propose improvement measures by collecting product return data.

[0034] The analysis department can analyze data collected by the data collection department to identify the causes of returns. For example, the analysis department can use AI to analyze collected data in real time and identify the causes of returns. The analysis department can also estimate user sentiment and adjust the return cause analysis method based on the estimated user sentiment. The analysis department can also analyze past return causes and predict the return risk of specific products. The analysis department can also apply different return cause analysis algorithms for each product category. This allows the analysis department to identify the causes of returns and propose improvements to reduce the return rate.

[0035] The proposal department can propose improvement measures based on the causes identified by the analysis department. For example, the proposal department can use AI to suggest improvements to products or services based on the identified causes. The proposal department can also estimate user emotions and adjust the method of suggesting improvement measures based on the estimated user emotions. The proposal department can also analyze past success and failure examples of improvement measures to improve the accuracy of its suggestions. The proposal department can also propose different improvement measures for each product category. In this way, the proposal department can reduce return rates and improve customer satisfaction by proposing improvement measures.

[0036] The data collection unit can collect customer behavior data. For example, it can collect browsing history, purchase history, and clickstream data when customers visit websites. The data collection unit can also use AI to dynamically adjust the scope and priority of the data it collects. The data collection unit can also analyze past behavior data and predict specific behavioral patterns. When collecting behavioral data, the data collection unit can also add customer social media activity and online reviews. This allows the data collection unit to analyze customer behavior and suggest the best shipping options by collecting customer behavior data.

[0037] The proposal department can analyze data collected by the data collection department and propose the optimal delivery option. For example, the proposal department can use AI to analyze the collected data in real time and propose the optimal delivery option based on the customer's past delivery choices and behavior. The proposal department can also estimate the user's emotions and adjust how it proposes delivery options based on those emotions. The proposal department can also analyze past successes and failures of delivery options to improve the accuracy of its proposals. The proposal department can also propose different delivery options for each product category. In this way, the proposal department can improve customer satisfaction and optimize delivery costs by proposing the optimal delivery option.

[0038] The data collection unit can dynamically adjust the scope of data collected by analyzing past market trends and competitive price fluctuation patterns. For example, the data collection unit can analyze past market trends and adjust the scope of data collected for a specific season. The data collection unit can analyze competitive price fluctuation patterns and adjust the scope of data collected at a specific event. The data collection unit can analyze past purchase history and adjust the scope of data collected for a specific customer segment. In this way, the data collection unit can dynamically adjust the scope of data collected by analyzing past market trends and competitive price fluctuation patterns.

[0039] The data collection unit can optimize the data collected at specific times of day or seasons based on customer purchase history. For example, the unit can analyze customer purchase history and optimize the data collected at specific times of day. It can also analyze customer purchase history and optimize the data collected during specific seasons. Furthermore, it can analyze customer purchase history and optimize the data collected during specific events. This allows the data collection unit to achieve more effective data collection by optimizing data based on customer purchase history.

[0040] The data collection unit can add customer social media activity and online reviews to the data it collects. For example, the unit can collect customer social media activity and analyze it in combination with purchase history. The unit can collect online reviews and analyze them in combination with competitive pricing. The unit can collect customer social media activity and online reviews and analyze them in combination with market trends. This allows the data collection unit to collect more diverse data by adding customer social media activity and online reviews.

[0041] The data collection unit can include information on regional market trends and competitive pricing in the data it collects. For example, the data collection unit can collect regional market trends and analyze them in combination with competitive pricing. The data collection unit can collect regional competitive pricing and analyze it in combination with customer purchase history. The data collection unit can collect regional market trends and competitive pricing and analyze them in combination with purchase history. This allows the data collection unit to collect more detailed data by including information on regional market trends and competitive pricing.

[0042] The analysis department can improve the accuracy of pricing by analyzing past successful and unsuccessful pricing examples. For example, the analysis department can analyze past successful pricing examples and identify the factors contributing to their success. The analysis department can analyze past unsuccessful pricing examples and identify the factors contributing to their failure. The analysis department can improve the accuracy of pricing by comparing and analyzing successful and unsuccessful examples. In this way, the analysis department can improve the accuracy of pricing by analyzing past successful and unsuccessful pricing examples.

[0043] The analysis department can apply different pricing algorithms to each product category. For example, it can apply an algorithm that emphasizes competitive pricing to high-priced products, an algorithm that emphasizes customer purchase history to low-priced products, and an algorithm that emphasizes market trends to mid-priced products. By applying different pricing algorithms to each product category, the analysis department can achieve more effective pricing.

[0044] The analysis department can consider competitors' promotional information when setting prices. For example, the analysis department can collect competitors' promotional information and reflect it in pricing. The analysis department can adjust prices during competitors' promotional periods. The analysis department can analyze competitors' promotional content and use it in pricing. As a result, the analysis department can set more effective prices by considering competitors' promotional information.

[0045] The analytics department can offer individual discounts and benefits based on customer purchase history when setting prices. For example, the analytics department analyzes customer purchase history and offers individual discounts. The analytics department offers benefits based on customer purchase history. The analytics department considers customer purchase history and reflects it in pricing. In this way, the analytics department can improve customer satisfaction by offering individual discounts and benefits based on customer purchase history.

[0046] The application unit can dynamically change the applied price according to specific time periods or seasons. For example, the application unit can dynamically change the price according to specific time periods. The application unit can dynamically change the price according to specific seasons. The application unit can dynamically change the price according to specific events. This allows the application unit to maximize revenue by dynamically changing the price according to specific time periods or seasons.

[0047] The application unit can personalize the price it applies based on the customer's purchase history. For example, the application unit personalizes the price based on the customer's purchase history. The application unit considers the customer's purchase history and offers a special price. The application unit analyzes the customer's purchase history and applies the optimal price. In this way, the application unit can improve customer satisfaction by personalizing prices based on the customer's purchase history.

[0048] The application unit can reflect regional market trends in the price it applies. For example, the application unit can apply prices by reflecting regional market trends. The application unit can apply prices by considering regional competitive prices. The application unit can apply prices based on regional purchase history. As a result, the application unit can achieve more effective pricing by reflecting regional market trends.

[0049] The application unit can take into account customer loyalty program information when determining the price to be applied. For example, the application unit can apply a price by taking into account customer loyalty program information. The application unit can apply a price according to the loyalty program rank. The application unit can apply a price that reflects the benefits of the loyalty program. In this way, the application unit can improve customer satisfaction by taking into account customer loyalty program information.

[0050] The data collection unit can analyze past return data and predict return trends for specific products. For example, the data collection unit analyzes past return data to predict return trends for specific products. Based on past return data, the data collection unit identifies products with a high return risk. The data collection unit analyzes past return data to identify the reasons for returns. Thus, by analyzing past return data, the data collection unit can predict return trends for specific products.

