system

Abstract

The system according to this embodiment aims to improve customer satisfaction and operational efficiency in e-commerce businesses. [Solution] The system according to the embodiment comprises a data collection unit, a recommendation unit, a forecasting unit, an ordering unit, and an optimization unit. The data collection unit collects customer purchase history and behavioral patterns. The recommendation unit proposes the most suitable products based on the data collected by the data collection unit. The forecasting unit predicts inventory levels based on the products proposed by the recommendation unit. The ordering unit places orders for inventory based on the inventory levels predicted by the forecasting unit. The optimization unit optimizes prices based on the inventory ordered by the ordering unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a system for improving customer satisfaction and operation efficiency in e-commerce business has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to improve customer satisfaction and operation efficiency in e-commerce business.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a recommendation unit, a forecasting unit, an ordering unit, and an optimization unit. The data collection unit collects customer purchase history and behavioral patterns. The recommendation unit proposes the most suitable products based on the data collected by the data collection unit. The forecasting unit predicts inventory levels based on the products proposed by the recommendation unit. The ordering unit places orders for inventory based on the inventory levels predicted by the forecasting unit. The optimization unit optimizes prices based on the inventory ordered by the ordering unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve customer satisfaction and operational efficiency in e-commerce businesses. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI-driven platform according to an embodiment of the present invention is a system that improves the operational efficiency of e-commerce businesses. This system can suggest optimal products based on customer purchase history and behavioral patterns, and efficiently manage inventory and pricing. The AI-driven platform can be seamlessly integrated with major e-commerce platforms and payment systems, and its modular design allows for the flexible addition of necessary functions. For example, the AI-driven platform collects customer purchase history and behavioral patterns and suggests optimal products. For instance, it recommends products that customers are likely to be interested in based on past purchases and browsing history. It also provides real-time notifications of new products and sales information to promote customer engagement. This enables the rapid and accurate delivery of products that customers want. Next, the AI-driven platform provides an automated inventory management function. Using AI technology, it analyzes sales trends in real time and automatically predicts and orders appropriate inventory levels. For example, it predicts the appropriate amount of inventory by considering past sales data and seasonal demand. This reduces the risk of inventory shortages and excess inventory, and improves operational efficiency. Furthermore, the AI-driven platform provides a price optimization function. It uses AI to analyze market demand and competitive conditions and sets optimal prices. For example, it analyzes competitor pricing and market demand in real time to set optimal prices. This maximizes sales and profits. Furthermore, AI-driven platforms offer automated customer support. They utilize AI to provide automated responses to inquiries and FAQs. For instance, the AI ​​instantly provides appropriate answers to customer inquiries. This reduces the burden on customer support and improves service quality. Finally, AI-driven platforms provide data analysis and reporting capabilities. Operators can view data such as total sales, sales per product, visitor numbers, and conversion rates in real time through a dashboard. This allows operators to make data-driven decisions. In this way, using an AI-driven platform can solve the challenges faced by e-commerce businesses and significantly improve their competitiveness and profitability.This allows AI-driven platforms to improve the operational efficiency of e-commerce businesses and increase customer satisfaction.

[0029] The AI-driven platform according to this embodiment comprises a data collection unit, a recommendation unit, a prediction unit, an order unit, and an optimization unit. The data collection unit collects customer purchase history and behavioral patterns. Customer purchase history and behavioral patterns include, but are not limited to, purchase date and time, purchased items, browsing history, and click history. The data collection unit tracks customer behavior on websites and collects purchase history and behavioral patterns. The data collection unit can also analyze a customer's past purchase history to understand their preferences. Furthermore, the data collection unit can analyze a customer's social media activity and collect data that reflects their interests. For example, the data collection unit prioritizes collecting data on product categories mentioned by the customer on social media. The recommendation unit suggests the most suitable products based on the data collected by the data collection unit. The recommendation unit recommends products that a customer is likely to be interested in, based on the customer's past purchase history and behavioral patterns. For example, the recommendation unit recommends products that a customer is likely to be interested in, based on products the customer has previously purchased and their browsing history. The recommendation unit can also notify customers of new products and sales information in real time to promote customer engagement. Furthermore, the recommendation department can provide personalized product recommendations based on customer preferences. For example, the recommendation department recommends products that customers are likely to be interested in based on their preferences. The forecasting department predicts inventory levels based on the products suggested by the recommendation department. The forecasting department predicts the appropriate amount of inventory by considering, for example, past sales data and seasonal demand. For example, the forecasting department analyzes past sales data to predict seasonal demand. The forecasting department can also analyze sales trends in real time to predict inventory levels. Furthermore, the forecasting department can use AI technology to automatically predict inventory levels. For example, the forecasting department uses AI technology to analyze sales trends in real time to predict inventory levels. The ordering department places orders for inventory based on the inventory levels predicted by the forecasting department. For example, the ordering department places orders for the appropriate amount of inventory based on the predicted inventory levels. For example, the ordering department automatically places orders for inventory based on the predicted inventory levels. The ordering department can also adjust inventory levels to mitigate the risk of insufficient or excessive inventory.Furthermore, the ordering department can automate inventory ordering using AI technology. For example, the ordering department can use AI technology to analyze inventory levels in real time and order the appropriate amount of inventory. The optimization department optimizes prices based on the inventory ordered by the ordering department. The optimization department can, for example, analyze competitor prices and market demand in real time and set the optimal price. For example, the optimization department can set the optimal price based on competitor prices. The optimization department can also analyze market demand and optimize prices. Furthermore, the optimization department can optimize prices using AI technology. For example, the optimization department can use AI technology to analyze market demand and competitor prices in real time and set the optimal price. As a result, the AI-driven platform according to this embodiment can propose the optimal products based on customer purchase history and behavior patterns, and efficiently manage inventory and set prices.

[0030] The data collection unit collects customer purchase history and behavioral patterns. This includes, but is not limited to, purchase date and time, purchased items, browsing history, and click history. For example, the unit tracks customer behavior on websites to collect purchase history and behavioral patterns. Specifically, it uses cookies and session data on the website to record in detail which pages customers view and which links they click. It also collects information on items customers add to their cart and items they complete purchases of. Furthermore, the data collection unit can analyze customers' past purchase history to understand their preferences. For example, it can identify product categories and brands that customers frequently purchase and profile their preferences based on this information. Additionally, the data collection unit can analyze customers' social media activity to collect data that reflects their interests. For example, it prioritizes collecting data on product categories mentioned by customers on social media. This includes using social media APIs to analyze customer posts and "liked" content to understand their interests and preferences. This allows the data collection department to comprehensively analyze customers' online behavior and social media activity, gaining a detailed understanding of their purchasing intent and preferences. The collected data is stored in a cloud-based database, making it accessible to other departments. This enables the data collection department to centrally manage diverse customer data and provide real-time updated information.

[0031] The recommendation department suggests the most suitable products based on data collected by the data collection department. For example, the recommendation department recommends products that customers are likely to be interested in based on their past purchase history and behavioral patterns. Specifically, it uses machine learning algorithms to analyze customers' past purchase and browsing history and predict products that customers are likely to purchase next. For example, it uses collaborative filtering technology to refer to the purchase history of other customers with similar preferences and provides personalized product recommendations to customers. The recommendation department can also promote customer engagement by notifying customers of new products and sales information in real time. For example, if a new product related to a product a customer has previously purchased is released, the department will notify the customer via push notification or email. Furthermore, the recommendation department can provide personalized product recommendations based on customer preferences. For example, if a customer prefers products from a particular brand or category, it will prioritize recommending products from that brand or category. In this way, the recommendation department can suggest the most suitable products based on customer preferences and behavioral patterns, thereby increasing customer purchasing intent. In addition, the recommendation department can use AI technology to continuously learn customer preferences and behavioral patterns and improve recommendation accuracy. For example, by using deep learning technology to analyze customer behavior data, more sophisticated personalized recommendations can be achieved. This allows the recommendation department to consistently suggest the most suitable products to customers, thereby improving customer satisfaction.

[0032] The forecasting unit predicts inventory levels based on products suggested by the recommendation unit. The forecasting unit predicts optimal inventory levels by considering, for example, historical sales data and seasonal demand. Specifically, it analyzes historical sales data using time series analysis and regression analysis to predict future demand. For example, it adjusts inventory levels by considering seasonal sales trends and demand fluctuations during specific events. The forecasting unit can also analyze sales trends in real time and predict inventory levels. For example, it analyzes current sales and inventory consumption rates based on real-time updated sales data to maintain appropriate inventory levels. Furthermore, the forecasting unit can automatically predict inventory levels using AI technology. For example, it uses deep learning technology to build complex forecasting models that consider multiple factors, predicting inventory levels with high accuracy. This allows the forecasting unit to mitigate the risks of inventory shortages and excesses, enabling efficient inventory management. Additionally, the forecasting unit can adjust inventory levels by considering external market data and competitor trends. For example, it monitors competitor sales trends and market demand fluctuations in real time and optimizes inventory strategies based on this information. This allows the forecasting unit to provide highly accurate inventory forecasts based on the latest information at all times, supporting quick and appropriate inventory management.

[0033] The ordering department places inventory orders based on inventory levels predicted by the forecasting department. For example, the ordering department orders the appropriate amount of inventory based on the predicted inventory levels. Specifically, it calculates the order quantity based on the inventory forecast data provided by the forecasting department and places the order with the supplier. For example, it can be set up so that an order is automatically placed when inventory falls below a certain threshold. The ordering department can also adjust inventory levels to mitigate the risk of inventory shortages or excesses. For example, if a surge in demand or supply delays are predicted, it can prevent inventory shortages by placing additional orders in advance. Furthermore, the ordering department can automate inventory ordering using AI technology. For example, it can use machine learning algorithms to analyze past ordering data and supply chain performance to determine the optimal ordering timing and quantity. This allows the ordering department to achieve efficient and accurate inventory ordering and improve the accuracy of inventory management. In addition, the ordering department can strengthen collaboration with suppliers and improve the efficiency of the entire supply chain. For example, through real-time information sharing with suppliers, it can detect supply delays and shortages early and respond quickly. This allows the ordering department to optimize the efficiency of the entire supply chain and minimize inventory management risks.