[0051] The data collection unit can obtain detailed information about the reason for a return when collecting return data. For example, the data collection unit obtains detailed information about the reason for a return when collecting return data. The data collection unit obtains customer feedback when collecting return data. The data collection unit obtains detailed information about the return process when collecting return data. As a result, the data collection unit can more accurately identify the cause of a return by obtaining detailed information about the reason for a return.

[0052] The data collection unit can add customer social media activity and online reviews when collecting return data. For example, the data collection unit can add customer social media activity when collecting return data. The data collection unit can add online reviews when collecting return data. The data collection unit can add customer feedback when collecting return data. This allows the data collection unit to collect more diverse data by adding customer social media activity and online reviews.

[0053] The data collection unit can consider regional return trends when collecting return data. For example, the data collection unit considers regional return trends when collecting return data. The data collection unit considers regional customer feedback when collecting return data. The data collection unit considers regional reasons for returns when collecting return data. As a result, the data collection unit can collect more accurate data by considering regional return trends.

[0054] The analysis department can analyze past return causes and predict the return risk of specific products. For example, the analysis department analyzes past return causes to predict the return risk of specific products. Based on past return causes, the analysis department identifies products with a high return risk. The analysis department analyzes past return causes to identify measures to reduce return risk. In this way, the analysis department can predict the return risk of specific products by analyzing past return causes.

[0055] The analysis department can apply different return cause analysis algorithms to each product category. For example, the analysis department can apply a detailed return cause analysis algorithm to high-priced products, a simplified return cause analysis algorithm to low-priced products, and a standard return cause analysis algorithm to mid-priced products. This allows the analysis department to perform more effective cause analysis by applying different return cause analysis algorithms to each product category.

[0056] The analytics department can consider customer social media activity and online reviews when analyzing the reasons for returns. For example, the analytics department considers customer social media activity when analyzing the reasons for returns. The analytics department considers online reviews when analyzing the reasons for returns. The analytics department considers customer feedback when analyzing the reasons for returns. This allows the analytics department to conduct more accurate root cause analysis by considering customer social media activity and online reviews.

[0057] The analysis department can incorporate regional return trends when analyzing the causes of returns. For example, the analysis department can incorporate regional return trends when analyzing the causes of returns. The analysis department can incorporate regional customer feedback when analyzing the causes of returns. The analysis department can incorporate regional reasons for returns when analyzing the causes of returns. This allows the analysis department to conduct more accurate cause analysis by incorporating regional return trends.

[0058] The proposal department can improve the accuracy of its proposals by analyzing past success and failure examples of improvement measures. For example, the proposal department can analyze past success examples of improvement measures and identify the factors for success. The proposal department can analyze past failure examples of improvement measures and identify the factors for failure. The proposal department can improve the accuracy of its proposals by comparing and analyzing success and failure examples. In this way, the proposal department can improve the accuracy of its proposals by analyzing past success and failure examples of improvement measures.

[0059] The proposal department can propose different improvement measures for each product category. For example, the proposal department can propose detailed improvement measures for high-priced products, simple improvement measures for low-priced products, and standard improvement measures for mid-priced products. This allows the proposal department to make more effective proposals by offering different improvement measures for each product category.

[0060] The proposal department can consider the customer's social media activity and online reviews when proposing improvement measures. For example, the proposal department considers the customer's social media activity when proposing improvement measures. The proposal department considers online reviews when proposing improvement measures. The proposal department considers customer feedback when proposing improvement measures. This allows the proposal department to make more accurate proposals by considering the customer's social media activity and online reviews.

[0061] The proposal department can incorporate regional return trends when proposing improvement measures. For example, the proposal department can incorporate regional return trends when proposing improvement measures. The proposal department can incorporate regional customer feedback when proposing improvement measures. The proposal department can incorporate regional return reasons when proposing improvement measures. This allows the proposal department to make more accurate proposals by incorporating regional return trends.

[0062] The data collection unit can analyze past behavioral data and predict specific behavioral patterns. For example, the data collection unit analyzes past behavioral data to predict specific behavioral patterns. Based on past behavioral data, the data collection unit predicts changes in behavioral patterns. The data collection unit analyzes past behavioral data to identify trends in behavioral patterns. Thus, by analyzing past behavioral data, the data collection unit can predict specific behavioral patterns.

[0063] The data collection unit can obtain detailed customer behavior history when collecting behavioral data. For example, the data collection unit obtains detailed customer behavior history when collecting behavioral data. The data collection unit obtains past customer behavior history when collecting behavioral data. The data collection unit records customer behavior history in detail when collecting behavioral data. As a result, the data collection unit can collect more accurate data by obtaining detailed customer behavior history.

[0064] The data collection unit can add customer social media activity and online reviews when collecting behavioral data. For example, the data collection unit can add customer social media activity when collecting behavioral data. The data collection unit can add online reviews when collecting behavioral data. The data collection unit can add customer feedback when collecting behavioral data. This allows the data collection unit to collect more diverse data by adding customer social media activity and online reviews.

[0065] The data collection unit can consider regional behavioral trends when collecting behavioral data. For example, the data collection unit considers regional behavioral trends when collecting behavioral data. The data collection unit considers regional customer feedback when collecting behavioral data. The data collection unit considers regional behavioral patterns when collecting behavioral data. As a result, the data collection unit can collect more accurate data by considering regional behavioral trends.

[0066] The proposal department can improve the accuracy of its proposals by analyzing past successes and failures of delivery options. For example, the proposal department can analyze past successes of delivery options and identify the factors contributing to their success. The proposal department can also analyze past failures of delivery options and identify the factors contributing to their failure. The proposal department can improve the accuracy of its proposals by comparing and analyzing successes and failures of delivery options. In this way, the proposal department can improve the accuracy of its proposals by analyzing past successes and failures of delivery options.

[0067] The proposal department can suggest different shipping options for each product category. For example, it might suggest expedited shipping for high-priced items, standard shipping for low-priced items, and standard shipping for mid-priced items. This allows the proposal department to make more effective suggestions by offering different shipping options for each product category.

[0068] The proposal department can consider the customer's social media activity and online reviews when proposing delivery options. For example, the proposal department considers the customer's social media activity when proposing delivery options. The proposal department considers online reviews when proposing delivery options. The proposal department considers customer feedback when proposing delivery options. This allows the proposal department to make more accurate recommendations by considering the customer's social media activity and online reviews.

[0069] The proposal department can reflect regional delivery trends when proposing delivery options. For example, the proposal department can reflect regional delivery trends when proposing delivery options. The proposal department can reflect regional customer feedback when proposing delivery options. The proposal department can reflect regional delivery patterns when proposing delivery options. As a result, the proposal department can make more accurate proposals by reflecting regional delivery trends.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The system can also include a behavior prediction unit to predict customer purchasing behavior. This unit, for example, analyzes a customer's past purchase and browsing history to predict the next product they are most likely to purchase. The unit can also estimate customer emotions and improve prediction accuracy based on these estimations. For instance, if a customer is excited, the system can determine their purchase intent is high and enhance its recommendations for related products. If a customer is relaxed, the system can suggest products at a slower pace to maintain their purchase intent. This allows the behavior prediction unit to predict customer purchasing behavior and suggest products at the appropriate time, thereby contributing to increased revenue.