[0034] The optimization department optimizes prices based on inventory ordered by the ordering department. For example, the optimization department analyzes competitor prices and market demand in real time to set optimal prices. Specifically, it uses price optimization algorithms to analyze competitor price data and market demand data to formulate optimal pricing strategies. For instance, if competitor prices fall, it adjusts its own prices accordingly to maintain competitiveness. The optimization department can also analyze market demand and optimize prices. For example, it adjusts prices to match seasons with high demand or specific events to maximize profits. Furthermore, the optimization department can use AI technology to optimize prices. For example, it uses deep learning technology to build complex price optimization models that consider multiple factors and adjust prices in real time. This allows the optimization department to always provide optimal prices in line with market trends, maximizing revenue. Additionally, the optimization department can analyze customer price sensitivity and purchasing behavior to implement personalized pricing strategies. For example, it can offer special discounts and promotions to specific customer groups to increase purchasing intent. This allows the optimization department to improve customer satisfaction while maximizing revenue. Furthermore, the optimization unit can continuously monitor the effectiveness of the pricing strategy and modify it as needed. This allows the optimization unit to maintain the optimal pricing strategy at all times, thereby enhancing competitiveness.

[0035] The support department can provide automated customer support functions. For example, the support department can use a chatbot to respond to customer inquiries. For example, the support department can use AI to provide immediate and appropriate answers to customer inquiries. The support department can also provide an automated FAQ response function. For example, the support department can use AI to automatically answer frequently asked questions from customers. Furthermore, the support department can provide a ticket management system. For example, the support department can manage customer inquiries as tickets and assign them to the appropriate person in charge. This allows the support department to reduce the burden on customer support and improve the quality of service. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input customer inquiries into AI, and the AI ​​can automatically generate answers.

[0036] The analytics department can provide data analysis and reporting functions. For example, the analytics department can analyze data using statistical analysis. For instance, it can analyze data such as total sales, sales per product, visitor numbers, and conversion rates in real time. The analytics department can also analyze data using machine learning. For example, it can use machine learning models to extract data patterns and predict future trends. Furthermore, the analytics department can analyze data using data mining. For example, it can extract useful information from large amounts of data and provide it to operators. The analytics department can provide data in various formats, such as graphs, text reports, and dashboards. This allows operators to make data-driven decisions. Some or all of the above processes in the analytics department may be performed using AI, or not. For example, the analytics department can input data into an AI, which can then automatically analyze the data and generate reports.

[0037] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit may prioritize collecting product categories that the user has frequently purchased in the past. For example, the data collection unit may concentrate data collection during specific time periods based on the user's purchase history. The data collection unit can also analyze the user's purchase patterns and suggest the optimal data collection method. For example, the data collection unit may analyze the user's purchase patterns and select the optimal data collection method. This enables efficient data collection by allowing the data collection unit to select the optimal data collection method based on the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI, which can then automatically select the optimal data collection method.

[0038] The data collection unit can filter the collected purchase history and behavioral patterns based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting product data in categories that the user is currently interested in. For example, the data collection unit can filter highly relevant data according to the user's lifestyle. The data collection unit can also adjust the scope of data collected based on the user's areas of interest. For example, the data collection unit can adjust the scope of data collected based on the user's areas of interest. This allows the data collection unit to collect highly relevant data by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then automatically filter the data.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting purchase history and behavioral patterns. For example, the data collection unit can prioritize the collection of data from specific stores in the user's current location. For example, the data collection unit can collect highly relevant product data based on the user's geographical location. The data collection unit can also adjust the scope of data collected by considering the user's travel history. For example, the data collection unit can adjust the scope of data collected by considering the user's travel history. This allows the data collection unit to collect more accurate data by collecting highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then automatically prioritize the collection of highly relevant data.

[0040] The data collection unit can analyze the user's social media activity and collect relevant data when collecting purchase history and behavioral patterns. For example, the data collection unit can prioritize collecting data on product categories mentioned by the user on social media. For example, the data collection unit can collect product data that the user might be interested in based on the user's social media activity. The data collection unit can also collect relevant data by referring to the activities of the user's followers and friends. For example, the data collection unit can collect relevant data by referring to the activities of the user's followers and friends. This allows the data collection unit to collect data that reflects the user's interests by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can automatically collect relevant data.

[0041] The recommendation system can adjust the level of detail in recommendations based on the importance of the products. For example, for highly important products, the recommendation system will provide recommendations that include detailed descriptions. For example, the recommendation system will prioritize recommending highly important products based on sales revenue or profit margins. The recommendation system can also provide concise recommendations for less important products. For example, the recommendation system will concisely recommend less important products based on their inventory turnover rate. Furthermore, the recommendation system can adjust the level of detail in recommendations in stages according to the importance of the products. For example, the recommendation system can adjust recommendations in stages, from those with detailed descriptions to concise recommendations, depending on the importance of the products. This allows the recommendation system to provide users with the most relevant information by adjusting the level of detail in recommendations according to the importance of the products. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input product importance data into a generating AI, which can then automatically adjust the level of detail in recommendations.

[0042] The recommendation system can apply different recommendation algorithms depending on the product category. For example, for fashion products, the system can apply a recommendation algorithm that takes trends into account. For example, the system can recommend fashion products based on the latest trend data. The system can also apply a recommendation algorithm that emphasizes technical specifications for electronic devices. For example, the system can recommend electronic devices based on technical specification data. Furthermore, the system can apply a recommendation algorithm that takes expiration dates and seasonality into account for food products. For example, the system can recommend food products based on expiration date and seasonality data. This allows the system to make more accurate product recommendations by applying a recommendation algorithm appropriate to the product category. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input product category data into a generating AI, which can then automatically apply different recommendation algorithms.

[0043] The recommendation department can determine the priority of recommendations based on the product submission timing. For example, the recommendation department may prioritize recommending new products. For example, it may prioritize recommending new products based on product release date data. The recommendation department may also prioritize recommending products that are on sale. For example, it may prioritize recommending products that are on sale based on product campaign period data. Furthermore, the recommendation department may also prioritize recommending seasonal products according to the season. For example, it may prioritize recommending seasonal products based on product season data. In this way, the recommendation department can recommend products at the optimal time for the user by determining the priority of recommendations based on the product submission timing. Some or all of the above processing in the recommendation department may be performed using AI, or not. For example, the recommendation department can input product submission timing data into a generating AI, and the generating AI can automatically determine the recommendation priority.

[0044] The recommendation system can adjust the order of recommendations based on product relevance. For example, the recommendation system can prioritize recommending highly relevant products based on the user's past purchase history. The recommendation system can also prioritize recommending highly relevant products based on the user's behavioral patterns. Furthermore, the recommendation system can prioritize recommending products that the user is likely to be interested in. For example, the recommendation system can prioritize recommending products that the user is likely to be interested in based on their preference data. In this way, the recommendation system can recommend products in the optimal order for the user by adjusting the order of recommendations based on product relevance. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input product relevance data into a generating AI, which can then automatically adjust the order of recommendations.

[0045] The prediction unit can optimize its prediction algorithm by referring to past sales data during the prediction process. For example, the prediction unit can select the optimal prediction algorithm based on past sales data. For example, the prediction unit analyzes past sales data and optimizes the prediction algorithm. The prediction unit can also improve the accuracy of its predictions by referring to past sales data. For example, the prediction unit improves the accuracy of its predictions based on past sales data. As a result, the prediction unit improves the accuracy of its predictions by optimizing its prediction algorithm based on past sales data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past sales data into a generating AI, and the generating AI can automatically optimize the prediction algorithm.

[0046] The forecasting unit can predict inventory levels while considering seasonal demand. For example, the forecasting unit predicts the optimal inventory level based on seasonal demand. For example, the forecasting unit predicts seasonal demand based on past sales data. The forecasting unit can also analyze seasonal demand and adjust inventory levels. For example, the forecasting unit adjusts inventory levels based on seasonal demand data. Furthermore, the forecasting unit can predict the appropriate amount of inventory while considering seasonal demand. For example, the forecasting unit predicts the appropriate amount of inventory based on seasonal demand data. As a result, by predicting inventory levels while considering seasonal demand, the forecasting unit enables proper inventory management. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input seasonal demand data into a generating AI, and the generating AI can automatically predict inventory levels.

[0047] The forecasting unit can predict inventory levels while considering the geographical distribution of products. For example, the forecasting unit predicts the optimal inventory level based on the geographical distribution of products. For example, the forecasting unit predicts the geographical distribution of products based on sales data for each region. The forecasting unit can also analyze the geographical distribution of products and adjust inventory levels. For example, the forecasting unit analyzes the geographical distribution of products based on logistics data and adjusts inventory levels. Furthermore, the forecasting unit can predict the appropriate amount of inventory while considering the geographical distribution of products. For example, the forecasting unit predicts the appropriate amount of inventory while considering the geographical distribution of products based on population data. As a result, by predicting inventory levels while considering the geographical distribution of products, the forecasting unit enables proper inventory management. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input geographical distribution data of products into a generating AI, and the generating AI can automatically predict inventory levels.

[0048] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on the product during the prediction process. For example, the prediction unit can select the optimal prediction algorithm based on relevant literature on the product. For example, the prediction unit can select a prediction algorithm by referring to relevant literature on the product based on academic papers. The prediction unit can also analyze relevant literature on the product and optimize the prediction algorithm. For example, the prediction unit can analyze relevant literature on the product based on industry reports and optimize the prediction algorithm. Furthermore, the prediction unit can improve the accuracy of its predictions by referring to relevant literature on the product. For example, the prediction unit can improve the accuracy of its predictions by referring to relevant literature on the product based on patent documents. In this way, the prediction unit improves the accuracy of its predictions by referring to relevant literature on the product. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input relevant literature data on the product into a generating AI, and the generating AI can automatically improve the accuracy of the predictions.

[0049] The ordering department can select the optimal ordering method by referring to past ordering history when placing an order. For example, the ordering department selects the optimal ordering method based on past ordering history. For example, the ordering department analyzes past ordering history and optimizes the ordering method. The ordering department can also improve the accuracy of orders by referring to past ordering history. For example, the ordering department improves the accuracy of orders based on past ordering history. As a result, the ordering department improves the accuracy of orders by selecting the optimal ordering method based on past ordering history. Some or all of the above processes in the ordering department may be performed using AI, for example, or without using AI. For example, the ordering department can input past ordering history data into a generating AI, and the generating AI can automatically select the optimal ordering method.