[0072] The system can also include a feedback collection unit to gather customer feedback. This unit collects customer opinions, for example, through post-purchase surveys or reviews. The feedback collection unit can also estimate customer emotions and adjust the feedback collection method based on these estimations. For example, if a customer is satisfied, requesting detailed feedback can gather more positive opinions. If a customer is dissatisfied, requesting concise feedback can quickly identify the problem. This allows the feedback collection unit to effectively gather customer opinions and use them to improve services.

[0073] The system can also include a loyalty management department to further enhance customer loyalty. This department could, for example, analyze customer purchase history and behavioral data to provide loyalty program benefits. It could also estimate customer emotions and adjust the content and timing of benefits based on those emotions. For instance, if a customer is excited, immediately available benefits could be offered to further increase their purchasing intent. If a customer is relaxed, long-term benefits could be proposed to maintain their loyalty. This allows the loyalty management department to improve customer loyalty and contribute to increased revenue.

[0074] The system can also include an experience enhancement unit to further improve the customer's purchasing experience. This unit, for example, analyzes customer purchase history and behavioral data to provide a personalized purchasing experience. It can also estimate customer emotions and adjust the content and timing of the purchasing experience based on those emotions. For example, if a customer is excited, it can suggest special offers or limited-edition products to further increase their desire to purchase. If a customer is relaxed, it can suggest products at a slower pace to allow them to enjoy the purchasing experience. In this way, the experience enhancement unit can improve the customer's purchasing experience and contribute to increased revenue.

[0075] The system can also include a behavior prediction unit to predict customer purchasing behavior. This unit, for example, analyzes a customer's past purchase and browsing history to predict the next product they are most likely to purchase. The unit can also estimate customer emotions and improve prediction accuracy based on these estimations. For instance, if a customer is excited, the system can determine their purchase intent is high and enhance its recommendations for related products. If a customer is relaxed, the system can suggest products at a slower pace to maintain their purchase intent. This allows the behavior prediction unit to predict customer purchasing behavior and suggest products at the appropriate time, thereby contributing to increased revenue.

[0076] The following briefly describes the processing flow for example form 1.

[0077] Step 1: The data collection unit gathers market trends, competitor pricing, and customer purchase history. For example, it can collect data from publicly available data on the internet, competitor pricing information, and customer purchase history databases. The data collection unit can also use AI to dynamically adjust the scope and priority of the data to be collected. Step 2: The analysis unit analyzes the data collected by the collection unit and sets the optimal price. For example, AI can be used to analyze the collected data in real time and perform demand forecasting and competitor analysis. Furthermore, it can estimate user sentiment and adjust the pricing algorithm based on the estimated user sentiment. Step 3: The application unit applies the price set by the analysis unit. For example, AI can be used to automatically apply the set price and adjust how the price is displayed and how notifications are made. Furthermore, the price can be dynamically changed according to specific time periods or seasons.

[0078] (Example of form 2) The system according to an embodiment of the present invention is an AI-powered system designed to solve challenges faced by merchants, such as pricing difficulties, high return rates, and inefficient shipping options. This system provides three main functions: a dynamic pricing agent, return forecasting and management, and personalized shipping options. The dynamic pricing agent uses AI to analyze market trends, competitor pricing, and customer purchase history in real time, automatically setting the optimal price. This allows merchants to maximize revenue while maintaining competitiveness. Return forecasting and management uses an AI model to predict the likelihood of product returns and suggest improvements. Based on past return data, it identifies which products are prone to returns and analyzes the causes. Personalized shipping options analyze customers' past shipping choices and behavior to suggest the optimal shipping option. This improves customer satisfaction and optimizes shipping costs. These functions are expected to improve merchant revenue, customer satisfaction, online shop operational efficiency, and platform reputation and revenue. For example, the dynamic pricing agent uses AI to analyze market trends, competitor pricing, and customer purchase history in real time, automatically setting the optimal price. This allows merchants to maximize revenue while maintaining competitiveness. Return prediction and management uses AI models to predict the likelihood of product returns and suggest improvements. Based on historical return data, it identifies which products are most likely to be returned and analyzes the causes. Personalized shipping options analyze customers' past shipping choices and behavior to suggest the optimal shipping option. This improves customer satisfaction and optimizes shipping costs. As a result, the system can help merchants increase revenue, improve customer satisfaction, enhance online store operational efficiency, and improve platform reputation and revenue.

[0079] The system according to this embodiment comprises a data collection unit, an analysis unit, and an application unit. The data collection unit collects market trends, competitor prices, and customer purchase history. The data collection unit can collect data from, for example, publicly available data on the internet, competitor price information, and customer purchase history databases. The data collection unit can also dynamically adjust the scope and priority of the data to be collected using AI. The analysis unit analyzes the data collected by the data collection unit and sets the optimal price. The analysis unit can, for example, use AI to analyze the collected data in real time and perform demand forecasting and competitor analysis. The analysis unit can also estimate user sentiment and adjust the pricing algorithm based on the estimated user sentiment. The application unit applies the price set by the analysis unit. The application unit can, for example, use AI to automatically apply the set price and adjust the price display method and notification method. The application unit can also dynamically change the price according to specific time periods or seasons. As a result, the system can collect market trends, competitor prices, and customer purchase history, and set and apply the optimal price to maximize revenue while maintaining competitiveness.

[0080] The data collection unit collects market trends, competitor pricing, and customer purchase history. Specifically, it can collect data from publicly available data on the internet, competitor pricing information, and customer purchase history databases. Publicly available data on the internet includes information from news sites, blogs, and social media, and this information is automatically collected using crawling technology. Competitor pricing information is obtained from websites and online stores using scraping technology. Customer purchase history databases are accessed from customer management systems and sales management systems owned by companies to obtain past purchase history and customer attribute information. The data collection unit can also use AI to dynamically adjust the scope and priority of the data to be collected. For example, the AI ​​evaluates the quality and reliability of the collected data and prioritizes the collection of high-priority data. It can also optimize the frequency and timing of collection to obtain the latest information in real time. This allows the data collection unit to efficiently collect data from a wide range of data sources and improve the accuracy and reliability of the overall system. Furthermore, the data collection unit preprocesses the data, removing noise and missing values ​​to prepare it in a format that is easy for the analysis unit to use. This allows the data collection unit to handle everything from data collection to preprocessing in a consistent manner, thereby improving the overall data quality of the system.

[0081] The analysis department analyzes data collected by the data collection department to set optimal prices. Specifically, it uses AI to analyze collected data in real time and perform demand forecasting and competitive analysis. In demand forecasting, future demand is predicted based on past purchase history and market trend data. The AI ​​uses time series analysis and machine learning algorithms to learn demand fluctuation patterns and make highly accurate predictions. In competitive analysis, the price information and market share of competitors are analyzed to optimize the company's pricing strategy. The AI ​​can monitor competitor price fluctuations and promotional activities in real time and respond quickly. Furthermore, the analysis department can estimate user sentiment and adjust the pricing algorithm based on the estimated user sentiment. For example, it analyzes text data from social media and review sites to classify user sentiment as positive, negative, or neutral. The AI ​​uses natural language processing technology to extract sentiment from the text data and adjusts pricing according to changes in sentiment. This allows the analysis department to set optimal prices by combining demand forecasting, competitive analysis, and sentiment estimation. Furthermore, the analysis department monitors the effect of the set price and readjusts the pricing as needed. This allows the analysis department to constantly adapt to market changes and maintain the optimal pricing strategy.

[0082] The application unit applies the prices set by the analysis unit. Specifically, it can use AI to automatically apply the set prices and adjust how prices are displayed and notified. For example, in an online store, it can reflect the set prices on product pages in real time, providing customers with the latest price information. The AI ​​can also personalize price displays based on customers' browsing and purchase history. For example, it can increase purchasing intent by offering exclusive discounts or special offers to specific customers. The application unit can also dynamically change prices according to specific times of day or seasons. For example, it can maximize revenue by raising prices during times of high demand or seasons and lowering prices during times of low demand. Furthermore, the application unit can adjust how price changes are notified to effectively communicate information to customers. For example, it can use email or push notifications to inform customers of price changes or special offers. This allows the application unit to quickly and effectively apply the set prices and provide customers with optimal price information. In addition, the application unit monitors the effectiveness of price application and readjusts the pricing strategy as needed. This allows the application unit to constantly respond to market changes and maintain an optimal pricing strategy.

[0083] The data collection unit can collect product return data. For example, when a customer requests a return, the unit collects data such as the reason for the return, the date and time of the return, and the returned product. The data collection unit can also use AI to dynamically adjust the scope and priority of the data it collects. The data collection unit can also analyze past return data to predict return trends for specific products. When collecting return data, the data collection unit can also add customer social media activity and online reviews. This allows the data collection unit to identify the causes of returns and propose improvement measures by collecting product return data.

[0084] The analysis department can analyze data collected by the data collection department to identify the causes of returns. For example, the analysis department can use AI to analyze collected data in real time and identify the causes of returns. The analysis department can also estimate user sentiment and adjust the return cause analysis method based on the estimated user sentiment. The analysis department can also analyze past return causes and predict the return risk of specific products. The analysis department can also apply different return cause analysis algorithms for each product category. This allows the analysis department to identify the causes of returns and propose improvements to reduce the return rate.

[0085] The proposal department can propose improvement measures based on the causes identified by the analysis department. For example, the proposal department can use AI to suggest improvements to products or services based on the identified causes. The proposal department can also estimate user emotions and adjust the method of suggesting improvement measures based on the estimated user emotions. The proposal department can also analyze past success and failure examples of improvement measures to improve the accuracy of its suggestions. The proposal department can also propose different improvement measures for each product category. In this way, the proposal department can reduce return rates and improve customer satisfaction by proposing improvement measures.

[0086] The data collection unit can collect customer behavior data. For example, it can collect browsing history, purchase history, and clickstream data when customers visit websites. The data collection unit can also use AI to dynamically adjust the scope and priority of the data it collects. The data collection unit can also analyze past behavior data and predict specific behavioral patterns. When collecting behavioral data, the data collection unit can also add customer social media activity and online reviews. This allows the data collection unit to analyze customer behavior and suggest the best shipping options by collecting customer behavior data.

[0087] The proposal department can analyze data collected by the data collection department and propose the optimal delivery option. For example, the proposal department can use AI to analyze the collected data in real time and propose the optimal delivery option based on the customer's past delivery choices and behavior. The proposal department can also estimate the user's emotions and adjust how it proposes delivery options based on those emotions. The proposal department can also analyze past successes and failures of delivery options to improve the accuracy of its proposals. The proposal department can also propose different delivery options for each product category. In this way, the proposal department can improve customer satisfaction and optimize delivery costs by proposing the optimal delivery option.

[0088] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is excited, the unit will prioritize collecting data on fluctuations in competitive pricing. If the user is anxious, the unit will prioritize collecting data based on the customer's purchase history. If the user is relaxed, the unit will prioritize collecting data on market trends. This allows the data collection unit to collect data more effectively by prioritizing data 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.

[0089] The data collection unit can dynamically adjust the scope of data collected by analyzing past market trends and competitive price fluctuation patterns. For example, the data collection unit can analyze past market trends and adjust the scope of data collected for a specific season. The data collection unit can analyze competitive price fluctuation patterns and adjust the scope of data collected at a specific event. The data collection unit can analyze past purchase history and adjust the scope of data collected for a specific customer segment. In this way, the data collection unit can dynamically adjust the scope of data collected by analyzing past market trends and competitive price fluctuation patterns.

[0090] The data collection unit can optimize the data collected at specific times of day or seasons based on customer purchase history. For example, the unit can analyze customer purchase history and optimize the data collected at specific times of day. It can also analyze customer purchase history and optimize the data collected during specific seasons. Furthermore, it can analyze customer purchase history and optimize the data collected during specific events. This allows the data collection unit to achieve more effective data collection by optimizing data based on customer purchase history.

[0091] The data collection unit can estimate the user's emotions and filter the data it collects based on those emotions. For example, if the user is excited, the unit will prioritize filtering data related to fluctuations in competitive prices. If the user is anxious, the unit will prioritize filtering data based on the customer's purchase history. If the user is relaxed, the unit will prioritize filtering data related to market trends. This allows the data collection unit to collect data more effectively by filtering it 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.

[0092] The data collection unit can add customer social media activity and online reviews to the data it collects. For example, the unit can collect customer social media activity and analyze it in combination with purchase history. The unit can collect online reviews and analyze them in combination with competitive pricing. The unit can collect customer social media activity and online reviews and analyze them in combination with market trends. This allows the data collection unit to collect more diverse data by adding customer social media activity and online reviews.

[0093] The data collection unit can include information on regional market trends and competitive pricing in the data it collects. For example, the data collection unit can collect regional market trends and analyze them in combination with competitive pricing. The data collection unit can collect regional competitive pricing and analyze it in combination with customer purchase history. The data collection unit can collect regional market trends and competitive pricing and analyze them in combination with purchase history. This allows the data collection unit to collect more detailed data by including information on regional market trends and competitive pricing.

[0094] The analytics unit can estimate the user's emotions and adjust the pricing algorithm based on those emotions. For example, if the user is excited, the analytics unit will aggressively adjust the pricing algorithm. If the user is anxious, the analytics unit will conservatively adjust the pricing algorithm. If the user is relaxed, the analytics unit will neutrally adjust the pricing algorithm. This allows the analytics unit to achieve more effective pricing by adjusting the pricing algorithm 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.