[0050] The ordering department can adjust the order quantity when placing an order, taking into account the supply status of the goods. For example, the ordering department can determine the optimal order quantity based on the supply status of the goods. For example, the ordering department can determine the order quantity based on the inventory level of the supplier, taking into account the supply status of the goods. The ordering department can also analyze the supply status of the goods and adjust the order quantity. For example, the ordering department can analyze the supply status of the goods and adjust the order quantity based on the lead time. Furthermore, the ordering department can improve the accuracy of orders by taking the supply status of the goods into account. For example, the ordering department can improve the accuracy of orders by taking the supply status of the goods into account, taking into account the supply capacity. As a result, the ordering department can make appropriate orders by adjusting the order quantity while taking the supply status of the goods into account. Some or all of the above processes in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input the supply status of goods data into a generating AI, and the generating AI can automatically adjust the order quantity.

[0051] The ordering department can adjust order quantities when placing orders, taking into account the geographical distribution of the products. For example, the ordering department can determine the optimal order quantity based on the geographical distribution of the products. For example, the ordering department can predict the geographical distribution of products and determine the order quantity based on sales data for each region. The ordering department can also analyze the geographical distribution of products and adjust the order quantity. For example, the ordering department can analyze the geographical distribution of products and adjust the order quantity based on logistics data. Furthermore, the ordering department can improve the accuracy of orders by taking into account the geographical distribution of products. For example, the ordering department can improve the accuracy of orders by taking into account the geographical distribution of products based on population data. As a result, the ordering department can make appropriate orders by adjusting order quantities while taking into account the geographical distribution of products. Some or all of the above processes in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input geographical distribution data of products into a generating AI, and the generating AI can automatically adjust the order quantity.

[0052] The ordering department can improve the accuracy of orders by referring to relevant literature on products when placing orders. For example, the ordering department can select the optimal ordering method based on relevant literature on products. For example, the ordering department can select an ordering method by referring to relevant literature on products based on academic papers. The ordering department can also analyze relevant literature on products and optimize the ordering method. For example, the ordering department can analyze relevant literature on products based on industry reports and optimize the ordering method. Furthermore, the ordering department can improve the accuracy of orders by referring to relevant literature on products. For example, the ordering department can improve the accuracy of orders by referring to relevant literature on products based on patent documents. In this way, the ordering department can improve the accuracy of orders by referring to relevant literature on products. Some or all of the above processes in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input relevant literature data on products into a generating AI, and the generating AI can automatically improve the accuracy of orders.

[0053] The optimization unit can optimize the optimization algorithm by referring to historical price data during the optimization process. For example, the optimization unit can select the optimal optimization algorithm based on historical price data. For example, the optimization unit analyzes historical price data and optimizes the optimization algorithm. The optimization unit can also improve the accuracy of optimization by referring to historical price data. For example, the optimization unit improves the accuracy of optimization based on historical price data. As a result, the optimization unit improves the accuracy of pricing by optimizing the optimization algorithm based on historical price data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input historical price data into a generating AI, and the generating AI can automatically optimize the optimization algorithm.

[0054] The optimization unit can optimize prices by considering the prices of competitors during the optimization process. For example, the optimization unit can set an optimal price based on the prices of competitors. For example, the optimization unit can analyze competitor prices and optimize them. The optimization unit can also improve the accuracy of prices by considering the prices of competitors. For example, the optimization unit can improve the accuracy of prices based on competitor price data. As a result, the optimization unit can set competitive prices by optimizing prices while considering competitor prices. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input competitor price data into a generating AI, and the generating AI can automatically optimize prices.

[0055] The optimization unit can optimize prices by considering the geographical distribution of products during the optimization process. For example, the optimization unit can set the optimal price based on the geographical distribution of products. For example, the optimization unit can set prices considering the geographical distribution of products based on sales data for each region. The optimization unit can also analyze the geographical distribution of products and optimize prices. For example, the optimization unit can analyze the geographical distribution of products based on logistics data and optimize prices. Furthermore, the optimization unit can improve the accuracy of pricing by considering the geographical distribution of products. For example, the optimization unit can improve the accuracy of pricing by considering the geographical distribution of products based on population data. As a result, the optimization unit can set appropriate prices by optimizing prices while considering the geographical distribution of products. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input geographical distribution data of products into a generating AI, and the generating AI can automatically optimize prices.

[0056] The optimization unit can improve the accuracy of pricing by referring to relevant literature on the product during optimization. For example, the optimization unit selects the optimal pricing method based on relevant literature on the product. For example, the optimization unit selects a pricing method by referring to relevant literature on the product based on academic papers. The optimization unit can also analyze relevant literature on the product and optimize the pricing method. For example, the optimization unit analyzes relevant literature on the product based on industry reports and optimizes the pricing method. Furthermore, the optimization unit can improve the accuracy of pricing by referring to relevant literature on the product. For example, the optimization unit improves the accuracy of pricing by referring to relevant literature on the product based on patent documents. In this way, the optimization unit improves the accuracy of pricing by referring to relevant literature on the product. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input relevant literature data on the product into a generating AI, and the generating AI can automatically improve the accuracy of pricing.

[0057] The support department can select the optimal response method when handling inquiries by referring to past inquiry history. For example, the support department can select the optimal response method based on past inquiry history. For example, the support department can analyze past inquiry history and optimize the response method. The support department can also improve the accuracy of its responses by referring to past inquiry history. For example, the support department can improve the accuracy of its responses based on past inquiry history. As a result, the support department improves the accuracy of its responses by selecting the optimal response method based on past inquiry history. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can input past inquiry history data into a generating AI, and the generating AI can automatically select the optimal response method.

[0058] The support department can select the most appropriate response method when handling inquiries, taking into account the user's device information. For example, if the user is using a smartphone, the support department can provide a response method adapted to the screen size. For example, based on the user's device information, the support department can provide a response method optimized for smartphones. Furthermore, if the user is using a tablet, the support department can also provide a response method optimized for larger screens. For example, based on the user's device information, the support department can provide a response method optimized for tablets. Additionally, if the user is using a smartwatch, the support department can provide a concise and highly visible response method. For example, based on the user's device information, the support department can provide a response method optimized for smartwatches. This allows the support department to provide more appropriate responses by selecting the most suitable method based on the user's device information. Some or all of the above processing in the support department may be performed using AI, or not. For example, the support department can input the user's device information into a generating AI, which can then automatically select the most appropriate response method.

[0059] The analysis unit can select the optimal analysis method by referring to past analysis data during data analysis. For example, the analysis unit selects the optimal analysis method based on past analysis data. For example, the analysis unit analyzes past analysis data and optimizes the analysis method. The analysis unit can also improve the accuracy of the analysis by referring to past analysis data. For example, the analysis unit improves the accuracy of the analysis based on past analysis data. As a result, the analysis unit improves the accuracy of the analysis by selecting the optimal analysis method based on past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI, and the generating AI can automatically select the optimal analysis method.

[0060] The analysis unit can select the optimal analysis method when analyzing data, taking into account the user's device information. For example, if the user is using a smartphone, the analysis unit can provide an analysis method adapted to the screen size. For example, based on the user's device information, the analysis unit can provide an analysis method optimized for smartphones. Furthermore, if the user is using a tablet, the analysis unit can also provide an analysis method optimized for larger screens. For example, based on the user's device information, the analysis unit can provide an analysis method optimized for tablets. Additionally, if the user is using a smartwatch, the analysis unit can provide a concise and highly visible analysis method. For example, based on the user's device information, the analysis unit can provide an analysis method optimized for smartwatches. This allows the analysis unit to perform more appropriate data analysis by selecting the optimal analysis method based on the user's device information. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the user's device information into a generating AI, which can then automatically select the optimal analysis method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] AI-driven platforms can prioritize the collection of highly relevant data by considering the user's geographical location when gathering user purchase history and behavioral patterns. For example, they can prioritize the collection of data from specific stores in the user's current location. Specifically, they can collect highly relevant product data based on the user's geographical location. They can also adjust the scope of data collected by considering the user's travel history. For example, they can adjust the scope of data collected by considering the user's travel history. As a result, the data collection unit can collect more accurate data by gathering highly relevant data based on the user's geographical location.

[0063] AI-driven platforms can collect relevant data by analyzing users' social media activity when gathering user purchase history and behavioral patterns. For example, they can prioritize collecting data on product categories mentioned by users on social media. Specifically, they can collect data on products that users are likely to be interested in based on their social media activity. They can also collect relevant data by referring to the activities of users' followers and friends. For example, they can collect relevant data by referring to the activities of users' followers and friends. As a result, the data collection unit can collect relevant data based on users' social media activity, enabling data collection that reflects users' interests.

[0064] AI-driven platforms can filter user purchase history and behavioral patterns based on the user's current lifestyle and areas of interest. For example, they can prioritize collecting product data in categories the user is currently interested in. Specifically, they filter data based on the user's lifestyle. They can also adjust the scope of data collected based on the user's areas of interest. For example, they can adjust the scope of data collected based on the user's areas of interest. This allows the data collection unit to collect highly relevant data by filtering it based on the user's lifestyle and areas of interest.

[0065] AI-driven platforms can analyze a user's past purchase history and select the optimal data collection method when gathering user purchase history and behavioral patterns. For example, they can prioritize collecting product categories that a user has frequently purchased in the past. Specifically, they can concentrate data collection during certain time periods based on the user's purchase history. They can also analyze the user's purchase patterns and suggest the optimal data collection method. For example, they can analyze the user's purchase patterns and select the most suitable collection method. As a result, the data collection unit can efficiently collect data by selecting the optimal collection method based on the user's past purchase history.

[0066] AI-driven platforms can prioritize the collection of highly relevant data by considering the user's geographical location when gathering user purchase history and behavioral patterns. For example, they can prioritize the collection of data from specific stores in the user's current location. Specifically, they can collect highly relevant product data based on the user's geographical location. They can also adjust the scope of data collected by considering the user's travel history. For example, they can adjust the scope of data collected by considering the user's travel history. As a result, the data collection unit can collect more accurate data by gathering highly relevant data based on the user's geographical location.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects customer purchase history and behavioral patterns. This includes purchase date and time, purchased items, browsing history, and click history. The data collection unit tracks customer behavior on the website and collects purchase history and behavioral patterns. It can also analyze customers' past purchase history and social media activity to collect data that reflects customer preferences and interests. Step 2: The recommendation department suggests the most suitable products based on the data collected by the data collection department. This includes recommending products that customers are likely to be interested in, based on their past purchase history and behavioral patterns. It can also provide real-time notifications of new products and sales information to promote customer engagement. Step 3: The forecasting unit predicts inventory levels based on the products suggested by the recommendation unit. This includes predicting the appropriate amount of inventory by considering historical sales data and seasonal demand. It can also analyze sales trends in real time and automatically predict inventory levels using AI technology. Step 4: The ordering department places orders for inventory based on the inventory levels predicted by the forecasting department. This includes ordering the appropriate amount of inventory based on the predicted inventory levels. It can also adjust inventory levels to mitigate the risk of insufficient or excess inventory, and automate inventory ordering using AI technology. Step 5: The optimization unit optimizes prices based on inventory ordered by the ordering unit. This includes analyzing competitor prices and market demand in real time to set the optimal price. AI technology can also be used to optimize prices.

[0069] (Example of form 2) An AI-driven platform according to an embodiment of the present invention is a system that improves the operational efficiency of e-commerce businesses. This system can suggest optimal products based on customer purchase history and behavioral patterns, and efficiently manage inventory and pricing. The AI-driven platform can be seamlessly integrated with major e-commerce platforms and payment systems, and its modular design allows for the flexible addition of necessary functions. For example, the AI-driven platform collects customer purchase history and behavioral patterns and suggests optimal products. For instance, it recommends products that customers are likely to be interested in based on past purchases and browsing history. It also provides real-time notifications of new products and sales information to promote customer engagement. This enables the rapid and accurate delivery of products that customers want. Next, the AI-driven platform provides an automated inventory management function. Using AI technology, it analyzes sales trends in real time and automatically predicts and orders appropriate inventory levels. For example, it predicts the appropriate amount of inventory by considering past sales data and seasonal demand. This reduces the risk of inventory shortages and excess inventory, and improves operational efficiency. Furthermore, the AI-driven platform provides a price optimization function. It uses AI to analyze market demand and competitive conditions and sets optimal prices. For example, it analyzes competitor pricing and market demand in real time to set optimal prices. This maximizes sales and profits. Furthermore, AI-driven platforms offer automated customer support. They utilize AI to provide automated responses to inquiries and FAQs. For instance, the AI ​​instantly provides appropriate answers to customer inquiries. This reduces the burden on customer support and improves service quality. Finally, AI-driven platforms provide data analysis and reporting capabilities. Operators can view data such as total sales, sales per product, visitor numbers, and conversion rates in real time through a dashboard. This allows operators to make data-driven decisions. In this way, using an AI-driven platform can solve the challenges faced by e-commerce businesses and significantly improve their competitiveness and profitability.This allows AI-driven platforms to improve the operational efficiency of e-commerce businesses and increase customer satisfaction.

[0070] The AI-driven platform according to this embodiment comprises a data collection unit, a recommendation unit, a prediction unit, an order unit, and an optimization unit. The data collection unit collects customer purchase history and behavioral patterns. Customer purchase history and behavioral patterns include, but are not limited to, purchase date and time, purchased items, browsing history, and click history. The data collection unit tracks customer behavior on websites and collects purchase history and behavioral patterns. The data collection unit can also analyze a customer's past purchase history to understand their preferences. Furthermore, the data collection unit can analyze a customer's social media activity and collect data that reflects their interests. For example, the data collection unit prioritizes collecting data on product categories mentioned by the customer on social media. The recommendation unit suggests the most suitable products based on the data collected by the data collection unit. The recommendation unit recommends products that a customer is likely to be interested in, based on the customer's past purchase history and behavioral patterns. For example, the recommendation unit recommends products that a customer is likely to be interested in, based on products the customer has previously purchased and their browsing history. The recommendation unit can also notify customers of new products and sales information in real time to promote customer engagement. Furthermore, the recommendation department can provide personalized product recommendations based on customer preferences. For example, the recommendation department recommends products that customers are likely to be interested in based on their preferences. The forecasting department predicts inventory levels based on the products suggested by the recommendation department. The forecasting department predicts the appropriate amount of inventory by considering, for example, past sales data and seasonal demand. For example, the forecasting department analyzes past sales data to predict seasonal demand. The forecasting department can also analyze sales trends in real time to predict inventory levels. Furthermore, the forecasting department can use AI technology to automatically predict inventory levels. For example, the forecasting department uses AI technology to analyze sales trends in real time to predict inventory levels. The ordering department places orders for inventory based on the inventory levels predicted by the forecasting department. For example, the ordering department places orders for the appropriate amount of inventory based on the predicted inventory levels. For example, the ordering department automatically places orders for inventory based on the predicted inventory levels. The ordering department can also adjust inventory levels to mitigate the risk of insufficient or excessive inventory.Furthermore, the ordering department can automate inventory ordering using AI technology. For example, the ordering department can use AI technology to analyze inventory levels in real time and order the appropriate amount of inventory. The optimization department optimizes prices based on the inventory ordered by the ordering department. The optimization department can, for example, analyze competitor prices and market demand in real time and set the optimal price. For example, the optimization department can set the optimal price based on competitor prices. The optimization department can also analyze market demand and optimize prices. Furthermore, the optimization department can optimize prices using AI technology. For example, the optimization department can use AI technology to analyze market demand and competitor prices in real time and set the optimal price. As a result, the AI-driven platform according to this embodiment can propose the optimal products based on customer purchase history and behavior patterns, and efficiently manage inventory and set prices.

[0071] The data collection unit collects customer purchase history and behavioral patterns. This includes, but is not limited to, purchase date and time, purchased items, browsing history, and click history. For example, the unit tracks customer behavior on websites to collect purchase history and behavioral patterns. Specifically, it uses cookies and session data on the website to record in detail which pages customers view and which links they click. It also collects information on items customers add to their cart and items they complete purchases of. Furthermore, the data collection unit can analyze customers' past purchase history to understand their preferences. For example, it can identify product categories and brands that customers frequently purchase and profile their preferences based on this information. Additionally, the data collection unit can analyze customers' social media activity to collect data that reflects their interests. For example, it prioritizes collecting data on product categories mentioned by customers on social media. This includes using social media APIs to analyze customer posts and "liked" content to understand their interests and preferences. This allows the data collection department to comprehensively analyze customers' online behavior and social media activity, gaining a detailed understanding of their purchasing intent and preferences. The collected data is stored in a cloud-based database, making it accessible to other departments. This enables the data collection department to centrally manage diverse customer data and provide real-time updated information.

[0072] The recommendation department suggests the most suitable products based on data collected by the data collection department. For example, the recommendation department recommends products that customers are likely to be interested in based on their past purchase history and behavioral patterns. Specifically, it uses machine learning algorithms to analyze customers' past purchase and browsing history and predict products that customers are likely to purchase next. For example, it uses collaborative filtering technology to refer to the purchase history of other customers with similar preferences and provides personalized product recommendations to customers. The recommendation department can also promote customer engagement by notifying customers of new products and sales information in real time. For example, if a new product related to a product a customer has previously purchased is released, the department will notify the customer via push notification or email. Furthermore, the recommendation department can provide personalized product recommendations based on customer preferences. For example, if a customer prefers products from a particular brand or category, it will prioritize recommending products from that brand or category. In this way, the recommendation department can suggest the most suitable products based on customer preferences and behavioral patterns, thereby increasing customer purchasing intent. In addition, the recommendation department can use AI technology to continuously learn customer preferences and behavioral patterns and improve recommendation accuracy. For example, by using deep learning technology to analyze customer behavior data, more sophisticated personalized recommendations can be achieved. This allows the recommendation department to consistently suggest the most suitable products to customers, thereby improving customer satisfaction.

[0073] The forecasting unit predicts inventory levels based on products suggested by the recommendation unit. The forecasting unit predicts optimal inventory levels by considering, for example, historical sales data and seasonal demand. Specifically, it analyzes historical sales data using time series analysis and regression analysis to predict future demand. For example, it adjusts inventory levels by considering seasonal sales trends and demand fluctuations during specific events. The forecasting unit can also analyze sales trends in real time and predict inventory levels. For example, it analyzes current sales and inventory consumption rates based on real-time updated sales data to maintain appropriate inventory levels. Furthermore, the forecasting unit can automatically predict inventory levels using AI technology. For example, it uses deep learning technology to build complex forecasting models that consider multiple factors, predicting inventory levels with high accuracy. This allows the forecasting unit to mitigate the risks of inventory shortages and excesses, enabling efficient inventory management. Additionally, the forecasting unit can adjust inventory levels by considering external market data and competitor trends. For example, it monitors competitor sales trends and market demand fluctuations in real time and optimizes inventory strategies based on this information. This allows the forecasting unit to provide highly accurate inventory forecasts based on the latest information at all times, supporting quick and appropriate inventory management.

[0074] The ordering department places inventory orders based on inventory levels predicted by the forecasting department. For example, the ordering department orders the appropriate amount of inventory based on the predicted inventory levels. Specifically, it calculates the order quantity based on the inventory forecast data provided by the forecasting department and places the order with the supplier. For example, it can be set up so that an order is automatically placed when inventory falls below a certain threshold. The ordering department can also adjust inventory levels to mitigate the risk of inventory shortages or excesses. For example, if a surge in demand or supply delays are predicted, it can prevent inventory shortages by placing additional orders in advance. Furthermore, the ordering department can automate inventory ordering using AI technology. For example, it can use machine learning algorithms to analyze past ordering data and supply chain performance to determine the optimal ordering timing and quantity. This allows the ordering department to achieve efficient and accurate inventory ordering and improve the accuracy of inventory management. In addition, the ordering department can strengthen collaboration with suppliers and improve the efficiency of the entire supply chain. For example, through real-time information sharing with suppliers, it can detect supply delays and shortages early and respond quickly. This allows the ordering department to optimize the efficiency of the entire supply chain and minimize inventory management risks.

[0075] The optimization department optimizes prices based on inventory ordered by the ordering department. For example, the optimization department analyzes competitor prices and market demand in real time to set optimal prices. Specifically, it uses price optimization algorithms to analyze competitor price data and market demand data to formulate optimal pricing strategies. For instance, if competitor prices fall, it adjusts its own prices accordingly to maintain competitiveness. The optimization department can also analyze market demand and optimize prices. For example, it adjusts prices to match seasons with high demand or specific events to maximize profits. Furthermore, the optimization department can use AI technology to optimize prices. For example, it uses deep learning technology to build complex price optimization models that consider multiple factors and adjust prices in real time. This allows the optimization department to always provide optimal prices in line with market trends, maximizing revenue. Additionally, the optimization department can analyze customer price sensitivity and purchasing behavior to implement personalized pricing strategies. For example, it can offer special discounts and promotions to specific customer groups to increase purchasing intent. This allows the optimization department to improve customer satisfaction while maximizing revenue. Furthermore, the optimization unit can continuously monitor the effectiveness of the pricing strategy and modify it as needed. This allows the optimization unit to maintain the optimal pricing strategy at all times, thereby enhancing competitiveness.