[0095] The analysis department can improve the accuracy of pricing by analyzing past successful and unsuccessful pricing examples. For example, the analysis department can analyze past successful pricing examples and identify the factors contributing to their success. The analysis department can analyze past unsuccessful pricing examples and identify the factors contributing to their failure. The analysis department can improve the accuracy of pricing by comparing and analyzing successful and unsuccessful examples. In this way, the analysis department can improve the accuracy of pricing by analyzing past successful and unsuccessful pricing examples.

[0096] The analysis department can apply different pricing algorithms to each product category. For example, it can apply an algorithm that emphasizes competitive pricing to high-priced products, an algorithm that emphasizes customer purchase history to low-priced products, and an algorithm that emphasizes market trends to mid-priced products. By applying different pricing algorithms to each product category, the analysis department can achieve more effective pricing.

[0097] The analytics unit can estimate user emotions and adjust pricing timing based on those emotions. For example, if a user is excited, the analytics unit will advance the pricing timing. If a user is anxious, the analytics unit will delay the pricing timing. If a user is relaxed, the analytics unit will maintain a neutral pricing timing. This allows the analytics unit to achieve more effective pricing by adjusting pricing timing based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The analysis department can consider competitors' promotional information when setting prices. For example, the analysis department can collect competitors' promotional information and reflect it in pricing. The analysis department can adjust prices during competitors' promotional periods. The analysis department can analyze competitors' promotional content and use it in pricing. As a result, the analysis department can set more effective prices by considering competitors' promotional information.

[0099] The analytics department can offer individual discounts and benefits based on customer purchase history when setting prices. For example, the analytics department analyzes customer purchase history and offers individual discounts. The analytics department offers benefits based on customer purchase history. The analytics department considers customer purchase history and reflects it in pricing. In this way, the analytics department can improve customer satisfaction by offering individual discounts and benefits based on customer purchase history.

[0100] The application can estimate the user's emotions and adjust the price display method based on the estimated emotions. For example, if the user is excited, the application will highlight the price. If the user is anxious, the application will display the price subtly. If the user is relaxed, the application will display the price neutrally. This allows the application to display prices more effectively by adjusting the price display 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The application unit can dynamically change the applied price according to specific time periods or seasons. For example, the application unit can dynamically change the price according to specific time periods. The application unit can dynamically change the price according to specific seasons. The application unit can dynamically change the price according to specific events. This allows the application unit to maximize revenue by dynamically changing the price according to specific time periods or seasons.

[0102] The application unit can personalize the price it applies based on the customer's purchase history. For example, the application unit personalizes the price based on the customer's purchase history. The application unit considers the customer's purchase history and offers a special price. The application unit analyzes the customer's purchase history and applies the optimal price. In this way, the application unit can improve customer satisfaction by personalizing prices based on the customer's purchase history.

[0103] The application can estimate the user's emotions and adjust the price notification method based on the estimated emotions. For example, if the user is excited, the application will emphasize the notification. If the user is anxious, the application will downplay the notification. If the user is relaxed, the application will be neutral. This allows the application to provide more effective price notifications by adjusting the notification 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The application unit can reflect regional market trends in the price it applies. For example, the application unit can apply prices by reflecting regional market trends. The application unit can apply prices by considering regional competitive prices. The application unit can apply prices based on regional purchase history. As a result, the application unit can achieve more effective pricing by reflecting regional market trends.

[0105] The application unit can take into account customer loyalty program information when determining the price to be applied. For example, the application unit can apply a price by taking into account customer loyalty program information. The application unit can apply a price according to the loyalty program rank. The application unit can apply a price that reflects the benefits of the loyalty program. In this way, the application unit can improve customer satisfaction by taking into account customer loyalty program information.

[0106] The data collection unit can estimate the user's emotions and adjust the timing of return data collection based on the estimated emotions. For example, if the user is dissatisfied, the data collection unit will collect return data quickly. If the user is relaxed, the data collection unit will collect return data at the normal timing. If the user is agitated, the data collection unit will collect return data cautiously. This allows the data collection unit to collect data more effectively by adjusting the timing of return data collection 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.

[0107] The data collection unit can analyze past return data and predict return trends for specific products. For example, the data collection unit analyzes past return data to predict return trends for specific products. Based on past return data, the data collection unit identifies products with a high return risk. The data collection unit analyzes past return data to identify the reasons for returns. Thus, by analyzing past return data, the data collection unit can predict return trends for specific products.

[0108] The data collection unit can obtain detailed information about the reason for a return when collecting return data. For example, the data collection unit obtains detailed information about the reason for a return when collecting return data. The data collection unit obtains customer feedback when collecting return data. The data collection unit obtains detailed information about the return process when collecting return data. As a result, the data collection unit can more accurately identify the cause of a return by obtaining detailed information about the reason for a return.

[0109] The data collection unit can estimate the user's emotions and prioritize return data based on those emotions. For example, if the user is dissatisfied, the data collection unit will prioritize the return data highly. If the user is relaxed, the data collection unit will prioritize the return data normally. If the user is agitated, the data collection unit will prioritize the return data cautiously. This allows the data collection unit to collect data more effectively by prioritizing return data 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.

[0110] The data collection unit can add customer social media activity and online reviews when collecting return data. For example, the data collection unit can add customer social media activity when collecting return data. The data collection unit can add online reviews when collecting return data. The data collection unit can add customer feedback when collecting return data. This allows the data collection unit to collect more diverse data by adding customer social media activity and online reviews.

[0111] The data collection unit can consider regional return trends when collecting return data. For example, the data collection unit considers regional return trends when collecting return data. The data collection unit considers regional customer feedback when collecting return data. The data collection unit considers regional reasons for returns when collecting return data. As a result, the data collection unit can collect more accurate data by considering regional return trends.

[0112] The analysis unit can estimate the user's emotions and adjust the return cause analysis method based on the estimated user emotions. For example, if the user is dissatisfied, the analysis unit will perform a rapid return cause analysis. If the user is relaxed, the analysis unit will perform a return cause analysis in the usual way. If the user is agitated, the analysis unit will perform a return cause analysis carefully. This allows the analysis unit to perform a more effective cause analysis by adjusting the return cause analysis 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.

[0113] The analysis department can analyze past return causes and predict the return risk of specific products. For example, the analysis department analyzes past return causes to predict the return risk of specific products. Based on past return causes, the analysis department identifies products with a high return risk. The analysis department analyzes past return causes to identify measures to reduce return risk. In this way, the analysis department can predict the return risk of specific products by analyzing past return causes.

[0114] The analysis department can apply different return cause analysis algorithms to each product category. For example, the analysis department can apply a detailed return cause analysis algorithm to high-priced products, a simplified return cause analysis algorithm to low-priced products, and a standard return cause analysis algorithm to mid-priced products. This allows the analysis department to perform more effective cause analysis by applying different return cause analysis algorithms to each product category.

[0115] The analysis unit can estimate the user's emotions and adjust how the return reason is displayed based on the estimated emotions. For example, if the user is dissatisfied, the analysis unit will display the return reason concisely. If the user is relaxed, the analysis unit will display the return reason in detail. If the user is agitated, the analysis unit will display the return reason carefully. This allows the analysis unit to display the return reason more effectively by adjusting how it is displayed 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The analytics department can consider customer social media activity and online reviews when analyzing the reasons for returns. For example, the analytics department considers customer social media activity when analyzing the reasons for returns. The analytics department considers online reviews when analyzing the reasons for returns. The analytics department considers customer feedback when analyzing the reasons for returns. This allows the analytics department to conduct more accurate root cause analysis by considering customer social media activity and online reviews.

[0117] The analysis department can incorporate regional return trends when analyzing the causes of returns. For example, the analysis department can incorporate regional return trends when analyzing the causes of returns. The analysis department can incorporate regional customer feedback when analyzing the causes of returns. The analysis department can incorporate regional reasons for returns when analyzing the causes of returns. This allows the analysis department to conduct more accurate cause analysis by incorporating regional return trends.

[0118] The suggestion unit can estimate the user's emotions and adjust how it suggests improvements based on those emotions. For example, if the user is dissatisfied, the suggestion unit will quickly suggest improvements. If the user is relaxed, the suggestion unit will suggest improvements in a normal manner. If the user is agitated, the suggestion unit will suggest improvements cautiously. This allows the suggestion unit to make more effective suggestions by adjusting how it suggests improvements 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.

[0119] The proposal department can improve the accuracy of its proposals by analyzing past success and failure examples of improvement measures. For example, the proposal department can analyze past success examples of improvement measures and identify the factors for success. The proposal department can analyze past failure examples of improvement measures and identify the factors for failure. The proposal department can improve the accuracy of its proposals by comparing and analyzing success and failure examples. In this way, the proposal department can improve the accuracy of its proposals by analyzing past success and failure examples of improvement measures.

[0120] The proposal department can propose different improvement measures for each product category. For example, the proposal department can propose detailed improvement measures for high-priced products, simple improvement measures for low-priced products, and standard improvement measures for mid-priced products. This allows the proposal department to make more effective proposals by offering different improvement measures for each product category.

[0121] The suggestion unit can estimate the user's emotions and prioritize improvement measures based on those emotions. For example, if the user is dissatisfied, the suggestion unit will set the priority of improvement measures to a high level. If the user is relaxed, the suggestion unit will set the priority to a normal level. If the user is agitated, the suggestion unit will set the priority to a cautious level. This allows the suggestion unit to make more effective suggestions by prioritizing improvement measures based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The proposal department can consider the customer's social media activity and online reviews when proposing improvement measures. For example, the proposal department considers the customer's social media activity when proposing improvement measures. The proposal department considers online reviews when proposing improvement measures. The proposal department considers customer feedback when proposing improvement measures. This allows the proposal department to make more accurate proposals by considering the customer's social media activity and online reviews.

[0123] The proposal department can incorporate regional return trends when proposing improvement measures. For example, the proposal department can incorporate regional return trends when proposing improvement measures. The proposal department can incorporate regional customer feedback when proposing improvement measures. The proposal department can incorporate regional return reasons when proposing improvement measures. This allows the proposal department to make more accurate proposals by incorporating regional return trends.

[0124] The data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the user is excited, the data collection unit will collect behavioral data quickly. If the user is relaxed, the data collection unit will collect behavioral data at normal intervals. If the user is feeling anxious, the data collection unit will collect behavioral data cautiously. This allows the data collection unit to collect data more effectively by adjusting the timing of behavioral data collection 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The data collection unit can analyze past behavioral data and predict specific behavioral patterns. For example, the data collection unit analyzes past behavioral data to predict specific behavioral patterns. Based on past behavioral data, the data collection unit predicts changes in behavioral patterns. The data collection unit analyzes past behavioral data to identify trends in behavioral patterns. Thus, by analyzing past behavioral data, the data collection unit can predict specific behavioral patterns.

[0126] The data collection unit can obtain detailed customer behavior history when collecting behavioral data. For example, the data collection unit obtains detailed customer behavior history when collecting behavioral data. The data collection unit obtains past customer behavior history when collecting behavioral data. The data collection unit records customer behavior history in detail when collecting behavioral data. As a result, the data collection unit can collect more accurate data by obtaining detailed customer behavior history.

[0127] The data collection unit can estimate the user's emotions and prioritize behavioral data based on the estimated emotions. For example, if the user is excited, the data collection unit will set a high priority for the behavioral data. If the user is relaxed, the data collection unit will set a normal priority for the behavioral data. If the user is anxious, the data collection unit will set a cautious priority for the behavioral data. This allows the data collection unit to collect data more effectively by prioritizing behavioral data 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.

[0128] The data collection unit can add customer social media activity and online reviews when collecting behavioral data. For example, the data collection unit can add customer social media activity when collecting behavioral data. The data collection unit can add online reviews when collecting behavioral data. The data collection unit can add customer feedback when collecting behavioral data. This allows the data collection unit to collect more diverse data by adding customer social media activity and online reviews.

[0129] The data collection unit can consider regional behavioral trends when collecting behavioral data. For example, the data collection unit considers regional behavioral trends when collecting behavioral data. The data collection unit considers regional customer feedback when collecting behavioral data. The data collection unit considers regional behavioral patterns when collecting behavioral data. As a result, the data collection unit can collect more accurate data by considering regional behavioral trends.

[0130] The suggestion unit can estimate the user's emotions and adjust how it suggests delivery options based on those emotions. For example, if the user is excited, the suggestion unit suggests a fast delivery option. If the user is relaxed, the suggestion unit suggests a standard delivery option. If the user is anxious, the suggestion unit suggests a cautious delivery option. This allows the suggestion unit to make more effective suggestions by adjusting how it suggests delivery options 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.

[0131] The proposal department can improve the accuracy of its proposals by analyzing past successes and failures of delivery options. For example, the proposal department can analyze past successes of delivery options and identify the factors contributing to their success. The proposal department can also analyze past failures of delivery options and identify the factors contributing to their failure. The proposal department can improve the accuracy of its proposals by comparing and analyzing successes and failures of delivery options. In this way, the proposal department can improve the accuracy of its proposals by analyzing past successes and failures of delivery options.

[0132] The proposal department can suggest different shipping options for each product category. For example, it might suggest expedited shipping for high-priced items, standard shipping for low-priced items, and standard shipping for mid-priced items. This allows the proposal department to make more effective suggestions by offering different shipping options for each product category.