[0076] The support department can provide automated customer support functions. For example, the support department can use a chatbot to respond to customer inquiries. For example, the support department can use AI to provide immediate and appropriate answers to customer inquiries. The support department can also provide an automated FAQ response function. For example, the support department can use AI to automatically answer frequently asked questions from customers. Furthermore, the support department can provide a ticket management system. For example, the support department can manage customer inquiries as tickets and assign them to the appropriate person in charge. This allows the support department to reduce the burden on customer support and improve the quality of service. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input customer inquiries into AI, and the AI ​​can automatically generate answers.

[0077] The analytics department can provide data analysis and reporting functions. For example, the analytics department can analyze data using statistical analysis. For instance, it can analyze data such as total sales, sales per product, visitor numbers, and conversion rates in real time. The analytics department can also analyze data using machine learning. For example, it can use machine learning models to extract data patterns and predict future trends. Furthermore, the analytics department can analyze data using data mining. For example, it can extract useful information from large amounts of data and provide it to operators. The analytics department can provide data in various formats, such as graphs, text reports, and dashboards. This allows operators to make data-driven decisions. Some or all of the above processes in the analytics department may be performed using AI, or not. For example, the analytics department can input data into an AI, which can then automatically analyze the data and generate reports.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of collecting purchase history and behavioral patterns based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency to lessen the user's burden. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also increase the collection frequency and acquire more detailed data if the user is relaxed. For example, the data collection unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. The data collection unit can also shorten the collection timing and acquire data quickly if the user is in a hurry. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows the data collection unit to reduce the user's burden and acquire detailed data by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into the generative AI, which can automatically estimate the emotions and adjust the collection timing.

[0079] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit may prioritize collecting product categories that the user has frequently purchased in the past. For example, the data collection unit may concentrate data collection during specific time periods based on the user's purchase history. The data collection unit can also analyze the user's purchase patterns and suggest the optimal data collection method. For example, the data collection unit may analyze the user's purchase patterns and select the optimal data collection method. This enables efficient data collection by allowing the data collection unit to select the optimal data collection method based on the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI, which can then automatically select the optimal data collection method.

[0080] The data collection unit can filter the collected purchase history and behavioral patterns based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting product data in categories that the user is currently interested in. For example, the data collection unit can filter highly relevant data according to the user's lifestyle. The data collection unit can also adjust the scope of data collected based on the user's areas of interest. For example, the data collection unit can adjust the scope of data collected based on the user's areas of interest. This allows the data collection unit to collect highly relevant data by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then automatically filter the data.

[0081] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For instance, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions in real time using an emotion estimation algorithm. For instance, the data collection unit can calculate an emotion score based on changes in facial expressions and provide feedback. The data collection unit can also prioritize the collection of detailed data if the user is relaxed. For instance, the data collection unit can record the user's voice and estimate their emotions in real time using voice analysis technology. For instance, the data collection unit can analyze the tone and speed of the voice, calculate an emotion score, and provide feedback. The data collection unit can also quickly collect high-priority data if the user is in a hurry. For instance, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions in real time using an emotion estimation algorithm. For instance, the data collection unit can calculate an emotion score based on fluctuations in heart rate and provide feedback. This allows the data collection unit to prioritize important data by determining data priorities according to 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. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then automatically estimate emotions and determine data priorities.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting purchase history and behavioral patterns. For example, the data collection unit can prioritize the collection of data from specific stores in the user's current location. For example, the data collection unit can collect highly relevant product data based on the user's geographical location. The data collection unit can also adjust the scope of data collected by considering the user's travel history. For example, the data collection unit can adjust the scope of data collected by considering the user's travel history. This allows the data collection unit to collect more accurate data by collecting highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then automatically prioritize the collection of highly relevant data.

[0083] The data collection unit can analyze the user's social media activity and collect relevant data when collecting purchase history and behavioral patterns. For example, the data collection unit can prioritize collecting data on product categories mentioned by the user on social media. For example, the data collection unit can collect product data that the user might be interested in based on the user's social media activity. The data collection unit can also collect relevant data by referring to the activities of the user's followers and friends. For example, the data collection unit can collect relevant data by referring to the activities of the user's followers and friends. This allows the data collection unit to collect data that reflects the user's interests by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can automatically collect relevant data.

[0084] The recommendation system can estimate the user's emotions and adjust its product recommendation method based on those emotions. For example, if the user is relaxed, the recommendation system will provide recommendations that include detailed explanations. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The recommendation system might then calculate an emotion score based on changes in facial expressions and adjust its recommendation method accordingly. Furthermore, if the user is in a hurry, the recommendation system can provide concise recommendations. For example, it might record the user's voice and estimate their emotions using voice analysis technology. The recommendation system might then analyze the tone and speed of the voice to calculate an emotion score and adjust its recommendation method. Finally, if the user is excited, the recommendation system can provide visually appealing recommendations. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The recommendation system might then calculate an emotion score based on fluctuations in heart rate and adjust its recommendation method accordingly. This allows the recommendation system to provide more appropriate product recommendations by adjusting its recommendation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using 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. Some or all of the above processing in the recommendation unit may be performed using AI or not using AI. For example, the recommendation unit can input user emotion data into a generative AI, which can then automatically estimate emotions and adjust the recommendation method.

[0085] The recommendation system can adjust the level of detail in recommendations based on the importance of the products. For example, for highly important products, the recommendation system will provide recommendations that include detailed descriptions. For example, the recommendation system will prioritize recommending highly important products based on sales revenue or profit margins. The recommendation system can also provide concise recommendations for less important products. For example, the recommendation system will concisely recommend less important products based on their inventory turnover rate. Furthermore, the recommendation system can adjust the level of detail in recommendations in stages according to the importance of the products. For example, the recommendation system can adjust recommendations in stages, from those with detailed descriptions to concise recommendations, depending on the importance of the products. This allows the recommendation system to provide users with the most relevant information by adjusting the level of detail in recommendations according to the importance of the products. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input product importance data into a generating AI, which can then automatically adjust the level of detail in recommendations.

[0086] The recommendation system can apply different recommendation algorithms depending on the product category. For example, for fashion products, the system can apply a recommendation algorithm that takes trends into account. For example, the system can recommend fashion products based on the latest trend data. The system can also apply a recommendation algorithm that emphasizes technical specifications for electronic devices. For example, the system can recommend electronic devices based on technical specification data. Furthermore, the system can apply a recommendation algorithm that takes expiration dates and seasonality into account for food products. For example, the system can recommend food products based on expiration date and seasonality data. This allows the system to make more accurate product recommendations by applying a recommendation algorithm appropriate to the product category. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input product category data into a generating AI, which can then automatically apply different recommendation algorithms.

[0087] The recommendation system can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, if the user is relaxed, the recommendation system will provide longer recommendations. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The recommendation system might calculate an emotion score based on changes in facial expressions and adjust the recommendation length accordingly. The recommendation system can also provide shorter recommendations if the user is in a hurry. For example, it might record the user's voice and estimate their emotions using voice analysis technology. The recommendation system might analyze the tone and speed of the voice, calculate an emotion score, and adjust the recommendation length accordingly. Furthermore, if the user is excited, the recommendation system can provide visually appealing recommendations. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The recommendation system might calculate an emotion score based on fluctuations in heart rate and adjust the recommendation length accordingly. This allows the recommendation system to provide optimal recommendations by adjusting the recommendation length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using 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. Some or all of the above processing in the recommendation section may be performed using AI or not using AI. For example, the recommendation section can input user emotion data into a generative AI, which can automatically estimate the emotion and adjust the length of the recommendation.

[0088] The recommendation department can determine the priority of recommendations based on the product submission timing. For example, the recommendation department may prioritize recommending new products. For example, it may prioritize recommending new products based on product release date data. The recommendation department may also prioritize recommending products that are on sale. For example, it may prioritize recommending products that are on sale based on product campaign period data. Furthermore, the recommendation department may also prioritize recommending seasonal products according to the season. For example, it may prioritize recommending seasonal products based on product season data. In this way, the recommendation department can recommend products at the optimal time for the user by determining the priority of recommendations based on the product submission timing. Some or all of the above processing in the recommendation department may be performed using AI, or not. For example, the recommendation department can input product submission timing data into a generating AI, and the generating AI can automatically determine the recommendation priority.

[0089] The recommendation system can adjust the order of recommendations based on product relevance. For example, the recommendation system can prioritize recommending highly relevant products based on the user's past purchase history. The recommendation system can also prioritize recommending highly relevant products based on the user's behavioral patterns. Furthermore, the recommendation system can prioritize recommending products that the user is likely to be interested in. For example, the recommendation system can prioritize recommending products that the user is likely to be interested in based on their preference data. In this way, the recommendation system can recommend products in the optimal order for the user by adjusting the order of recommendations based on product relevance. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input product relevance data into a generating AI, which can then automatically adjust the order of recommendations.

[0090] The prediction unit can estimate the user's emotions and adjust the inventory level prediction method based on the estimated user emotions. For example, if the user is relaxed, the prediction unit can make predictions based on detailed data. For example, the prediction unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the prediction unit can calculate an emotion score based on changes in facial expressions and adjust the prediction method. The prediction unit can also make predictions based on concise data if the user is in a hurry. For example, the prediction unit can record the user's voice and estimate emotions using voice analysis technology. For example, the prediction unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the prediction method. The prediction unit can also make visually appealing predictions if the user is excited. For example, the prediction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the prediction unit can calculate an emotion score based on fluctuations in heart rate and adjust the prediction method. This allows the prediction unit to adjust its inventory level prediction method according to the user's emotions, enabling more accurate inventory forecasts. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input user emotion data into the generative AI, which can then automatically estimate the emotions and adjust the prediction method.

[0091] The prediction unit can optimize its prediction algorithm by referring to past sales data during the prediction process. For example, the prediction unit can select the optimal prediction algorithm based on past sales data. For example, the prediction unit analyzes past sales data and optimizes the prediction algorithm. The prediction unit can also improve the accuracy of its predictions by referring to past sales data. For example, the prediction unit improves the accuracy of its predictions based on past sales data. As a result, the prediction unit improves the accuracy of its predictions by optimizing its prediction algorithm based on past sales data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past sales data into a generating AI, and the generating AI can automatically optimize the prediction algorithm.

[0092] The forecasting unit can predict inventory levels while considering seasonal demand. For example, the forecasting unit predicts the optimal inventory level based on seasonal demand. For example, the forecasting unit predicts seasonal demand based on past sales data. The forecasting unit can also analyze seasonal demand and adjust inventory levels. For example, the forecasting unit adjusts inventory levels based on seasonal demand data. Furthermore, the forecasting unit can predict the appropriate amount of inventory while considering seasonal demand. For example, the forecasting unit predicts the appropriate amount of inventory based on seasonal demand data. As a result, by predicting inventory levels while considering seasonal demand, the forecasting unit enables proper inventory management. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input seasonal demand data into a generating AI, and the generating AI can automatically predict inventory levels.

[0093] The prediction unit can estimate the user's emotions and determine prediction priorities based on the estimated emotions. For example, if the user is relaxed, the prediction unit prioritizes detailed predictions. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The prediction unit might calculate an emotion score based on changes in facial expressions and determine prediction priorities. The prediction unit can also prioritize concise predictions if the user is in a hurry. For example, it might record the user's voice and estimate their emotions using voice analysis technology. The prediction unit might analyze the tone and speed of the voice, calculate an emotion score, and determine prediction priorities. The prediction unit can also prioritize visually appealing predictions if the user is excited. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The prediction unit might calculate an emotion score based on fluctuations in heart rate and determine prediction priorities. This allows the prediction unit to make more accurate predictions by determining the priority of predictions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input user emotion data into the generative AI, which can then automatically estimate emotions and determine the priority of predictions.

[0094] The forecasting unit can predict inventory levels while considering the geographical distribution of products. For example, the forecasting unit predicts the optimal inventory level based on the geographical distribution of products. For example, the forecasting unit predicts the geographical distribution of products based on sales data for each region. The forecasting unit can also analyze the geographical distribution of products and adjust inventory levels. For example, the forecasting unit analyzes the geographical distribution of products based on logistics data and adjusts inventory levels. Furthermore, the forecasting unit can predict the appropriate amount of inventory while considering the geographical distribution of products. For example, the forecasting unit predicts the appropriate amount of inventory while considering the geographical distribution of products based on population data. As a result, by predicting inventory levels while considering the geographical distribution of products, the forecasting unit enables proper inventory management. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input geographical distribution data of products into a generating AI, and the generating AI can automatically predict inventory levels.

[0095] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on the product during the prediction process. For example, the prediction unit can select the optimal prediction algorithm based on relevant literature on the product. For example, the prediction unit can select a prediction algorithm by referring to relevant literature on the product based on academic papers. The prediction unit can also analyze relevant literature on the product and optimize the prediction algorithm. For example, the prediction unit can analyze relevant literature on the product based on industry reports and optimize the prediction algorithm. Furthermore, the prediction unit can improve the accuracy of its predictions by referring to relevant literature on the product. For example, the prediction unit can improve the accuracy of its predictions by referring to relevant literature on the product based on patent documents. In this way, the prediction unit improves the accuracy of its predictions by referring to relevant literature on the product. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input relevant literature data on the product into a generating AI, and the generating AI can automatically improve the accuracy of the predictions.

[0096] The ordering unit can estimate the user's emotions and adjust the timing of orders based on those emotions. For example, if the user is relaxed, the ordering unit can place orders based on detailed data. For example, the ordering unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the ordering unit can calculate an emotion score based on changes in facial expressions and adjust the timing of orders. The ordering unit can also place orders based on concise data if the user is in a hurry. For example, the ordering unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the ordering unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the timing of orders. The ordering unit can also place visually appealing orders if the user is excited. For example, the ordering unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the ordering unit can calculate an emotion score based on fluctuations in heart rate and adjust the timing of orders. This allows the ordering department to adjust the timing of orders according to the user's emotions, enabling more appropriate ordering. 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. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input user emotion data into a generative AI, which can automatically estimate the emotion and adjust the timing of the order.

[0097] The ordering department can select the optimal ordering method by referring to past ordering history when placing an order. For example, the ordering department selects the optimal ordering method based on past ordering history. For example, the ordering department analyzes past ordering history and optimizes the ordering method. The ordering department can also improve the accuracy of orders by referring to past ordering history. For example, the ordering department improves the accuracy of orders based on past ordering history. As a result, the ordering department improves the accuracy of orders by selecting the optimal ordering method based on past ordering history. Some or all of the above processes in the ordering department may be performed using AI, for example, or without using AI. For example, the ordering department can input past ordering history data into a generating AI, and the generating AI can automatically select the optimal ordering method.

[0098] The ordering department can adjust the order quantity when placing an order, taking into account the supply status of the goods. For example, the ordering department can determine the optimal order quantity based on the supply status of the goods. For example, the ordering department can determine the order quantity based on the inventory level of the supplier, taking into account the supply status of the goods. The ordering department can also analyze the supply status of the goods and adjust the order quantity. For example, the ordering department can analyze the supply status of the goods and adjust the order quantity based on the lead time. Furthermore, the ordering department can improve the accuracy of orders by taking the supply status of the goods into account. For example, the ordering department can improve the accuracy of orders by taking the supply status of the goods into account, taking into account the supply capacity. As a result, the ordering department can make appropriate orders by adjusting the order quantity while taking the supply status of the goods into account. Some or all of the above processes in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input the supply status of goods data into a generating AI, and the generating AI can automatically adjust the order quantity.

[0099] The ordering department can estimate the user's emotions and determine the priority of orders based on those emotions. For example, if the user is relaxed, the ordering department will prioritize detailed orders. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The ordering department might then calculate an emotion score based on changes in facial expressions and determine the order priority. The ordering department can also prioritize concise orders if the user is in a hurry. For example, it might record the user's voice and estimate their emotions using voice analysis technology. The ordering department might then analyze the tone and speed of the voice to calculate an emotion score and determine the order priority. Furthermore, if the user is excited, the ordering department can prioritize visually appealing orders. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The ordering department might then calculate an emotion score based on fluctuations in heart rate and determine the order priority. This allows the ordering department to prioritize orders based on the user's emotions, enabling more appropriate ordering. 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. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input user emotion data into a generative AI, which can then automatically estimate emotions and determine order priorities.

[0100] The ordering department can adjust order quantities when placing orders, taking into account the geographical distribution of the products. For example, the ordering department can determine the optimal order quantity based on the geographical distribution of the products. For example, the ordering department can predict the geographical distribution of products and determine the order quantity based on sales data for each region. The ordering department can also analyze the geographical distribution of products and adjust the order quantity. For example, the ordering department can analyze the geographical distribution of products and adjust the order quantity based on logistics data. Furthermore, the ordering department can improve the accuracy of orders by taking into account the geographical distribution of products. For example, the ordering department can improve the accuracy of orders by taking into account the geographical distribution of products based on population data. As a result, the ordering department can make appropriate orders by adjusting order quantities while taking into account the geographical distribution of products. Some or all of the above processes in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input geographical distribution data of products into a generating AI, and the generating AI can automatically adjust the order quantity.

[0101] The ordering department can improve the accuracy of orders by referring to relevant literature on products when placing orders. For example, the ordering department can select the optimal ordering method based on relevant literature on products. For example, the ordering department can select an ordering method by referring to relevant literature on products based on academic papers. The ordering department can also analyze relevant literature on products and optimize the ordering method. For example, the ordering department can analyze relevant literature on products based on industry reports and optimize the ordering method. Furthermore, the ordering department can improve the accuracy of orders by referring to relevant literature on products. For example, the ordering department can improve the accuracy of orders by referring to relevant literature on products based on patent documents. In this way, the ordering department can improve the accuracy of orders by referring to relevant literature on products. Some or all of the above processes in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input relevant literature data on products into a generating AI, and the generating AI can automatically improve the accuracy of orders.

[0102] The optimization unit can estimate the user's emotions and adjust the price optimization method based on the estimated emotions. For example, if the user is relaxed, the optimization unit can optimize the price based on detailed data. For example, the optimization unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the optimization unit can calculate an emotion score based on changes in facial expressions and adjust the price optimization method. The optimization unit can also optimize the price based on concise data if the user is in a hurry. For example, the optimization unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the optimization unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the price optimization method. The optimization unit can also set a visually appealing price if the user is excited. For example, the optimization unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the optimization unit can calculate an emotion score based on fluctuations in heart rate and adjust the price optimization method. This allows the optimization unit to adjust the price optimization method according to the user's emotions, enabling more appropriate pricing. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input user emotion data into the generative AI, which can then automatically estimate the emotion and adjust the price optimization method.

[0103] The optimization unit can optimize the optimization algorithm by referring to historical price data during the optimization process. For example, the optimization unit can select the optimal optimization algorithm based on historical price data. For example, the optimization unit analyzes historical price data and optimizes the optimization algorithm. The optimization unit can also improve the accuracy of optimization by referring to historical price data. For example, the optimization unit improves the accuracy of optimization based on historical price data. As a result, the optimization unit improves the accuracy of pricing by optimizing the optimization algorithm based on historical price data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input historical price data into a generating AI, and the generating AI can automatically optimize the optimization algorithm.

[0104] The optimization unit can optimize prices by considering the prices of competitors during the optimization process. For example, the optimization unit can set an optimal price based on the prices of competitors. For example, the optimization unit can analyze competitor prices and optimize them. The optimization unit can also improve the accuracy of prices by considering the prices of competitors. For example, the optimization unit can improve the accuracy of prices based on competitor price data. As a result, the optimization unit can set competitive prices by optimizing prices while considering competitor prices. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input competitor price data into a generating AI, and the generating AI can automatically optimize prices.

[0105] The optimization unit can estimate the user's emotions and determine price priorities based on those emotions. For example, if the user is relaxed, the optimization unit prioritizes detailed pricing. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The optimization unit might then calculate an emotion score based on changes in facial expressions and determine price priorities. The optimization unit can also prioritize concise pricing if the user is in a hurry. For example, it might record the user's voice and estimate their emotions using voice analysis technology. The optimization unit might then analyze the tone and speed of the voice to calculate an emotion score and determine price priorities. Furthermore, if the user is excited, the optimization unit can prioritize visually appealing pricing. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The optimization unit might then calculate an emotion score based on fluctuations in heart rate and determine price priorities. This allows the optimization unit to determine price priorities according to the user's emotions, enabling more appropriate pricing. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into a generative AI, which can then automatically estimate emotions and determine price priorities.