[0133] The suggestion unit can estimate the user's emotions and prioritize delivery options based on those emotions. For example, if the user is excited, the suggestion unit will prioritize the expedited delivery option. If the user is relaxed, the suggestion unit will prioritize the standard delivery option. If the user is anxious, the suggestion unit will prioritize the cautious delivery option. This allows the suggestion unit to make more effective suggestions by prioritizing delivery options based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0134] The proposal department can consider the customer's social media activity and online reviews when proposing delivery options. For example, the proposal department considers the customer's social media activity when proposing delivery options. The proposal department considers online reviews when proposing delivery options. The proposal department considers customer feedback when proposing delivery options. This allows the proposal department to make more accurate recommendations by considering the customer's social media activity and online reviews.

[0135] The proposal department can reflect regional delivery trends when proposing delivery options. For example, the proposal department can reflect regional delivery trends when proposing delivery options. The proposal department can reflect regional customer feedback when proposing delivery options. The proposal department can reflect regional delivery patterns when proposing delivery options. As a result, the proposal department can make more accurate proposals by reflecting regional delivery trends.

[0136] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0137] The system can also include an incentive provision unit to further enhance customer purchasing intent. This unit, for example, analyzes customer purchase history and behavioral data and offers discount coupons or points to customers who meet specific criteria. The incentive provision unit can also estimate customer emotions and adjust the content and timing of incentives based on those emotions. For example, if a customer is excited, an immediately usable discount coupon can be offered to further increase purchasing intent. If a customer is feeling anxious, a point reward program can be proposed to provide reassurance. In this way, the incentive provision unit can increase customer purchasing intent and contribute to increased revenue.

[0138] The system can also include a behavior prediction unit to predict customer purchasing behavior. This unit, for example, analyzes a customer's past purchase and browsing history to predict the next product they are most likely to purchase. The unit can also estimate customer emotions and improve prediction accuracy based on these estimations. For instance, if a customer is excited, the system can determine their purchase intent is high and enhance its recommendations for related products. If a customer is relaxed, the system can suggest products at a slower pace to maintain their purchase intent. This allows the behavior prediction unit to predict customer purchasing behavior and suggest products at the appropriate time, thereby contributing to increased revenue.

[0139] The system can also include a feedback collection unit to gather customer feedback. This unit collects customer opinions, for example, through post-purchase surveys or reviews. The feedback collection unit can also estimate customer emotions and adjust the feedback collection method based on these estimations. For example, if a customer is satisfied, requesting detailed feedback can gather more positive opinions. If a customer is dissatisfied, requesting concise feedback can quickly identify the problem. This allows the feedback collection unit to effectively gather customer opinions and use them to improve services.

[0140] The system can also include a loyalty management department to further enhance customer loyalty. This department could, for example, analyze customer purchase history and behavioral data to provide loyalty program benefits. It could also estimate customer emotions and adjust the content and timing of benefits based on those emotions. For instance, if a customer is excited, immediately available benefits could be offered to further increase their purchasing intent. If a customer is relaxed, long-term benefits could be proposed to maintain their loyalty. This allows the loyalty management department to improve customer loyalty and contribute to increased revenue.

[0141] The system can also include an experience enhancement unit to further improve the customer's purchasing experience. This unit, for example, analyzes customer purchase history and behavioral data to provide a personalized purchasing experience. It can also estimate customer emotions and adjust the content and timing of the purchasing experience based on those emotions. For example, if a customer is excited, it can suggest special offers or limited-edition products to further increase their desire to purchase. If a customer is relaxed, it can suggest products at a slower pace to allow them to enjoy the purchasing experience. In this way, the experience enhancement unit can improve the customer's purchasing experience and contribute to increased revenue.

[0142] The system can also include a behavior prediction unit to predict customer purchasing behavior. This unit, for example, analyzes a customer's past purchase and browsing history to predict the next product they are most likely to purchase. The unit can also estimate customer emotions and improve prediction accuracy based on these estimations. For instance, if a customer is excited, the system can determine their purchase intent is high and enhance its recommendations for related products. If a customer is relaxed, the system can suggest products at a slower pace to maintain their purchase intent. This allows the behavior prediction unit to predict customer purchasing behavior and suggest products at the appropriate time, thereby contributing to increased revenue.

[0143] The system can also include a feedback collection unit to gather customer feedback. This unit collects customer opinions, for example, through post-purchase surveys or reviews. The feedback collection unit can also estimate customer emotions and adjust the feedback collection method based on these estimations. For example, if a customer is satisfied, requesting detailed feedback can gather more positive opinions. If a customer is dissatisfied, requesting concise feedback can quickly identify the problem. This allows the feedback collection unit to effectively gather customer opinions and use them to improve services.

[0144] The system can also include a loyalty management department to further enhance customer loyalty. This department could, for example, analyze customer purchase history and behavioral data to provide loyalty program benefits. It could also estimate customer emotions and adjust the content and timing of benefits based on those emotions. For instance, if a customer is excited, immediately available benefits could be offered to further increase their purchasing intent. If a customer is relaxed, long-term benefits could be proposed to maintain their loyalty. This allows the loyalty management department to improve customer loyalty and contribute to increased revenue.

[0145] The system can also include an experience enhancement unit to further improve the customer's purchasing experience. This unit, for example, analyzes customer purchase history and behavioral data to provide a personalized purchasing experience. It can also estimate customer emotions and adjust the content and timing of the purchasing experience based on those emotions. For example, if a customer is excited, it can suggest special offers or limited-edition products to further increase their desire to purchase. If a customer is relaxed, it can suggest products at a slower pace to allow them to enjoy the purchasing experience. In this way, the experience enhancement unit can improve the customer's purchasing experience and contribute to increased revenue.

[0146] The system can also include a behavior prediction unit to predict customer purchasing behavior. This unit, for example, analyzes a customer's past purchase and browsing history to predict the next product they are most likely to purchase. The unit can also estimate customer emotions and improve prediction accuracy based on these estimations. For instance, if a customer is excited, the system can determine their purchase intent is high and enhance its recommendations for related products. If a customer is relaxed, the system can suggest products at a slower pace to maintain their purchase intent. This allows the behavior prediction unit to predict customer purchasing behavior and suggest products at the appropriate time, thereby contributing to increased revenue.

[0147] The following briefly describes the processing flow for example form 2.

[0148] Step 1: The data collection unit gathers market trends, competitor pricing, and customer purchase history. For example, it can collect data from publicly available data on the internet, competitor pricing information, and customer purchase history databases. The data collection unit can also use AI to dynamically adjust the scope and priority of the data to be collected. Step 2: The analysis unit analyzes the data collected by the collection unit and sets the optimal price. For example, AI can be used to analyze the collected data in real time and perform demand forecasting and competitor analysis. Furthermore, it can estimate user sentiment and adjust the pricing algorithm based on the estimated user sentiment. Step 3: The application unit applies the price set by the analysis unit. For example, AI can be used to automatically apply the set price and adjust how the price is displayed and how notifications are made. Furthermore, the price can be dynamically changed according to specific time periods or seasons.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, and application unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects market trends, competitive prices, and customer purchase history using the control unit 46A of the smart device 14, and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in real time using the specific processing unit 290 of the data processing unit 12 and sets the optimal price. The application unit is implemented in real time using the control unit 46A of the smart device 14 and automatically applies the set price and adjusts the price display method and notification method. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, and application unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects market trends, competitive prices, and customer purchase history by the control unit 46A of the smart glasses 214, and analyzes the collected data by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data in real time to set the optimal price. The application unit is implemented, for example, in the control unit 46A of the smart glasses 214, and automatically applies the set price and adjusts the price display method and notification method. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0169] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the collection unit, analysis unit, and application unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects market trends, competitive prices, and customer purchase history using the control unit 46A of the headset terminal 314, and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in real time using the specific processing unit 290 of the data processing unit 12 and sets the optimal price. The application unit is implemented in real time using the control unit 46A of the headset terminal 314 and automatically applies the set price and adjusts the price display method and notification method. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0185] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] Each of the multiple elements described above, including the collection unit, analysis unit, and application unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects market trends, competitive prices, and customer purchase history by the control unit 46A of the robot 414, and analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in real time to set the optimal price. The application unit is implemented in, for example, the control unit 46A of the robot 414, and automatically applies the set price and adjusts the price display method and notification method. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] (Note 1) A data collection unit that collects market trends, competitive pricing, and customer purchase history, An analysis unit analyzes the data collected by the aforementioned collection unit and sets the optimal price, The system includes an application unit that applies the price set by the analysis unit. A system characterized by the following features. (Note 2) It includes a data collection unit for collecting product return data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system includes an analysis unit that analyzes the data collected by the aforementioned collection unit and identifies the cause of the return. The system described in Appendix 2, characterized by the features described herein. (Note 4) The system includes a proposal unit that proposes improvement measures based on the causes identified by the aforementioned analysis unit. The system described in Appendix 3, characterized by the features described herein. (Note 5) It includes a data collection unit for collecting customer behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The collection unit analyzes the data collected by the collection unit and includes a proposal unit that suggests the optimal delivery option. The system described in Appendix 5, characterized by the features described herein. (Note 7) 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 8) The aforementioned collection unit is Analyze past market trends and competitive price fluctuation patterns, and dynamically adjust the scope of data collected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Based on customer purchase history, we optimize the data collected during specific time periods and seasons. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and filters the data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Add customer social media activity and online reviews to the data we collect. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The data collected should include information on regional market trends and competitive pricing. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates user sentiment and adjusts the pricing algorithm based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We analyze past successful and unsuccessful pricing examples to improve the accuracy of our pricing. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Apply different pricing algorithms to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates user sentiment and adjusts pricing timing based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When setting prices, take into account the promotional information of competitors. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When setting prices, offer individual discounts and benefits based on the customer's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned application unit is It estimates user sentiment and adjusts how prices are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned application unit is The applicable price is dynamically changed depending on the time of day or season. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned application unit is Personalize the price applied based on the customer's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned application unit is It estimates the user's sentiment and adjusts the price notification method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned application unit is The applicable price will reflect regional market trends. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned application unit is We take into account customer loyalty program information when determining the applicable price. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of return data collection based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned collection unit is By analyzing past return data, we can predict return trends for specific products. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned collection unit is When collecting return data, obtain detailed information about the reason for the return. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned collection unit is The system estimates user sentiment and prioritizes return data based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned collection unit is When collecting return data, add customer social media activity and online reviews. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned collection unit is When collecting return data, consider return trends by region. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis method for return causes based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned analysis unit is By analyzing past return reasons, we can predict the return risk for specific products. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned analysis unit is Apply a different return cause analysis algorithm to each product category. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned analysis unit is We estimate the user's emotions and adjust how the return reason is displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned analysis unit is When analyzing the reasons for returns, consider the customer's social media activity and online reviews. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned analysis unit is When analyzing the reasons for returns, reflect return trends by region. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned proposal section is, It estimates the user's emotions and adjusts the method of suggesting improvement measures based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned proposal section is, Analyze past successes and failures of improvement measures to improve the accuracy of proposals. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned proposal section is, We propose different improvement measures for each product category. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned proposal section is, We estimate user emotions and prioritize improvement measures based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned proposal section is, When proposing improvement measures, consider the customer's social media activity and online reviews. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned proposal section is, When proposing improvement measures, reflect the return trends of each region. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system according to appended note 5, characterized in that... (Appended note 44) The collection unit analyzes past behavior data and predicts specific behavior patterns The system according to appended note 5, characterized in that... (Appended note 45) The collection unit acquires the detailed behavior history of the customer when collecting behavior data The system according to appended note 5, characterized in that... (Appended note 46) The collection unit estimates the user's sentiment and determines the priority of behavior data based on the estimated user's sentiment The system according to appended note 5, characterized in that... (Appended note 47) The collection unit adds the customer's social media activities and online reviews when collecting behavior data The system according to appended note 5, characterized in that... (Appended note 48) The collection unit considers the behavior trends for each region when collecting behavior data The system according to appended note 5, characterized in that... (Appended note 49) The proposal unit estimates the user's sentiment and adjusts the method of proposing delivery options based on the estimated user's sentiment The system according to appended note 6, characterized in that... (Appended note 50) The proposal unit analyzes the success and failure cases of past delivery options to improve the accuracy of the proposal The system according to appended note 6, characterized in that... (Appended note 51) The proposal unit proposes different delivery options for each category of goods The system according to appended note 6, characterized in that... (Appended note 52) The aforementioned proposal section is, The system estimates the user's emotions and prioritizes delivery options based on those emotions. The system described in Appendix 6, characterized by the features described herein. (Note 53) The aforementioned proposal section is, When proposing delivery options, we consider the customer's social media activity and online reviews. The system described in Appendix 6, characterized by the features described herein. (Note 54) The aforementioned proposal section is, When proposing delivery options, reflect delivery trends for each region. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0221] 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 market trends, competitive pricing, and customer purchase history, An analysis unit analyzes the data collected by the aforementioned collection unit and sets the optimal price, The system includes an application unit that applies the price set by the analysis unit. A system characterized by the following features.

2. It includes a data collection unit for collecting product return data. The system according to feature 1.

3. The system includes an analysis unit that analyzes the data collected by the aforementioned collection unit and identifies the cause of the return. The system according to feature 2.

4. The system includes a proposal unit that proposes improvement measures based on the causes identified by the aforementioned analysis unit. The system according to claim 3.

5. It includes a data collection unit for collecting customer behavior data. The system according to feature 1.

6. The collection unit analyzes the data collected by the collection unit and includes a proposal unit that suggests the optimal delivery option. The system according to claim 5, characterized in that it is the same as described in claim 5.

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past market trends and competitive price fluctuation patterns, and dynamically adjust the scope of data collected. The system according to feature 1.

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