[0106] The optimization unit can optimize prices by considering the geographical distribution of products during the optimization process. For example, the optimization unit can set the optimal price based on the geographical distribution of products. For example, the optimization unit can set prices considering the geographical distribution of products based on sales data for each region. The optimization unit can also analyze the geographical distribution of products and optimize prices. For example, the optimization unit can analyze the geographical distribution of products based on logistics data and optimize prices. Furthermore, the optimization unit can improve the accuracy of pricing by considering the geographical distribution of products. For example, the optimization unit can improve the accuracy of pricing by considering the geographical distribution of products based on population data. As a result, the optimization unit can set appropriate prices by optimizing prices while considering the geographical distribution of products. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input geographical distribution data of products into a generating AI, and the generating AI can automatically optimize prices.

[0107] The optimization unit can improve the accuracy of pricing by referring to relevant literature on the product during optimization. For example, the optimization unit selects the optimal pricing method based on relevant literature on the product. For example, the optimization unit selects a pricing method by referring to relevant literature on the product based on academic papers. The optimization unit can also analyze relevant literature on the product and optimize the pricing method. For example, the optimization unit analyzes relevant literature on the product based on industry reports and optimizes the pricing method. Furthermore, the optimization unit can improve the accuracy of pricing by referring to relevant literature on the product. For example, the optimization unit improves the accuracy of pricing by referring to relevant literature on the product based on patent documents. In this way, the optimization unit improves the accuracy of pricing by referring to relevant literature on the product. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input relevant literature data on the product into a generating AI, and the generating AI can automatically improve the accuracy of pricing.

[0108] The support department can estimate the user's emotions and adjust its response method based on those emotions. For example, if the user is nervous, the support department will respond in a calm voice. For example, the support department can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the support department can calculate an emotion score based on changes in facial expressions and adjust its response method. The support department can also respond in a cheerful voice if the user is relaxed. For example, the support department can record the user's voice and estimate their emotions using voice analysis technology. For example, the support department can analyze the tone and speed of the voice, calculate an emotion score, and adjust its response method. The support department can also provide a quick and concise response if the user is in a hurry. For example, the support department can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the support department can calculate an emotion score based on fluctuations in heart rate and adjust its response method. This allows the support department to provide more appropriate responses by adjusting its response methods according to 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. Some or all of the above processing in the support department may be performed using AI or not. For example, the support department can input user emotion data into a generative AI, which can then automatically estimate the emotion and adjust its response method accordingly.

[0109] The support department can select the optimal response method when handling inquiries by referring to past inquiry history. For example, the support department can select the optimal response method based on past inquiry history. For example, the support department can analyze past inquiry history and optimize the response method. The support department can also improve the accuracy of its responses by referring to past inquiry history. For example, the support department can improve the accuracy of its responses based on past inquiry history. As a result, the support department improves the accuracy of its responses by selecting the optimal response method based on past inquiry history. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can input past inquiry history data into a generating AI, and the generating AI can automatically select the optimal response method.

[0110] The support department can estimate the user's emotions and determine the priority of handling inquiries based on those emotions. For example, if the user is nervous, the support department will prioritize a quick response. For instance, the support department can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For instance, the support department can calculate an emotion score based on changes in facial expressions and determine the priority of handling inquiries. The support department can also prioritize a detailed response if the user is relaxed. For instance, the support department can record the user's voice and estimate their emotions using voice analysis technology. For instance, the support department can analyze the tone and speed of the voice to calculate an emotion score and determine the priority of handling inquiries. The support department can also prioritize a concise response if the user is in a hurry. For instance, the support department can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For instance, the support department can calculate an emotion score based on fluctuations in heart rate and determine the priority of handling inquiries. This allows the support department to prioritize inquiries based on the user's emotions, enabling more appropriate responses. 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. Some or all of the above processing in the support department may be performed using AI, or not. For example, the support department can input user emotion data into a generative AI, which can then automatically estimate the emotion and determine the priority of inquiries.

[0111] The support department can select the most appropriate response method when handling inquiries, taking into account the user's device information. For example, if the user is using a smartphone, the support department can provide a response method adapted to the screen size. For example, based on the user's device information, the support department can provide a response method optimized for smartphones. Furthermore, if the user is using a tablet, the support department can also provide a response method optimized for larger screens. For example, based on the user's device information, the support department can provide a response method optimized for tablets. Additionally, if the user is using a smartwatch, the support department can provide a concise and highly visible response method. For example, based on the user's device information, the support department can provide a response method optimized for smartwatches. This allows the support department to provide more appropriate responses by selecting the most suitable method based on the user's device information. Some or all of the above processing in the support department may be performed using AI, or not. For example, the support department can input the user's device information into a generating AI, which can then automatically select the most appropriate response method.

[0112] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the data analysis method. The analysis unit can also perform a concise data analysis if the user is in a hurry. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the data analysis method. The analysis unit can also perform a visually appealing data analysis if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and adjust the data analysis method. This allows the analysis unit to perform more appropriate data analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then automatically estimate emotions and adjust the data analysis method.

[0113] The analysis unit can select the optimal analysis method by referring to past analysis data during data analysis. For example, the analysis unit selects the optimal analysis method based on past analysis data. For example, the analysis unit analyzes past analysis data and optimizes the analysis method. The analysis unit can also improve the accuracy of the analysis by referring to past analysis data. For example, the analysis unit improves the accuracy of the analysis based on past analysis data. As a result, the analysis unit improves the accuracy of the analysis by selecting the optimal analysis method based on past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI, and the generating AI can automatically select the optimal analysis method.

[0114] The analysis unit can estimate the user's emotions and prioritize data analysis based on those estimated emotions. For example, if the user is relaxed, the analysis unit prioritizes detailed data analysis. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit might calculate an emotion score based on changes in facial expressions and prioritize data analysis. The analysis unit can also prioritize concise data analysis if the user is in a hurry. For example, it might record the user's voice and estimate their emotions using voice analysis technology. The analysis unit might analyze the tone and speed of the voice to calculate an emotion score and prioritize data analysis. The analysis unit can also prioritize visually appealing data analysis if the user is excited. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The analysis unit might calculate an emotion score based on heart rate variability and prioritize data analysis. This allows the analysis unit to prioritize data analysis according to the user's emotions, enabling more appropriate data analysis. 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. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then automatically estimate emotions and determine the priority of data analysis.

[0115] The analysis unit can select the optimal analysis method when analyzing data, taking into account the user's device information. For example, if the user is using a smartphone, the analysis unit can provide an analysis method adapted to the screen size. For example, based on the user's device information, the analysis unit can provide an analysis method optimized for smartphones. Furthermore, if the user is using a tablet, the analysis unit can also provide an analysis method optimized for larger screens. For example, based on the user's device information, the analysis unit can provide an analysis method optimized for tablets. Additionally, if the user is using a smartwatch, the analysis unit can provide a concise and highly visible analysis method. For example, based on the user's device information, the analysis unit can provide an analysis method optimized for smartwatches. This allows the analysis unit to perform more appropriate data analysis by selecting the optimal analysis method based on the user's device information. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the user's device information into a generating AI, which can then automatically select the optimal analysis method.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] AI-driven platforms can estimate a user's emotions and adjust customer support responses based on those estimates. For example, if a user is stressed, the support team can provide a more courteous and calm response. Specifically, the system can capture the user's facial expressions with a camera, use an emotion estimation algorithm to estimate their emotions, and adjust its response accordingly. Conversely, if a user is relaxed, the support team can provide a quick and concise response. For example, it can record the user's voice, use voice analysis technology to estimate their emotions, and adjust its response accordingly. Furthermore, if a user is agitated, the support team can provide visually appealing information. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to estimate their emotions, and adjust its response accordingly. This allows the support team to provide the most appropriate response based on the user's emotions.

[0118] AI-driven platforms can prioritize the collection of highly relevant data by considering the user's geographical location when gathering user purchase history and behavioral patterns. For example, they can prioritize the collection of data from specific stores in the user's current location. Specifically, they can collect highly relevant product data based on the user's geographical location. They can also adjust the scope of data collected by considering the user's travel history. For example, they can adjust the scope of data collected by considering the user's travel history. As a result, the data collection unit can collect more accurate data by gathering highly relevant data based on the user's geographical location.

[0119] AI-driven platforms can estimate a user's emotions and adjust product recommendations based on those emotions. For example, if a user is relaxed, the platform can provide recommendations with detailed descriptions. Specifically, it can capture the user's facial expressions with a camera, use an emotion estimation algorithm to estimate their emotions, and adjust the recommendation method accordingly. If a user is in a hurry, it can provide concise recommendations. For example, it can record the user's voice, use voice analysis technology to estimate their emotions, and adjust the recommendation method accordingly. Furthermore, if a user is excited, it can provide visually appealing recommendations. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to estimate their emotions, and adjust the recommendation method accordingly. This allows the recommendation system to provide optimal product recommendations based on the user's emotions.

[0120] AI-driven platforms can collect relevant data by analyzing users' social media activity when gathering user purchase history and behavioral patterns. For example, they can prioritize collecting data on product categories mentioned by users on social media. Specifically, they can collect data on products that users are likely to be interested in based on their social media activity. They can also collect relevant data by referring to the activities of users' followers and friends. For example, they can collect relevant data by referring to the activities of users' followers and friends. As a result, the data collection unit can collect relevant data based on users' social media activity, enabling data collection that reflects users' interests.

[0121] The AI-driven platform can estimate the user's emotions and adjust the inventory level prediction method based on those emotions. For example, if the user is relaxed, it can make predictions based on detailed data. Specifically, it can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and adjust the prediction method. If the user is in a hurry, it can also make predictions based on concise data. For example, it can record the user's voice, estimate their emotions using voice analysis technology, and adjust the prediction method. Furthermore, if the user is excited, it can make visually appealing predictions. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and adjust the prediction method. As a result, the prediction unit can adjust the inventory level prediction method according to the user's emotions, enabling more accurate inventory predictions.

[0122] AI-driven platforms can filter user purchase history and behavioral patterns based on the user's current lifestyle and areas of interest. For example, they can prioritize collecting product data in categories the user is currently interested in. Specifically, they filter data based on the user's lifestyle. They can also adjust the scope of data collected based on the user's areas of interest. For example, they can adjust the scope of data collected based on the user's areas of interest. This allows the data collection unit to collect highly relevant data by filtering it based on the user's lifestyle and areas of interest.

[0123] The AI-driven platform can estimate the user's emotions and adjust the timing of orders based on those emotions. For example, if the user is relaxed, orders can be placed based on detailed data. Specifically, the user's facial expressions are captured by a camera, an emotion estimation algorithm is used to estimate their emotions, and the timing of orders is adjusted accordingly. If the user is in a hurry, orders can be placed based on concise data. For example, the user's voice is recorded, and emotion is estimated using voice analysis technology to adjust the timing of orders. Furthermore, if the user is excited, orders can be placed that are visually appealing. For example, the user's biometric data (heart rate and skin electrical activity) is collected by sensors, an emotion estimation algorithm is used to estimate their emotions, and the timing of orders is adjusted accordingly. As a result, the ordering unit can adjust the timing of orders according to the user's emotions, enabling more appropriate orders.

[0124] AI-driven platforms can analyze a user's past purchase history and select the optimal data collection method when gathering user purchase history and behavioral patterns. For example, they can prioritize collecting product categories that a user has frequently purchased in the past. Specifically, they can concentrate data collection during certain time periods based on the user's purchase history. They can also analyze the user's purchase patterns and suggest the optimal data collection method. For example, they can analyze the user's purchase patterns and select the most suitable collection method. As a result, the data collection unit can efficiently collect data by selecting the optimal collection method based on the user's past purchase history.

[0125] An AI-driven platform can estimate a user's emotions and adjust the data analysis method based on those emotions. For example, if the user is relaxed, a detailed data analysis can be performed. Specifically, the user's facial expressions are captured by a camera, an emotion estimation algorithm is used to estimate the emotions, and the data analysis method is adjusted accordingly. If the user is in a hurry, a concise data analysis can be performed. For example, the user's voice is recorded, and emotion is estimated using voice analysis technology, and the data analysis method is adjusted accordingly. Furthermore, if the user is excited, a visually engaging data analysis can be performed. For example, the user's biometric data (heart rate and skin electrical activity) is collected by sensors, an emotion estimation algorithm is used to estimate the emotions, and the data analysis method is adjusted accordingly. This allows the analysis unit to perform more appropriate data analysis by adjusting the data analysis method according to the user's emotions.

[0126] AI-driven platforms can prioritize the collection of highly relevant data by considering the user's geographical location when gathering user purchase history and behavioral patterns. For example, they can prioritize the collection of data from specific stores in the user's current location. Specifically, they can collect highly relevant product data based on the user's geographical location. They can also adjust the scope of data collected by considering the user's travel history. For example, they can adjust the scope of data collected by considering the user's travel history. As a result, the data collection unit can collect more accurate data by gathering highly relevant data based on the user's geographical location.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects customer purchase history and behavioral patterns. This includes purchase date and time, purchased items, browsing history, and click history. The data collection unit tracks customer behavior on the website and collects purchase history and behavioral patterns. It can also analyze customers' past purchase history and social media activity to collect data that reflects customer preferences and interests. Step 2: The recommendation department suggests the most suitable products based on the data collected by the data collection department. This includes recommending products that customers are likely to be interested in, based on their past purchase history and behavioral patterns. It can also provide real-time notifications of new products and sales information to promote customer engagement. Step 3: The forecasting unit predicts inventory levels based on the products suggested by the recommendation unit. This includes predicting the appropriate amount of inventory by considering historical sales data and seasonal demand. It can also analyze sales trends in real time and automatically predict inventory levels using AI technology. Step 4: The ordering department places orders for inventory based on the inventory levels predicted by the forecasting department. This includes ordering the appropriate amount of inventory based on the predicted inventory levels. It can also adjust inventory levels to mitigate the risk of insufficient or excess inventory, and automate inventory ordering using AI technology. Step 5: The optimization unit optimizes prices based on inventory ordered by the ordering unit. This includes analyzing competitor prices and market demand in real time to set the optimal price. AI technology can also be used to optimize prices.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, recommendation unit, forecasting unit, ordering unit, optimization unit, support unit, analysis unit, and sentiment estimation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects customer behavior patterns using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal product based on the collected data. The forecasting unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and forecasts inventory levels. The ordering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and orders inventory based on the predicted inventory levels. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and optimizes prices. The support unit is implemented, for example, by the control unit 46A of the smart device 14 and responds to customer inquiries. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the data and generates a report. The emotion estimation unit estimates the user's emotions using, for example, the camera 42 and microphone 38B of the smart device 14 and adjusts the timing of data collection. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, recommendation unit, forecasting unit, ordering unit, optimization unit, support unit, analysis unit, and sentiment estimation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects customer behavior patterns using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 via the control unit 46A. The recommendation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes the optimal product based on the collected data. The forecasting unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and forecasts inventory levels. The ordering unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and orders inventory based on the predicted inventory levels. The optimization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and optimizes prices. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and responds to customer inquiries. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the data and generates a report. The emotion estimation unit estimates the user's emotions using, for example, the camera 42 and microphone 238 of the smart glasses 214 and adjusts the timing of data collection. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, recommendation unit, forecasting unit, ordering unit, optimization unit, support unit, analysis unit, and sentiment estimation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects customer behavior patterns using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 via the control unit 46A. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal product based on the collected data. The forecasting unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and forecasts inventory levels. The ordering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and orders inventory based on the predicted inventory levels. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and optimizes prices. The support unit is implemented, for example, by the control unit 46A of the headset terminal 314 and responds to customer inquiries. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the data and generates a report. The emotion estimation unit estimates the user's emotions using, for example, the camera 42 and microphone 238 of the headset terminal 314 and adjusts the timing of data collection. 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.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the data collection unit, recommendation unit, forecasting unit, ordering unit, optimization unit, support unit, analysis unit, and sentiment estimation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects customer behavior patterns using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 by the control unit 46A. The recommendation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes the optimal product based on the collected data. The forecasting unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and forecasts inventory levels. The ordering unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and orders inventory based on the predicted inventory levels. The optimization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and optimizes prices. The support unit is implemented, for example, by the control unit 46A of the robot 414 and responds to customer inquiries. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the data and generates a report. The emotion estimation unit estimates the user's emotions using, for example, the camera 42 and microphone 238 of the robot 414 and adjusts the timing of data collection. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) The collection department collects customer purchase history and behavioral patterns, A recommendation unit proposes the most suitable product based on the data collected by the aforementioned collection unit, A forecasting unit that predicts inventory levels based on the products proposed by the aforementioned recommendation unit, An ordering unit that places an order for inventory based on the inventory level predicted by the forecasting unit, The system includes an optimization unit that optimizes prices based on the inventory ordered by the ordering unit. A system characterized by the following features. (Note 2) It includes a support department that provides automated customer support features. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an analysis unit that provides data analysis and reporting functions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting purchase history and behavioral patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting purchase history and behavioral patterns, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 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 When collecting purchase history and behavioral patterns, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting purchase history and behavioral patterns, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recommendation department, It estimates the user's emotions and adjusts the product recommendation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, When making a recommendation, we will prioritize recommendations based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, We estimate user sentiment and adjust the inventory level forecasting method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, When making predictions, the prediction algorithm is optimized by referring to historical sales data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The prediction unit, When forecasting, inventory levels are predicted by taking seasonal demand into account. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, It estimates the user's emotions and determines the priority of predictions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When forecasting, the geographical distribution of products is taken into consideration when predicting inventory levels. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, we improve the accuracy of the predictions by referring to relevant literature on the product. The system described in Appendix 1, characterized by the features described herein. (Note 22) The ordering department said, It estimates the user's emotions and adjusts the timing of orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The ordering department said, When placing an order, the optimal ordering method is selected by referring to past order history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The ordering department said, When placing an order, adjust the order quantity considering the product supply situation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The ordering department said, It estimates user sentiment and determines order priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The ordering department said, When placing an order, adjust the order quantity considering the geographical distribution of the product. The system described in Appendix 1, characterized by the features described herein. (Note 27) The ordering department said, When placing an order, refer to relevant literature on the product to improve the accuracy of the order. The system described in Appendix 1, characterized by the features described herein. (Note 28) The optimization unit, It estimates user sentiment and adjusts the pricing optimization method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, During optimization, the optimization algorithm is optimized by referring to historical price data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The optimization unit, During optimization, the price is optimized by taking into account the prices of competitors. The system described in Appendix 1, characterized by the features described herein. (Note 31) The optimization unit, It estimates user sentiment and determines price priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit, During optimization, the price is optimized considering the geographical distribution of the product. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, During optimization, we improve pricing accuracy by referring to relevant literature on the product. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is The system estimates the user's emotions and adjusts the response method to inquiries based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned support unit is When responding to inquiries, refer to past inquiry history to select the most appropriate response method. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned support unit is The system estimates the user's emotions and determines the priority of inquiry responses based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned support unit is When responding to inquiries, the optimal response method is selected by considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned analysis unit is When analyzing data, the optimal analysis method is selected by referring to past analysis data. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned analysis unit is We estimate user sentiment and prioritize data analysis based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned analysis unit is When analyzing data, the optimal analysis method is selected by considering the user's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0201] 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. The collection department collects customer purchase history and behavioral patterns, A recommendation unit proposes the most suitable product based on the data collected by the aforementioned collection unit, A forecasting unit that predicts inventory levels based on the products proposed by the aforementioned recommendation unit, An ordering unit that places an order for inventory based on the inventory level predicted by the forecasting unit, The system includes an optimization unit that optimizes prices based on the inventory ordered by the ordering unit. A system characterized by the following features.

2. It includes a support department that provides automated customer support features. The system according to feature 1.

3. It includes an analysis unit that provides data analysis and reporting functions. The system according to feature 1.

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

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

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

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 When collecting purchase history and behavioral patterns, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

9. The aforementioned collection unit is When collecting purchase history and behavioral patterns, analyze users' social media activity and collect relevant data. The system according to feature 1.

10. The aforementioned recommendation department, It estimates the user's emotions and adjusts the product recommendation method based on the estimated user emotions. The system according to feature 1.

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